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ARXIV:2604.17849 · AI AGENTS · SUBMITTED 21 APR · 02:41 UTC · FRESHNESS STALE
ARXIV:2604.17849AI AGENTSSUBMITTED 21 APR · 02:41 UTCFRESHNESS STALEGonzalo Gonzalez-Pumariega · Saaket Agashe · Jiachen Yang · Ang Li · Xin Eric Wang · arXiv
This paper analyzes the sources of unreliability in computer-use agents, focusing on stochasticity, ambiguity, and behavioral variability.
Opportunity summary
Pain This paper analyzes the sources of unreliability in computer-use agents, focusing on stochasticity, ambiguity, and behavioral variability.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
This paper analyzes the sources of unreliability in computer-use agents, focusing on stochasticity, ambiguity, and behavioral variability. Yet even when the task and model are unchanged, an agent that succeeds once may fail on…
Computer-use agents have rapidly improved on real-world tasks such as web navigation, desktop automation, and software interaction, in some cases surpassing human performance. Yet even when the task and model are unchanged, an agent…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Our analysis shows that reliability depends on both how tasks are specified and how agent behavior varies across executions.
AI Agents moved forward this cycle; last verified April 2026. Public score 3.0/10.
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Score3.0Analysis summary
This paper analyzes the sources of unreliability in computer-use agents, focusing on stochasticity, ambiguity, and behavioral variability.
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10.48550/arXiv.2604.17849This paper analyzes the sources of unreliability in computer-use agents, focusing on stochasticity, ambiguity, and behavioral variability.
Abstract
Computer-use agents have rapidly improved on real-world tasks such as web navigation, desktop automation, and software interaction, in some cases surpassing human performance. Yet even when the task and model are unchanged, an agent that succeeds once may fail on a repeated execution of the same task. This raises a fundamental question: if an agent can succeed at a task once, what prevents it from doing so reliably? In this work, we study the sources of unreliability in computer-use agents through three factors: stochasticity during execution, ambiguity in task specification, and variability in agent behavior. We analyze these factors on OSWorld using repeated executions of the same task together with paired statistical tests that capture task-level changes across settings. Our analysis shows that reliability depends on both how tasks are specified and how agent behavior varies across executions. These findings suggest the need to evaluate agents under repeated execution, to allow agents to resolve task ambiguity through interaction, and to favor strategies that remain stable across runs.
Source availability
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Proof status
unverified0 refs; 3 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Commercial
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Dimensions overall score 3.0
PROBLEM
This paper analyzes the sources of unreliability in computer-use agents, focusing on stochasticity, ambiguity, and behavioral variability. Yet even when the task and model are unchanged, an agent that succeeds once may fail on a repeated execution of the same task.
METHOD
Computer-use agents have rapidly improved on real-world tasks such as web navigation, desktop automation, and software interaction, in some cases surpassing human performance. Yet even when the task and model are unchanged, an agent that succeeds once may fail on a repeated exec...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Our analysis shows that reliability depends on both how tasks are specified and how agent behavior varies across executions.
WHY NOW
AI Agents moved forward this cycle; last verified April 2026. Public score 3.0/10.
{"file name": "input.pdf", "number of pages": 33, "author": "Gonzalo Gonzalez-Pumariega; Saaket Agashe; Jiachen Yang; Ang Li; Xin Eric Wang", "title": "On the Reliability of Computer Use Agents", "creation date": null
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Concepts
Methods
Materials
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This paper analyzes the sources of unreliability in computer-use agents, focusing on stochasticity, ambiguity, and behavioral variability.
Segment
AI Agents
Adoption evidence
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Commercial read
3.0/10 public viability
Direct
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CITED BY
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2/3 checks · 67%
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status
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reason
passport_row_missing
proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Evidence coverage
OpportunityKernel evidence_receipt
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stale
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Build readiness
BuildPassport EvidenceState
passport absent
stale
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Artifact maturity
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stale
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Technical feasibility
partial
Current read
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Gaps
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Market urgency
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Evidence
0 references, 3 sources, 50% evidence coverage.
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Buyer clarity
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Evidence
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Defensibility
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Defensibility signals are missing.
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Gaps
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Current read
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Regulatory load
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
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Operator workflow not sourced.
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People
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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