Catching One in Five: LLM-as-Judge Blind Spots in Production Multi-Turn Transaction Agents

• URL: http://arxiv.org/abs/2606.10315v1
• Date: Tue, 09 Jun 2026 02:11:01 GMT
• Title: Catching One in Five: LLM-as-Judge Blind Spots in Production Multi-Turn Transaction Agents
• Abstract summary: 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.
• Score: 45.148328075418156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM-as-judge is the default instrument for evaluating conversational agents, yet its reliability is almost always reported as agreement with human ratings, not recall of real defects. We study a deployed multi-turn food-and-beverage ordering agent and measure how many genuine quality problems its built-in LLM judge catches, using exhaustive human transcript review as ground truth. Across three batches the judge surfaces well under a quarter of human-confirmed systematic problems -- 2 of 9 patterns (22%) in one batch, and its operational gate flagged zero of 100 rounds in a batch where humans confirmed 23 distinct defects and 7 new cross-cutting patterns. Our blind-spot taxonomy shows the failure is structured, not random: the judge catches turn-local issues (a fabricated statistic, a wrong language) but misses cross-turn state issues (confirm-gate lockout, cart hallucination, escalation lockout, stale referents). The mechanism: the scoring rubric exposes only three coarse axes (intent, brand-voice, personalization) and has no category for the behavioural dimensions -- state-tracking, guardrails, recovery -- where most defects cluster. The failure is routing, not perception: 113 of 114 rounds whose raw judge note describes a confirm-gate or cart-state defect are scored "brand voice", and none reach an operational failure -- the gate is wired to hangs and hard assertions, not the rubric -- so the 0% is a routing-and-wiring failure, not blindness. The consequence for prevalence estimation is sharp: when the apparent defect rate is zero the Rogan-Gladen correction degenerates -- no signal can recover the true rate -- while where the gate reports a nonzero rate the same estimator implies a 3-6x undercount under our measured sensitivity. For production multi-turn agents, automated judging is a regression floor, not a substitute for human review.

Related papers

• trajectory-judge: What Outcome-Only LLM Judges Miss on Agent Trajectories [0.0]
  Outcome-only evaluation is the production default for LLM agents.<n>We measure that blind spot where ground truth is known by construction.<n>We argue that judge evaluations must stratify recall by outcome survival.
  arXiv  Detail & Related papers  (2026-08-29T10:14:05Z)
• Located but Not Releasable: Silent Gate Inversion and Bounded Linear Release [0.0]
  We submit the complete pipeline -- detect, localize, and release -- to a fully preregistered stress test on a 25.7M transformer.<n> Interventions at observation-evidence channels of mid layers restore target behavior on otherwise-suppressed worlds.<n>Gating fails out of distribution: a detector calibrated to trigger on zero out-of-distribution calibration worlds triggers on 6.9-7.3% of held-out in-distribution generations and on zero of the 2,400 held-out generations that actually need it.
  arXiv  Detail & Related papers  (2026-08-12T09:04:54Z)
• 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)
• PerceptionRubrics: Calibrating Multimodal Evaluation to Human Perception [75.70273182182397]
  PerceptionRubrics is a rubric-based evaluation framework.<n>It addresses the gap between saturated benchmark scores and real-world brittleness.<n>It pairs 1,038 information-dense images with over 10,000 instance-specific rubrics.
  arXiv  Detail & Related papers  (2026-06-26T17:59:15Z)
• BabelJudge: Measuring LLM-as-a-Judge Reliability Across Languages and Agent Trajectories [0.0]
  LLM-as-a-judge has become the dominant approach to scalable evaluation in NLP pipelines.<n>We introduce BabelJudge, an open-source benchmark and reliability audit framework.<n>It measures all four failure modes -- position bias, verbosity bias, order inconsistency, and cross-lingual degradation.
  arXiv  Detail & Related papers  (2026-06-21T04:35:43Z)
• 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)
• Evaluating Tool-Using Language Agents: Judge Reliability, Propagation Cascades, and Runtime Mitigation in AgentProp-Bench [0.0]
  We introduce AgentProp-Bench, a 2,000-task benchmark with 2,300 traces across four domains.<n>We quantify judge reliability, characterize error propagation, and evaluate a runtime mitigation.<n>All code, data, traces, and human labels are released at https://github.com/bhaskargurram-ai/agenthallu-bench.
  arXiv  Detail & Related papers  (2026-04-17T21:15:35Z)
• Automated Self-Testing as a Quality Gate: Evidence-Driven Release Management for LLM Applications [51.56484100374058]
  We present an automated self-testing framework that introduces quality gates with evidence-based release decisions.<n>We evaluate the framework through a longitudinal case study of an internally deployed multi-agent conversational AI system.
  arXiv  Detail & Related papers  (2026-03-13T20:44:15Z)
• A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness [57.510025257780306]
  We show that existing validation protocols fail to account for substantial distribution shifts inherent to red-teaming.<n>We propose ReliableBench, a benchmark of behaviors that remain more consistently judgeable, and JudgeStressTest, a dataset designed to expose judge failures.
  arXiv  Detail & Related papers  (2026-02-04T15:13:35Z)
• Evaluative Fingerprints: Stable and Systematic Differences in LLM Evaluator Behavior [0.0]
  We find that judges are consistent, but not with each other; they are consistent with themselves.<n>Across 3,240 evaluations, inter-judge agreement is near-zero.<n>Averaging judges' scores produces a synthetic verdict that corresponds to no judge's actual values.
  arXiv  Detail & Related papers  (2026-01-08T17:02:22Z)
• 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)
• TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them [58.04324690859212]
  Large Language Models (LLMs) as automated evaluators (LLM-as-a-judge) has revealed critical inconsistencies in current evaluation frameworks.<n>We identify two fundamental types of inconsistencies: Score-Comparison Inconsistency and Pairwise Transitivity Inconsistency.<n>We propose TrustJudge, a probabilistic framework that addresses these limitations through two key innovations.
  arXiv  Detail & Related papers  (2025-09-25T13:04:29Z)
• When Judgment Becomes Noise: How Design Failures in LLM Judge Benchmarks Silently Undermine Validity [21.192000569821943]
  We argue that without tight objectives and verifiable constructions, benchmark rankings can produce high-confidence rankings that are in fact largely noise.<n>We show that the ELO-style aggregation used by Arena-Hard Auto collapses and masks genuine ranking uncertainty.<n>Our results highlight design failures that undermine validity and offer actionable principles for building better-scoped, reliability-aware benchmarks.
  arXiv  Detail & Related papers  (2025-09-24T16:26:47Z)

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.