A Paired Testing Protocol for Batch-Conditioned Refusal Robustness in LLM Serving
- URL: http://arxiv.org/abs/2605.27763v1
- Date: Tue, 26 May 2026 23:22:55 GMT
- Title: A Paired Testing Protocol for Batch-Conditioned Refusal Robustness in LLM Serving
- Abstract summary: Safety evaluations of language models often treat serving configuration as fixed background infrastructure.<n>We synthesize four artifact-backed studies into a paired testing protocol.<n>Standard vLLM reproduces 22/55 label flips on current score-flip candidates, while enabling VLLM_BATCH_INIANT=1 reduces the same test to 0/55 flips.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Safety evaluations of language models often treat serving configuration as fixed background infrastructure, but batch condition is an untested treatment variable whenever the same prompt may be evaluated alone, in a synchronized batch, or inside a continuous-batching scheduler. We synthesize four artifact-backed studies into a paired testing protocol: Study A combines local discovery, scorer-corrected adjudication, and true-batching confirmation; Study B tests cross-model generalization; Study C tests continuous-batch composition; and Study D runs a batch-invariant-kernel ablation. The local test finds safety-label changes more often than capability-label changes (0.51% vs. 0.14%), but adjudication of 63 candidate rows leaves only 17 genuine behavioral flips, implying a corrected full-set rate of 0.16%. The 15-model extension finds no detectable universal safety-over-capability skew: flips are near parity (0.94x), alignment type has no detectable association ($p=0.942$, $η^2=0.033$), and output instability is the strongest tested fragility screen ($r=0.909$, bootstrap 95% CI [0.65, 0.97]). In the targeted kernel ablation, standard vLLM reproduces 22/55 label flips on current score-flip candidates, while enabling VLLM_BATCH_INVARIANT=1 reduces the same test to 0/55 flips; the composition test separately finds no aggregate effect at 4.7pp sensitivity. The testing recommendation is exact-stack validation: evaluate refusal at the served batch setting, pair safety prompts with capability controls, and report low-rate directional flips separately from aggregate null effects.
Related papers
- Beyond Local Accuracy: A Protocol-Level Identifiability Audit for Controlled LLM Reasoning Evaluation [6.047519836191728]
We formalize a protocol-level identifiability audit over a finite behavioral policy class.<n>The audit requires zero model calls and resolves our diagnostic case.<n>This case shows how evaluation-design validity can be checked structurally before model inference.
arXiv Detail & Related papers (2026-08-13T14:49:47Z) - Beyond Pass@k: Measuring Reliability and Security of Agentic Code Generation [2.1734195143282693]
We propose reliability@k, the same estimator applied correctly, with n = independent rollouts and c = fully-passing rollouts per (task, agent) pair.<n>Motivated by evidence that functional correctness does not imply security safety, we additionally propose security-adjusted reliability@k.
arXiv Detail & Related papers (2026-08-11T16:01:15Z) - Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion [46.262619407930266]
We train a multi-label classifier on a curated gold dataset of 1,207 CVEs from expert MITRE Center for Threat-Informed Defense mappings.<n>The resulting model approximately doubles recall@5 compared with a zero-shot embedding-similarity baseline.<n>We then investigate whether LLM-assisted labeling can extend the gold dataset.
arXiv Detail & Related papers (2026-07-28T10:59:04Z) - Deterministic Decisions for High-Stakes AI. A Zero-Egress Pipeline with the Deployability of RAG and the Accuracy of Machine Learning [0.0]
Intervention bias is a previously unquantified failure mode of large-language-model educational advisory agents.<n>We show that supervised policy learning eliminates this bias.<n>We also show an Evaluation Gap: LLM-as-judge scoring is blind to intervention bias, rewarding fluent over-prescription rather than decision quality.
arXiv Detail & Related papers (2026-06-28T08:58:17Z) - BOKBO (Best of K Bad Options): Calibrated Abstention for VLA Policies [1.3918848543076061]
Test-time scaling for vision-suite-action (VLA) policies, methods such as RoboMonkey, SEAL, MG-Select, and V-GPS, samples K candidate action chunks at inference and executes the verifier-best.<n>We propose BOKBO, the first conformal abstention layer for K-sample VLA inference, providing finite-sample distribution-free guarantees on executed-violation rate.
arXiv Detail & Related papers (2026-05-28T23:39:09Z) - Let the Results Speak: A Replication-First Paradigm for LLM Behavioral Benchmarking [22.825786049667602]
We propose a replication-first paradigm to anchor validity to a single human-rater consensus.<n>We certify the instrument via four properties -- reliability across K runs, cross-instrument replication across architecturally distinct judges, historical-footprint calibration via judges from earlier training cohorts, and pre-registered prediction.<n>We test it on emotional accompaniment by letting the self-evolve data-driven across: the dimensions are not pre-stipulated and the procedure stabilizes to a 9-dimension set.
arXiv Detail & Related papers (2026-05-27T03:41:11Z) - 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) - Prompt-Induced Score Variance in Zero-Shot Binary Vision-Language Safety Classification [43.86209535250233]
Cross-prompt variance is strongly associated with prompt-level disagreement and higher error.<n>A training-free mean ensemble improves NLL on all 14 dataset-model evaluation pairs.<n>We frame this as a reliability stress test for zero-shot VLM first-token safety scores.
arXiv Detail & Related papers (2026-05-01T01:06:30Z) - 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) - Are Benchmark Tests Strong Enough? Mutation-Guided Diagnosis and Augmentation of Regression Suites [49.16055123488827]
Insufficiently strong test suites can admit plausible yet semantically incorrect patches, inflating reported success rates.<n>We introduce STING, a framework for targeted test augmentation that uses semantically altered program variants as diagnostic stressors.<n>STING produces 1,014 validated tests spanning 211 instances and increases patch-region line and branch coverage by 10.8% and 9.5%.
arXiv Detail & Related papers (2026-04-02T01:13:40Z) - SpatialBench-UC: Uncertainty-Aware Evaluation of Spatial Prompt Following in Text-to-Image Generation [0.0]
SpatialBench-UC is a small, reproducible benchmark for pairwise spatial relations.<n>We release a benchmark package, versioned prompts, pinned configs, per-sample checker outputs, and report tables.<n>We evaluate three baselines, Stable Diffusion 1.5, SD 1.5 BoxDiff, and SD 1.4 GLIGEN.
arXiv Detail & Related papers (2026-01-19T23:37:10Z) - CARE What Fails: Contrastive Anchored-REflection for Verifiable Multimodal [84.71254539482369]
Group-relative reinforcement learning with verifiable rewards (RLVR) often wastes the most informative data it already has the failures.<n>We present CARE, a failure-centric post-training framework for multimodal reasoning that turns errors into supervision.<n> CARE improves accuracy and training smoothness while explicitly increasing the share of learning signal that comes from failures.
arXiv Detail & Related papers (2025-12-22T16:34:21Z) - Robust Conformal Prediction with a Single Binary Certificate [58.450154976190795]
Conformal prediction (CP) converts any model's output to prediction sets with a guarantee to cover the true label with (adjustable) high probability.<n>We propose a robust conformal prediction that produces smaller sets even with significantly lower MC samples.
arXiv Detail & Related papers (2025-03-07T08:41:53Z) - STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay [76.06127233986663]
Test-time adaptation (TTA) aims to address the distribution shift between the training and test data with only unlabeled data at test time.
This paper pays attention to the problem that conducts both sample recognition and outlier rejection during inference while outliers exist.
We propose a new approach called STAble Memory rePlay (STAMP), which performs optimization over a stable memory bank instead of the risky mini-batch.
arXiv Detail & Related papers (2024-07-22T16:25:41Z) - Sequential Kernelized Independence Testing [77.237958592189]
We design sequential kernelized independence tests inspired by kernelized dependence measures.<n>We demonstrate the power of our approaches on both simulated and real data.
arXiv Detail & Related papers (2022-12-14T18:08:42Z)
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.