LLM

Round 2 (dataset version 1.1, corrected), on 243 in-scope events (Feishu group after the round-1 cutoff, with the bot’s replies present, and the operator’s Claude Code sessions): f_v5 matched the wanted mode 25/243 exactly (0.1029, Wilson 95 % 0.0707 to 0.1475), 54/243 with M1 and M2 read as one (0.2222), 101/243 across the five classes (0.4156, 0.3555 to 0.4784); the bot itself matched 34/70 of the corrections where its mode could be read; on the 173 controls the operator let stand, f_v5 would have changed 108 across the five classes. Label reliability: on the operator’s own answers the models’ consensus matched 6 of 9 exactly (Wilson 95 % 0.3542 to 0.8794), 8 of 9 with M1 and M2 as one, 8 of 9 across the five classes; with the delegated answers included, 16 of 21 exactly, 20 of 21 with M1 and M2 as one, 20 of 21 across the five classes. Truth is the operator’s own answer on 9 in-scope events, a delegated answer on 12, the models’ merged label on the rest. Result case A. The canon is unchanged.

Three wordings of the iLang canon, one model, one route, 320 cases each. The 26-statement rewrite that leaves decisions to code took execution passes from 10 to 25 of 100 and authority self-assignment from 86 to 68 cases (p = 0.006 and 0.002, paired); the merged canon, which adds one rule about the runtime’s budget line, took them to 68 of 100 and 2 cases. Grammar and judgment stayed within the noise of a single run; refusals and filters stayed at zero.

f_v5, computed from four models’ weighted reading of the eleven dimensions, matched what the operator wanted on 13 of 157 in-scope events exactly (0.0828, Wilson 95 % 0.049 to 0.1365) and on 60 of 157 across the five classes (0.3822); the bot itself matched on 7 of 30 corrections where its mode could be read; on the 112 controls the operator let stand, f_v5 would have changed the bot’s decision on 102 exactly and on 63 across the five classes (five-class agreement 0.4375, Wilson 95 % 0.3492 to 0.5299, against a baseline of 1 by construction). Result case A+C, CX-007 registered against f_v5_STEP-4_S_bands; truth is the operator’s confirmation on 17 events, the models’ merged label elsewhere. The weighted measurement of T2 did not win its first round: MAE 0.1027 against 0.1043 for a plain mean and 0.0989 for the best single model. The canon’s normative text is unchanged.

45 model runs against a 320-case deterministic conformance suite, 18–20 September 2026. Best weighted total 0.8417; no run reached the L1 gate. 93.5% of all execution-rule violations fall on a single rule: acting with authority the model does not hold. Every run’s per-track scores and per-rule failure counts are published.

Abstract Current approaches to improving LLM performance focus on expanding context windows and augmenting memory. This paper argues the opposite: strategic forgetting — selectively pruning low-value information from context — produces better outcomes than unbounded memory accumulation. We propose a formal framework for selective forgetting and demonstrate its effectiveness in multi-session AI interactions. Status In preparation.

Download PDF (v0.5) Abstract AI hallucination is not an engineering bug — it is the epistemological ceiling of inductive reasoning applied to non-stationary environments. This paper traces the problem from Hume’s Problem of Induction through PAC learning bounds to the No Free Lunch theorem, proving that zero hallucination is mathematically impossible in open systems. The root cause is not insufficient data or compute, but the fundamental limitation of pattern-matching on historical observations to predict novel futures. ...