FailureScope: Cross-Regime Behavioral Diagnosis of Language Model Weaknesses

• URL: http://arxiv.org/abs/2606.09878v1
• Date: Wed, 03 Jun 2026 01:28:00 GMT
• Title: FailureScope: Cross-Regime Behavioral Diagnosis of Language Model Weaknesses
• Abstract summary: FailureScope is a behavioral-diagnosis method that clusters evaluation probes by their cross-model pass/fail patterns.<n>We show it yields stable, interpretable failure across three regimes usually studied separately: single-turn benchmarks, multi-turn dialogue, and adversarial agent attacks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Standard benchmarks report aggregate accuracy, but practitioners need to know which specific capabilities a model lacks. We introduce FailureScope, a behavioral-diagnosis method that clusters evaluation probes by their cross-model pass/fail patterns (leave-one-model-out, LOMO), and show it yields stable, interpretable failure taxonomies across three regimes usually studied separately: single-turn benchmarks, multi-turn dialogue, and adversarial agent attacks. On 2,664 single-turn tasks across 18 models, taxonomy-conditioned sampling reaches Kendall's tau = 0.81 at 50 tasks (versus 0.34 for random selection), and cross-model failure prediction reaches AUC 0.88. The same primitive recovers interpretable clusters on a 363-task multi-turn corpus and on 630 adversarial agent traces, where it exposes a meta-failure mode: a 73-100 percentage-point gap between LLM-judge ASR and real execution. Cluster cohesion remains strong across all three regimes, which we take as evidence that behavioral clustering is a portable diagnosis primitive that generalizes beyond any single benchmark. We release the pipeline, three annotated corpora, and the cross-regime taxonomies.

Related papers

• Behavioral Grammar: Detecting Adaptive Malware via Tiny Language Model Priors and Second-Order Temporal Analysis [1.560152792366037]
  This paper presents Behavioral Grammar, a detection architecture that treats host runtime behavior as a structured language.<n>Each system event is discretized into an 8-token representation spanning event type, process, argument skeleton, path category, parent process, user, destination, and inter-event timing.<n>We frame these findings within a coevolutionary economics model, arguing that behavior-grammar detection shifts the evasion cost from rule circumvention to distribution matching.
  arXiv  Detail & Related papers  (2026-08-01T16:28:38Z)
• SingGuard-NSFA: Extensible Guardrails for Agentic AI via Generative Reasoning and Real-Time Classification [0.0]
  nsfaguard is a guardrail framework for securing agentic AI systems against operational threats.<n>We first introduce the NSFA taxonomy, which organizes 185 risk variants into a CIA-triad-grounded hierarchy.<n>We develop a dual-mode approach combining SFT-based generative reasoning for interpretable offline auditing with discriminative classification heads on the frozen backbone.
  arXiv  Detail & Related papers  (2026-07-13T07:28:45Z)
• Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems [47.027290803102666]
  Normal behaviour is the union of many imbalanced, curved, thin-fringed operating regimes.<n>We model the normal law with a jointly learned latent representation plus explicit Gaussian-mixture mode clustering.<n>We evaluate under a deliberately fair protocol: raw point-wise metrics with no point adjustment, a trivial-detector difficulty split, prevalence-matched F1, and train-normal-only calibration.
  arXiv  Detail & Related papers  (2026-07-07T10:10:00Z)
• AdversaBench: Automated LLM Red-Teaming with Multi-Judge Confirmation and Cross-Model Transferability [16.27940179594792]
  AdversaBench is an end-to-end red-teaming pipeline that mutates seed prompts with five structured operators.<n>We report experiments on 45 seeds across three categories: reasoning, instruction-following, and tool use.
  arXiv  Detail & Related papers  (2026-06-23T13:50:51Z)
• AgentAtlas: Beyond Outcome Leaderboards for LLM Agents [0.025718125188898048]
  AgentAtlas reframes agent evaluation as a diagnostic vocabulary and audit protocol.<n>The paper contributes: (i) a six-state control-decision taxonomy (Act / Ask / Refuse / Stop / Confirm / Recover); (ii) a trajectory-failure vocabulary with primary error source and downstream impact; and (iv) an illustrative protocol study on a synthetic 1,342-item set evaluated with eight models.
  arXiv  Detail & Related papers  (2026-05-19T22:05:12Z)
• When the Ruler is Broken: Parsing-Induced Suppression in LLM-Based Security Log Evaluation [0.0]
  We demonstrate a class of silent, systematic evaluation errors that can cause a fully functional model to appear completely non-functional categories.<n>We propose SOC-Bench v0, a benchmark framework comprising a standardized 13 category threat taxonomy, minimum statistical power requirements, fuzzy field extraction specification, and a public scoring script intended to prevent specific accuracy distortion in future SOC research.
  arXiv  Detail & Related papers  (2026-05-08T06:03:11Z)
• CUJBench: Benchmarking LLM-Agent on Cross-Modal Failure Diagnosis from Browser to Backend [2.9612444540570113]
  We present CUJBench, the first benchmark to combine browser-visible failure evidence with backend observability in a diagnostic framing.<n>The benchmark yields an overall accuracy of 19.7% with a ceiling of 52%, well below saturation.
  arXiv  Detail & Related papers  (2026-04-25T22:10:53Z)
• Claw-Eval: Toward Trustworthy Evaluation of Autonomous Agents [66.97968363332465]
  We introduce Claw-Eval, an end-to-end evaluation suite addressing all three gaps in agent benchmarks.<n>Claw-Eval comprises 300 human-verified tasks spanning 9 categories across three groups.<n>Every agent action is recorded through three independent evidence channels.
  arXiv  Detail & Related papers  (2026-04-07T17:43:18Z)
• OrgForge-IT: A Verifiable Synthetic Benchmark for LLM-Based Insider Threat Detection [0.0]
  We present a verifiable synthetic benchmark in which a deterministic simulation engine maintains ground truth and language models generate only surface prose.<n>The corpus spans 51 simulated days, 2,904 telemetry records at a 96.4% noise rate, and four detection scenarios designed to defeat single-surface and single-day triage strategies.
  arXiv  Detail & Related papers  (2026-03-23T19:03:53Z)
• Cross-LLM Generalization of Behavioral Backdoor Detection in AI Agent Supply Chains [0.0]
  We present the first systematic study of cross-LLM behavioral backdoor detection.<n>We show that single-model detectors achieve 92.7% accuracy within their training distribution but only 49.2% across different LLMs.<n>We show that model-aware detection model identity as an additional feature achieves 90.6% accuracy universally across all evaluated models.
  arXiv  Detail & Related papers  (2025-11-25T03:33:04Z)
• AttentiveGRUAE: An Attention-Based GRU Autoencoder for Temporal Clustering and Behavioral Characterization of Depression from Wearable Data [46.262619407930266]
  We present AttentiveGRUAE, a novel attention-based gated recurrent unit (GRU) autoencoder designed for temporal clustering and prediction of outcome from longitudinal wearable data.<n>We evaluate AttentiveGRUAE on longitudinal sleep data from 372 participants (GLOBEM 2018-2019)<n>It demonstrates superior performance over baseline clustering, domain-aligned self-supervised, and ablated models in both clustering quality and depression classification.
  arXiv  Detail & Related papers  (2025-10-02T20:52:16Z)
• CLIP Meets Diffusion: A Synergistic Approach to Anomaly Detection [49.11819337853632]
  Anomaly detection is a complex problem due to the ambiguity in defining anomalies, the diversity of anomaly types, and the scarcity of training data.<n>We propose CLIPfusion, a method that leverages both discriminative and generative foundation models.<n>We believe that our method underscores the effectiveness of multi-modal and multi-model fusion in tackling the multifaceted challenges of anomaly detection.
  arXiv  Detail & Related papers  (2025-06-13T13:30:15Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Learning from Aggregate Observations [82.44304647051243]
  We study the problem of learning from aggregate observations where supervision signals are given to sets of instances. We present a general probabilistic framework that accommodates a variety of aggregate observations. Simple maximum likelihood solutions can be applied to various differentiable models.
  arXiv  Detail & Related papers  (2020-04-14T06:18:50Z)

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.