PRIOR-ART.md

Prior art: LLMs as decision models / self-verification (scan 2026-10-06)

Scope: before building the "generator verifies itself" experiments, check what is already established for (a) reading typed decisions out of general LLMs, (b) self-verification vs an independent verifier, (c) whether model size matters, and (d) whether readouts survive quantization. Confidence tags: [verified] = primary abstract/repo read here; [single-source] = one write-up; [inferred] = our synthesis.

1. Typed decisions from general LLMs — settled, recent

• [verified] LLM-as-Jev (Li & Wagle, arXiv:2610.02076, 1 Oct 2026): architecture-preserving readout of bracketed option identifiers from next-token probabilities. Qwen3.5-4B without training matches community Jev-style models built on the same backbone; fine-tuning gives targeted benefits (weaker models, many-option intent routing) and diminishing returns on strong backbones; KL anchors protect conversational generation.
• [verified] AnyJev (Nokia, arXiv:2610.00831, 30 Sep 2026): L0 (rotation marginalisation + label-prior correction, zero labels) and L2 (closed-form LDA/ridge head on a hidden state ~2/3 depth, 100-300 labels per question). Qwen3-1.7B L2 0.730 ~ Jev's published 0.727; Qwen3-4B 0.786 > fine-tuned Laya 0.768; zero-label L0 is weak at small scale (1.7B 0.494, 8B 0.647, 32B 0.700). Heads do not transfer across questions; labels are needed per question.

Consequence: the premise of the Clef-shrinking line (train a separate decision model on a capable base) is superseded. The readout is the product.

2. Self-verification vs independent verification — the live debate

• [verified] Kadavath et al. 2022 (arXiv:2207.05221): P(True) self-evaluation of own samples works, is well calibrated few-shot, and improves with model size; models find their own samples more plausible. This is the origin of "the ability is latent".
• [verified] Panickssery et al. 2024 (arXiv:2404.13076): self-preference and self-recognition bias are real and causally linked; evaluators favour their own outputs.
• [verified] Huang et al. 2023 (arXiv:2310.01798): prompted intrinsic self-correction of reasoning fails, at times degrading.
• [verified] Lu et al. 2025/26 (arXiv:2512.02304): across 37 models and 9 benchmarks, cross-family verification is more effective than self-verification; benefits decrease as solver and verifier become more similar; reasoning post-training weakens self-improvement. This is the strongest argument to keep an independent verifier in the cascade.
• [single-source] Zhang et al. 2025 (arXiv:2504.05419): probes on reasoning-model hidden states verify answers with high accuracy and calibration, and encode future correctness.
• [single-source] Gnosis (arXiv:2512.20578): ~5M-parameter head on frozen internal traces (hidden-state evolution + attention), backbones 1.7B-20B, outperforms large external judges on accuracy and calibration; generalises to partial generations.

Synthesis: readout-based self-evaluation (logits, probes) >> prompted self-critique; independent verifiers still beat self-verification. The "generator judges itself for free" hypothesis is the weakest variant.

3. Does size matter?

• [verified] LLM-as-Jev: 4B is the practical knee; 0.6B needs fine-tuning.
• [verified] AnyJev: L2 lifts 1.7B to Jev-level; zero-label L0 needs size.
• [verified] Kadavath: P(True) improves with scale.
• [verified] Lu et al.: verifier diversity matters more than raw size for selection gains.
• [inferred] For our verifier: a 4B-class base + readout is the expected sweet spot; below that, fine-tuning or a task-specific head is required.

4. Readouts under quantization — our former blocker, largely answered

• [verified] Aiersilan 2026 (arXiv:2606.02628): on three 7-8B instruction-tuned models in 4-bit NF4, a single linear probe on a mid-network layer reaches 0.904-1.000 AUROC on held-out hallucination benchmarks; MLP probes rarely beat linear by >0.01 AUROC; peak layers are consistent (Qwen2.5-7B blocks 19-25 of 28). Sampling-based detectors were far worse under the paired-label protocol.
• [single-source] SAPLMA (Azaria & Mitchell 2023): linear probes on hidden states detect statement truthfulness; middle layers best.
• [single-source] arXiv:2510.09033 (counterpoint): hidden states may reflect knowledge recall rather than truthfulness; unknown-unknown hallucinations overlap with factual outputs.
• [single-source] arXiv:2609.31796: probes are sensitive to inference numerics/precision; fix instrumentation across comparisons.

Consequence for us: the earlier "ternary kills the decision signal" conclusion was measured through Clef's frozen final-layer head. Mid-layer linear readouts are documented to survive 4-bit quantization; 2-bit ternary survival is the genuinely open question, and our QAT/V2 artifacts are ready-made subjects.

5. What remains for this project

1. Self vs independent on our task: cascade verifier with checker gold, mixed math/code/QA, on the actual local generator. Prediction (Lu et al.): independent wins; if it does, the "no separate model" claim fails for the hallucination lever even though typed decisions need no dedicated model.
2. Ternary/2-bit readout survival (novel): linear probes at mid-depth on 2-bit ternary bodies, fitted on bf16 and refit on ternary.
3. Footprint fit on this box (RAM/VRAM floor beside the generator) — systems question, not in the literature.
4. Calibration/threshold for the cascade's reject/escalate lever.

Test plan (frozen set first): see PLAN.md in this directory.