DualityCert: Verifier-Gated Language-Model Repair of Broken Duality Claims in Quantum Field Theory
- URL: http://arxiv.org/abs/2607.23614v1
- Date: Sun, 26 Jul 2026 11:39:49 GMT
- Title: DualityCert: Verifier-Gated Language-Model Repair of Broken Duality Claims in Quantum Field Theory
- Abstract summary: We present DualityCert, a symbolic verifier for candidate Seiberg-duality claims in four-dimensional N=1 quiver gauge theories.<n>A claim that passes receives a consistency certificate, which states that no tested inconsistency was found, not that the duality is proven.<n>We use the verifier as a repair environment for language-model agents, which receive a deliberately broken claim and must edit it until it certifies.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present DualityCert, a symbolic verifier for candidate Seiberg-duality claims in four-dimensional N=1 quiver gauge theories. The verifier evaluates 't Hooft anomaly matching, superpotential R-charge consistency, central-charge matching, and a bounded chiral-ring proxy. A claim that passes receives a consistency certificate, which states that no tested inconsistency was found, not that the duality is proven. We use the verifier as a repair environment for language-model agents, which receive a deliberately broken claim and must edit it until it certifies. On a preregistered benchmark of 145 broken claims, with the analysis fixed before the first confirmatory model call, verifier-gated retry improves final repair success over a single attempt by +8.3 percentage points (pp) on deepseek-chat and +7.1 pp on qwen-plus (Holm-adjusted p<0.002). Under an equal budget of eleven attempts, the stop-first strategy portfolio underperforms independent verifier-filtered resampling by 10.3 percentage points on deepseek-chat but outperforms it by 14.7 points on qwen-plus, reversing the ordering of the two tested verifier-exploitation policies across the two confirmatory models. On qwen-plus, category-level verifier feedback is worth +8.7 pp over content-free retry, and interpretable obligation identities alone are worth +6.4 pp over structurally identical masked feedback. Neither effect is detected on deepseek-chat. Separately, a preregistered MiniMax-M2.5 extension again finds an iteration gain and independent verifier-filtered resampling outperforming the strategy portfolio. Which policy is better thus differs between the two models, while every winning policy uses the same cheap certificate. The verifier, benchmark, protocol, and all per-attempt records are released.
Related papers
- ReDraft, Don't Just Distill: Reference-Driven Revision for Continual VLLM Post-Training [66.72708893922605]
Post-training of large multimodal models should add new capabilities while preserving those from pre-training.<n>SFT gives explicit target supervision that learns a task from near-zero accuracy, but its off-policy targets move the model far enough to cause forgetting.<n>We introduce ReDraft, which obtains both from the model's own failures.
arXiv Detail & Related papers (2026-09-15T04:59:03Z) - Certifying Model Upgrades with Slice-Wise Non-Regression and Incumbent Fallback [2.8716825293247665]
We study checkpoint selection subject to non-regression tolerances relative to a retained incumbent.<n>We give a reproducible release procedure that separates candidate search from independent, paired evaluation.<n>A joint release decision does not require a slice-count Bonferroni penalty, although certification power can still decrease with the number of slices.
arXiv Detail & Related papers (2026-09-12T05:00:50Z) - GANDR: Claim Auditing for Verifiable Legal Answer Generation [21.842813905857863]
Grounded-generation pipelines score the answer as a whole, so a correct conclusion can rest on fabricated or loosely matched citations and still score well.<n>We introduce GANDR, a two-agent system in which a Drafter writes an answer in a structured legal-reasoning format and a Critic, with the same view as a human verifier, audits each claim against its cited source.<n>On a 185-item legal benchmark, GANDR ranks first on every primary metric, reaching 70.8% strict accuracy and leading the strongest baseline by 11.3 points (p0.01)
arXiv Detail & Related papers (2026-09-09T15:08:09Z) - Same Agent, Different Answers: A Repeat-Aware Audit of Corpus-Induced Answer Churn in Retrieval-Augmented QA [0.4351760066527804]
A retrieval-augmented QA system can return different answers after an index expansion even when its requested model identifier, prompt, retrieval policy, evidence depth, rendering, and exposed generation controls are held fixed.<n>We call the hidden phenomenon accuracy-blind answer churn and introduce the emphSnapshot Compatibility Audit, which estimates excess answer churn by subtracting same-snapshot repeat disagreement from cross-snapshot disagreement.
arXiv Detail & Related papers (2026-08-24T06:40:09Z) - Looping Is Not Reliability: State-Bound Evidence and Typed Revision Contracts for Agentic Code Repair [36.56438114281786]
We study the gap between finding a correct patch and retaining, verifying, and submitting it.<n>We derive an evidence-bound typed loop contract and instantiate its mechanically enforceable subset in a reference implementation.
arXiv Detail & Related papers (2026-07-27T16:05:23Z) - Evaluating and Guarding Citation Faithfulness in Agentic Scientific Synthesis [2.7092559671391]
Agentic LLM systems such as OpenScholar and PaperQA2 read the scientific literature and return cited answers.<n>Neither audits the reliability of that check itself.<n>We show it is not reliable, and that this matters.<n>We present a gold-anchored evaluation protocol and a deployable guard.
arXiv Detail & Related papers (2026-07-10T02:05:17Z) - SEVA: Self-Evolving Verification Agent with Process Reward for Fact Attribution [6.908637308550535]
SEVA is a structured verification agent that emits evidence alignments, step-by-step reasoning chains, calibrated confidence, and a six-category error diagnosis.<n>On ClearFacts, SEVA-3B matches GPT-4o-mini (69.0 vs. 69.8 F1) while producing substantially richer, auditable output.
arXiv Detail & Related papers (2026-06-29T02:37:13Z) - What Accuracy and Gradient Cosine Miss: Evaluating Feedback Alignment via Scale Stability, Reference Validity, and Depth Utility [48.10132234701036]
We propose a diagnostic evaluation protocol based on three checks -- scale stability, reference validity, and depth utility -- together with per-layer rather than aggregate cosine reporting.<n>Across multiple architectures and methods, our protocol identifies all failures with wide calibration margins.
arXiv Detail & Related papers (2026-06-19T06:04:17Z) - PACE: Anytime-Valid Acceptance Tests for Self-Evolving Agents [0.0]
Self-evolving agents improve by repeatedly proposing changes to their own prompts, skills, ormodifications.<n>On Qwen2.5 agents (0.5B-3B) self-evolving at the prompt level on GSM8K, SVAMP, and ARC-Challenge, greedy acceptance commits 30-42% false and 10-33% harmful edits.<n> PACE commits the real one and essentially nothing else, matching greedy's held-out accuracy at sharply lower variance and about 18% lower evaluation cost.
arXiv Detail & Related papers (2026-06-06T11:12:11Z) - Trust but Verify: Prover-Verifier Deliberation for Selective LLM Prediction [4.273094752480624]
We introduce prover-verifier deliberation (PVD), an inference-time protocol grounded in interactive proof theory.<n>PVD produces both an answer and a structured confidence verdict, allowing a system to report high-confidence answers while abstaining on uncertain cases.
arXiv Detail & Related papers (2026-05-24T15:23:27Z) - Hallucination as Exploit: Evidence-Carrying Multimodal Agents [10.441697487723568]
Multimodal agents increasingly choose tool calls from screenshots, documents, and webpages, where a false perceptual claim can turn from an answer-quality error into an authorization failure.<n>We propose evidence-carrying multimodal agents (ECA), which treat free-form model text as inadmissible evidence.<n>ECA converts opaque model belief into auditable residuals at the verifier, schema, and implementation levels.
arXiv Detail & Related papers (2026-05-18T23:40:43Z) - 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) - HLE-Verified: A Systematic Verification and Structured Revision of Humanity's Last Exam [63.84155758655084]
Humanity's Last Exam (HLE) has become a widely used benchmark for evaluating frontier large language models.<n>We introduce HLE-Verified, a verified and revised version of HLE with a transparent verification protocol and fine-grained error taxonomy.<n>We evaluate seven state-of-the-art language models on HLE and HLE-Verified, observing an average absolute accuracy gain of 7--10 percentage points.
arXiv Detail & Related papers (2026-02-15T02:50:15Z) - PRIME: A Process-Outcome Alignment Benchmark for Verifiable Reasoning in Mathematics and Engineering [71.15346406323827]
We introduce PRIME, a benchmark for evaluating verifiers on Process-Outcome Alignment verification.<n>We find that current verifiers frequently fail to detect derivation flaws.<n>We propose a process-aware RLVR training paradigm utilizing verifiers selected via PRIME.
arXiv Detail & Related papers (2026-02-12T04:45:01Z) - Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers [90.50039419576807]
Reinforcement Learning with Verifiable Rewards (RLVR) trains policies against automated verifiers to avoid costly human labeling.<n>To reduce vulnerability to verifier hacking, many RLVR systems collapse rewards to binary $0,1$ during training.<n>This choice carries a cost: it introduces textitfalse negatives (rejecting correct answers, FNs) and textitfalse positives (accepting incorrect ones, FPs)
arXiv Detail & Related papers (2025-10-01T13:56:44Z) - CertDW: Towards Certified Dataset Ownership Verification via Conformal Prediction [48.82467166657901]
We propose the first certified dataset watermark (i.e., CertDW) and CertDW-based certified dataset ownership verification method.<n>Inspired by conformal prediction, we introduce two statistical measures, including principal probability (PP) and watermark robustness (WR)<n>We prove there exists a provable lower bound between PP and WR, enabling ownership verification when a suspicious model's WR value significantly exceeds the PP values of benign models trained on watermark-free datasets.
arXiv Detail & Related papers (2025-06-16T07:17:23Z)
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.