From 3c080aa5b12a590b39762d58e2878e7f6e27efca Mon Sep 17 00:00:00 2001 From: Assistente Malpagato Date: Mon, 22 Jun 2026 14:49:40 +0200 Subject: [PATCH 1/7] feat: add pith-skill context compression server for Windows environments Introduces mcp-server-pith, a zero-dependency Python MCP server that compresses inter-agent payloads using Zipf word-density scoring validated by Benford's Law structural integrity check. Exposes two tools: `compress` (text output with header) and `compress_with_metadata` (JSON with token counts and Benford MAD values). Applies sys.stdout.reconfigure(encoding="utf-8") at startup to prevent UnicodeEncodeError on Windows CP1252 terminals. All 7 eval cases pass (core compression, code preservation, passthrough, JSON preservation, aggressive compression, URL preservation, Benford metadata). --- src/pith/.python-version | 1 + src/pith/LICENSE | 7 + src/pith/README.md | 134 +++++++++++ src/pith/pyproject.toml | 36 +++ src/pith/src/mcp_server_pith/__init__.py | 3 + src/pith/src/mcp_server_pith/__main__.py | 3 + src/pith/src/mcp_server_pith/compress.py | 279 +++++++++++++++++++++++ src/pith/src/mcp_server_pith/server.py | 110 +++++++++ src/pith/tests/__init__.py | 0 src/pith/tests/evals.json | 88 +++++++ src/pith/tests/run_evals.py | 151 ++++++++++++ 11 files changed, 812 insertions(+) create mode 100644 src/pith/.python-version create mode 100644 src/pith/LICENSE create mode 100644 src/pith/README.md create mode 100644 src/pith/pyproject.toml create mode 100644 src/pith/src/mcp_server_pith/__init__.py create mode 100644 src/pith/src/mcp_server_pith/__main__.py create mode 100644 src/pith/src/mcp_server_pith/compress.py create mode 100644 src/pith/src/mcp_server_pith/server.py create mode 100644 src/pith/tests/__init__.py create mode 100644 src/pith/tests/evals.json create mode 100644 src/pith/tests/run_evals.py diff --git a/src/pith/.python-version b/src/pith/.python-version new file mode 100644 index 0000000000..2c0733315e --- /dev/null +++ b/src/pith/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/src/pith/LICENSE b/src/pith/LICENSE new file mode 100644 index 0000000000..3161f4afd3 --- /dev/null +++ b/src/pith/LICENSE @@ -0,0 +1,7 @@ +Copyright (c) 2024 Albert (VjAlbert) + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/src/pith/README.md b/src/pith/README.md new file mode 100644 index 0000000000..a2e0e739ce --- /dev/null +++ b/src/pith/README.md @@ -0,0 +1,134 @@ +# PITH MCP Server + +An MCP server that compresses inter-agent payloads to eliminate token waste in multi-agent pipelines. Uses Zipf word-density scoring validated by Benford's Law structural integrity check. Zero external dependencies beyond the MCP SDK. + +## Overview + +In multi-agent AI pipelines, agents pass verbose outputs — tool results, reasoning traces, search summaries — to downstream agents without compression. PITH fills this gap: it compresses those inter-agent payloads aggressively while preserving all structured content (code, JSON, URLs, file paths, numbers). + +``` +AGENT A — verbose output (487 tokens) + ↓ +[PITH MCP Server] + ↓ +AGENT B — compressed payload (284 tokens, -42%) +[PITH | ✓ | -42% tokens | benford:4.3% | compressed] +``` + +## Tools + +### `compress` + +Compress a verbose payload. Returns compressed text with a metadata header. + +**Input:** +- `payload` (string, required): Text to compress +- `ratio` (number, optional, default: `0.6`): Fraction of sentences to keep (0.1–1.0) + +**Output example:** +``` +[PITH | ✓ | -42% tokens | benford:4.3% | compressed] + +``` + +### `compress_with_metadata` + +Same as `compress` but returns a JSON object with full metadata including token counts and Benford MAD values. Useful for programmatic pipelines. + +**Output example:** +```json +{ + "compressed": "...", + "meta": { + "action": "compressed", + "original_tokens": 487, + "compressed_tokens": 284, + "ratio": 0.583, + "saved_pct": 41.7, + "sentences_original": 22, + "sentences_kept": 13, + "original_benford_mad": 4.1, + "compressed_benford_mad": 4.3, + "benford_ok": true, + "preserved_blocks": 0 + } +} +``` + +## Compression Ratios + +| Ratio | Mode | Best For | +|-------|------|----------| +| `0.8` | Conservative | Sensitive reasoning traces | +| `0.6` | Default | Most agent tool results | +| `0.4` | Aggressive | Bulk search results, summaries | +| `0.3` | Maximum | Context window critical | + +## What is Always Preserved + +Code blocks, inline code, JSON objects/arrays, URLs, file paths, XML/HTML tags, numbers. These are extracted before compression and reinserted after — never touched. + +## How it Works + +1. **Extract** — code, JSON, URLs, paths quarantined before processing +2. **Score** — each sentence scored by Zipf density (word length >= 7 chars as rarity proxy) +3. **Filter** — top N% sentences by density selected (default: 60%) +4. **Benford Gate** — if compression increases MAD vs Benford's Law by >2×, relax ratio and retry (max 3 attempts) +5. **Reassemble** — original sentence order restored, preserved blocks reinserted + +Benford's Law is used as a structural integrity signal: natural text has sentence-length distributions approximating the logarithmic Benford distribution. Over-compressed text deviates. The gate prevents producing text more artificial than the input. + +## Installation + +### Claude Desktop + +```json +{ + "mcpServers": { + "pith": { + "command": "uvx", + "args": ["mcp-server-pith"] + } + } +} +``` + +On Windows: +```json +{ + "mcpServers": { + "pith": { + "command": "cmd", + "args": ["/c", "uvx", "mcp-server-pith"] + } + } +} +``` + +### Direct + +```bash +# With uvx (recommended) +uvx mcp-server-pith + +# With pip +pip install mcp-server-pith +python -m mcp_server_pith +``` + +## Windows Encoding + +`sys.stdout.reconfigure(encoding="utf-8", errors="replace")` is applied at startup, preventing `UnicodeEncodeError: 'charmap' codec can't encode character` errors common in Windows CP1252 terminals when handling Unicode characters in LLM outputs. + +## Testing + +```bash +cd src/pith +python tests/run_evals.py +``` + +All 7 eval cases cover: core compression, code preservation, passthrough (short payloads), JSON preservation, aggressive compression, URL preservation, and Benford metadata output. + +## License + +MIT diff --git a/src/pith/pyproject.toml b/src/pith/pyproject.toml new file mode 100644 index 0000000000..faccf04adb --- /dev/null +++ b/src/pith/pyproject.toml @@ -0,0 +1,36 @@ +[project] +name = "mcp-server-pith" +version = "0.1.0" +description = "A Model Context Protocol server providing token-aware context compression for inter-agent payloads, optimized for Windows environments" +readme = "README.md" +requires-python = ">=3.10" +authors = [{ name = "Albert", email = "dotthofmann@gmail.com" }] +keywords = ["compression", "mcp", "llm", "agents", "tokens", "context"] +license = { text = "MIT" } +classifiers = [ + "Development Status :: 3 - Alpha", + "Intended Audience :: Developers", + "License :: OSI Approved :: MIT License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", +] +dependencies = [ + "mcp>=1.1.3", +] + +[project.scripts] +mcp-server-pith = "mcp_server_pith:main" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.uv] +dev-dependencies = ["pyright>=1.1.389", "ruff>=0.7.3", "pytest>=8.0.0"] + +[tool.pytest.ini_options] +testpaths = ["tests"] + +[tool.hatch.build.targets.wheel] +packages = ["src/mcp_server_pith"] diff --git a/src/pith/src/mcp_server_pith/__init__.py b/src/pith/src/mcp_server_pith/__init__.py new file mode 100644 index 0000000000..8635928aa2 --- /dev/null +++ b/src/pith/src/mcp_server_pith/__init__.py @@ -0,0 +1,3 @@ +from .server import main, serve + +__all__ = ["main", "serve"] diff --git a/src/pith/src/mcp_server_pith/__main__.py b/src/pith/src/mcp_server_pith/__main__.py new file mode 100644 index 0000000000..ba841506a8 --- /dev/null +++ b/src/pith/src/mcp_server_pith/__main__.py @@ -0,0 +1,3 @@ +from mcp_server_pith import main + +main() diff --git a/src/pith/src/mcp_server_pith/compress.py b/src/pith/src/mcp_server_pith/compress.py new file mode 100644 index 0000000000..065874bb62 --- /dev/null +++ b/src/pith/src/mcp_server_pith/compress.py @@ -0,0 +1,279 @@ +#!/usr/bin/env python3 +""" +PITH — Inter-Agent Payload Compressor +Zipf word-density scoring + Benford structural validation + +Usage: + echo "" | python3 compress.py + python3 compress.py --payload "" --ratio 0.6 + python3 compress.py --payload "" --json +""" + +import sys +import re +import json +import math +import argparse +import heapq +from collections import Counter + +if hasattr(sys.stdout, "reconfigure"): + sys.stdout.reconfigure(encoding="utf-8", errors="replace") + +# ── Configuration ──────────────────────────────────────────────────── +DEFAULT_RATIO = 0.60 # Keep top 60% of sentences by density +BENFORD_TOLERANCE = 2.0 # Max MAD multiplier before Benford gate triggers +MIN_SENTENCES = 5 # Pass through if fewer sentences than this +DENSE_WORD_CHARS = 7 # Words >= N chars = rare = high information proxy +MAX_RETRIES = 3 # Benford gate retry attempts + +BENFORD = {d: math.log10(1 + 1 / d) * 100 for d in range(1, 10)} + +STOPWORDS = set(""" +a about above after again against all am an and any are as at be because been before +being below between both but by can cannot could did do does doing down during each few +for from further get got had has have having he her here him his how i if in into is it +its itself let me more most my myself no nor not of off on once only or other our out +over own same she should so some such than that the their them then there these they this +those through to too under until up very was we were what when where which while who +whose why will with would you your yours +""".split()) + +COMMON_LONG_WORDS = { + "encountered", "completed", "successfully", "produced", "happened", + "computation", "discussed", "following", "together", "available", + "including", "currently", "adequate", "correctly", "proceeded", + "retrieved", "processed", "sequentially", "executed", +} + +FILLER_PATTERNS = re.compile( + r"^(i believe\b|i think\b|i found\b|i searched\b|i need to\b|i am\b" + r"|the search (was|is|has been|returned|completed)\b" + r"|no (errors?|issues?|problems?|warnings?)\b)", + re.IGNORECASE, +) + +_STRIP_NON_ALPHA = re.compile(r'[^a-zA-Z\s]') +_SPLIT_SENT_RE = re.compile(r'(?<=[.!?])\s+(?=[A-ZÀ-ɏ])') + +PRESERVE_PATTERNS = [ + ("code_block", re.compile(r"```[\s\S]*?```")), + ("inline_code", re.compile(r"`[^`\n]+`")), + ("json_obj", re.compile(r"\{[^{}]{10,}\}")), + ("json_arr", re.compile(r"\[[^\[\]]{10,}\]")), + ("url", re.compile(r"https?://\S+")), + ("filepath", re.compile(r"(?:/[\w.\-_]+){2,}")), + ("xml_tag", re.compile(r"<[a-zA-Z][^>]*>[\s\S]*?")), +] + + +def extract_preserved(text): + preserved = {} + idx = [0] + + def replacer(m, kind): + key = f"\x00PITH_{kind}_{idx[0]}\x00" + preserved[key] = m.group(0) + idx[0] += 1 + return key + + for kind, pattern in PRESERVE_PATTERNS: + text = pattern.sub(lambda m, k=kind: replacer(m, k), text) + + return text, preserved + + +def restore_preserved(text, preserved): + for key, value in preserved.items(): + text = text.replace(key, value) + return text + + +def split_sentences(text): + raw = _SPLIT_SENT_RE.split(text.strip()) + result = [] + for part in raw: + lines = part.split('\n') + for line in lines: + line = line.strip() + if len(line.split()) >= 2: + result.append(line) + return result + + +def zipf_density(sentence): + """Score sentence information density using word length as Zipf proxy.""" + words = _STRIP_NON_ALPHA.sub(' ', sentence.lower()).split() + content = [w for w in words if w not in STOPWORDS and len(w) > 2] + + if not content: + return 0.0 + + n_dense = 0 + total_len = 0 + for w in content: + wl = len(w) + total_len += wl + if wl >= DENSE_WORD_CHARS and w not in COMMON_LONG_WORDS: + n_dense += 1 + mean_len = total_len / len(content) + + score = (n_dense / len(content)) * 0.6 + min(mean_len / 12.0, 1.0) * 0.4 + + if FILLER_PATTERNS.match(sentence.strip()): + score *= 0.25 + + return round(min(score, 1.0), 4) + + +def benford_mad(sentences): + """Mean Absolute Deviation of sentence-length first-digit distribution from Benford's Law.""" + lengths = [] + for s in sentences: + parts = s.split() + if parts: + lengths.append(len(parts)) + if len(lengths) < 5: + return 0.0 + + first_digits = [int(str(max(l, 1))[0]) for l in lengths] + dist = Counter(first_digits) + total = len(first_digits) + return round( + sum(abs(dist.get(d, 0) / total * 100 - BENFORD[d]) for d in range(1, 10)) / 9, + 2 + ) + + +def compress(text, target_ratio=DEFAULT_RATIO): + """ + Main PITH compression pipeline. + + Returns: + compressed_text (str): The compressed payload + meta (dict): Compression metadata + """ + original_tokens = len(text.split()) + passthrough_meta = { + "action": "passthrough", + "original_tokens": original_tokens, + "compressed_tokens": original_tokens, + "ratio": 1.0, + "saved_pct": 0.0, + "benford_ok": True, + } + + working, preserved = extract_preserved(text) + n_preserved = len(preserved) + + sentences = split_sentences(working) + + if len(sentences) < MIN_SENTENCES: + passthrough_meta["reason"] = f"only {len(sentences)} sentences (< {MIN_SENTENCES})" + passthrough_meta["benford_mad"] = benford_mad(sentences) + passthrough_meta["preserved_blocks"] = n_preserved + return text, passthrough_meta + + scored = [(i, s, zipf_density(s)) for i, s in enumerate(sentences)] + original_mad = benford_mad(sentences) + keep_n = max(2, round(len(sentences) * target_ratio)) + + kept_sentences = sentences + compressed_mad = original_mad + + for attempt in range(MAX_RETRIES): + top = heapq.nlargest(keep_n, scored, key=lambda x: x[2]) + kept_indices = sorted(x[0] for x in top) + candidate = [sentences[i] for i in kept_indices] + candidate_mad = benford_mad(candidate) + + if original_mad > 0 and candidate_mad > original_mad * BENFORD_TOLERANCE: + keep_n = min(len(sentences), keep_n + 2) + else: + kept_sentences = candidate + compressed_mad = candidate_mad + break + else: + kept_sentences = candidate + compressed_mad = candidate_mad + + compressed_working = " ".join(kept_sentences) + compressed_text = restore_preserved(compressed_working, preserved) + + for key, value in preserved.items(): + if key not in compressed_working: + compressed_text += f"\n{value}" + + compressed_tokens = len(compressed_text.split()) + ratio = round(compressed_tokens / original_tokens, 3) if original_tokens > 0 else 1.0 + + meta = { + "action": "compressed", + "original_tokens": original_tokens, + "compressed_tokens": compressed_tokens, + "ratio": ratio, + "saved_pct": round((1 - ratio) * 100, 1), + "sentences_original": len(sentences), + "sentences_kept": len(kept_sentences), + "original_benford_mad": original_mad, + "compressed_benford_mad": compressed_mad, + "benford_ok": compressed_mad <= original_mad * BENFORD_TOLERANCE or original_mad == 0, + "preserved_blocks": n_preserved, + } + + return compressed_text, meta + + +def main(): + parser = argparse.ArgumentParser( + description="PITH - Inter-Agent Payload Compressor", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + echo "Your verbose agent output..." | python3 compress.py + python3 compress.py --payload "Long text..." --ratio 0.5 + python3 compress.py --payload "Long text..." --json + """ + ) + parser.add_argument("--payload", type=str, + help="Text to compress (alternative to stdin)") + parser.add_argument("--ratio", type=float, default=DEFAULT_RATIO, + help=f"Target sentence keep ratio 0.0-1.0 (default: {DEFAULT_RATIO})") + parser.add_argument("--json", action="store_true", + help="Output full JSON with compressed text + metadata") + args = parser.parse_args() + + if args.payload: + text = args.payload + elif not sys.stdin.isatty(): + text = sys.stdin.read() + else: + parser.print_help() + sys.exit(1) + + text = text.strip() + if not text: + print('{"error": "empty input"}' if args.json else "Error: empty input", + file=sys.stderr) + sys.exit(1) + + if not 0.1 <= args.ratio <= 1.0: + print("Error: --ratio must be between 0.1 and 1.0", file=sys.stderr) + sys.exit(1) + + compressed, meta = compress(text, target_ratio=args.ratio) + + if args.json: + print(json.dumps({"compressed": compressed, "meta": meta}, indent=2)) + else: + benford_icon = "✓" if meta.get("benford_ok", True) else "⚠" + action = meta.get("action", "compressed") + saved = meta.get("saved_pct", 0) + b_mad = meta.get("compressed_benford_mad", meta.get("benford_mad", 0)) + header = f"[PITH | {benford_icon} | -{saved:.0f}% tokens | benford:{b_mad:.1f}% | {action}]" + print(header) + print(compressed) + + +if __name__ == "__main__": + main() diff --git a/src/pith/src/mcp_server_pith/server.py b/src/pith/src/mcp_server_pith/server.py new file mode 100644 index 0000000000..3d19e5a31b --- /dev/null +++ b/src/pith/src/mcp_server_pith/server.py @@ -0,0 +1,110 @@ +import asyncio +import json +import sys + +from mcp.server import Server +from mcp.server.stdio import stdio_server +from mcp.types import TextContent, Tool + +from .compress import compress, DEFAULT_RATIO + +if hasattr(sys.stdout, "reconfigure"): + sys.stdout.reconfigure(encoding="utf-8", errors="replace") + +app = Server("mcp-server-pith") + + +@app.list_tools() +async def list_tools() -> list[Tool]: + return [ + Tool( + name="compress", + description=( + "Compress a verbose inter-agent payload using PITH (Zipf density scoring " + "validated by Benford's Law). Removes filler sentences while preserving all " + "code blocks, JSON, URLs, file paths, and numbers. Typical savings: 30-60%. " + "Use before passing large tool results or reasoning traces to the next agent." + ), + inputSchema={ + "type": "object", + "properties": { + "payload": { + "type": "string", + "description": "The text to compress.", + }, + "ratio": { + "type": "number", + "description": ( + "Fraction of sentences to keep (0.1-1.0). " + "0.6=default, 0.8=conservative, 0.4=aggressive, 0.3=maximum." + ), + "default": DEFAULT_RATIO, + }, + }, + "required": ["payload"], + }, + ), + Tool( + name="compress_with_metadata", + description=( + "Compress a payload with PITH and return full JSON metadata including " + "token counts, Benford MAD values, and compression ratio. " + "Use for programmatic pipelines or when you need to inspect compression quality." + ), + inputSchema={ + "type": "object", + "properties": { + "payload": { + "type": "string", + "description": "The text to compress.", + }, + "ratio": { + "type": "number", + "description": ( + "Fraction of sentences to keep (0.1-1.0). " + "0.6=default, 0.8=conservative, 0.4=aggressive, 0.3=maximum." + ), + "default": DEFAULT_RATIO, + }, + }, + "required": ["payload"], + }, + ), + ] + + +@app.call_tool() +async def call_tool(name: str, arguments: dict) -> list[TextContent]: + payload = arguments.get("payload", "").strip() + if not payload: + return [TextContent(type="text", text="Error: empty payload")] + + ratio = float(arguments.get("ratio", DEFAULT_RATIO)) + if not 0.1 <= ratio <= 1.0: + return [TextContent(type="text", text="Error: ratio must be between 0.1 and 1.0")] + + compressed_text, meta = compress(payload, target_ratio=ratio) + + if name == "compress": + benford_icon = "✓" if meta.get("benford_ok", True) else "⚠" + action = meta.get("action", "compressed") + saved = meta.get("saved_pct", 0) + b_mad = meta.get("compressed_benford_mad", meta.get("benford_mad", 0)) + header = f"[PITH | {benford_icon} | -{saved:.0f}% tokens | benford:{b_mad:.1f}% | {action}]" + return [TextContent(type="text", text=f"{header}\n{compressed_text}")] + + if name == "compress_with_metadata": + result = json.dumps({"compressed": compressed_text, "meta": meta}, indent=2, + ensure_ascii=False) + return [TextContent(type="text", text=result)] + + return [TextContent(type="text", text=f"Unknown tool: {name}")] + + +async def serve() -> None: + async with stdio_server() as (read_stream, write_stream): + await app.run(read_stream, write_stream, app.create_initialization_options()) + + +def main() -> None: + asyncio.run(serve()) diff --git a/src/pith/tests/__init__.py b/src/pith/tests/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/src/pith/tests/evals.json b/src/pith/tests/evals.json new file mode 100644 index 0000000000..b40f373048 --- /dev/null +++ b/src/pith/tests/evals.json @@ -0,0 +1,88 @@ +[ + { + "id": "TC01", + "category": "core_compression", + "description": "Verbose web search result — default ratio", + "prompt": "Compress this tool result before passing it to the synthesis agent:\n\nI searched the web for the latest AI research developments. The search returned several relevant results. According to the first result from MIT Technology Review, researchers have developed a new transformer architecture that reduces computational costs by approximately 40 percent while maintaining comparable performance on benchmark tasks. The article notes that this is a significant advancement in the field. The second result from Arxiv described a paper on reinforcement learning from human feedback, noting that the technique has been further refined to reduce reward hacking. The third result discussed multimodal models and their applications in robotics. The fourth result was about AI safety research at several institutions. The fifth result discussed token efficiency in large language models. The search was completed successfully and all five results appear to be from credible sources published within the last six months. I believe this information is sufficient to answer the user's question about recent AI developments.", + "assertions": [ + "output contains [PITH", + "output contains -", + "output contains % tokens", + "output does NOT contain 'The search was completed successfully'", + "output does NOT contain 'I believe this information is sufficient'", + "output contains 'transformer architecture' OR output contains 'reinforcement learning' OR output contains 'multimodal'" + ] + }, + { + "id": "TC02", + "category": "code_preservation", + "description": "Mixed code + verbose explanation — code must be untouched", + "prompt": "Pith this output from the code execution agent:\n\nThe script ran and produced output. Here is what happened during execution. The function was called with the input parameters as specified. The algorithm processed each element in the list sequentially. No errors were encountered during runtime. The computation completed successfully.\n\n```python\ndef binary_search(arr, target):\n left, right = 0, len(arr) - 1\n while left <= right:\n mid = (left + right) // 2\n if arr[mid] == target:\n return mid\n elif arr[mid] < target:\n left = mid + 1\n else:\n right = mid - 1\n return -1\n\nresult = binary_search([1, 3, 5, 7, 9, 11], 7)\nprint(result) # Output: 3\n```\n\nThe result was 3, which is the correct index of the value 7 in the sorted array. The binary search algorithm has O(log n) time complexity, which makes it efficient for large datasets. The function returns -1 if the target is not found.", + "assertions": [ + "output contains ```python", + "output contains def binary_search", + "output contains return mid", + "output contains O(log n)", + "output does NOT contain 'The script ran and produced output. Here is what happened'", + "output does NOT contain 'No errors were encountered during runtime'" + ] + }, + { + "id": "TC03", + "category": "passthrough", + "description": "Short payload — must pass through unchanged", + "prompt": "Compress this agent result: The API returned status 200. Authentication succeeded.", + "assertions": [ + "output contains passthrough", + "output contains -0% OR output contains The API returned" + ] + }, + { + "id": "TC04", + "category": "json_preservation", + "description": "JSON payload — must be preserved and not broken", + "prompt": "Slim down this payload before the next agent processes it:\n\nThe database query returned successfully. Here are the results in structured format. The query took 45 milliseconds to complete. All records were retrieved without errors.\n\n{\"users\": [{\"id\": 1, \"name\": \"Alice\", \"role\": \"admin\"}, {\"id\": 2, \"name\": \"Bob\", \"role\": \"viewer\"}], \"total\": 2, \"page\": 1}\n\nThe data looks correct and matches the expected schema. You can now pass this to the processing agent.", + "assertions": [ + "output contains \"id\": 1", + "output contains \"name\": \"Alice\"", + "output contains \"role\": \"admin\"", + "output does NOT contain 'The data looks correct and matches the expected schema'" + ] + }, + { + "id": "TC05", + "category": "aggressive_compression", + "description": "Long reasoning trace — aggressive mode", + "prompt": "Use pith at ratio 0.4 to compress this reasoning trace before forwarding:\n\nI need to analyze the user's request carefully. The user has asked about climate change mitigation strategies. Let me think through this systematically. First, I should consider the major categories of mitigation. These include renewable energy transition, carbon capture and storage, reforestation and land use changes, and behavioral and policy interventions. Each of these categories has distinct mechanisms, timelines, and costs. Renewable energy is perhaps the most discussed category. Solar photovoltaic costs have fallen by over 90 percent in the last decade, making it the cheapest electricity source in history in many regions. Wind energy has also seen dramatic cost reductions. Battery storage technology is improving rapidly, addressing the intermittency problem. Carbon capture and storage technology exists but remains expensive at approximately 100 to 300 dollars per ton of CO2. Direct air capture is even more expensive. Reforestation has significant co-benefits for biodiversity and water cycles. Policy frameworks such as carbon pricing, regulations, and international agreements like the Paris Agreement play a crucial role in coordinating action. I believe the most impactful near-term strategies combine rapid renewable deployment with demand reduction. The evidence strongly supports this conclusion.", + "assertions": [ + "output contains -", + "output contains % tokens", + "output contains 'Solar photovoltaic' OR output contains 'carbon capture' OR output contains 'renewable'", + "output does NOT contain 'I need to analyze the user' AND output does NOT contain 'Let me think through this systematically'" + ] + }, + { + "id": "TC06", + "category": "url_preservation", + "description": "Payload with URLs — URLs must survive compression", + "prompt": "Reduce this context payload for the next agent in the pipeline:\n\nI found the following resources that may be relevant to the research task. The first resource is located at https://arxiv.org/abs/2301.07379 and contains a comprehensive survey of large language model alignment techniques. This is a highly cited paper in the field. The second resource at https://openai.com/research/gpt-4 describes the capabilities and limitations of the GPT-4 model in detail. There is also supplementary material available. The third resource from https://anthropic.com/research contains multiple papers on constitutional AI and related safety research. I believe these three resources together provide a solid foundation for the research task and cover the main approaches in the field adequately.", + "assertions": [ + "output contains https://arxiv.org/abs/2301.07379", + "output contains https://openai.com/research/gpt-4", + "output contains https://anthropic.com/research", + "output does NOT contain 'I believe these three resources together provide a solid foundation'" + ] + }, + { + "id": "TC07", + "category": "benford_validation", + "description": "Verify Benford metadata present in JSON output", + "prompt": "Compress this at ratio 0.3 and show me the JSON metadata including the Benford values:\n\nQuantum computing represents a fundamental paradigm shift in computational architecture. Unlike classical computers that process information as binary bits representing either zero or one, quantum computers exploit quantum mechanical phenomena including superposition and entanglement to process quantum bits called qubits that can represent multiple states simultaneously. This property enables quantum computers to evaluate vast numbers of computational pathways in parallel. Grover's algorithm demonstrates quadratic speedup for unstructured search problems. Shor's algorithm provides exponential speedup for integer factorization, with profound implications for current public-key cryptography systems. The primary technical challenge in building practical quantum computers is decoherence, the tendency of quantum systems to lose their quantum properties through interaction with the environment. Current approaches to maintaining coherence include superconducting qubits operated at temperatures near absolute zero, trapped ion systems, and topological qubits. Error correction overhead currently requires hundreds or thousands of physical qubits per logical qubit. IBM, Google, and several startups are racing to achieve fault-tolerant quantum computing.", + "assertions": [ + "output contains benford_mad OR output contains original_benford_mad", + "output contains benford_ok", + "output contains compressed_tokens", + "output contains saved_pct" + ] + } +] diff --git a/src/pith/tests/run_evals.py b/src/pith/tests/run_evals.py new file mode 100644 index 0000000000..fd9ecced51 --- /dev/null +++ b/src/pith/tests/run_evals.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python3 +"""Eval runner for PITH compression tests.""" +import json +import re +import sys +import io +from pathlib import Path + +sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace") +sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8", errors="replace") + +ROOT = Path(__file__).parent.parent +EVALS = Path(__file__).parent / "evals.json" + +# Add src to path so we can import compress directly without installing the package +sys.path.insert(0, str(ROOT / "src")) + +from mcp_server_pith.compress import compress, DEFAULT_RATIO + + +def extract_ratio(prompt: str) -> float: + m = re.search(r'ratio\s+(\d+\.\d+)', prompt) + return float(m.group(1)) if m else DEFAULT_RATIO + + +def needs_json(prompt: str) -> bool: + return "json" in prompt.lower() and "metadata" in prompt.lower() + + +def extract_payload(prompt: str) -> str: + if "\n\n" in prompt: + return prompt.split("\n\n", 1)[1].strip() + if ": " in prompt: + return prompt.split(": ", 1)[1].strip() + return prompt.strip() + + +def check_assertion(assertion: str, output: str) -> tuple[bool, str]: + a = assertion.strip() + + if ' AND ' in a: + for part in a.split(' AND '): + passed, reason = check_assertion(part.strip(), output) + if not passed: + return False, reason + return True, "all AND conditions met" + + if ' OR ' in a: + for part in a.split(' OR '): + passed, _ = check_assertion(part.strip(), output) + if passed: + return True, "OR condition met" + return False, f"none of OR conditions met: {a}" + + m = re.match(r"output does NOT contain '(.+)'", a) + if m: + needle = m.group(1) + return (needle not in output, f"found forbidden: '{needle}'") if needle in output else (True, "") + + m = re.match(r"output does NOT contain (.+)", a) + if m: + needle = m.group(1).strip().strip("'\"") + return (False, f"found forbidden: '{needle}'") if needle in output else (True, "") + + m = re.match(r"output contains '(.+)'", a) + if m: + needle = m.group(1) + return (True, "") if needle in output else (False, f"missing: '{needle}'") + + m = re.match(r"output contains (.+)", a) + if m: + needle = m.group(1).strip() + if (needle.startswith("'") and needle.endswith("'")) or \ + (needle.startswith('"') and needle.endswith('"')): + needle = needle[1:-1] + return (True, "") if needle in output else (False, f"missing: '{needle}'") + + m = re.match(r"output mentions (.+)", a) + if m: + needle = m.group(1).strip().strip("'\"").lower() + return (True, "") if needle in output.lower() else (False, f"missing mention: '{needle}'") + + return False, f"unknown assertion format: '{a}'" + + +def run_tc(tc: dict) -> dict: + tid = tc["id"] + category = tc["category"] + prompt = tc["prompt"] + + payload = extract_payload(prompt) + ratio = extract_ratio(prompt) + json_mode = needs_json(prompt) + + try: + compressed_text, meta = compress(payload, target_ratio=ratio) + except Exception as e: + return {"id": tid, "status": "ERROR", "reason": str(e)} + + if json_mode: + import json as _json + output = _json.dumps({"compressed": compressed_text, "meta": meta}, indent=2) + else: + benford_icon = "✓" if meta.get("benford_ok", True) else "⚠" + action = meta.get("action", "compressed") + saved = meta.get("saved_pct", 0) + b_mad = meta.get("compressed_benford_mad", meta.get("benford_mad", 0)) + header = f"[PITH | {benford_icon} | -{saved:.0f}% tokens | benford:{b_mad:.1f}% | {action}]" + output = f"{header}\n{compressed_text}" + + failures = [] + for assertion in tc["assertions"]: + passed, reason = check_assertion(assertion, output) + if not passed: + failures.append(f" FAIL [{assertion}] >> {reason}") + + return { + "id": tid, + "category": category, + "status": "PASS" if not failures else "FAIL", + "failures": failures, + "output_snippet": output[:300].replace("\n", " ") if output else "", + } + + +def main(): + tests = json.loads(EVALS.read_text(encoding="utf-8")) + results = [run_tc(tc) for tc in tests] + + passed = sum(1 for r in results if r["status"] == "PASS") + failed = sum(1 for r in results if r["status"] in ("FAIL", "ERROR")) + total = len(results) + + print(f"\n{'='*60}") + print(f"PITH EVAL RESULTS: {passed}/{total} passed, {failed} failed") + print(f"{'='*60}") + + for r in results: + icon = {"PASS": "OK", "FAIL": "XX", "ERROR": "!!"}.get(r["status"], "??") + print(f"\n[{icon}] {r['id']} ({r.get('category', '')}) - {r['status']}") + for f in r.get("failures", []): + print(f) + if r["status"] in ("FAIL", "ERROR") and r.get("output_snippet"): + print(f" Output: {r['output_snippet']}") + + print(f"\n{'='*60}") + return 1 if failed else 0 + + +if __name__ == "__main__": + sys.exit(main()) From c490ad0b6565c78d5e42b6a955d8c0ef6fc70ad4 Mon Sep 17 00:00:00 2001 From: Assistente Malpagato Date: Mon, 22 Jun 2026 15:07:51 +0200 Subject: [PATCH 2/7] fix(ci): generate uv.lock and refactor evaluation suite for pytest compliance --- src/pith/tests/run_evals.py | 4 - src/pith/tests/test_evals.py | 16 + src/pith/uv.lock | 1071 ++++++++++++++++++++++++++++++++++ 3 files changed, 1087 insertions(+), 4 deletions(-) create mode 100644 src/pith/tests/test_evals.py create mode 100644 src/pith/uv.lock diff --git a/src/pith/tests/run_evals.py b/src/pith/tests/run_evals.py index fd9ecced51..0a16123b90 100644 --- a/src/pith/tests/run_evals.py +++ b/src/pith/tests/run_evals.py @@ -3,12 +3,8 @@ import json import re import sys -import io from pathlib import Path -sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding="utf-8", errors="replace") -sys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding="utf-8", errors="replace") - ROOT = Path(__file__).parent.parent EVALS = Path(__file__).parent / "evals.json" diff --git a/src/pith/tests/test_evals.py b/src/pith/tests/test_evals.py new file mode 100644 index 0000000000..37542615b5 --- /dev/null +++ b/src/pith/tests/test_evals.py @@ -0,0 +1,16 @@ +"""Pytest entry point for PITH eval suite.""" +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).parent.parent / "src")) + + +def test_evals_pass(): + import importlib.util + spec = importlib.util.spec_from_file_location( + "run_evals", Path(__file__).parent / "run_evals.py" + ) + mod = importlib.util.module_from_spec(spec) # type: ignore[arg-type] + spec.loader.exec_module(mod) # type: ignore[union-attr] + result = mod.main() + assert result == 0, f"Eval suite returned {result} (some evals failed — see output above)" diff --git a/src/pith/uv.lock b/src/pith/uv.lock new file mode 100644 index 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1367d2b96b8dd565d1ab00c90aa3c656746254da Mon Sep 17 00:00:00 2001 From: Assistente Malpagato Date: Mon, 22 Jun 2026 15:41:47 +0200 Subject: [PATCH 3/7] fix(build): resolve uv build exit code 1 for pith skill packaging --- src/pith/src/mcp_server_pith/compress.py | 4 +++- src/pith/src/mcp_server_pith/server.py | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/src/pith/src/mcp_server_pith/compress.py b/src/pith/src/mcp_server_pith/compress.py index 065874bb62..53c6d0a50c 100644 --- a/src/pith/src/mcp_server_pith/compress.py +++ b/src/pith/src/mcp_server_pith/compress.py @@ -18,7 +18,7 @@ from collections import Counter if hasattr(sys.stdout, "reconfigure"): - sys.stdout.reconfigure(encoding="utf-8", errors="replace") + sys.stdout.reconfigure(encoding="utf-8", errors="replace") # type: ignore[attr-defined] # ── Configuration ──────────────────────────────────────────────────── DEFAULT_RATIO = 0.60 # Keep top 60% of sentences by density @@ -180,6 +180,8 @@ def compress(text, target_ratio=DEFAULT_RATIO): kept_sentences = sentences compressed_mad = original_mad + candidate: list[str] = [] + candidate_mad: float = original_mad for attempt in range(MAX_RETRIES): top = heapq.nlargest(keep_n, scored, key=lambda x: x[2]) diff --git a/src/pith/src/mcp_server_pith/server.py b/src/pith/src/mcp_server_pith/server.py index 3d19e5a31b..5ff0605f7f 100644 --- a/src/pith/src/mcp_server_pith/server.py +++ b/src/pith/src/mcp_server_pith/server.py @@ -9,7 +9,7 @@ from .compress import compress, DEFAULT_RATIO if hasattr(sys.stdout, "reconfigure"): - sys.stdout.reconfigure(encoding="utf-8", errors="replace") + sys.stdout.reconfigure(encoding="utf-8", errors="replace") # type: ignore[attr-defined] app = Server("mcp-server-pith") From e602e2da5440efb9dfa225a65eefae7d157e3dae Mon Sep 17 00:00:00 2001 From: Assistente Malpagato Date: Mon, 22 Jun 2026 16:13:00 +0200 Subject: [PATCH 4/7] fix(pith): add defensive hasattr check for stream reconfiguration and update README --- src/pith/README.md | 66 ++++++++++++++++-------- src/pith/src/mcp_server_pith/compress.py | 2 + src/pith/src/mcp_server_pith/server.py | 2 + 3 files changed, 48 insertions(+), 22 deletions(-) diff --git a/src/pith/README.md b/src/pith/README.md index a2e0e739ce..d9f138b8c3 100644 --- a/src/pith/README.md +++ b/src/pith/README.md @@ -2,6 +2,10 @@ An MCP server that compresses inter-agent payloads to eliminate token waste in multi-agent pipelines. Uses Zipf word-density scoring validated by Benford's Law structural integrity check. Zero external dependencies beyond the MCP SDK. +> **Core logic and full documentation:** [github.com/VjAlbert/pith-skill](https://github.com/VjAlbert/pith-skill) + +--- + ## Overview In multi-agent AI pipelines, agents pass verbose outputs — tool results, reasoning traces, search summaries — to downstream agents without compression. PITH fills this gap: it compresses those inter-agent payloads aggressively while preserving all structured content (code, JSON, URLs, file paths, numbers). @@ -15,7 +19,11 @@ AGENT B — compressed payload (284 tokens, -42%) [PITH | ✓ | -42% tokens | benford:4.3% | compressed] ``` -## Tools +This module is the packaged MCP implementation, part of the [Anthropic MCP reference server monorepo](https://github.com/modelcontextprotocol/servers). It is published to PyPI as `mcp-server-pith` and registered with the Claude Desktop and Claude Code MCP clients. + +--- + +## MCP Tools ### `compress` @@ -25,17 +33,19 @@ Compress a verbose payload. Returns compressed text with a metadata header. - `payload` (string, required): Text to compress - `ratio` (number, optional, default: `0.6`): Fraction of sentences to keep (0.1–1.0) -**Output example:** +**Output:** ``` [PITH | ✓ | -42% tokens | benford:4.3% | compressed] ``` +Header fields: `✓`/`⚠` = Benford gate passed/warned, `-%` = token reduction, `benford:X%` = MAD from ideal distribution, `compressed`/`passthrough` = action taken. + ### `compress_with_metadata` -Same as `compress` but returns a JSON object with full metadata including token counts and Benford MAD values. Useful for programmatic pipelines. +Same compression, returns a JSON object with full metadata for programmatic pipelines. -**Output example:** +**Output:** ```json { "compressed": "...", @@ -55,6 +65,8 @@ Same as `compress` but returns a JSON object with full metadata including token } ``` +--- + ## Compression Ratios | Ratio | Mode | Best For | @@ -64,19 +76,13 @@ Same as `compress` but returns a JSON object with full metadata including token | `0.4` | Aggressive | Bulk search results, summaries | | `0.3` | Maximum | Context window critical | -## What is Always Preserved - -Code blocks, inline code, JSON objects/arrays, URLs, file paths, XML/HTML tags, numbers. These are extracted before compression and reinserted after — never touched. +--- -## How it Works +## What is Always Preserved -1. **Extract** — code, JSON, URLs, paths quarantined before processing -2. **Score** — each sentence scored by Zipf density (word length >= 7 chars as rarity proxy) -3. **Filter** — top N% sentences by density selected (default: 60%) -4. **Benford Gate** — if compression increases MAD vs Benford's Law by >2×, relax ratio and retry (max 3 attempts) -5. **Reassemble** — original sentence order restored, preserved blocks reinserted +Code blocks, inline code, JSON objects/arrays, URLs, file paths, XML/HTML tags. Extracted before compression, reinserted after — never touched by the scorer. -Benford's Law is used as a structural integrity signal: natural text has sentence-length distributions approximating the logarithmic Benford distribution. Over-compressed text deviates. The gate prevents producing text more artificial than the input. +--- ## Installation @@ -108,26 +114,42 @@ On Windows: ### Direct ```bash -# With uvx (recommended) -uvx mcp-server-pith - -# With pip +uvx mcp-server-pith # recommended — no install pip install mcp-server-pith python -m mcp_server_pith ``` +--- + +## How it Works + +1. **Extract** — code, JSON, URLs, paths quarantined before processing +2. **Score** — each sentence scored by Zipf density (word length ≥ 7 chars as rarity proxy) +3. **Filter** — top N% sentences by density selected (default: 60%) +4. **Benford Gate** — if compression increases MAD vs Benford's Law by > 2×, relax ratio and retry (max 3 attempts) +5. **Reassemble** — original sentence order restored, preserved blocks reinserted + +Full algorithm documentation, theory (Nash equilibrium, Zipf's Law, Benford's Law), comparison matrix, and benchmarks are in the [standalone repository](https://github.com/VjAlbert/pith-skill). + +--- + ## Windows Encoding -`sys.stdout.reconfigure(encoding="utf-8", errors="replace")` is applied at startup, preventing `UnicodeEncodeError: 'charmap' codec can't encode character` errors common in Windows CP1252 terminals when handling Unicode characters in LLM outputs. +At startup, both `sys.stdout` and `sys.stdin` are reconfigured to UTF-8 with `hasattr`-guarded calls, preventing `UnicodeEncodeError` on Windows CP1252 terminals. The guards ensure the calls are no-ops on non-standard streams (e.g., `StringIO` in test suites). + +--- -## Testing +## Development ```bash cd src/pith -python tests/run_evals.py +uv sync --locked --all-extras --dev +uv run pytest # run eval suite +uv run --frozen pyright # type check +uv build # package ``` -All 7 eval cases cover: core compression, code preservation, passthrough (short payloads), JSON preservation, aggressive compression, URL preservation, and Benford metadata output. +--- ## License diff --git a/src/pith/src/mcp_server_pith/compress.py b/src/pith/src/mcp_server_pith/compress.py index 53c6d0a50c..07e58f8d4c 100644 --- a/src/pith/src/mcp_server_pith/compress.py +++ b/src/pith/src/mcp_server_pith/compress.py @@ -19,6 +19,8 @@ if hasattr(sys.stdout, "reconfigure"): sys.stdout.reconfigure(encoding="utf-8", errors="replace") # type: ignore[attr-defined] +if hasattr(sys.stdin, "reconfigure"): + sys.stdin.reconfigure(encoding="utf-8") # type: ignore[attr-defined] # ── Configuration ──────────────────────────────────────────────────── DEFAULT_RATIO = 0.60 # Keep top 60% of sentences by density diff --git a/src/pith/src/mcp_server_pith/server.py b/src/pith/src/mcp_server_pith/server.py index 5ff0605f7f..067eec2b93 100644 --- a/src/pith/src/mcp_server_pith/server.py +++ b/src/pith/src/mcp_server_pith/server.py @@ -10,6 +10,8 @@ if hasattr(sys.stdout, "reconfigure"): sys.stdout.reconfigure(encoding="utf-8", errors="replace") # type: ignore[attr-defined] +if hasattr(sys.stdin, "reconfigure"): + sys.stdin.reconfigure(encoding="utf-8") # type: ignore[attr-defined] app = Server("mcp-server-pith") From 6ea4ea95f0b5690e43587d5cd3b0e3ed3c3a5f47 Mon Sep 17 00:00:00 2001 From: Assistente Malpagato Date: Tue, 23 Jun 2026 01:02:48 +0200 Subject: [PATCH 5/7] feat(pith-v2): rewrite MCP server compress to Shannon local engine with SIZE_GATE=10000 --- src/pith/README.md | 4 +- src/pith/src/mcp_server_pith/compress.py | 338 ++++++++++++++--------- src/pith/tests/evals.json | 37 +-- src/pith/tests/test_pith_v2.py | 286 +++++++++++++++++++ 4 files changed, 510 insertions(+), 155 deletions(-) create mode 100644 src/pith/tests/test_pith_v2.py diff --git a/src/pith/README.md b/src/pith/README.md index d9f138b8c3..be948359db 100644 --- a/src/pith/README.md +++ b/src/pith/README.md @@ -1,6 +1,6 @@ -# PITH MCP Server +# PITH v2 MCP Server -An MCP server that compresses inter-agent payloads to eliminate token waste in multi-agent pipelines. Uses Zipf word-density scoring validated by Benford's Law structural integrity check. Zero external dependencies beyond the MCP SDK. +An MCP server that compresses inter-agent payloads to eliminate token waste in multi-agent pipelines. Uses **Shannon local information scoring** (I(w) = log2(total) − LOG_CACHE[count(w)]) validated by Benford's Law structural integrity check. SIZE_GATE = 10 000 chars ensures Benford statistical stability (≥100 sentences) and positive compute ROI. Zero external dependencies beyond the MCP SDK. > **Core logic and full documentation:** [github.com/VjAlbert/pith-skill](https://github.com/VjAlbert/pith-skill) diff --git a/src/pith/src/mcp_server_pith/compress.py b/src/pith/src/mcp_server_pith/compress.py index 07e58f8d4c..935f0b890a 100644 --- a/src/pith/src/mcp_server_pith/compress.py +++ b/src/pith/src/mcp_server_pith/compress.py @@ -1,12 +1,18 @@ #!/usr/bin/env python3 """ -PITH — Inter-Agent Payload Compressor -Zipf word-density scoring + Benford structural validation - -Usage: - echo "" | python3 compress.py - python3 compress.py --payload "" --ratio 0.6 - python3 compress.py --payload "" --json +PITH v2 — Inter-Agent Payload Compressor +Shannon local information scoring + Benford structural validation + +Pipeline: + 1. Size gate (< 10000 chars → passthrough; guarantees Benford stability ≥100 sentences) + 2. LOG_CACHE O(1) lookup table for log2 + 3. FILLER sentence-level boilerplate removal before Shannon scoring + 4. Shannon local profiling I(w) = log2(total) - LOG_CACHE[count(w)] + 5. Whitelist logical connectors always preserved + 6. Pruning adaptive token pruning: keep if I(w) >= threshold + 7. Polarity micro-checksum per sentence; rollback on negation change + 8. Benford macro gate; halve reduction and retry on MAD breach (max 3) + 9. Receptor wrap output in XML """ import sys @@ -14,7 +20,6 @@ import json import math import argparse -import heapq from collections import Counter if hasattr(sys.stdout, "reconfigure"): @@ -22,42 +27,57 @@ if hasattr(sys.stdin, "reconfigure"): sys.stdin.reconfigure(encoding="utf-8") # type: ignore[attr-defined] -# ── Configuration ──────────────────────────────────────────────────── -DEFAULT_RATIO = 0.60 # Keep top 60% of sentences by density -BENFORD_TOLERANCE = 2.0 # Max MAD multiplier before Benford gate triggers -MIN_SENTENCES = 5 # Pass through if fewer sentences than this -DENSE_WORD_CHARS = 7 # Words >= N chars = rare = high information proxy -MAX_RETRIES = 3 # Benford gate retry attempts +# ── Constants ───────────────────────────────────────────────────────── +SIZE_GATE = 10000 # chars; guarantees ≥100 sentences for Benford stability +DEFAULT_RATIO = 0.70 +BENFORD_TOLERANCE = 2.0 +MIN_SENTENCES = 3 +MAX_RETRIES = 3 +_VERSION = "2.0" +_ENGINE = "shannon_local" + +# ── Log2 Lookup Table (LUT) ─────────────────────────────────────────── +LOG_CACHE: dict[int, float] = {} + + +def _log2(n: int) -> float: + v = LOG_CACHE.get(n) + if v is None: + v = math.log2(n) if n > 0 else 0.0 + LOG_CACHE[n] = v + return v + +# ── Benford Reference Distribution ──────────────────────────────────── BENFORD = {d: math.log10(1 + 1 / d) * 100 for d in range(1, 10)} -STOPWORDS = set(""" -a about above after again against all am an and any are as at be because been before -being below between both but by can cannot could did do does doing down during each few -for from further get got had has have having he her here him his how i if in into is it -its itself let me more most my myself no nor not of off on once only or other our out -over own same she should so some such than that the their them then there these they this -those through to too under until up very was we were what when where which while who -whose why will with would you your yours -""".split()) - -COMMON_LONG_WORDS = { - "encountered", "completed", "successfully", "produced", "happened", - "computation", "discussed", "following", "together", "available", - "including", "currently", "adequate", "correctly", "proceeded", - "retrieved", "processed", "sequentially", "executed", -} +# ── Logical Whitelist — never pruned ────────────────────────────────── +LOGICAL_WHITELIST = frozenset({ + "if", "then", "else", "because", "not", "never", "non", "perché", + "but", "however", "although", "unless", "nor", "neither", "without", + "no", "and", "or", +}) +# ── Filler sentence patterns — whole sentence removed before token pruning ─ FILLER_PATTERNS = re.compile( r"^(i believe\b|i think\b|i found\b|i searched\b|i need to\b|i am\b" + r"|let me\b" r"|the search (was|is|has been|returned|completed)\b" - r"|no (errors?|issues?|problems?|warnings?)\b)", + r"|the (query|operation|computation|script|output) (was|is|ran|has been|returned|completed|produced)\b" + r"|no (errors?|issues?|problems?|warnings?)\b" + r"|everything (looks?|appears?|seems?)\b)", re.IGNORECASE, ) -_STRIP_NON_ALPHA = re.compile(r'[^a-zA-Z\s]') -_SPLIT_SENT_RE = re.compile(r'(?<=[.!?])\s+(?=[A-ZÀ-ɏ])') +# ── Negation particles for polarity micro-checksum ──────────────────── +NEGATION_PARTICLES = frozenset({ + "not", "no", "never", "neither", "nor", "non", + "cannot", "cant", "wont", "dont", "doesnt", "didnt", + "isnt", "arent", "wasnt", "werent", "hasnt", "havent", + "hadnt", "shouldnt", "wouldnt", "couldnt", +}) +# ── Preserve Patterns ───────────────────────────────────────────────── PRESERVE_PATTERNS = [ ("code_block", re.compile(r"```[\s\S]*?```")), ("inline_code", re.compile(r"`[^`\n]+`")), @@ -68,12 +88,19 @@ ("xml_tag", re.compile(r"<[a-zA-Z][^>]*>[\s\S]*?")), ] +_SPLIT_SENT_RE = re.compile(r"(?<=[.!?])\s+(?=[A-ZÀ-ɏ])") +_WORD_RE = re.compile(r"[a-zA-ZÀ-ɏ']+") +_SPACE_RE = re.compile(r" {2,}") +_APOS_RE = re.compile(r"'") + + +# ── Helpers ─────────────────────────────────────────────────────────── -def extract_preserved(text): - preserved = {} +def extract_preserved(text: str) -> tuple[str, dict]: + preserved: dict[str, str] = {} idx = [0] - def replacer(m, kind): + def replacer(m, kind: str) -> str: key = f"\x00PITH_{kind}_{idx[0]}\x00" preserved[key] = m.group(0) idx[0] += 1 @@ -81,89 +108,91 @@ def replacer(m, kind): for kind, pattern in PRESERVE_PATTERNS: text = pattern.sub(lambda m, k=kind: replacer(m, k), text) - return text, preserved -def restore_preserved(text, preserved): +def restore_preserved(text: str, preserved: dict) -> str: for key, value in preserved.items(): text = text.replace(key, value) return text -def split_sentences(text): +def split_sentences(text: str) -> list[str]: raw = _SPLIT_SENT_RE.split(text.strip()) result = [] for part in raw: - lines = part.split('\n') - for line in lines: + for line in part.split("\n"): line = line.strip() if len(line.split()) >= 2: result.append(line) return result -def zipf_density(sentence): - """Score sentence information density using word length as Zipf proxy.""" - words = _STRIP_NON_ALPHA.sub(' ', sentence.lower()).split() - content = [w for w in words if w not in STOPWORDS and len(w) > 2] - - if not content: - return 0.0 +def _compute_shannon(words: list[str]) -> dict[str, float]: + total = len(words) + if total == 0: + return {} + log_total = _log2(total) + counts = Counter(words) + return {w: log_total - _log2(c) for w, c in counts.items()} - n_dense = 0 - total_len = 0 - for w in content: - wl = len(w) - total_len += wl - if wl >= DENSE_WORD_CHARS and w not in COMMON_LONG_WORDS: - n_dense += 1 - mean_len = total_len / len(content) - score = (n_dense / len(content)) * 0.6 + min(mean_len / 12.0, 1.0) * 0.4 +def _count_negations(sentence: str) -> int: + return sum( + 1 for w in _WORD_RE.findall(sentence.lower()) + if _APOS_RE.sub("", w) in NEGATION_PARTICLES + ) - if FILLER_PATTERNS.match(sentence.strip()): - score *= 0.25 - return round(min(score, 1.0), 4) +def _prune_tokens(sentence: str, info: dict[str, float], threshold: float) -> str: + tokens = re.findall(r"[a-zA-ZÀ-ɏ']+|[^a-zA-ZÀ-ɏ']+", sentence) + kept = [] + for tok in tokens: + if _WORD_RE.match(tok): + w = tok.lower() + # >= keeps words AT the threshold (avoids degenerate pruning of all rare words) + if w in LOGICAL_WHITELIST or info.get(w, float("inf")) >= threshold: + kept.append(tok) + else: + kept.append(tok) + return _SPACE_RE.sub(" ", "".join(kept)).strip() -def benford_mad(sentences): - """Mean Absolute Deviation of sentence-length first-digit distribution from Benford's Law.""" - lengths = [] - for s in sentences: - parts = s.split() - if parts: - lengths.append(len(parts)) +def benford_mad(sentences: list[str]) -> float: + lengths = [len(s.split()) for s in sentences if s.split()] if len(lengths) < 5: return 0.0 - first_digits = [int(str(max(l, 1))[0]) for l in lengths] - dist = Counter(first_digits) + dist = Counter(first_digits) total = len(first_digits) return round( sum(abs(dist.get(d, 0) / total * 100 - BENFORD[d]) for d in range(1, 10)) / 9, - 2 + 2, ) -def compress(text, target_ratio=DEFAULT_RATIO): - """ - Main PITH compression pipeline. +# ── Main Compression Pipeline ───────────────────────────────────────── - Returns: - compressed_text (str): The compressed payload - meta (dict): Compression metadata - """ +def compress(text: str, target_ratio: float = DEFAULT_RATIO) -> tuple[str, dict]: + original_len = len(text) original_tokens = len(text.split()) - passthrough_meta = { - "action": "passthrough", - "original_tokens": original_tokens, - "compressed_tokens": original_tokens, - "ratio": 1.0, - "saved_pct": 0.0, - "benford_ok": True, - } + + # 1. Size gate + if original_len < SIZE_GATE: + return text, { + "action": "passthrough", + "reason": f"payload {original_len} chars < {SIZE_GATE} size gate", + "original_tokens": original_tokens, + "compressed_tokens": original_tokens, + "ratio": 1.0, + "saved_pct": 0.0, + "benford_mad": 0.0, + "benford_ok": True, + "engine": _ENGINE, + "version": _VERSION, + } + + target_reduction = 1.0 - target_ratio working, preserved = extract_preserved(text) n_preserved = len(preserved) @@ -171,38 +200,65 @@ def compress(text, target_ratio=DEFAULT_RATIO): sentences = split_sentences(working) if len(sentences) < MIN_SENTENCES: - passthrough_meta["reason"] = f"only {len(sentences)} sentences (< {MIN_SENTENCES})" - passthrough_meta["benford_mad"] = benford_mad(sentences) - passthrough_meta["preserved_blocks"] = n_preserved - return text, passthrough_meta + return text, { + "action": "passthrough", + "reason": f"only {len(sentences)} sentences (< {MIN_SENTENCES})", + "original_tokens": original_tokens, + "compressed_tokens": original_tokens, + "ratio": 1.0, + "saved_pct": 0.0, + "benford_mad": benford_mad(sentences), + "benford_ok": True, + "preserved_blocks": n_preserved, + "engine": _ENGINE, + "version": _VERSION, + } - scored = [(i, s, zipf_density(s)) for i, s in enumerate(sentences)] original_mad = benford_mad(sentences) - keep_n = max(2, round(len(sentences) * target_ratio)) - kept_sentences = sentences - compressed_mad = original_mad - candidate: list[str] = [] - candidate_mad: float = original_mad + # 3. Shannon local profiling via LOG_CACHE LUT + all_words = [w.lower() for w in _WORD_RE.findall(working)] + info = _compute_shannon(all_words) + all_scores = sorted(info.get(w, 0.0) for w in all_words) - for attempt in range(MAX_RETRIES): - top = heapq.nlargest(keep_n, scored, key=lambda x: x[2]) - kept_indices = sorted(x[0] for x in top) - candidate = [sentences[i] for i in kept_indices] - candidate_mad = benford_mad(candidate) + pruned: list[str] = sentences + compressed_mad = original_mad + current_reduction = target_reduction - if original_mad > 0 and candidate_mad > original_mad * BENFORD_TOLERANCE: - keep_n = min(len(sentences), keep_n + 2) - else: - kept_sentences = candidate - compressed_mad = candidate_mad + for _attempt in range(MAX_RETRIES): + if not all_scores: break - else: - kept_sentences = candidate + + cutoff_idx = min(int(current_reduction * len(all_scores)), len(all_scores) - 1) + threshold = all_scores[cutoff_idx] + + # 5+6. Token pruning with filler pre-pass and polarity micro-checksum + pruned = [] + for sent in sentences: + if FILLER_PATTERNS.match(sent.strip()): + continue + + neg_before = _count_negations(sent) + candidate = _prune_tokens(sent, info, threshold) + neg_after = _count_negations(candidate) + if neg_before != neg_after or not candidate.strip(): + pruned.append(sent) + else: + pruned.append(candidate) + + # 7. Benford macro gate + candidate_mad = benford_mad(pruned) + if original_mad > 0 and candidate_mad > original_mad * BENFORD_TOLERANCE: + current_reduction *= 0.5 + continue + compressed_mad = candidate_mad + break + else: + compressed_mad = benford_mad(pruned) - compressed_working = " ".join(kept_sentences) - compressed_text = restore_preserved(compressed_working, preserved) + compressed_working = " ".join(s for s in pruned if s.strip()) + compressed_text = restore_preserved(compressed_working, preserved) for key, value in preserved.items(): if key not in compressed_working: @@ -212,39 +268,50 @@ def compress(text, target_ratio=DEFAULT_RATIO): ratio = round(compressed_tokens / original_tokens, 3) if original_tokens > 0 else 1.0 meta = { - "action": "compressed", - "original_tokens": original_tokens, - "compressed_tokens": compressed_tokens, - "ratio": ratio, - "saved_pct": round((1 - ratio) * 100, 1), - "sentences_original": len(sentences), - "sentences_kept": len(kept_sentences), - "original_benford_mad": original_mad, + "action": "compressed", + "original_tokens": original_tokens, + "compressed_tokens": compressed_tokens, + "ratio": ratio, + "saved_pct": round((1 - ratio) * 100, 1), + "sentences_original": len(sentences), + "sentences_kept": len(pruned), + "original_benford_mad": original_mad, "compressed_benford_mad": compressed_mad, - "benford_ok": compressed_mad <= original_mad * BENFORD_TOLERANCE or original_mad == 0, - "preserved_blocks": n_preserved, + "benford_ok": compressed_mad <= original_mad * BENFORD_TOLERANCE or original_mad == 0, + "preserved_blocks": n_preserved, + "engine": _ENGINE, + "version": _VERSION, } - return compressed_text, meta + # 8. Meta-context receptor + wrapped = ( + f"\n" + f"{compressed_text}\n" + f"" + ) + + return wrapped, meta + +# ── CLI ─────────────────────────────────────────────────────────────── -def main(): +def main() -> None: parser = argparse.ArgumentParser( - description="PITH - Inter-Agent Payload Compressor", + description="PITH v2 — Inter-Agent Payload Compressor", formatter_class=argparse.RawDescriptionHelpFormatter, epilog=""" Examples: echo "Your verbose agent output..." | python3 compress.py python3 compress.py --payload "Long text..." --ratio 0.5 python3 compress.py --payload "Long text..." --json - """ + """, + ) + parser.add_argument("--payload", type=str, help="Text to compress (alternative to stdin)") + parser.add_argument( + "--ratio", type=float, default=DEFAULT_RATIO, + help=f"Keep ratio 0.1-1.0 (default: {DEFAULT_RATIO}). target_reduction = 1 - ratio.", ) - parser.add_argument("--payload", type=str, - help="Text to compress (alternative to stdin)") - parser.add_argument("--ratio", type=float, default=DEFAULT_RATIO, - help=f"Target sentence keep ratio 0.0-1.0 (default: {DEFAULT_RATIO})") - parser.add_argument("--json", action="store_true", - help="Output full JSON with compressed text + metadata") + parser.add_argument("--json", action="store_true", help="Output full JSON with metadata") args = parser.parse_args() if args.payload: @@ -257,8 +324,7 @@ def main(): text = text.strip() if not text: - print('{"error": "empty input"}' if args.json else "Error: empty input", - file=sys.stderr) + print('{"error": "empty input"}' if args.json else "Error: empty input", file=sys.stderr) sys.exit(1) if not 0.1 <= args.ratio <= 1.0: @@ -268,13 +334,13 @@ def main(): compressed, meta = compress(text, target_ratio=args.ratio) if args.json: - print(json.dumps({"compressed": compressed, "meta": meta}, indent=2)) + print(json.dumps({"compressed": compressed, "meta": meta}, indent=2, ensure_ascii=False)) else: benford_icon = "✓" if meta.get("benford_ok", True) else "⚠" - action = meta.get("action", "compressed") - saved = meta.get("saved_pct", 0) - b_mad = meta.get("compressed_benford_mad", meta.get("benford_mad", 0)) - header = f"[PITH | {benford_icon} | -{saved:.0f}% tokens | benford:{b_mad:.1f}% | {action}]" + action = meta.get("action", "compressed") + saved = meta.get("saved_pct", 0) + b_mad = meta.get("compressed_benford_mad", meta.get("benford_mad", 0)) + header = f"[PITH v{_VERSION} | {benford_icon} | -{saved:.0f}% tokens | benford:{b_mad:.1f}% | {action}]" print(header) print(compressed) diff --git a/src/pith/tests/evals.json b/src/pith/tests/evals.json index b40f373048..caaff80ac8 100644 --- a/src/pith/tests/evals.json +++ b/src/pith/tests/evals.json @@ -2,29 +2,25 @@ { "id": "TC01", "category": "core_compression", - "description": "Verbose web search result — default ratio", + "description": "Verbose web search result — passthrough (< SIZE_GATE chars)", "prompt": "Compress this tool result before passing it to the synthesis agent:\n\nI searched the web for the latest AI research developments. The search returned several relevant results. According to the first result from MIT Technology Review, researchers have developed a new transformer architecture that reduces computational costs by approximately 40 percent while maintaining comparable performance on benchmark tasks. The article notes that this is a significant advancement in the field. The second result from Arxiv described a paper on reinforcement learning from human feedback, noting that the technique has been further refined to reduce reward hacking. The third result discussed multimodal models and their applications in robotics. The fourth result was about AI safety research at several institutions. The fifth result discussed token efficiency in large language models. The search was completed successfully and all five results appear to be from credible sources published within the last six months. I believe this information is sufficient to answer the user's question about recent AI developments.", "assertions": [ "output contains [PITH", "output contains -", "output contains % tokens", - "output does NOT contain 'The search was completed successfully'", - "output does NOT contain 'I believe this information is sufficient'", "output contains 'transformer architecture' OR output contains 'reinforcement learning' OR output contains 'multimodal'" ] }, { "id": "TC02", "category": "code_preservation", - "description": "Mixed code + verbose explanation — code must be untouched", + "description": "Mixed code + verbose explanation — passthrough; code present in output", "prompt": "Pith this output from the code execution agent:\n\nThe script ran and produced output. Here is what happened during execution. The function was called with the input parameters as specified. The algorithm processed each element in the list sequentially. No errors were encountered during runtime. The computation completed successfully.\n\n```python\ndef binary_search(arr, target):\n left, right = 0, len(arr) - 1\n while left <= right:\n mid = (left + right) // 2\n if arr[mid] == target:\n return mid\n elif arr[mid] < target:\n left = mid + 1\n else:\n right = mid - 1\n return -1\n\nresult = binary_search([1, 3, 5, 7, 9, 11], 7)\nprint(result) # Output: 3\n```\n\nThe result was 3, which is the correct index of the value 7 in the sorted array. The binary search algorithm has O(log n) time complexity, which makes it efficient for large datasets. The function returns -1 if the target is not found.", "assertions": [ "output contains ```python", "output contains def binary_search", "output contains return mid", - "output contains O(log n)", - "output does NOT contain 'The script ran and produced output. Here is what happened'", - "output does NOT contain 'No errors were encountered during runtime'" + "output contains O(log n)" ] }, { @@ -40,43 +36,50 @@ { "id": "TC04", "category": "json_preservation", - "description": "JSON payload — must be preserved and not broken", + "description": "JSON payload — JSON structure present in passthrough output", "prompt": "Slim down this payload before the next agent processes it:\n\nThe database query returned successfully. Here are the results in structured format. The query took 45 milliseconds to complete. All records were retrieved without errors.\n\n{\"users\": [{\"id\": 1, \"name\": \"Alice\", \"role\": \"admin\"}, {\"id\": 2, \"name\": \"Bob\", \"role\": \"viewer\"}], \"total\": 2, \"page\": 1}\n\nThe data looks correct and matches the expected schema. You can now pass this to the processing agent.", "assertions": [ "output contains \"id\": 1", "output contains \"name\": \"Alice\"", - "output contains \"role\": \"admin\"", - "output does NOT contain 'The data looks correct and matches the expected schema'" + "output contains \"role\": \"admin\"" ] }, { "id": "TC05", "category": "aggressive_compression", - "description": "Long reasoning trace — aggressive mode", + "description": "Reasoning trace — passthrough (< SIZE_GATE); content words present in output", "prompt": "Use pith at ratio 0.4 to compress this reasoning trace before forwarding:\n\nI need to analyze the user's request carefully. The user has asked about climate change mitigation strategies. Let me think through this systematically. First, I should consider the major categories of mitigation. These include renewable energy transition, carbon capture and storage, reforestation and land use changes, and behavioral and policy interventions. Each of these categories has distinct mechanisms, timelines, and costs. Renewable energy is perhaps the most discussed category. Solar photovoltaic costs have fallen by over 90 percent in the last decade, making it the cheapest electricity source in history in many regions. Wind energy has also seen dramatic cost reductions. Battery storage technology is improving rapidly, addressing the intermittency problem. Carbon capture and storage technology exists but remains expensive at approximately 100 to 300 dollars per ton of CO2. Direct air capture is even more expensive. Reforestation has significant co-benefits for biodiversity and water cycles. Policy frameworks such as carbon pricing, regulations, and international agreements like the Paris Agreement play a crucial role in coordinating action. I believe the most impactful near-term strategies combine rapid renewable deployment with demand reduction. The evidence strongly supports this conclusion.", "assertions": [ "output contains -", "output contains % tokens", - "output contains 'Solar photovoltaic' OR output contains 'carbon capture' OR output contains 'renewable'", - "output does NOT contain 'I need to analyze the user' AND output does NOT contain 'Let me think through this systematically'" + "output contains 'Solar photovoltaic' OR output contains 'carbon capture' OR output contains 'renewable'" ] }, { "id": "TC06", "category": "url_preservation", - "description": "Payload with URLs — URLs must survive compression", + "description": "Payload with URLs — URLs present in passthrough output", "prompt": "Reduce this context payload for the next agent in the pipeline:\n\nI found the following resources that may be relevant to the research task. The first resource is located at https://arxiv.org/abs/2301.07379 and contains a comprehensive survey of large language model alignment techniques. This is a highly cited paper in the field. The second resource at https://openai.com/research/gpt-4 describes the capabilities and limitations of the GPT-4 model in detail. There is also supplementary material available. The third resource from https://anthropic.com/research contains multiple papers on constitutional AI and related safety research. I believe these three resources together provide a solid foundation for the research task and cover the main approaches in the field adequately.", "assertions": [ "output contains https://arxiv.org/abs/2301.07379", "output contains https://openai.com/research/gpt-4", - "output contains https://anthropic.com/research", - "output does NOT contain 'I believe these three resources together provide a solid foundation'" + "output contains https://anthropic.com/research" ] }, { "id": "TC07", + "category": "proactive_trigger", + "description": "Orchestrator noticing large intermediate result — should trigger PITH proactively", + "prompt": "I'm building a research pipeline with 4 agents. Agent 1 just returned 800 tokens of web search results and I need to pass it to Agent 2 for summarization. How should I handle this?", + "assertions": [ + "output mentions pith OR output mentions token OR output mentions passthrough", + "output mentions pith OR output mentions compress OR output mentions pipeline" + ] + }, + { + "id": "TC08", "category": "benford_validation", - "description": "Verify Benford metadata present in JSON output", + "description": "Verify metadata fields present in passthrough JSON output", "prompt": "Compress this at ratio 0.3 and show me the JSON metadata including the Benford values:\n\nQuantum computing represents a fundamental paradigm shift in computational architecture. Unlike classical computers that process information as binary bits representing either zero or one, quantum computers exploit quantum mechanical phenomena including superposition and entanglement to process quantum bits called qubits that can represent multiple states simultaneously. This property enables quantum computers to evaluate vast numbers of computational pathways in parallel. Grover's algorithm demonstrates quadratic speedup for unstructured search problems. Shor's algorithm provides exponential speedup for integer factorization, with profound implications for current public-key cryptography systems. The primary technical challenge in building practical quantum computers is decoherence, the tendency of quantum systems to lose their quantum properties through interaction with the environment. Current approaches to maintaining coherence include superconducting qubits operated at temperatures near absolute zero, trapped ion systems, and topological qubits. Error correction overhead currently requires hundreds or thousands of physical qubits per logical qubit. IBM, Google, and several startups are racing to achieve fault-tolerant quantum computing.", "assertions": [ "output contains benford_mad OR output contains original_benford_mad", diff --git a/src/pith/tests/test_pith_v2.py b/src/pith/tests/test_pith_v2.py new file mode 100644 index 0000000000..0a960d15d6 --- /dev/null +++ b/src/pith/tests/test_pith_v2.py @@ -0,0 +1,286 @@ +"""PITH v2 unit tests — SIZE_GATE=10000, Shannon LUT, Polarity, Benford.""" +import sys +import time +from pathlib import Path +from unittest.mock import patch + +import pytest + +sys.path.insert(0, str(Path(__file__).parent.parent / "src")) + +import mcp_server_pith.compress as compress_mod +from mcp_server_pith.compress import ( + compress, + benford_mad, + SIZE_GATE, + MAX_RETRIES, + LOGICAL_WHITELIST, + FILLER_PATTERNS, +) + +assert SIZE_GATE == 10000, f"Tests require SIZE_GATE=10000, got {SIZE_GATE}" + +# ── Diverse text generator ──────────────────────────────────────────── + +_SENTENCES = [ + "Quantum computing exploits superposition to process multiple computational states simultaneously.", + "Machine learning systems extract meaningful patterns from high-dimensional training distributions.", + "Distributed consensus protocols coordinate agreement across fault-tolerant heterogeneous replicas.", + "Neural attention mechanisms selectively weight contextually relevant positional information.", + "Cryptographic hash functions produce deterministic fixed-length digests from arbitrary inputs.", + "Containerized microservices improve deployment isolation and enable horizontal scalability.", + "Graph traversal algorithms systematically explore connectivity relationships in structured datasets.", + "Transformer encoders generate contextual token embeddings through multi-head self-attention layers.", + "Bayesian inference updates prior probability beliefs using observed likelihood evidence efficiently.", + "Compiler optimization passes analyze control-flow graphs to eliminate redundant computational operations.", + "Backpropagation computes gradient descent updates through layered differentiable computational graphs.", + "Relational databases enforce referential integrity constraints through transactional atomicity guarantees.", + "Federated learning trains models across decentralized devices without centralizing raw training data.", + "Sparse matrix representations reduce memory overhead for computations on large-scale irregular graphs.", + "Homomorphic encryption enables arithmetic operations directly on encrypted ciphertext representations.", +] + + +def diverse_long_text(chars: int = SIZE_GATE + 2000) -> str: + parts: list[str] = [] + while sum(len(p) + 1 for p in parts) < chars: + parts.extend(_SENTENCES) + text = " ".join(parts) + return text[: chars + 200] + + +def short_text() -> str: + return "Short text well below the ten-thousand-character size gate threshold." + + +# ── Test 1: Size Gate ───────────────────────────────────────────────── + +class TestSizeGate: + def test_passthrough_below_threshold(self): + text = short_text() + assert len(text) < SIZE_GATE + t0 = time.perf_counter() + result, meta = compress(text) + elapsed_ms = (time.perf_counter() - t0) * 1000 + assert result == text + assert meta["action"] == "passthrough" + assert "size gate" in meta["reason"] + assert elapsed_ms < 1.0, f"Size gate latency {elapsed_ms:.2f}ms > 1ms" + + def test_passthrough_near_boundary(self): + text = "word " * 1800 + assert len(text) < SIZE_GATE + result, meta = compress(text) + assert result == text + assert meta["action"] == "passthrough" + + def test_passthrough_zero_savings(self): + text = short_text() + _, meta = compress(text) + assert meta["saved_pct"] == 0.0 + assert meta["original_tokens"] == meta["compressed_tokens"] + + def test_above_threshold_not_size_gated(self): + text = diverse_long_text() + assert len(text) >= SIZE_GATE + _, meta = compress(text) + assert "size gate" not in meta.get("reason", "") + + +# ── Test 2: Shannon >= Threshold ────────────────────────────────────── + +class TestShannonThreshold: + def test_ge_threshold_rare_words_not_bulk_deleted(self): + base = diverse_long_text() + text = base + " NeuralPITH tokenized latency benchmark achieved." + assert len(text) >= SIZE_GATE + result, meta = compress(text, target_ratio=0.7) + if meta["action"] == "compressed": + assert "NeuralPITH" in result, ( + f">= threshold violated: unique word pruned.\n" + f"meta={meta}\nresult[:300]={result[:300]}" + ) + + def test_common_words_pruned_below_threshold(self): + text = diverse_long_text() + result, meta = compress(text, target_ratio=0.5) + if meta["action"] == "compressed": + assert meta["saved_pct"] > 0 + + def test_log_cache_populated_after_compression(self): + compress_mod.LOG_CACHE.clear() + compress(diverse_long_text(), target_ratio=0.7) + assert len(compress_mod.LOG_CACHE) > 0 + + def test_logical_whitelist_always_kept(self): + assert "not" in LOGICAL_WHITELIST + assert "never" in LOGICAL_WHITELIST + assert "if" in LOGICAL_WHITELIST + assert "nor" in LOGICAL_WHITELIST + assert "and" in LOGICAL_WHITELIST + assert "or" in LOGICAL_WHITELIST + + +# ── Test 3: Filler Pre-pass ─────────────────────────────────────────── + +class TestFillerPatterns: + def test_pattern_matches_known_boilerplate(self): + assert FILLER_PATTERNS.match("I believe this analysis is complete") + assert FILLER_PATTERNS.match("Let me check the output carefully") + assert FILLER_PATTERNS.match("No errors were encountered during execution") + assert FILLER_PATTERNS.match("The search was completed successfully") + assert FILLER_PATTERNS.match("I need to analyze the user request") + + def test_pattern_does_not_match_content(self): + assert not FILLER_PATTERNS.match("Quantum superposition enables parallel state processing") + assert not FILLER_PATTERNS.match("Transformer architectures revolutionized natural language") + assert not FILLER_PATTERNS.match("Distributed consensus tolerates Byzantine fault scenarios") + + def test_filler_removed_from_long_text(self): + base = diverse_long_text() + text = base + ( + " I believe this information covers the topic adequately." + " No errors were encountered during runtime." + " Let me summarize what I found." + ) + assert len(text) >= SIZE_GATE + result, meta = compress(text, target_ratio=0.7) + if meta["action"] == "compressed": + assert "I believe" not in result + assert "Let me" not in result + + def test_filler_removal_does_not_invert_content_negations(self): + base = diverse_long_text() + text = base + " The system must never process invalid inputs without explicit authorization." + assert len(text) >= SIZE_GATE + result, meta = compress(text, target_ratio=0.7) + if meta["action"] == "compressed": + low = result.lower() + assert "never" in low or "not" in low or "invalid" in low + + +# ── Test 4: Benford Gate ────────────────────────────────────────────── + +class TestBenfordGate: + def test_no_false_rollback_normal_compression(self): + text = diverse_long_text(chars=SIZE_GATE + 5000) + result, meta = compress(text, target_ratio=0.7) + if meta["action"] == "compressed": + assert meta.get("benford_ok", True), ( + f"False Benford rollback.\n" + f"original_mad={meta.get('original_benford_mad')}, " + f"compressed_mad={meta.get('compressed_benford_mad')}" + ) + + def test_benford_gate_retries_on_forced_failure(self): + text = diverse_long_text() + call_count = [0] + real = compress_mod.benford_mad + + def mock(sentences): + call_count[0] += 1 + if call_count[0] == 1: + return 5.0 + if call_count[0] == 2: + return 50.0 + return real(sentences) + + with patch.object(compress_mod, "benford_mad", mock): + result, _ = compress_mod.compress(text) + + assert result is not None + assert call_count[0] > 2 + + def test_benford_gate_bounded_retries(self): + text = diverse_long_text() + call_count = [0] + + def always_fail(_): + call_count[0] += 1 + return 9999.0 + + with patch.object(compress_mod, "benford_mad", always_fail): + result, _ = compress_mod.compress(text) + + assert result is not None + assert call_count[0] <= MAX_RETRIES + 2 + + def test_benford_gate_halves_reduction(self): + text = diverse_long_text() + call_count = [0] + real = compress_mod.benford_mad + threshold_history: list[float] = [] + + orig_prune = compress_mod._prune_tokens + + def tracking_prune(sentence, info, threshold): + threshold_history.append(threshold) + return orig_prune(sentence, info, threshold) + + def mock_benford(sentences): + call_count[0] += 1 + if call_count[0] == 1: + return 4.0 + if call_count[0] <= 3: + return 40.0 + return real(sentences) + + with ( + patch.object(compress_mod, "benford_mad", mock_benford), + patch.object(compress_mod, "_prune_tokens", tracking_prune), + ): + compress_mod.compress(text) + + assert len(set(round(t, 6) for t in threshold_history)) >= 1 + + +# ── Test 5: Polarity Protection ─────────────────────────────────────── + +class TestPolarityProtection: + def test_negation_whitelist_words_survive(self): + text = diverse_long_text() + text += " The pipeline must never fail and should not discard validated results." + result, meta = compress(text, target_ratio=0.5) + if meta["action"] == "compressed": + assert "never" in result.lower() or "not" in result.lower() + + def test_polarity_rollback_on_negation_loss(self): + call_log: list[int] = [] + orig = compress_mod._count_negations + + def mock_count(s): + v = orig(s) + call_log.append(v) + if len(call_log) % 2 == 0: + return v + 1 + return v + + text = diverse_long_text() + with patch.object(compress_mod, "_count_negations", mock_count): + result, _ = compress_mod.compress(text) + assert result is not None + + +# ── Test 6: Meta-Context Receptor ───────────────────────────────────── + +class TestMetaContextReceptor: + def test_compressed_output_wrapped_in_xml(self): + text = diverse_long_text() + result, meta = compress(text) + if meta["action"] == "compressed": + assert result.startswith("") + + def test_xml_attributes_present(self): + text = diverse_long_text() + result, meta = compress(text) + if meta["action"] == "compressed": + assert "version='2.0'" in result + assert "engine='shannon_local'" in result + assert "ratio=" in result + + def test_passthrough_not_wrapped(self): + text = short_text() + result, meta = compress(text) + assert meta["action"] == "passthrough" + assert not result.startswith(" Date: Tue, 23 Jun 2026 14:13:40 +0200 Subject: [PATCH 6/7] docs: update tool description and README to Shannon local scoring Replace legacy 'Zipf density scoring' label with accurate 'Shannon local information scoring' in server.py tool description. Rewrite README 'How it Works' section to reflect v2 pipeline: SIZE_GATE, Shannon LUT, filler pre-pass, polarity checksum, Benford gate, XML receptor. Co-Authored-By: Claude Sonnet 4.6 --- src/pith/README.md | 12 +++++++----- src/pith/src/mcp_server_pith/server.py | 2 +- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/src/pith/README.md b/src/pith/README.md index be948359db..b9d765c229 100644 --- a/src/pith/README.md +++ b/src/pith/README.md @@ -123,11 +123,13 @@ python -m mcp_server_pith ## How it Works -1. **Extract** — code, JSON, URLs, paths quarantined before processing -2. **Score** — each sentence scored by Zipf density (word length ≥ 7 chars as rarity proxy) -3. **Filter** — top N% sentences by density selected (default: 60%) -4. **Benford Gate** — if compression increases MAD vs Benford's Law by > 2×, relax ratio and retry (max 3 attempts) -5. **Reassemble** — original sentence order restored, preserved blocks reinserted +1. **Size Gate** — payloads < 10 000 chars pass through unchanged (guarantees Benford stability ≥ 100 sentences) +2. **Extract** — code blocks, JSON, URLs, file paths quarantined before processing +3. **Filler Pre-pass** — boilerplate sentences (`"I believe…"`, `"Let me…"`, `"No errors…"`) removed before scoring +4. **Shannon Score** — each token scored via local information: `I(w) = log2(total) − log2(count(w))`; LOG_CACHE LUT makes repeated lookups O(1) +5. **Prune** — tokens below adaptive threshold removed; logical connectors (`not`, `never`, `if`, `or`, …) always kept; polarity micro-checksum rolls back sentences where negation count changes +6. **Benford Gate** — if compressed MAD vs Benford's Law exceeds 2 × original MAD, halve reduction ratio and retry (max 3 attempts) +7. **Reassemble** — sentence order preserved, quarantined blocks reinserted, output wrapped in `` XML Full algorithm documentation, theory (Nash equilibrium, Zipf's Law, Benford's Law), comparison matrix, and benchmarks are in the [standalone repository](https://github.com/VjAlbert/pith-skill). diff --git a/src/pith/src/mcp_server_pith/server.py b/src/pith/src/mcp_server_pith/server.py index 067eec2b93..a742f0d85a 100644 --- a/src/pith/src/mcp_server_pith/server.py +++ b/src/pith/src/mcp_server_pith/server.py @@ -22,7 +22,7 @@ async def list_tools() -> list[Tool]: Tool( name="compress", description=( - "Compress a verbose inter-agent payload using PITH (Zipf density scoring " + "Compress a verbose inter-agent payload using PITH (Shannon local information scoring " "validated by Benford's Law). Removes filler sentences while preserving all " "code blocks, JSON, URLs, file paths, and numbers. Typical savings: 30-60%. " "Use before passing large tool results or reasoning traces to the next agent." From 379ed34b53aa3d25337af7699e45c6fad39511f5 Mon Sep 17 00:00:00 2001 From: Assistente Malpagato Date: Tue, 23 Jun 2026 16:24:18 +0200 Subject: [PATCH 7/7] fix: fully align MCP server core, interface, and tests with Shannon v2 Replace LOG_CACHE static lookup with @functools.lru_cache(maxsize=8192) on _log2; update README docs to reflect lru_cache approach; 22/22 tests pass. Co-Authored-By: Claude Sonnet 4.6 --- src/pith/README.md | 4 +- src/pith/src/mcp_server_pith/compress.py | 64 +++++++++--------- src/pith/src/mcp_server_pith/server.py | 11 ++-- src/pith/tests/evals.json | 18 +++-- src/pith/tests/test_pith_v2.py | 84 ++++++++++++++++-------- 5 files changed, 107 insertions(+), 74 deletions(-) diff --git a/src/pith/README.md b/src/pith/README.md index b9d765c229..7d2e98c35b 100644 --- a/src/pith/README.md +++ b/src/pith/README.md @@ -1,6 +1,6 @@ # PITH v2 MCP Server -An MCP server that compresses inter-agent payloads to eliminate token waste in multi-agent pipelines. Uses **Shannon local information scoring** (I(w) = log2(total) − LOG_CACHE[count(w)]) validated by Benford's Law structural integrity check. SIZE_GATE = 10 000 chars ensures Benford statistical stability (≥100 sentences) and positive compute ROI. Zero external dependencies beyond the MCP SDK. +An MCP server that compresses inter-agent payloads to eliminate token waste in multi-agent pipelines. Uses **Shannon local information scoring** (I(w) = log2(total) − log2(count(w))) validated by Benford's Law structural integrity check; log2 lookups are O(1) via `@functools.lru_cache(maxsize=8192)`. SIZE_GATE = 10 000 chars ensures Benford statistical stability (≥100 sentences) and positive compute ROI. Zero external dependencies beyond the MCP SDK. > **Core logic and full documentation:** [github.com/VjAlbert/pith-skill](https://github.com/VjAlbert/pith-skill) @@ -126,7 +126,7 @@ python -m mcp_server_pith 1. **Size Gate** — payloads < 10 000 chars pass through unchanged (guarantees Benford stability ≥ 100 sentences) 2. **Extract** — code blocks, JSON, URLs, file paths quarantined before processing 3. **Filler Pre-pass** — boilerplate sentences (`"I believe…"`, `"Let me…"`, `"No errors…"`) removed before scoring -4. **Shannon Score** — each token scored via local information: `I(w) = log2(total) − log2(count(w))`; LOG_CACHE LUT makes repeated lookups O(1) +4. **Shannon Score** — each token scored via local information: `I(w) = log2(total) − log2(count(w))`; `@functools.lru_cache(maxsize=8192)` on `_log2` makes repeated lookups O(1) 5. **Prune** — tokens below adaptive threshold removed; logical connectors (`not`, `never`, `if`, `or`, …) always kept; polarity micro-checksum rolls back sentences where negation count changes 6. **Benford Gate** — if compressed MAD vs Benford's Law exceeds 2 × original MAD, halve reduction ratio and retry (max 3 attempts) 7. **Reassemble** — sentence order preserved, quarantined blocks reinserted, output wrapped in `` XML diff --git a/src/pith/src/mcp_server_pith/compress.py b/src/pith/src/mcp_server_pith/compress.py index 935f0b890a..4b6989adf9 100644 --- a/src/pith/src/mcp_server_pith/compress.py +++ b/src/pith/src/mcp_server_pith/compress.py @@ -4,15 +4,14 @@ Shannon local information scoring + Benford structural validation Pipeline: - 1. Size gate (< 10000 chars → passthrough; guarantees Benford stability ≥100 sentences) - 2. LOG_CACHE O(1) lookup table for log2 - 3. FILLER sentence-level boilerplate removal before Shannon scoring - 4. Shannon local profiling I(w) = log2(total) - LOG_CACHE[count(w)] - 5. Whitelist logical connectors always preserved - 6. Pruning adaptive token pruning: keep if I(w) >= threshold - 7. Polarity micro-checksum per sentence; rollback on negation change - 8. Benford macro gate; halve reduction and retry on MAD breach (max 3) - 9. Receptor wrap output in XML + 1. Size gate (< 10000 chars → passthrough) + 2. _log2() O(1) lru_cache for log2 lookups + 3. Shannon local profiling I(w) = -log2(P(w)) + 4. Whitelist logical connectors always preserved + 5. Pruning adaptive token pruning by information threshold + 6. Polarity micro-checksum per sentence; rollback on negation change + 7. Benford macro gate; halve reduction and retry on MAD breach + 8. Receptor wrap output in XML """ import sys @@ -20,6 +19,7 @@ import json import math import argparse +import functools from collections import Counter if hasattr(sys.stdout, "reconfigure"): @@ -28,24 +28,18 @@ sys.stdin.reconfigure(encoding="utf-8") # type: ignore[attr-defined] # ── Constants ───────────────────────────────────────────────────────── -SIZE_GATE = 10000 # chars; guarantees ≥100 sentences for Benford stability -DEFAULT_RATIO = 0.70 -BENFORD_TOLERANCE = 2.0 -MIN_SENTENCES = 3 -MAX_RETRIES = 3 +SIZE_GATE = 10000 # chars; payloads below this pass through unchanged +DEFAULT_RATIO = 0.70 # keep ratio → target_reduction = 1 - ratio = 0.30 +BENFORD_TOLERANCE = 2.0 # MAD multiplier before Benford gate fires +MIN_SENTENCES = 5 # minimum sentence count to attempt compression (≥5 for benford_mad to be non-zero) +MAX_RETRIES = 3 # Benford gate retry cap _VERSION = "2.0" _ENGINE = "shannon_local" -# ── Log2 Lookup Table (LUT) ─────────────────────────────────────────── -LOG_CACHE: dict[int, float] = {} - - +# ── Log2 LRU Cache ──────────────────────────────────────────────────── +@functools.lru_cache(maxsize=8192) def _log2(n: int) -> float: - v = LOG_CACHE.get(n) - if v is None: - v = math.log2(n) if n > 0 else 0.0 - LOG_CACHE[n] = v - return v + return math.log2(n) if n > 0 else 0.0 # ── Benford Reference Distribution ──────────────────────────────────── @@ -85,7 +79,7 @@ def _log2(n: int) -> float: ("json_arr", re.compile(r"\[[^\[\]]{10,}\]")), ("url", re.compile(r"https?://\S+")), ("filepath", re.compile(r"(?:/[\w.\-_]+){2,}")), - ("xml_tag", re.compile(r"<[a-zA-Z][^>]*>[\s\S]*?")), + ("xml_tag", re.compile(r"<[a-zA-Z_][^>]*>[\s\S]*?")), ] _SPLIT_SENT_RE = re.compile(r"(?<=[.!?])\s+(?=[A-ZÀ-ɏ])") @@ -129,6 +123,7 @@ def split_sentences(text: str) -> list[str]: def _compute_shannon(words: list[str]) -> dict[str, float]: + """I(w) = -log2(P(w)) via lru_cache.""" total = len(words) if total == 0: return {} @@ -154,11 +149,12 @@ def _prune_tokens(sentence: str, info: dict[str, float], threshold: float) -> st if w in LOGICAL_WHITELIST or info.get(w, float("inf")) >= threshold: kept.append(tok) else: - kept.append(tok) + kept.append(tok) # punctuation and spaces always preserved return _SPACE_RE.sub(" ", "".join(kept)).strip() def benford_mad(sentences: list[str]) -> float: + """Mean Absolute Deviation of sentence-length first digits from Benford's Law.""" lengths = [len(s.split()) for s in sentences if s.split()] if len(lengths) < 5: return 0.0 @@ -216,15 +212,18 @@ def compress(text: str, target_ratio: float = DEFAULT_RATIO) -> tuple[str, dict] original_mad = benford_mad(sentences) - # 3. Shannon local profiling via LOG_CACHE LUT + # 3. Shannon local profiling via _log2 lru_cache all_words = [w.lower() for w in _WORD_RE.findall(working)] info = _compute_shannon(all_words) - all_scores = sorted(info.get(w, 0.0) for w in all_words) + all_scores = sorted(info.get(w, 0.0) for w in all_words) # computed once pruned: list[str] = sentences compressed_mad = original_mad current_reduction = target_reduction + # Pre-compute per-sentence negation counts — invariant across Benford retries + orig_negations = {i: _count_negations(sent) for i, sent in enumerate(sentences)} + for _attempt in range(MAX_RETRIES): if not all_scores: break @@ -234,22 +233,23 @@ def compress(text: str, target_ratio: float = DEFAULT_RATIO) -> tuple[str, dict] # 5+6. Token pruning with filler pre-pass and polarity micro-checksum pruned = [] - for sent in sentences: + for i, sent in enumerate(sentences): + # Sentence-level filler removal (procedural / hedging boilerplate) if FILLER_PATTERNS.match(sent.strip()): - continue + continue # drop whole sentence; polarity check is for token pruning only - neg_before = _count_negations(sent) + neg_before = orig_negations[i] candidate = _prune_tokens(sent, info, threshold) neg_after = _count_negations(candidate) if neg_before != neg_after or not candidate.strip(): - pruned.append(sent) + pruned.append(sent) # local raw passthrough else: pruned.append(candidate) # 7. Benford macro gate candidate_mad = benford_mad(pruned) if original_mad > 0 and candidate_mad > original_mad * BENFORD_TOLERANCE: - current_reduction *= 0.5 + current_reduction *= 0.5 # halve reduction, retry continue compressed_mad = candidate_mad diff --git a/src/pith/src/mcp_server_pith/server.py b/src/pith/src/mcp_server_pith/server.py index a742f0d85a..af2cc8c7bd 100644 --- a/src/pith/src/mcp_server_pith/server.py +++ b/src/pith/src/mcp_server_pith/server.py @@ -37,8 +37,8 @@ async def list_tools() -> list[Tool]: "ratio": { "type": "number", "description": ( - "Fraction of sentences to keep (0.1-1.0). " - "0.6=default, 0.8=conservative, 0.4=aggressive, 0.3=maximum." + "Fraction of token information to retain (0.1-1.0). " + "Default = 0.7. Lower = more aggressive pruning." ), "default": DEFAULT_RATIO, }, @@ -49,7 +49,8 @@ async def list_tools() -> list[Tool]: Tool( name="compress_with_metadata", description=( - "Compress a payload with PITH and return full JSON metadata including " + "Compress a payload with PITH (Shannon local information scoring " + "validated by Benford's Law) and return full JSON metadata including " "token counts, Benford MAD values, and compression ratio. " "Use for programmatic pipelines or when you need to inspect compression quality." ), @@ -63,8 +64,8 @@ async def list_tools() -> list[Tool]: "ratio": { "type": "number", "description": ( - "Fraction of sentences to keep (0.1-1.0). " - "0.6=default, 0.8=conservative, 0.4=aggressive, 0.3=maximum." + "Fraction of token information to retain (0.1-1.0). " + "Default = 0.7. Lower = more aggressive pruning." ), "default": DEFAULT_RATIO, }, diff --git a/src/pith/tests/evals.json b/src/pith/tests/evals.json index caaff80ac8..7bf976229e 100644 --- a/src/pith/tests/evals.json +++ b/src/pith/tests/evals.json @@ -2,25 +2,29 @@ { "id": "TC01", "category": "core_compression", - "description": "Verbose web search result — passthrough (< SIZE_GATE chars)", - "prompt": "Compress this tool result before passing it to the synthesis agent:\n\nI searched the web for the latest AI research developments. The search returned several relevant results. According to the first result from MIT Technology Review, researchers have developed a new transformer architecture that reduces computational costs by approximately 40 percent while maintaining comparable performance on benchmark tasks. The article notes that this is a significant advancement in the field. The second result from Arxiv described a paper on reinforcement learning from human feedback, noting that the technique has been further refined to reduce reward hacking. The third result discussed multimodal models and their applications in robotics. The fourth result was about AI safety research at several institutions. The fifth result discussed token efficiency in large language models. The search was completed successfully and all five results appear to be from credible sources published within the last six months. I believe this information is sufficient to answer the user's question about recent AI developments.", + "description": "Verbose web search result — active compression (> SIZE_GATE chars); filler removed, signal preserved", + "prompt": "Compress this tool result before passing it to the synthesis agent:\n\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\n\nI searched the web for the latest AI research developments. The search returned several relevant results. According to the first result from MIT Technology Review, researchers have developed a new transformer architecture that reduces computational costs by approximately 40 percent while maintaining comparable performance on benchmark tasks. The article notes that this is a significant advancement in the field. The second result from Arxiv described a paper on reinforcement learning from human feedback, noting that the technique has been further refined to reduce reward hacking. The third result discussed multimodal models and their applications in robotics. The fourth result was about AI safety research at several institutions. The fifth result discussed token efficiency in large language models. The search was completed successfully and all five results appear to be from credible sources published within the last six months. I believe this information is sufficient to answer the user's question about recent AI developments.", "assertions": [ "output contains [PITH", "output contains -", "output contains % tokens", - "output contains 'transformer architecture' OR output contains 'reinforcement learning' OR output contains 'multimodal'" + "output contains 'transformer architecture' OR output contains 'reinforcement learning' OR output contains 'multimodal'", + "output does NOT contain 'The search was completed successfully'", + "output does NOT contain 'I believe this information is sufficient'" ] }, { "id": "TC02", "category": "code_preservation", - "description": "Mixed code + verbose explanation — passthrough; code present in output", - "prompt": "Pith this output from the code execution agent:\n\nThe script ran and produced output. Here is what happened during execution. The function was called with the input parameters as specified. The algorithm processed each element in the list sequentially. No errors were encountered during runtime. The computation completed successfully.\n\n```python\ndef binary_search(arr, target):\n left, right = 0, len(arr) - 1\n while left <= right:\n mid = (left + right) // 2\n if arr[mid] == target:\n return mid\n elif arr[mid] < target:\n left = mid + 1\n else:\n right = mid - 1\n return -1\n\nresult = binary_search([1, 3, 5, 7, 9, 11], 7)\nprint(result) # Output: 3\n```\n\nThe result was 3, which is the correct index of the value 7 in the sorted array. The binary search algorithm has O(log n) time complexity, which makes it efficient for large datasets. The function returns -1 if the target is not found.", + "description": "Mixed code + verbose explanation — active compression (> SIZE_GATE chars); code block preserved, filler removed", + "prompt": "Pith this output from the code execution agent:\n\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\nI searched the web for additional context.\nThe search was completed successfully.\nI found multiple relevant sources.\nNo errors were encountered during the search.\nI believe this covers the topic adequately.\nThe query was executed successfully.\nLet me now summarize the results.\nThe operation completed and produced the expected output.\nI need to review these results carefully.\nThe computation completed without errors.\nI am processing the information now.\nThe output was produced correctly.\nI found additional relevant data.\nNo warnings appeared during execution.\nI believe the results are comprehensive and accurate.\n\nThe script ran and produced output. Here is what happened during execution. The function was called with the input parameters as specified. The algorithm processed each element in the list sequentially. No errors were encountered during runtime. The computation completed successfully.\n\n```python\ndef binary_search(arr, target):\n left, right = 0, len(arr) - 1\n while left <= right:\n mid = (left + right) // 2\n if arr[mid] == target:\n return mid\n elif arr[mid] < target:\n left = mid + 1\n else:\n right = mid - 1\n return -1\n\nresult = binary_search([1, 3, 5, 7, 9, 11], 7)\nprint(result) # Output: 3\n```\n\nThe result was 3, which is the correct index of the value 7 in the sorted array. The binary search algorithm has O(log n) time complexity, which makes it efficient for large datasets. The function returns -1 if the target is not found.", "assertions": [ "output contains ```python", "output contains def binary_search", "output contains return mid", - "output contains O(log n)" + "output contains O(log n)", + "output does NOT contain 'The script ran and produced output'", + "output does NOT contain 'No errors were encountered during runtime'" ] }, { @@ -88,4 +92,4 @@ "output contains saved_pct" ] } -] +] \ No newline at end of file diff --git a/src/pith/tests/test_pith_v2.py b/src/pith/tests/test_pith_v2.py index 0a960d15d6..d3450aaa7b 100644 --- a/src/pith/tests/test_pith_v2.py +++ b/src/pith/tests/test_pith_v2.py @@ -1,13 +1,9 @@ """PITH v2 unit tests — SIZE_GATE=10000, Shannon LUT, Polarity, Benford.""" -import sys import time -from pathlib import Path from unittest.mock import patch import pytest -sys.path.insert(0, str(Path(__file__).parent.parent / "src")) - import mcp_server_pith.compress as compress_mod from mcp_server_pith.compress import ( compress, @@ -20,7 +16,7 @@ assert SIZE_GATE == 10000, f"Tests require SIZE_GATE=10000, got {SIZE_GATE}" -# ── Diverse text generator ──────────────────────────────────────────── +# ── Diverse text generator (> SIZE_GATE chars, varied vocabulary) ───── _SENTENCES = [ "Quantum computing exploits superposition to process multiple computational states simultaneously.", @@ -42,6 +38,7 @@ def diverse_long_text(chars: int = SIZE_GATE + 2000) -> str: + """Diverse technical text longer than SIZE_GATE with varied word frequencies.""" parts: list[str] = [] while sum(len(p) + 1 for p in parts) < chars: parts.extend(_SENTENCES) @@ -68,7 +65,7 @@ def test_passthrough_below_threshold(self): assert elapsed_ms < 1.0, f"Size gate latency {elapsed_ms:.2f}ms > 1ms" def test_passthrough_near_boundary(self): - text = "word " * 1800 + text = "word " * 1800 # ~9000 chars, < 10000 assert len(text) < SIZE_GATE result, meta = compress(text) assert result == text @@ -87,30 +84,39 @@ def test_above_threshold_not_size_gated(self): assert "size gate" not in meta.get("reason", "") -# ── Test 2: Shannon >= Threshold ────────────────────────────────────── +# ── Test 2: >= Threshold — rare words with equal Shannon score kept ─── class TestShannonThreshold: def test_ge_threshold_rare_words_not_bulk_deleted(self): + """ + All unique words share I(w) = log2(N). With >= at threshold boundary, + these must NOT be bulk-deleted. At least one rare technical term survives. + """ base = diverse_long_text() + # Add extra rare term that appears exactly once text = base + " NeuralPITH tokenized latency benchmark achieved." assert len(text) >= SIZE_GATE result, meta = compress(text, target_ratio=0.7) if meta["action"] == "compressed": + # "NeuralPITH" appears once → max I(w) → must survive >= threshold assert "NeuralPITH" in result, ( - f">= threshold violated: unique word pruned.\n" + f">= threshold violated: unique word 'NeuralPITH' pruned.\n" f"meta={meta}\nresult[:300]={result[:300]}" ) def test_common_words_pruned_below_threshold(self): + """High-frequency words fall below threshold → get pruned.""" text = diverse_long_text() + assert len(text) >= SIZE_GATE result, meta = compress(text, target_ratio=0.5) if meta["action"] == "compressed": - assert meta["saved_pct"] > 0 + assert meta["saved_pct"] > 0, "Expected token reduction on diverse text" def test_log_cache_populated_after_compression(self): - compress_mod.LOG_CACHE.clear() - compress(diverse_long_text(), target_ratio=0.7) - assert len(compress_mod.LOG_CACHE) > 0 + compress_mod._log2.cache_clear() + text = diverse_long_text() + compress(text, target_ratio=0.7) + assert compress_mod._log2.cache_info().currsize > 0, "_log2 lru_cache never populated" def test_logical_whitelist_always_kept(self): assert "not" in LOGICAL_WHITELIST @@ -138,41 +144,52 @@ def test_pattern_does_not_match_content(self): def test_filler_removed_from_long_text(self): base = diverse_long_text() - text = base + ( + filler = ( " I believe this information covers the topic adequately." " No errors were encountered during runtime." " Let me summarize what I found." ) + text = base + filler assert len(text) >= SIZE_GATE result, meta = compress(text, target_ratio=0.7) if meta["action"] == "compressed": - assert "I believe" not in result - assert "Let me" not in result + assert "I believe" not in result, "Filler 'I believe' survived into output" + assert "Let me" not in result, "Filler 'Let me' survived into output" def test_filler_removal_does_not_invert_content_negations(self): + """Filler removal is sentence-level; content negations in non-filler sentences kept.""" base = diverse_long_text() - text = base + " The system must never process invalid inputs without explicit authorization." + content = " The system must never process invalid inputs without explicit authorization." + text = base + content assert len(text) >= SIZE_GATE result, meta = compress(text, target_ratio=0.7) if meta["action"] == "compressed": low = result.lower() - assert "never" in low or "not" in low or "invalid" in low + assert "never" in low or "not" in low or "invalid" in low, ( + "Content negation incorrectly removed" + ) -# ── Test 4: Benford Gate ────────────────────────────────────────────── +# ── Test 4: Benford Gate Stability ─────────────────────────────────── class TestBenfordGate: def test_no_false_rollback_normal_compression(self): + """ + Long diverse text (>10000 chars → ~100+ sentences) must not trigger + spurious Benford rollback under default compression settings. + """ text = diverse_long_text(chars=SIZE_GATE + 5000) + assert len(text) >= SIZE_GATE result, meta = compress(text, target_ratio=0.7) if meta["action"] == "compressed": assert meta.get("benford_ok", True), ( - f"False Benford rollback.\n" + f"False Benford rollback on natural text.\n" f"original_mad={meta.get('original_benford_mad')}, " f"compressed_mad={meta.get('compressed_benford_mad')}" ) def test_benford_gate_retries_on_forced_failure(self): + """When MAD exceeds threshold, algorithm retries and reduces compression.""" text = diverse_long_text() call_count = [0] real = compress_mod.benford_mad @@ -180,18 +197,19 @@ def test_benford_gate_retries_on_forced_failure(self): def mock(sentences): call_count[0] += 1 if call_count[0] == 1: - return 5.0 + return 5.0 # original_mad if call_count[0] == 2: - return 50.0 + return 50.0 # force failure: 50 > 5 * 2 return real(sentences) with patch.object(compress_mod, "benford_mad", mock): result, _ = compress_mod.compress(text) assert result is not None - assert call_count[0] > 2 + assert call_count[0] > 2, "Benford gate never retried" def test_benford_gate_bounded_retries(self): + """Benford gate caps at MAX_RETRIES iterations — no infinite loop.""" text = diverse_long_text() call_count = [0] @@ -200,12 +218,13 @@ def always_fail(_): return 9999.0 with patch.object(compress_mod, "benford_mad", always_fail): - result, _ = compress_mod.compress(text) + result, meta = compress_mod.compress(text) assert result is not None assert call_count[0] <= MAX_RETRIES + 2 def test_benford_gate_halves_reduction(self): + """On gate failure, current_reduction halves → subsequent threshold is lower.""" text = diverse_long_text() call_count = [0] real = compress_mod.benford_mad @@ -220,9 +239,9 @@ def tracking_prune(sentence, info, threshold): def mock_benford(sentences): call_count[0] += 1 if call_count[0] == 1: - return 4.0 + return 4.0 # original_mad if call_count[0] <= 3: - return 40.0 + return 40.0 # force failure twice return real(sentences) with ( @@ -231,7 +250,9 @@ def mock_benford(sentences): ): compress_mod.compress(text) - assert len(set(round(t, 6) for t in threshold_history)) >= 1 + # Multiple iterations must have occurred and threshold must have changed + unique_thresholds = set(round(t, 6) for t in threshold_history) + assert len(unique_thresholds) >= 2 # halving must produce distinct thresholds across attempts # ── Test 5: Polarity Protection ─────────────────────────────────────── @@ -240,17 +261,22 @@ class TestPolarityProtection: def test_negation_whitelist_words_survive(self): text = diverse_long_text() text += " The pipeline must never fail and should not discard validated results." + assert len(text) >= SIZE_GATE result, meta = compress(text, target_ratio=0.5) if meta["action"] == "compressed": - assert "never" in result.lower() or "not" in result.lower() + assert "never" in result.lower() or "not" in result.lower(), ( + "Negation words removed from content sentence" + ) def test_polarity_rollback_on_negation_loss(self): + """Sentences that lose negation particles during pruning are restored.""" call_log: list[int] = [] orig = compress_mod._count_negations def mock_count(s): v = orig(s) call_log.append(v) + # Every "after" call (even index) returns divergent value → triggers rollback if len(call_log) % 2 == 0: return v + 1 return v @@ -268,7 +294,9 @@ def test_compressed_output_wrapped_in_xml(self): text = diverse_long_text() result, meta = compress(text) if meta["action"] == "compressed": - assert result.startswith("") def test_xml_attributes_present(self):