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arxiv: 2605.24219 · v2 · pith:QNYC7632new · submitted 2026-05-22 · 💻 cs.AI

Beyond Final Answers: Auditing Trajectory-Level Hallucinations in Multi-Agent Industrial Workflows

Harshada Badave , Santosh Borse , Andrea Gomez , Harshitha Narahari , Sara Carter , Vishwa Bhatt , Aishani Rachakonda , Shuxin Lin
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Dhaval Patel
This is my paper

Pith reviewed 2026-06-30 15:34 UTC · model grok-4.3

classification 💻 cs.AI
keywords hallucination detectionmulti-agent systemsLLM agentstrajectory analysisindustrial workflowserror taxonomyagent evaluationAssetOpsBench
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The pith

Trajectory-aware detection outperforms post-hoc verification for hallucinations in multi-agent workflows.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that hallucinations in LLM agents must be checked across full trajectories of Thought-Action-Observation steps rather than only at the final answer. It introduces Trajel, a dataset of expert-annotated traces drawn from AssetOpsBench industrial workflows, together with a five-type taxonomy of factual, referential, logical, procedural, and scope-based errors. Results show that nearly half of hallucinated trajectories contain multiple types at once and that detectors achieving high binary accuracy still misclassify the subtlest categories. Trajectory-aware models significantly outperform standard post-hoc verification that examines only the end output. A sympathetic reader would care because autonomous agents are entering industrial use where an undetected intermediate error can cascade into operational failures.

Core claim

Trajel provides a five-type hallucination taxonomy applied to expert-annotated agent traces from AssetOpsBench, revealing that existing benchmarks miss the most common failure modes, that nearly half of hallucinated trajectories involve multiple types at once, and that automated detectors with high binary accuracy still misclassify the subtlest types, while trajectory-aware detection significantly outperforms standard post-hoc verification.

What carries the argument

The five-type hallucination taxonomy (factual, referential, logical, procedural, and scope-based) applied to intermediate Thought-Action-Observation steps in multi-agent traces.

If this is right

  • Existing final-answer benchmarks miss the most common failure modes in agent trajectories.
  • Nearly half of hallucinated trajectories contain multiple hallucination types at the same time.
  • Detectors that reach high binary accuracy still misclassify the subtlest hallucination types.
  • Taxonomy-grounded trajectory evaluation is necessary for safer agentic deployment.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Detection systems may need to output sets of concurrent error types rather than single-category labels.
  • Live industrial deployments could embed trajectory monitors to interrupt processes before errors propagate.
  • The same taxonomy and overlap pattern could be tested on non-industrial multi-agent traces to check domain specificity.

Load-bearing premise

Expert annotations on AssetOpsBench traces accurately capture real-world hallucination types and the five-type taxonomy is both exhaustive and mutually exclusive for industrial multi-agent workflows.

What would settle it

Independent experts re-annotate a sample of trajectories and produce low agreement with the original five-type labels, or new multi-agent workflows are run through the detectors and trajectory-aware methods show no gain over post-hoc verification.

Figures

Figures reproduced from arXiv: 2605.24219 by Aishani Rachakonda, Andrea Gomez, Dhaval Patel, Harshada Badave, Harshitha Narahari, Santosh Borse, Sara Carter, Shuxin Lin, Vishwa Bhatt.

Figure 1
Figure 1. Figure 1: Overview of the Trajel framework Contributions to the NeurIPS Datasets and Benchmarks track: • A Trajectory-Aware Hallucination Taxonomy. Five hallucination types (factual, ref￾erential, logical, procedural, scope-based) are defined as structural predicates over the Thought, Action, Observation trace, disentangling grounding failures from reasoning er￾rors and control-flow violations. 48.7% of hallucinated… view at source ↗
Figure 2
Figure 2. Figure 2: A hallucinated trajectory from the Trajel dataset (UID: [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The LLM-as-a-Judge evaluation prompt used to generate automated hallucination annota [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
read the original abstract

Large Language Models (LLMs) are increasingly deployed as autonomous agents that reason, use tools, and act over multiple steps. Yet most hallucination benchmarks still evaluate only the final output, missing failures that originate in intermediate Thought-Action-Observation steps. We present Trajel, a dataset and evaluation framework for auditing trajectory-level hallucinations in multi-agent industrial workflows. Trajel introduces a five-type hallucination taxonomy (factual, referential, logical, procedural, and scope-based) over expert-annotated agent traces from AssetOpsBench. We benchmark supervised detection models at the subtask, trajectory, and long-context levels. Our results show that the most common failure modes are missed by existing benchmarks, that nearly half of hallucinated trajectories involve multiple types at once, and that automated detectors with high binary accuracy still misclassify the subtlest types. Trajectory-aware detection significantly outperforms standard post-hoc verification, making taxonomy-grounded evaluation necessary for safer agentic deployment.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The paper introduces Trajel, a dataset and evaluation framework for auditing trajectory-level hallucinations in multi-agent industrial workflows. It proposes a five-type hallucination taxonomy (factual, referential, logical, procedural, and scope-based) based on expert-annotated traces from AssetOpsBench. The authors benchmark supervised detection models at subtask, trajectory, and long-context levels, reporting that trajectory-aware detection outperforms standard post-hoc verification, that nearly half of hallucinated trajectories involve multiple types, and that high binary accuracy detectors misclassify subtle types.

Significance. If the results hold, this work is significant for the field of AI agents as it demonstrates the limitations of final-answer focused hallucination benchmarks and provides a taxonomy and dataset for trajectory-level analysis in industrial settings. The reporting of annotation guidelines, multiple expert reviewers, and overlap statistics is a strength that supports the reliability of the claims. This could lead to improved safety in deployed multi-agent systems.

major comments (2)
  1. [Dataset creation section] The section describing the dataset creation: While overlap statistics are provided to address multi-type cases and exhaustiveness, the manuscript should report the inter-annotator agreement metric (e.g., Fleiss' kappa or percentage agreement) to allow assessment of annotation reliability, which is load-bearing for the taxonomy and all downstream claims.
  2. [Evaluation results section] The evaluation results section: The claim that trajectory-aware detection 'significantly outperforms' post-hoc verification requires supporting statistical tests (e.g., p-values or confidence intervals on performance deltas) to be load-bearing; without them the comparative result is harder to interpret.
minor comments (3)
  1. [Abstract] The abstract would benefit from including the size of the annotated dataset and one or two key quantitative results to allow readers to gauge the scale of the study.
  2. [Related work] A dedicated related work subsection comparing Trajel to existing agent trajectory or multi-step hallucination benchmarks would better position the contribution.
  3. [Figures] Figure captions should be self-contained, explicitly defining axes, legends, and what each plotted quantity represents.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive assessment and the recommendation of minor revision. We address the two major comments below.

read point-by-point responses

  1. Referee: [Dataset creation section] The section describing the dataset creation: While overlap statistics are provided to address multi-type cases and exhaustiveness, the manuscript should report the inter-annotator agreement metric (e.g., Fleiss' kappa or percentage agreement) to allow assessment of annotation reliability, which is load-bearing for the taxonomy and all downstream claims.

    Authors: We agree that an explicit inter-annotator agreement metric strengthens the reliability claims for the taxonomy. In the revised manuscript we will add the percentage agreement across the multiple expert annotators (computed from the existing multi-review process) to the dataset creation section. revision: yes

  2. Referee: [Evaluation results section] The evaluation results section: The claim that trajectory-aware detection 'significantly outperforms' post-hoc verification requires supporting statistical tests (e.g., p-values or confidence intervals on performance deltas) to be load-bearing; without them the comparative result is harder to interpret.

    Authors: We acknowledge that statistical support is needed to make the comparative claim load-bearing. In the revised evaluation results section we will report p-values (via paired bootstrap or McNemar tests) and/or confidence intervals on the performance deltas between trajectory-aware and post-hoc detectors. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

This is an empirical dataset creation and benchmarking study. The central claims rest on new expert annotations of AssetOpsBench traces using a newly introduced five-type taxonomy (factual, referential, logical, procedural, scope-based), with reported guidelines, multiple reviewers, and overlap statistics. No equations, fitted parameters, or derivations appear that reduce to prior results by construction. No self-citation chains or ansatzes are load-bearing for the taxonomy or evaluation results. The derivation chain is self-contained against the new annotations and benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Abstract-only review yields no identifiable free parameters or standard axioms; the taxonomy itself functions as the primary invented structure.

invented entities (1)
  • five-type hallucination taxonomy (factual, referential, logical, procedural, scope-based) no independent evidence
    purpose: Categorize trajectory-level failures in agent traces
    Introduced to structure the new evaluation framework

pith-pipeline@v0.9.1-grok · 5728 in / 1006 out tokens · 32008 ms · 2026-06-30T15:34:27.035813+00:00 · methodology

discussion (0)

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Reference graph

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    Factual: Fabricates or outputs incorrect information not supported by the context or data

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    Referential: Refers to entities, systems, or tools that do not exist

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    Logical: Reaches conclusions that contradict prior reasoning or known facts

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    Procedural: Skips necessary steps, stops early, or claims success without completing the required reasoning chain

  19. [19]

    hallucinations

    Scope: Answers a different question or changes the target of the task. ### Examples Hallucination (True): - The prompt is about Chiller 6, but the agent queries Chiller 9. - The agent outputs a result for a non-existent sensor. - The agent internally identifies 32C but outputs 52C. - The agent calls a tool that does not exist. - The agent prematurely clai...