SafetyRepro: Configuration-Conditional Rank Instability on Alignment Benchmarks
- URL: http://arxiv.org/abs/2605.25492v1
- Date: Mon, 25 May 2026 06:54:44 GMT
- Title: SafetyRepro: Configuration-Conditional Rank Instability on Alignment Benchmarks
- Abstract summary: We show a finite-envelope proposition tying a measurable pairwise-disagreement rate to whether the strict ordering admits a configuration-pair reversal.<n>On every benchmark we test, configuration choice alone can flip the pairwise verdict; the proposition isolates this strict-reversal failure mode.
- Score: 0.30586855806896046
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Pairwise model comparisons drawn from foundation-model benchmarks ("A is safer than B") are read as quantitative verdicts but hinge on harness choices benchmark papers under-specify. We close one theory-benchmark loop on this primitive: a finite-envelope proposition tying a measurable pairwise-disagreement rate to whether the strict ordering admits a configuration-pair reversal, paired with a commit-stamped evaluation protocol that operationalises it on widely cited alignment benchmarks. On every benchmark we test, configuration choice alone can flip the pairwise verdict; the proposition isolates this strict-reversal failure mode.
Related papers
- RRPO: Reference-Relative Policy Optimization with Stratified Conditional Rollouts [84.20747697259326]
Group Relative Policy Optimization (GRPO) has shown strong effectiveness in reinforcement learning from verifiable feedback.<n>We propose textbfReference-Relative Policy Optimization (RRPO), which generalizes GRPO by replacing direct correctness-based advantage construction with reference-relative contrastive comparisons.<n>We evaluate RRPO using anchor-based contrastive advantages throughout policy optimization, without relying on task ground-truth verifiers.
arXiv Detail & Related papers (2026-07-20T19:40:25Z) - Prompt Perturbation for Reliable LLM Evaluation over Comparison Graphs [10.095945693317079]
evaluating large language models (LLMs) is important for understanding their capabilities, comparing competing systems, and supporting the deployment of reliable models in practice.<n>For open-ended tasks, pairwise evaluation has become a popular paradigm, in which two responses to the same prompt are compared and the resulting judgments are aggregated into an overall ranking.<n>A central challenge of this paradigm is intransitivity: the induced comparison outcomes may fail to support any coherent global ranking.
arXiv Detail & Related papers (2026-06-16T07:44:45Z) - CriterAlign: Criterion-Centric Rationale Alignment for Code Preference Judging [95.02210956333374]
We propose a criterion-centric framework that adapts rubric-based judging to pairwise preference evaluation.<n>On BigCodeReward, CriterAlign improves a Qwen2.5-VL-32B monolithic judge from 60.4% to 66.3% accuracy.
arXiv Detail & Related papers (2026-05-19T10:59:19Z) - The Generalized Turing Test: A Foundation for Comparing Intelligence [0.8929189891250133]
We introduce a formal framework for comparing the capabilities of arbitrary agents via indistinguishability.<n>For agents A and B, we define the Turing comparator A $geq$ B to hold if B, acting as a distinguisher, cannot reliably distinguish between interactions with A and another instance of B.<n>This yields a dataset- and task-agnostic notion of relative intelligence.
arXiv Detail & Related papers (2026-05-11T17:00:18Z) - Silicon Bureaucracy and AI Test-Oriented Education: Contamination Sensitivity and Score Confidence in LLM Benchmarks [43.45152572188735]
We propose an audit framework for analyzing contamination sensitivity and score confidence in language models.<n>We find widespread but heterogeneous above-baseline gains under noisy conditions.<n>These results suggest that similar benchmark scores may carry substantially different levels of confidence.
arXiv Detail & Related papers (2026-03-23T07:03:07Z) - IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation [85.56193980646981]
We propose IF-RewardBench, a comprehensive meta-evaluation benchmark for instruction-following.<n>For each instruction, we construct a preference graph containing all pairwise preferences among multiple responses.<n>Experiments on IF-RewardBench reveal significant deficiencies in current judge models.
arXiv Detail & Related papers (2026-03-05T02:21:17Z) - Beyond Arrow: From Impossibility to Possibilities in Multi-Criteria Benchmarking [8.399688944263843]
We formalize a social choice problem where each metric induces a preference ranking over models on each dataset.<n>We prove that on single-peaked, group-separable and distance-restricted preferences, the benchmark operator allows for the construction of well-behaved rankings.<n> Empirically, we investigate several modern benchmark suites like HELM MMLU and verify which structural conditions are fulfilled on which benchmark problems.
arXiv Detail & Related papers (2026-02-07T15:47:24Z) - On the Limits of Test-Time Compute: Sequential Reward Filtering for Better Inference [71.09125259964684]
Test-time compute (TTC) has become an increasingly prominent paradigm for enhancing large language models (LLMs)<n>We study reward-filtered sequential inference, a simple procedure that selectively incorporates only high-reward generations into the context.<n>On the theoretical side, we show that reward-filtered sequential inference yields strictly stronger guarantees than standard TTC paradigms.
arXiv Detail & Related papers (2025-12-04T08:21:33Z) - Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
arXiv Detail & Related papers (2025-09-26T23:40:47Z) - COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
arXiv Detail & Related papers (2025-06-25T07:04:49Z) - A Backdoor-based Explainable AI Benchmark for High Fidelity Evaluation of Attributions [60.06461883533697]
We first identify a set of fidelity criteria that reliable benchmarks for attribution methods are expected to fulfill.<n>We then introduce a Backdoor-based eXplainable AI benchmark (BackX) that adheres to the desired fidelity criteria.<n>Our analysis also offers insights into defending against neural Trojans by utilizing the attributions.
arXiv Detail & Related papers (2024-05-02T13:48:37Z)
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.