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Locating Hidden Failures Makes Long-Horizon Agents More Reliable

Salman Rahman, Yubin Kim, Mihir Parmar, A. Ali Heydari, Genglin Liu, Simon A. Lee, Weizhi Zhang, Arian Hosseini, Ahmed A. Metwally, Yuzhe Yang, Baharan Mirzasoleiman, Xin Liu, Pavel Izmailov, Saadia Gabriel, Mark Malhotra, Shwetak Patel, Daniel McDuff, Hamid Palangi

arXiv:2609.17930v1cs.LG

TL;DR

Outcome-only judgments cannot show where long-horizon agents went wrong or what harm they caused en route. The paper introduces a human-verified benchmark and trains a verifier to locate mistakes; frontier judges struggle, while Scout transfers to an unseen domain and improves test-time run selection. The authors note that their evidence covers three domains and does not establish whether the patterns extend to embodied, multimodal, or multi-user settings.

  • Problem

    Outcome-only evaluation collapses a long agent trajectory into a pass-or-fail verdict, leaving its intermediate steps unmeasured as agents take on more autonomous work.

    Source: §1 · p. 1

  • Method

    The authors build Traverse from human-labeled agent steps and first mistakes, then train a verifier to locate failures in long-horizon trajectories.

    Source: §1 · pp. 4–5

  • Results

    The strongest frontier judge locates first mistakes in only 26.8% of software-engineering runs and 32.3% of computer-use runs, while Scout outperforms much larger judges.

    Source: §1 · pp. 4–5

  • Takeaways & Limitations

    Process-level verification matters because an outcome-accurate reward model can still misjudge the steps that produced the outcome.

    Source: §4 · p. 18

    The study covers three domains, leaving open whether its reported patterns hold in embodied, multimodal, and multi-user settings.

    Source: §4 · p. 19

Abstract

from arXiv · show

As AI agents take on long, autonomous tasks, we increasingly oversee rather than perform the work, yet we still judge them almost entirely by whether they finally succeed. An outcome cannot reveal where a run went wrong, whether the agent recovered, or the irreversible harm it caused along the way, and where long-horizon agents fail remains unmapped. We study $2518$ agent trajectories across software engineering, computer use, and science, close to real deployment, and classify $6967$ mistakes into $78$ failure types. Failure follows a recurring signature: after its first mistake an agent often fails to recover and rarely catches the error itself, so the run continues unchecked while still looking correct; whether an agent recovers depends on the task and the environment's feedback, not on the agent framework running it. Long-horizon agents can do real harm on the way to a passing result: even runs scored as solved delete data, corrupt systems, or fabricate success rather than earning it. We release these human-verified annotations as Traverse, a benchmark on which six frontier judges struggle to locate failure regardless of scale: even the strongest correctly identifies the first mistake in fewer than a third of runs. Yet Scout, a $4$B verifier we trained, locates failure far better than these judges and transfers to domains it never saw. Used at test time to select among an agent's candidate runs, it raises task success above the agent's own single-attempt performance, without retraining the agent. By making failure cheap to locate and correct, this work is a foundation for more trustworthy long-horizon agents that learn from their own mistakes, and a practical path to overseeing increasingly autonomous AI.

1. Introduction

Outcome-only evaluation misses where long-horizon agents fail and the harm they cause along the way. The paper maps these failures, introduces Traverse to measure first-mistake localization, and trains Scout to locate failures and improve test-time run selection.

Failure analysis

  • Across software engineering, computer use, and AI-for-science, the study labels every step in 2,620 real trajectories and marks each failed run’s first mistake.The collection spans closed- and open-weight models and multiple agent harnesses.Source: §1 · p. 3

Failure signature

  • After the first mistake, agents recover in only 30.5% of runs and never detect it in 38.5%; each wrong step makes the next roughly 11–14 times likelier to be wrong.Reasoning and planning errors most often start and doom failures, while invalid actions are usually downstream symptoms.Source: §1 · p. 4

Safety and recovery

  • Failure patterns depend on task and environmental feedback, and some solved runs cause irreversible harm or fabricate success rather than earning it.Software agents may recover through rich environmental feedback, while computer-use agents can notice an error yet remain stuck without it.Source: §1 · p. 4

Traverse benchmark

  • Traverse pairs long-horizon trajectories and naturally occurring failures with human labels for each step and the first mistake, enabling process- and outcome-level evaluation.Annotators agree with Cohen’s κ=0.77 on whether a run failed and κ=0.73 on where its first mistake occurred.Source: §1 · p. 4

Frontier judges

  • Even the strongest of six frontier judges locates first mistakes in only 26.8% of software-engineering runs and 32.3% of computer-use runs.Accuracy falls further as trajectories lengthen, and every judge shows systematic bias when judging whether a run failed.Source: §1 · p. 4

Scout verifier

  • Scout raises task success from 81.8% to 90.2% on Terminal-Bench, 75.6% to 78.0% on SWE-bench, and 50.1% to 53.2% on BixBench.Selecting among candidate trajectories improves performance without retraining the agent.Source: §1 · p. 5

2. Methods

The methods assemble long-horizon trajectories from three deployment-relevant domains, annotate and analyze failures step by step, and use the resulting labels to build Traverse and train Scout. They evaluate failure localization, error detection, and verifier-guided candidate selection.

2.1. Collecting long-horizon agent trajectories

  • Trajectories come from SWE-bench, TerminalBench, and BixBench, with multiple models and harnesses; their steps average 28, 33, and 10 respectively, reaching 200.The corpus includes 1,851 TerminalBench, 565 SWE-bench, and 102 BixBench trajectories.Source: §2.1 · p. 6

2.2. Analyzing failures

  • Annotators mark a step wrong when it was an unjustified state-changing decision that moved the task away from any still-solvable path, then locate each run’s earliest such step.Correctness is judged using evidence available at the time, and later recovery can still yield a successful run.Source: §2.2 · p. 7
  • A judge assigns each annotated mistake a codebook category, root-fault or cascade status, and run phase; low agreement on fine categories leads analyses to use coarser levels.The judge types mistakes located by annotators rather than deciding which steps are mistakes.Source: §2.2 · pp. 7–8
  • The failure analysis measures recovery, self-detection, persistence, safety, error propagation, and silent failures using distinct operational criteria.A run has the full failure signature when it fails to recover, never self-detects, and persists; safety actions are audited across solved and failed runs.Source: §2.2 · pp. 7–8

2.3. From failure analysis to a benchmark

  • 600 SWE-bench, 721 TerminalBench, and 102 BixBench trajectories form Traverse, with full step labels for the benchmark and first-mistake localization across three domains.Trajectories were selected from the larger corpus and balanced between correct and failed runs where possible.Source: §2.3 · p. 9

2.4. Training a failure-localization verifier

  • Scout is trained to reason through each trajectory and label every step, first with supervised fine-tuning on label-faithful analyses and then with reinforcement learning.The training set uses 1,904 trajectories, while agentic science is held out to test transfer.Source: §2.4 · pp. 9–10
  • The reinforcement-learning reward combines per-step correct- and incorrect-step recall with a trajectory-verdict term, avoiding rewards that favor labeling every step correct.A correct overall verdict supplies an early signal when no sampled analysis on a failed trajectory identifies a mistake.Source: §2.4 · p. 11

2.5. Evaluation protocol

  • First-mistake localization is tested on the full Traverse benchmark and held-out Scout data, including science trajectories unseen during training.Localization uses exact match, within-k tolerance, and mean absolute error.Source: §2.5 · pp. 11–12

3. Results

Long-horizon agents often fail to recover from or detect early mistakes, while their runs can still appear successful and conceal harm. Frontier judges struggle to locate failures, but Scout improves test-time selection among candidate runs.

3.1. How long-horizon agents fail

  • The taxonomy groups 6,967 mistakes into 78 observed categories across 10 families, with action and planning faults accounting for about 69% of mistakes.Reasoning and planning, tool and environment, and memory faults are more often root faults than action errors, which are mostly cascades.Source: §3.1 · pp. 12–13, Figure 2 · p. 13
  • After a first mistake, agents recover in only 30.5% of runs, never detect the error in 38.5%, and keep acting or declare completion in 72.6%.About 43% of failed runs have no single turning point and instead fail gradually.Source: §3.1 · p. 13
  • A wrong step raises the chance that the next step is wrong 13.7× in software engineering and 10.9× in computer use; 84.1% of failed runs end with a locally correct-looking final step.The share of mistakes that are root faults falls from 58% in the first third of a run to 39% in the last third.Source: §3.1 · p. 14
  • Failure patterns differ by domain: software-engineering agents often miss mistakes but can use tests and tracebacks, while computer-use agents notice mistakes but rarely verify fixes.The observed differences follow task and environmental feedback rather than the agent harness.Source: §3.1 · p. 14
  • The safety audit found 65 unnecessary unsafe actions; 75% were irreversible, and some solved runs concealed destructive actions or fabricated success.A regex scan missed 77% of unsafe actions.Source: §3.1 · p. 14

3.2. Frontier models cannot locate failure

  • Six frontier judges identify first mistakes on at most a third of runs in the coding domains, while the strongest reaches 63.2% exact match on shorter science runs.On software engineering, exact-match accuracy ranges from 7.2% to 26.8%; on computer use, it ranges from 22.7% to 32.3%.Source: §3.2 · p. 15
  • Detection F1 reaches 69.9% in software engineering, 88.2% in computer use, and 80.0% in science, but judges trade over-flagging against missed failures.GPT-5.5 has high recall but software-engineering precision of 60.9%; Claude Opus 4.6 has software-engineering recall of 31.7%.Source: §3.2 · p. 16

3.3. Trained verifiers improve long-horizon agents

  • Scout selection reaches 90.2% on Terminal-Bench 2.0 and 78.0% on SWE-Bench Verified, exceeding frontier verifiers and agents’ pass@1 from fixed candidate pools.The candidate pool is held fixed across selectors, and the agent is not retrained.Source: Figure 4 · p. 17, §3.3 · pp. 17–18

4. Discussion

Long-horizon agents are often derailed not by making more mistakes, but because an early mistake goes uncaught and unreversed. Human-verified first-mistake labels make this failure measurable, while the discussion emphasizes limits in both the labels and the evidence for some findings.

4. Discussion

  • An early mistake is rarely caught or reversed, so long-horizon failure reflects compounding errors more than a higher error frequency.The paper argues that locating and correcting the first error is central to improving reliability.Source: §4 · p. 18
  • Human-verified first-mistake labels let verifiers be scored on where a run went wrong, turning failure localization into a quantity the field can optimize.The same step-level signal maps failures and evaluates models that try to catch them.Source: §4 · p. 18
  • First-mistake labels are human judgments, and a single decisive index simplifies failures that are diffuse, multi-causal, or lack one point of origin.Annotator agreement is substantial but imperfect; the single-index model captures common compounding errors.Source: §4 · pp. 18–19
  • Recovery, self-detection, and cross-domain findings rely on smaller samples and are presented as directional, unlike the strongest claims.The paper says these analyses are being developed further.Source: §4 · p. 19
  • A verifier that locates an error could support in-progress correction and provide dense, process-level rewards for training long-horizon agents.The proposed uses include stopping and reconsidering a run before an error compounds.Source: §4 · p. 19
  • The verifier is external to the agent, but its failure-localization ability could eventually be turned inward so agents can identify and learn from their own failures.The paper presents this as a future possibility, not a demonstrated capability.Source: §4 · p. 19

Appendix

The appendix details Traverse’s failure taxonomy and presents supplementary analyses of failure patterns, recovery, and unsafe actions across long-horizon agent runs.

Appendix

  • The taxonomy groups 88 mistake categories into 10 families and reports family prevalence, counts, category totals, root share, and run-phase information.Table 3 provides the category definitions; greyed rows mark categories defined in advance that did not occur in the corpus.Source: Table 3 · p. 26, Appendix · p. 26
  • Traverse combines long-horizon trajectories (T=20–200), naturally occurring failures, and human-annotated first-mistake localization across multiple domains and scaffolds; Tables 4, 6, and 7 present dataset statistics, corpus composition, and Scout training-data composition by source.Source: Appendix · p. 30, Table 4 · p. 30, Table 6 · p. 30
  • First mistakes span a wide range of step positions in SWE-bench and TerminalBench, making exact localization challenging.The figure plots 0-based first-mistake positions, truncated at step 40, with a dashed median line.Source: Appendix · p. 31, Figure 6 · p. 31
  • 343 runs (14%) were solved despite a flagged mistake, while 588 (23%) failed without a single decisive mistake; both patterns appeared across domains.The first-mistake analysis describes only the flawed-and-failed subset.Source: Appendix · p. 32
  • When agents noticed a first mistake, recovery rates were similar to when they did not: 76% versus 76% in software engineering and 18% versus 16% in computer use.Pooling domains reverses this comparison because self-detection is concentrated in computer use, where recovery is low.Source: Appendix · p. 32
  • Tool and environment faults fell from 14% early to about 3% late, while termination and verification faults emerged only late at about 6% and 4%.Action and reasoning remained dominant across run phases, with shares of 41%, 48%, 45% and 39%, 34%, 33%, respectively.Source: Appendix · p. 33
  • 54% of flagged unsafe actions were invisible to gold step labels and 77% would evade regex-only scans; file overwrites, data deletions, and database destructions were common.The audit identified 19 file overwrites, 17 data deletions, and 13 database destructions, most high severity.Source: Appendix · p. 34

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