EVEMISSLAB

ResearchRES-2026-0004v0.1

A PACC-style runtime on language models: reasoning, intent and creative breadth

Does wrapping candidate selection in a canonical hard/derived-constraint commit space change what a generator produces? A synthetic A/B/C witness (generator only / hard verifier / PACC runtime) shows derived coherence and constraint-satisfying novelty up, soft-preference fit slightly down, and a creative-breadth collapse that a post-hoc 'elastic' exploration policy recovers. The real-language-model version of the benchmark has a validated, fail-closed harness but has not been executed.

Research status
ACTIVE under continuous research
Evidence level
E2 Controlled experiment
Data basis
SYNTHETIC Synthetic data and theoretical reasoning. Many now treat synthetic data as if it were real; this laboratory says the opposite deliberately — until a real hybrid model exists, an inference is only an inference, and theoretically possible is not actually possible.
Version
0.1
Updated
2026-09-09
Created
2026-09-09
Domain
Reasoning, Evaluation, Agent Systems
Program
PRG-2026-0001 Adaptive Epistemic Systems
Authors
Neo.K (EveMissLab)
AI collaborators
Sol (GPT-5.6, OpenAI ChatGPT)

Research questions

research_questions
  • Is there a reasoning-up / imagination-down trade-off, or is the observed breadth loss a selection-policy artefact separable from the commit constraints?
  • Will a frontier language model show the same effect as the synthetic witness?

Claims

claims
  • Within the synthetic witness: reasoning coherence ↑, valid novelty ↑, soft-preference fit slightly ↓, creative breadth ↓ under naive selection and recoverable with exploration separated from commitment (8/8 seeds sign-stable).
  • Real local 9B model, thinking off, two runs (32 and 64 rows per condition): the three runtime conditions are practically equivalent on every judge axis, and by label-free measures the PACC runtime does not narrow creative breadth. The synthetic v0.1 prediction did not appear on this model.
  • Real local 9B model with thinking enabled (32 rows per condition): the PACC runtime has the highest valid novelty (+0.069 vs the hard verifier, +0.038 vs the plain model) and repair, governance axes are flat to slightly lower, and breadth is again not reduced — half of the synthetic prediction, unreplicated.

Limitations

limitations
  • Zero real LLM calls so far; no claim about real-model reasoning, intent understanding, imagination, hallucination or human-rated usefulness is permitted before a real-model result exists.
  • The v0.1 numbers are synthetic data and theoretical reasoning; until a real hybrid model exists, an inference is only an inference — theoretically possible is not actually possible.

Relations

SourceRelationTargetStatusID
RES-2026-0004 A PACC-style runtime on language models: reasoning, intent and creative breadthbelongs_toPRG-2026-0001 Adaptive Epistemic SystemsACTIVEREL-2026-0004
RES-2026-0004 A PACC-style runtime on language models: reasoning, intent and creative breadthextendsRES-2026-0002 PACC conjecture — do non-probabilistic primitives converge to probability-like structure?ACTIVEREL-2026-0006
SYS-2026-0003 PACC-LLM Hybrid LabsupportsRES-2026-0004 A PACC-style runtime on language models: reasoning, intent and creative breadthACTIVEREL-2026-0087
DAT-2026-0002 PACC-Hybrid v0.1 shared candidate poolssupportsRES-2026-0004 A PACC-style runtime on language models: reasoning, intent and creative breadthACTIVEREL-2026-0093

History and provenance

Canonical URL
https://evemisslab.com/ai/research/RES-2026-0004/
Machine-readable
/ai/research/RES-2026-0004/index.json
Snapshot
AI-SNAPSHOT-v0.1-fe85b9694a45
Provenance
source
EveMissLab research collection: Adaptive Epistemic Systems (真本體論13)
extracted_by
Splice (Claude Code), reading the canonical UTF-8 sources and each lab's own result reports
extracted_at
2026-09-11
generator
tools/extract_aes/extract.py
claim_boundary
status, evidence level and result type follow the source artifact's own stated claim boundary; nothing is upgraded beyond what the report supports