Sehgal, Yuvraj, Panahandeh, Mahsa, Ezzati-Jivan, Naser · Oct 26, 2026
Authors report tool-output loss in LLM agent tasks
Markdown · JSON · Original source
Yuvraj Sehgal, Mahsa Panahandeh and Naser Ezzati-Jivan report that an agent’s tool results can be cut before they reach the model, even when its is mostly unused. In their Linux kernel trace task, 3,827 of 11,189 tool results were cut across 720 runs, and each cut result became broken JSON. The authors say JSON text was cut at a fixed length, disproportionately removing data near the end. In controlled experiments, the cut was associated first with more work rather than wrong answers: 43% more tool calls, twice the computation, and yield falling from 0.61 to 0.31. The agent could still find the right answer when another route to the evidence was available. The paper also reports that two of seven surveyed agent frameworks used the same cutting approach. The authors leave open whether data loss truly causes the observed accuracy drop; these findings concern the tested agent and task, and do not establish the effect across other systems. How often does cutting tool output cause agents to miss the evidence they need when no alternative route is available?
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Oct 26, 2026 (2026-10-26).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-05T08:49:24.219Z. First observed: 2026-10-28T05:00:35.341Z. Retrieved: 2026-10-29T23:48:01.429Z.
Referenced by
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Limits
The evidence describes one agent diagnosing injected faults from Linux kernel traces; the abstract does not establish whether the findings generalize to other agents or tasks. The authors explicitly leave open whether data loss causes the accuracy drop. The reported controlled-experiment results show extra work and lower yield, not a general measure of real-world reliability.
Open question
How often does cutting tool output cause agents to miss the evidence they need when no alternative route is available?
Source-stated
The authors report that 3,827 of 11,189 tool results were cut before reaching the model across 720 runs, despite the context window being lightly used.
across 720 runs, the single largest prompt used only 5.75% of it. Yet a third of all tool results (3,827 of 11,189) were cut before they reached the model
Source-stated
The harness cut JSON text at a fixed length, which the authors say removed data near the end rather than shortening results evenly.
The harness cut each result by slicing its JSON text at a fixed length. This does not shorten a result evenly. It deletes whatever data sits near the end.
Source-stated
In the controlled experiment, the authors report increased tool calls and computation, and lower yield; they say the agent could still find the right answer when another route was available.
43% more tool calls, twice the computation, and yield falling from 0.61 to 0.31. The agent still finds the right answer when it has another way to reach it.
Source-stated
The authors report that two of the seven surveyed agent frameworks cut tool output in the same unsafe way.
a survey of seven agent frameworks finds two that cut tool output the same unsafe way.
Source-stated
Whether the data cut causes the accuracy drop remains unresolved according to the authors.
Whether the data cut truly causes the accuracy drop is still an open question.
Source and contributor context
Sehgal, Yuvraj, Panahandeh, Mahsa, Ezzati-Jivan, Naser · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Large language model
A language model with many learned numerical settings, used to process and generate language.
Context window
How much text a model can take in and consider at one time.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: deba69e6-56e9-48e6-8826-491e0991d228.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-05T08:49:24.219Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 7c1f01931bed73779b2960e94d83f32509598bb609f73247f8cefa042f9df930. Source text hash: a82cfab93c8a7ccc7f9a182a3e2d7777c3abae0e3f748188b288e41f1a4a1094.
Revision and source-check history
Version 1 · 2026-11-05T08:49:24.219Z
Authors report tool-output loss in LLM agent tasks
Yuvraj Sehgal, Mahsa Panahandeh and Naser Ezzati-Jivan report that an agent’s tool results can be cut before they reach the model, even when its is mostly unused. In their Linux kernel trace task, 3,827 of 11,189 tool results were cut across 720 runs, and each cut result became broken JSON. The authors say JSON text was cut at a fixed length, disproportionately removing data near the end. In controlled experiments, the cut was associated first with more work rather than wrong answers: 43% more tool calls, twice the computation, and yield falling from 0.61 to 0.31. The agent could still find the right answer when another route to the evidence was available. The paper also reports that two of seven surveyed agent frameworks used the same cutting approach. The authors leave open whether data loss truly causes the observed accuracy drop; these findings concern the tested agent and task, and do not establish the effect across other systems. How often does cutting tool output cause agents to miss the evidence they need when no alternative route is available?
Original source
cs.AI · Article
Source details
Original source date: Oct 26, 2026 (2026-10-26).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-05T08:49:24.219Z. First observed: 2026-10-28T05:00:35.341Z. Retrieved: 2026-10-29T23:48:01.429Z.
Limits
The evidence describes one agent diagnosing injected faults from Linux kernel traces; the abstract does not establish whether the findings generalize to other agents or tasks. The authors explicitly leave open whether data loss causes the accuracy drop. The reported controlled-experiment results show extra work and lower yield, not a general measure of real-world reliability.
Open question
How often does cutting tool output cause agents to miss the evidence they need when no alternative route is available?
Source-stated
The authors report that 3,827 of 11,189 tool results were cut before reaching the model across 720 runs, despite the context window being lightly used.
across 720 runs, the single largest prompt used only 5.75% of it. Yet a third of all tool results (3,827 of 11,189) were cut before they reached the model
Source-stated
The harness cut JSON text at a fixed length, which the authors say removed data near the end rather than shortening results evenly.
The harness cut each result by slicing its JSON text at a fixed length. This does not shorten a result evenly. It deletes whatever data sits near the end.
Source-stated
In the controlled experiment, the authors report increased tool calls and computation, and lower yield; they say the agent could still find the right answer when another route was available.
43% more tool calls, twice the computation, and yield falling from 0.61 to 0.31. The agent still finds the right answer when it has another way to reach it.
Source-stated
The authors report that two of the seven surveyed agent frameworks cut tool output in the same unsafe way.
a survey of seven agent frameworks finds two that cut tool output the same unsafe way.
Source-stated
Whether the data cut causes the accuracy drop remains unresolved according to the authors.
Whether the data cut truly causes the accuracy drop is still an open question.
Source and contributor context
Sehgal, Yuvraj, Panahandeh, Mahsa, Ezzati-Jivan, Naser · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Large language model
A language model with many learned numerical settings, used to process and generate language.
Context window
How much text a model can take in and consider at one time.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: deba69e6-56e9-48e6-8826-491e0991d228.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-05T08:49:24.219Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 7c1f01931bed73779b2960e94d83f32509598bb609f73247f8cefa042f9df930. Source text hash: a82cfab93c8a7ccc7f9a182a3e2d7777c3abae0e3f748188b288e41f1a4a1094.
uncertain · 2026-11-05T08:49:24.219Z · anthropic/claude-opus-5.5
Publication: deba69e6-56e9-48e6-8826-491e0991d228
attribution — pass: The reading correctly attributes the work to Yuvraj Sehgal, Mahsa Panahandeh and Naser Ezzati-Jivan. It keeps the findings framed as the authors' reports rather than established fact.
Authors:Yuvraj Sehgal, Mahsa Panahandeh, Naser Ezzati-Jivan
summary — uncertain: The core idea, that tool output is cut before the model sees it, is clear and faithful. But the summary ends by saying the authors leave open whether data loss causes 'the observed accuracy drop' without first describing that drop. Its omitted context is the 4.7% vs 99.3% field split: which field held the evidence depended on the faulty process's start time, and accuracy followed that split. The task, diagnosing injected faults, is only alluded to, and 'yield' and 'broken JSON' go unexplained for general readers. Smallest repair: one sentence on the field split and the matching accuracy pattern, plus brief plain glosses or tooltips for JSON and yield.
One field reached the model in only 4.7% of calls. A second field, in the very same results, reached the model in 99.3% of calls.
date — pass: The originalPublished date of 2026-10-26 matches the arXiv v1 submission date. The later feed date is correctly not used.
[Submitted on 26 Oct 2026]
support — pass: Each claim is directly supported by the abstract: the counts, the fixed-length JSON slicing, the extra work and lower yield, the alternative route, the two of seven frameworks, and the open causal question. The limitations and the question stay within the source's scope and do not invent benefits or costs.
the cut's real cost appears first as extra work, not as wrong answers: 43% more tool calls, twice the computation, and yield falling from 0.61 to 0.31
media — pass: The selected media is the verified direct arXiv abstract page for this paper. It is the most direct access to the source.
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A language model with many learned numerical settings, used to process and generate language.
Definition referenceHow much text a model can take in and consider at one time.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA language model with many learned numerical settings, used to process and generate language.
Definition referenceHow much text a model can take in and consider at one time.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXiv