The Answer Is Not the Argument
- URL: http://arxiv.org/abs/2609.00264v1
- Date: Mon, 31 Aug 2026 19:06:44 GMT
- Title: The Answer Is Not the Argument
- Abstract summary: Chain-of-thought monitoring is proposed for AI oversight, yet evaluations often provide monitors with a trusted reference answer.<n>We ask whether answer access improves reasoning verification or mainly exposes incorrect conclusions.
- Score: 0.00897008283486962
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Chain-of-thought monitoring is proposed for AI oversight, yet evaluations often provide monitors with a trusted reference answer. We ask whether answer access improves reasoning verification or mainly exposes incorrect conclusions. We collected 237 step-numbered solutions to 79 Humanity's Last Exam physics questions from three frontier models, with no inserted errors, and independently labelled final-answer correctness and the first false step. The reference standard combined physicist annotations, an independent LLM debate, and source-masked adjudication. This yielded 24 critical traces in which the answer was correct but the trace contained a genuine error. 8 LLM monitors evaluated traces blind, with an unverified or certified answer, or after a blind commitment. Certification raised mean balanced accuracy from 0.637 to 0.796, while exact first-error localization rose from 0.261 to 0.379. Certification changed recall (the fraction of error traces flagged as erroneous) from 0.653 to 0.951 on wrong-answer traces but from 0.521 to 0.438 on critical traces; the contrast had the same direction for all 8 monitors (question-bootstrap 95% CI [+0.256, +0.506]). After blind commitment, monitors shown the answer newly flagged 93.8% of previously passed wrong-answer traces as erroneous, but only 18.0% of critical traces. Answer access therefore improves conclusion-consistency checking rather than independent verification of the supporting argument. For AI safety, these traces provide a benign analogue of reward hacking: an acceptable output does not establish that the process producing it was sound. Although the errors studied here were ordinary and mostly non-load-bearing rather than adversarial, trusted-answer evaluations may similarly overstate monitoring capability when acceptable outputs conceal unsound reasoning.
Related papers
- The Model Proposes, the Code Disposes: A Pre-Registered Ablation of a Verifier-and-Acceptance Stage in an LLM-Orchestrated Offensive-Security Agent [0.0]
We evaluate whether a verifier-and-acceptance stage - a model verifier whose verdicts are enforced by deterministic code - changes what an LLM-driven offensive-security agent reports.<n>We report a 15-run exploratory pilot, a pre-registered 20-run confirmatory ablation, and a pre-registered 2 x 2 factorial study with 40 runs across two deliberately vulnerable lab targets.<n>The results support a narrow conclusion: the verifier changes what the system ships, while deterministic code supplies enforcement and auditability.
arXiv Detail & Related papers (2026-09-14T17:13:05Z) - Decision Shifts, Lost Label Functionality, and an Inconclusive Grounding Audit in Correctness-Gated Multi-Teacher Distillation [0.0]
Candidate decision correctness and rationale grounding are different objectives.<n>We examine correctness-gated multi-teacher distillation in a fixed experiment.<n>Relative to unfiltered distillation, the correctness-weighted arm differed in accuracy by +0.1660.
arXiv Detail & Related papers (2026-09-09T04:39:56Z) - When Self-Consistency Backfires: Majority Vote Hurts the Majority of Hard Science Problems for Small LLMs [0.0]
Self-consistency via majority vote reduces per-problem accuracy on most GPQA Diamond problems for small instruction-tuned models.<n>This version reports that the most natural repair also fails, and separates three signal failures that v1 treated as one.
arXiv Detail & Related papers (2026-08-11T20:08:51Z) - Cybersecurity Detection Classification with Reasoning-enabled Language Models [50.72675435043546]
A major issue in Security Operations Centers (SOCs) is alert fatigue.<n>Prior work prompts or fine-tunes large language models (LLMs) to emit a triage label directly, but does not train them to reason about whether a detection is a genuine threat.<n>We train a chain-of-thought (CoT) reasoning-enabled triage classifier on real, human-labeled Windows endpoint detections.
arXiv Detail & Related papers (2026-07-30T16:22:13Z) - Visual Credit Audit for Multimodal Spatial Reasoning [70.16915309526443]
Visual Credit Audit separates two Estimands: whether the benchmark image gives the model's declared decision more support than text-only and blank controls, and whether the model responds to relation-specific visual evidence.<n>Applying labels yields dependence-credited correctness (D-CC)<n>Across four open MLLMs and two spatial benchmarks, 12.73-26.25% of decisions are correct yet uncredited.
arXiv Detail & Related papers (2026-07-29T15:55:31Z) - Two Regimes of Chain-of-Thought Unfaithfulness: Behavioral Detection Fails Where Models Are Wrong [1.2744523252873352]
Black-box (behavioral) detection of unfaithful CoT against FaithCoT-Bench's human annotations.<n>We find answer correctness structures the problem at every level.
arXiv Detail & Related papers (2026-07-26T04:43:53Z) - Catching One in Five: LLM-as-Judge Blind Spots in Production Multi-Turn Transaction Agents [45.148328075418156]
We study a deployed multi-turn food-and-beverage ordering agent and measure how many genuine quality problems it catches.<n>Our blind-spot taxonomy shows the failure is structured, not random.<n>For production multi-turn agents, automated judging is a regression floor, not a substitute for human review.
arXiv Detail & Related papers (2026-06-09T02:11:01Z) - Evidence-Grounded Ensemble Diagnosis of 802.11 Packet Captures: A Multi-Stage Pipeline with Deterministic Reliability Scoring [1.0170129555792935]
Diagnosing 802.11 packet captures requires expert protocol knowledge, is slow, inconsistent across engineers, and unscalable.<n>LLM-based approaches sound plausible but fabricate protocol events absent from captures, produce uncalibrated confidence scores, and suffer evaluation bias when golden references are co-produced by the model under test.<n>We introduce PROBE, a multi-stage pipeline addressing all three failures.
arXiv Detail & Related papers (2026-06-05T03:39:58Z) - Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
arXiv Detail & Related papers (2026-05-15T17:08:27Z) - Tracing Uncertainty in Language Model "Reasoning" [46.61902399979181]
Language model (LM) "reasoning", commonly described as Chain-of-Thought or test-time scaling, often improves benchmark performance.<n>We study these dynamics by treating the "reasoning" traces, the intermediate token sequences generated by LMs, as evolving model states.
arXiv Detail & Related papers (2026-05-08T14:16:37Z) - The Verification Tax: Fundamental Limits of AI Auditing in the Rare-Error Regime [0.0]
Most cited calibration result in deep learning -- post-temperature-scaling ECE of 0.012 on CIFAR-100 -- is below the statistical noise floor.<n>We prove this is not a failure of the experiment but a law: the minimax rate for estimating calibration error with model error rate epsilon is Theta((Lepsilon/m)2/3), and no estimator can beat it.
arXiv Detail & Related papers (2026-04-14T16:48:24Z) - SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratio [0.038379177968040606]
We propose SELFDOUBT, a single-pass uncertainty framework for reasoning language models.<n>Our key signal, the Hedge-to-Verify Ratio (HVR), detects whether a reasoning trace contains uncertainty markers and, if so, whether they are offset by explicit selfchecking behavior.<n> SELFDOUBT operates on a single observed reasoning trajectory, making it suitable for latency- and cost-constrained deployment over any proprietary API.
arXiv Detail & Related papers (2026-04-07T19:19:29Z) - Causal Understanding by LLMs: The Role of Uncertainty [43.87879175532034]
Recent papers show LLMs achieve near-random accuracy in causal relation classification.<n>We investigate whether pretraining exposure to causal examples improves causal understanding.
arXiv Detail & Related papers (2025-09-24T13:06:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.