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WCY — Watch → Compute → Yield

A token-native reasoning format with theorem-backed semantics.

DOI (Paper) DOI (Dataset) DOI (Repository, v2) License: CC BY 4.0 Python 3.10+

The name is the grammar. The grammar encodes the capacity to not-know.


What is WCY?

WCY is a line-oriented, phase-tagged format for AI reasoning and agent-to-agent data exchange, designed for how transformer-based LLMs actually process information. Every line begins with a phase marker (. observe, : infer, > act, ~ meta, ! exception), and the ? marker gives the unknown an explicit, structural representation.

WCY-2 (this release) keeps that surface unchanged and adds what v1 left as convention: a value model, merge semantics, a mutation discipline, an audit rule, and a mechanical rule for when ? must appear. Each of these is backed by a published, machine-verified theorem from the Dual-Rail Carrier Program — a 12-release series of kernel-checked mathematics that grew out of this format's original design questions and now, closing the circle, answers them. The full specification is SPEC.md.

Why WCY-2 (the one-paragraph pitch)

JSON's null conflates unknown, inapplicable, and conflicting; JSON has no defined merge; deletion destroys history; and nothing tells an agent when it should admit it doesn't know. In WCY-2: every atom is a pair of evidence rails, so unknown (no evidence) and conflict (opposing evidence) are different values; merge is the railwise join — associative, commutative, idempotent by kernel-checked proof (lean/wcymerge/, 10 theorems, all axiom-free), so replicas merge in any order and conflict surfaces as a value instead of a lost update; retraction adds refuting evidence rather than deleting, making stores append-only as a theorem consequence; only resolution steps (unknown/conflict → resolved) carry an essential obstruction — so logging exactly those is a provably sufficient audit trail; and a query that leaves the safe (self-dual) fragment must either resolve-and-log or emit ? — turning v1's weakest point (? as unenforced style) into grammar.

The theorem-backed rules

Rule Backing theorem (DOI)
unknown ≠ conflict: dual-rail atoms 10.5281/zenodo.21800031
merge = railwise join, CRDT laws kernel-checked this repo (lean/wcymerge/) + 10.5281/zenodo.21870654
retraction is monotone; append-only derived, not decreed 10.5281/zenodo.21800033
audit = resolution records only, and that is sufficient 10.5281/zenodo.21870654 (Theorem 0)
confidence/version history is level-indexed, never compressed 10.5281/zenodo.21871946
safe query fragment = self-dual; outside it, ? is mandatory 10.5281/zenodo.21866741
schema evolution = conservative pointed extension 10.5281/zenodo.21800031, 21866478
no "verified once, trusted forever" across levels 10.5281/zenodo.21869871

Empirical results (v1 series, unchanged by the upgrade)

Experiment Finding
Token reduction vs JSON (structured data) 50–60%
Token reduction for tool-call schemas 65–71%
Full MCP protocol exchange reduction 61%
Agent output token reduction 40%
from= provenance validity (3-agent pipeline) 45/45 (100%)
WCY format acquisition (0-shot → 3-shot) parse_r: 0.29 → 1.00
Void-B resolution rate 67–97%
Pipeline quality gate pass rate (528 traces) 528/528 (100%)

The v2 semantic layer adds no surface tokens to resolved-face documents, so these figures carry over; conflict and level annotations cost tokens only where the corresponding information exists.

Quick example (merge, conflict, resolution)

agent A:   . allergy=penicillin        | from=intake_form
agent B:   . allergy!=penicillin       | from=lab_panel_2

merged:    . allergy=penicillin^C  from=A1,B1     ← conflict is a value
           > order  skin_test  reason=resolve_allergy
           . skin_test=negative
           : resolve allergy  was=C  now=F  from=3  why=skin_test_negative

Nothing was overwritten, nothing was lost, and the single resolve line is the entire audit obligation for this exchange.

Repository contents

SPEC.md                         The WCY-2 specification (v0.1 draft):
                                value model, merge, mutation, audit,
                                levels, safe fragment, JSON interop;
                                normative references = the series DOIs
wcy_parser.py                   Reference parser v2 (v1-compatible)
wcy_merge.py                    Merge / JSON embed & project / law mirror
wcy_eval.py                     3-axis evaluation (v1)
lean/wcymerge/                  Kernel-checked merge laws: 10 theorems,
                                all axiom-free (Lean 4, core only)
tests/                          v1-compat + v2 semantics test suite
data/                           v1 trace corpus (540 traces)
experiments/                    v1 experiment scripts
papers/                         papers; second_edition/ = the v2 paper
                                (Zenodo: 10.5281/zenodo.19068378, latest)

Conformance

Four classes — Core (value model + syntax + JSON projection), Merge (+ join semantics + mutation discipline), Audit (+ resolution records + level families), Full (+ safe-fragment query discipline + conservative schema evolution). A v1 document is a valid Core document. See SPEC.md §2, §12.

Authorship & provenance

Won Chul Yang, independent researcher (wcy0969@gmail.com). The format and the underlying mathematics are the author's; the carrier-theory series backing the v2 semantics is published with complete proofs, kernel-checked Lean artifacts, and independent replays (series hub: https://github.com/ycmath/dual-rail-carrier-program). AI assistance (Anthropic Claude family) was used for the machine-verification layer and implementation, with the Lean 4 kernel as the acceptance gate for verified components. Corrections are invited.

License

CC BY 4.0 (text, data); code files carry their headers.

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A Token-Native Reasoning Format and Epistemic Substrate for AI Systmes.

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