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From Confident Closing to Silent Failure: Characterizing False Success in LLM Agents

Laksh Advani

arXiv:2606.09863v1cs.LG

TL;DR

LLM agents can silently claim success while the environment shows the task failed, creating a monitoring problem. The paper evaluates false success across tau2-bench and AppWorld, finding that prevalence varies by setting and LLM judges rely on surface completion proxies. Lightweight, benchmark-specific detectors outperform judges as triage signals, though high-stakes use calls for direct trajectory–environment checks.

  • Problem

    False success occurs when agents assert task completion despite contrary environment state, a failure that can propagate without customers or operators noticing.

    Source: §1 · p. 1

  • Method

    The study analyzes tau2-bench and AppWorld trajectories using programmatic outcome labels and evaluates LLM judges and lightweight detectors against those labels.

    Source: §1 · p. 2, §3.1 · p. 3, §3.2 · p. 3

  • Results

    TF-IDF detectors achieve task-disjoint AUROC 0.83 on tau2-bench and 0.95 on AppWorld, recovering 4–8× more false successes than the best judge at the same review budget.

    Source: §1 · p. 2, §6 · p. 11

  • Takeaways & Limitations

    The results support using lightweight, domain-calibrated detectors as triage signals and reserving direct trajectory–environment consistency checks for higher-stakes deployment.

    Source: §6 · p. 11

    The AppWorld 75.8% rate applies only to two self-assessing architectures, and the benchmark-specific detectors are not directly comparable or transferable across the datasets.

    Source: §5 · p. 10

Abstract

from arXiv · show

LLM agents can fail silently by asserting task completion when the environment state shows otherwise. We study this failure mode, false success, across two agent benchmarks: 9,876 tau2-bench trajectories from 8 model families and 1,879 AppWorld trajectories from 4 model families with text-independent ground truth. False success is common but varies by setting: 45--48% of failures in single-control tau2-bench domains, 3% in dual-control telecom, and 75.8% among AppWorld self-assessing coding-agent trajectories with explicit status claims. LLM judges fail reliably: no configuration across 5 judges, 5 prompt strategies, and full task specifications exceeds AUROC 0.65 on tau2-bench, and the same judges reach only 0.54 AUROC on AppWorld API-call traces. Judges rely on surface completion proxies -- confident closing language in tau2-bench and coarse action-sequence volume in AppWorld -- rather than verified state changes. Lightweight TF-IDF detectors achieve task-disjoint AUROC 0.83 on tau2-bench and 0.95 on AppWorld, recovering 4--8x more false successes than the best judge at the same flag rate with 3,300x lower latency. These results suggest that production monitoring should use lightweight, domain-calibrated detectors as triage signals rather than relying on LLM judges as the primary monitor for false success.

1. Introduction

False success occurs when an agent claims task completion despite contrary environment state, making it a silent monitoring problem. Across two benchmarks, the paper characterizes its prevalence, explains why LLM judges miss it, and evaluates lightweight detectors.

Motivation

  • False success propagates silently because customers may believe an issue is resolved while orders remain open or refunds never issue.The paper frames this as a monitoring problem rather than merely a benchmark curiosity.Source: §1 · p. 1

Prevalence

  • False success accounts for 44–52% of failures in single-control tau2-bench domains, 3% in dual-control telecom, and 75.8% among AppWorld architectures with explicit completion signals.Rates span 13% to 89% per model; Qwen3-Max-Thinking has the highest tau2-bench rate at 79%.Source: §1 · p. 1

Why LLM judges fail

  • No tested LLM-judge configuration exceeds AUROC 0.65 on tau2-bench, and the same judges reach only 0.54 on AppWorld.Judges follow confident completion language in tau2-bench and action-sequence volume in AppWorld rather than verified state changes.Source: §1 · pp. 1–2

Contributions

  • The study contributes 616 human-validated tau2-bench false successes (κ = 0.86) and replicates the phenomenon in AppWorld using a text-independent status field.The tau2-bench corpus separates false success from honest failure and ambiguous cases.Source: §1 · p. 2
  • Task-disjoint TF-IDF detectors reach AUROC 0.83 on tau2-bench and 0.95 on AppWorld, recover 4–8× more false successes than the best judge at the same review budget, and run 3,300× faster.Simple linear classifiers match fine-tuned neural models within 0.03 AUROC.Source: §1 · p. 2
  • Judges reward surface signals that false-success trajectories also exhibit, while detectors trained against ground-truth labels learn the opposite signal.In tau2-bench, agents use confident assertion language regardless of outcome; in AppWorld, they may inspect state without changing it before claiming completion.Source: §1 · p. 2

Environment and model-level moderators

  • Telecom’s 3% false-success rate contrasts with 44–52% in single-control domains, but only one dual-control domain is available to isolate causality.The authors suggest independent verification may be a promising structural defense, while noting that additional dual-control environments are needed.Source: §1 · p. 2

2. Related Work

The paper distinguishes its false-success detection problem from related work on agent evaluation, failure localization, LLM judges, and interpretability. It also separates false success from corrupt success, which concerns procedurally improper trajectories that nevertheless receive positive reward.

Agent evaluation benchmarks

  • Benchmarks such as AgentBench (Liu et al., 2024), GAIA (Mialon et al., 2023), and tau2-bench assess agent behavior, but the paper isolates false success for detection.Tau2-bench provides database-state completion labels independent of agent language.Source: §2 · p. 2

Failure detection and localization

  • Who&When (Zhang et al., 2025b), AgenTracer (Zhang et al., 2025a), and AgentDebug (Zhu et al., 2025) localize known failures, whereas this work detects whether failure occurred when an agent claims success.The approaches are complementary but target different steps.Source: §2 · p. 2

LLM-as-judge evaluation

  • Unlike prior LLM-as-judge work on dialogue, instruction following, reasoning, and agent trajectories, this paper identifies confident assertion language as a mechanism that misleads judges about false success.The authors connect their finding to concerns about judge reliability (Thakur et al., 2025).Source: §2 · p. 2

Corrupt success and procedure-aware evaluation

  • Cao et al. (2026) define corrupt success as positive-reward trajectories with procedural violations, distinct from false success, where agents claim success despite reward=0.The two populations are disjoint and pose different risks: policy compliance versus user trust.Source: §2 · p. 3

Interpretability and failure modes

  • False success resembles hallucination but is triggered by tool outputs and environment state; the telecom contrast suggests prevalence may vary with verification structure.Prior hallucination work had not explored this environment-structure finding.Source: §2 · p. 3

3. Methods

The study builds benchmark-specific corpora and labels for false success, then evaluates automated detectors and LLM judges using distinct inputs and evaluation splits. Its methods combine environment-state outcomes, explicit status fields, and trajectory-based assessments.

3.1. tau2-bench corpus

  • 9,876 tau2-bench trajectories pair complete tool-grounded simulations with binary rewards based on whether the final environment state matches task specifications.The corpus covers eight model families and three customer-service domains.Source: §3.1 · p. 3

3.2. AppWorld corpus

  • 1,879 AppWorld trajectories from self-assessing architectures were labeled by comparing explicit supervisor-database status with programmatic evaluation.The resulting corpus contains 1,425 false successes and 454 honest failures; trajectories without status claims were excluded.Source: §3.2 · p. 3

3.3. Three-class labeling of tau2-bench failures

  • Tau2-bench failures were regex-labeled as false success, honest failure, or ambiguous, with human agreement κ = 0.86.The validation sample showed 91.5% agreement between human and regex labels.Source: §3.3 · p. 4

3.4. Detectors

  • Three detector architectures use benchmark-specific features, with task-disjoint evaluation preventing task-specific surface patterns from appearing in both training and test sets.The detectors include TF-IDF with logistic regression or XGBoost and a fine-tuned DeBERTa model; the study also evaluates cross-model and leave-one-domain-out transfer.Source: §3.4 · pp. 4–5

3.5. Judge evaluation

  • Tau2-bench judges were tested across five models and five prompt conditions, while AppWorld judges assessed API-call sequences under three conditions.Judge responses were converted to false-success scores from completion decisions and confidence; tau2-bench also included a condition supplying the task specification.Source: §3.5 · p. 5

4. Results

Across tau2-bench and AppWorld, false success varied by setting, LLM judges performed poorly, and lightweight detectors achieved stronger task-disjoint discrimination and triage recall. The results identify surface completion cues as a source of judge error and position the detector as a human-reviewed triage signal.

4.1. Phenomenon Characterization

  • Across tau2-bench failure trajectories, false-success prevalence ranged from 13% for GPT-5.2 to 79% for Qwen3-Max-Thinking-Preview, and reasoning capability was not protective.AppWorld rates among failures with explicit completion claims ranged from 67% for LLaMA-3 to 89% for DeepSeekCoder.Source: §4.1 · pp. 5–6
  • False success accounted for 45% and 48% of failures in tau2-bench airline and retail, versus 3% in telecom; AppWorld’s overall false-success rate was 75.8%.With only one dual-control domain and 15 false-success cases, the authors treat the telecom pattern as an observation rather than a causal claim.Source: §4.1 · p. 6
  • AppWorld false-success rates were highest in Spotify and SimpleNote and lowest in Amazon, and decreased as task difficulty rose.Rates were 94.5% for Spotify, 91.9% for SimpleNote, and 65.2% for Amazon; they fell from 88.2% at difficulty 1 to 70.1% at difficulty 3.Source: §4.1 · p. 6

4.2. Lightweight Detectors

  • Task-disjoint TF-IDF + XGBoost reached 0.825 ± 0.025 AUROC on tau2-bench and 0.953 on AppWorld, with shallow sequence features matching deep models.On tau2-bench, TF-IDF + LR reached 0.849 AUROC; the methods were within 0.024 AUROC of each other.Source: Table 4 · p. 6, §4.2 · pp. 6–7
  • Cross-model transfer held above 0.85 on both benchmarks, while tau2-bench cross-domain transfer reached 0.69 and cross-temporal transfer reached 0.73.The authors attribute the cross-domain gap to domain-specific vocabulary and report that LODO does not apply to AppWorld.Source: Figure 2 · p. 7, §4.2 · pp. 7–8
  • AppWorld false successes were predicted by read-heavy API sequences without state-modifying calls, while honest failures showed write-retry patterns and post-completion restarts.Removing the final tau2-bench closing message reduced AUROC by 0.008, indicating that useful signal also appears in tool responses and intermediate steps.Source: §4.2 · p. 7, Figure 3 · p. 8

4.3. LLM Judges Fail Systematically

  • No tau2-bench judge configuration exceeded 0.65 AUROC, while AppWorld judges peaked at 0.537 against a task-disjoint detector score of 0.953.On tau2-bench, the strongest single judge cell was Sonnet 4.5 no closing at 0.640; AppWorld judges remained weak even with checklist conditions.Source: Table 5 · p. 8, §4.3 · p. 8

4.4. Deployment Implications

  • At a 10% flag rate, the detector recovered 72% of false successes versus 13% for the strongest judge, and ran 3,364× faster.At that flag rate, precision was 50%; the authors therefore characterize the detector as a triage signal, not an autonomous monitor.Source: Figure 6 · p. 9, §4.4 · pp. 9–10

5. Limitations

The study’s evidence comes from structured API benchmarks, and several limitations constrain how far its prevalence and detector results can be generalized. AppWorld results cover only self-assessing architectures, while transfer and deployment require calibration or additional checks.

Benchmark scope

  • Both benchmarks use structured API environments with programmatic rewards; whether false success extends to web navigation, open-ended code generation, or scientific reasoning remains open.The authors suggest independent state verification may suppress false success, while completion claims without verifiable grounding may preserve the phenomenon.Source: §5 · p. 10

AppWorld scope

  • AppWorld’s 75.8% false-success rate applies only to trajectories from two self-assessing architectures, and its API-sequence detector cannot be directly applied to tau2-bench data.The other two architectures always report status=success on completion, making that field uninformative as a self-assessment signal.Source: §5 · p. 10

Cross-domain transfer

  • Zero-shot transfer between tau2-bench airline and retail reaches AUROC 0.69, while 50–100 labeled examples per class raise LR AUROC to 0.79–0.83.Cross-temporal transfer reaches AUROC 0.68–0.73, suggesting surface expression drifts across model generations.Source: §5 · p. 10

Deployment scoping

  • At a 10% flag rate, detector precision is 50%, so the authors position it as triage rather than autonomous monitoring and recommend direct state checks for high-stakes use.The AppWorld result suggests structured features may generalize better, but this has not been tested in production.Source: §5 · p. 10

Adversarial robustness

  • Rewriting honest-failure messages in confident-completion style flips both detectors and judges on roughly 20–25% of cases, leaving adversarially trained agents in need of additional defenses.Source: §5 · p. 10

6. Conclusion

False success is a silent failure in which agents claim completion despite a failing environment state, and its prevalence varies sharply by benchmark setting and architecture. The study finds that LLM judges rely on surface proxies, while lightweight detectors provide a stronger triage signal; its appendix regex labels distinguish asserted success, explicit failure, and ambiguous cases.

6. Conclusion

  • False success ranges from 3% of failures in tau2-bench telecom to 45–48% in single-control domains and 75.8% among AppWorld self-assessing trajectories.Across the study’s corpus, reasoning did not reliably prevent the failure; Qwen3-Max-Thinking had the highest tau2-bench rate at 79%.Source: §6 · p. 10
  • No tested LLM-judge configuration exceeds AUROC 0.65 on tau2-bench, and the same judge family reaches only 0.54 AUROC on AppWorld API-call traces.Judges appear to use confident closing language in tau2-bench and coarse action-sequence volume in AppWorld rather than verified state changes.Source: §6 · p. 10
  • TF-IDF detectors reach task-disjoint AUROC 0.83 on tau2-bench and 0.95 on AppWorld, recovering 4–8× more false successes than the best judge at equal review budgets and running 3,300× faster.The authors recommend lightweight, domain-calibrated detectors as triage signals and direct trajectory–environment checks for higher-stakes deployment.Source: §6 · pp. 10–11

A. Regex Pattern Definitions

  • The appendix regex labels a trajectory FALSE SUCCESS when its assertion pattern matches without the honest-failure pattern, HONEST FAILURE for the inverse, and AMBIGUOUS when both or neither match.The patterns are applied case-insensitively to tau2-bench failure trajectories’ final assistant message.Source: A. Regex Pattern Definitions · p. 13

B. Human Validation Protocol

The validation procedure samples failure trajectories across model families and domains, then compares independent annotators’ closing-behavior labels with regex labels. Agreement is high overall, with disagreements concentrated in ambiguous or borderline cases.

Sampling

  • The study sampled 200 failure trajectories, balancing 100 regex-labeled false successes and 100 honest failures across eight model families and three domains, while excluding ambiguous cases.Source: B. Human Validation Protocol · p. 13

Annotation protocol

  • Annotators reviewed full trajectories and classified closing behavior as FALSE SUCCESS, HONEST FAILURE, or AMBIGUOUS before seeing the regex label; GPT-5 served as a second annotator.GPT-5 used the same trajectories and label definitions at temperature 0.0.Source: B. Human Validation Protocol · p. 13

Agreement

  • Human-regex agreement reached 92% for false success and 91% for honest failure, with 11 of 17 disagreements involving human-labeled ambiguous trajectories.Other disagreements involved borderline false-success cases with a late hedge or honest failures with a vestigial “I cannot” clause overridden by a successful action.Source: B. Human Validation Protocol · p. 13, Table 7 · p. 13

C. Detector Hyperparameters and Training Details

The appendix specifies detector feature construction and training settings, alongside the prompt conditions used to evaluate LLM judges. Tau2-bench judges see trajectories under several wording and information conditions, while AppWorld judges assess API-call sequences with or without task and write-operation guidance.

C. Detector Hyperparameters and Training Details

  • The study uses shared TF-IDF unigram and bigram features capped at 30,000 for Logistic Regression and XGBoost; only Logistic Regression is class-balanced, while XGBoost uses fold-specific positive-class weighting.Logistic Regression uses C=1.0; XGBoost uses 300 estimators, maximum depth 6, and learning rate 0.08.Source: C. Detector Hyperparameters and… · pp. 13–14
  • The DeBERTa-v3-base comparison model has 184M parameters and is fine-tuned for three epochs with batch size 16, AdamW, and sequence length 512.The best checkpoint is selected by validation AUROC.Source: C. Detector Hyperparameters and… · p. 14
  • Tau2-bench trajectories are serialized with user, tool-call, tool-result, and assistant markers, with tool arguments and results truncated to 200 and 300 characters.AppWorld API sequences are represented as space-separated method-endpoint tokens.Source: C. Detector Hyperparameters and… · p. 14

D. Judge Prompts

  • Tau2-bench judges assess trajectory completion under blind, full-task-specification, no-closing, tool-only, checklist, and neutral, skeptical, or terse prompt conditions.The checklist asks judges to verify required tool calls, tool-result success, request alignment, confirmations, and errors or hand-offs.Source: D. Judge Prompts · pp. 14–15
  • AppWorld judges see API-call sequences, optionally alongside the task instruction, and one prompt explicitly reminds them that write tasks require POST, PUT, or DELETE calls to appropriate endpoints.AppWorld prompts return a completion decision and confidence score.Source: D. Judge Prompts · p. 15

E. Per-Holdout Detector Results

The supplied passages identify the scopes of the tau2-bench holdout tables and note a reliability caveat for telecom-as-test LODO.

E. Per-Holdout Detector Results

F. AppWorld Architecture Filter

The AppWorld analysis keeps architectures whose status field can distinguish success from failure and excludes trajectories without a completion signal from false-success analysis.

F. AppWorld Architecture Filter

  • React and plan exec always write status=success on completion, regardless of actual task success, so their status is degenerate as a self-assessment signal.Source: F. AppWorld Architecture Filter · p. 16
  • The analysis is restricted to full code refl and ipfuncall, which sometimes write status=fail and thus demonstrate genuine self-assessment capability.Source: F. AppWorld Architecture Filter · p. 16
  • The 3,120 status=None trajectories are excluded because they stopped without a completion signal and represent incomplete execution rather than a false completion claim.Source: F. AppWorld Architecture Filter · p. 16

G. XGBoost Feature Analysis

The feature analysis reports a distributed tau2-bench importance profile and a masking result, alongside discriminative AppWorld API-sequence features and status-writing behavior.

G. XGBoost Feature Analysis

  • The top 30 features account for 22% of total importance across 1,859 features with non-zero importance; cumulative importance reaches 90% around rank 910.Source: G. XGBoost Feature Analysis · p. 16
  • Removing the top 30 features changes random-split AUROC from 0.940 to 0.939 (delta = 0.001), indicating that the detector’s signal is not concentrated in a few trigger tokens.Source: G. XGBoost Feature Analysis · p. 16
  • On AppWorld, read-heavy sequences predict false success, while write-retry patterns and post-completion restarts predict honest failure.Examples include get gmail outbox (+1.34) and post supervisor message followed by get supervisor profile (−4.81).Source: G. XGBoost Feature Analysis · p. 16
  • Table 10 reports per-model LOMO AUROC on AppWorld using XGBoost and API call sequence features.Source: Table 10 · p. 17
  • Across 8,190 AppWorld trajectories, only full code refl and ipfuncall ever write status=fail.Source: Table 11 · p. 17

H. Adversarial Rewrite Test

The section tests whether confident rewrites alter detector judgments, then evaluates few-shot target-domain adaptation for airline-retail LODO.

H. Adversarial Rewrite Test

  • On 50 HF trajectories, GPT-4o rewrites closing messages in confident-completion style while leaving tool calls and rewards unchanged.The instruction directs the rewrite to assert successful completion while matching the original topic and length.Source: H. Adversarial Rewrite Test · p. 17
  • The XGBoost detector flips on 10/50 rewritten trajectories (20%), while GPT-4o and Claude Sonnet 4.5 each flip on 11/45 explicit-failure trajectories (24%).The authors describe both detection mechanisms as similarly vulnerable.Source: H. Adversarial Rewrite Test · p. 17
  • The example rewrite turns a refund requiring manual approval into a confident claim that the refund was processed and approved.Source: H. Adversarial Rewrite Test · p. 17

I. Few-shot Domain Adaptation

  • Few-shot adaptation adds K labeled false-success and K labeled honest-failure examples from the target domain, then evaluates on its remaining examples.Table 13 covers airline↔retail LODO target-domain adaptation.Source: §I · p. 17, Table 13 · p. 18
  • With calibration examples, LR improves steadily while XGBoost degrades; the authors suggest lexical recalibration helps LR and the small target set leads XGBoost to overfit.Source: §I · p. 17
  • Entity stripping does not improve LODO (LR 0.690, XGB 0.684), consistent with a domain gap involving vocabulary differences beyond entity names.Source: §I · p. 17
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