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ARXIV:2605.09698 · DATA SCIENCE AGENTS · SUBMITTED 12 MAY · 20:16 UTC · FRESHNESS FRESH
ARXIV:2605.09698DATA SCIENCE AGENTSSUBMITTED 12 MAY · 20:16 UTCFRESHNESS FRESHJosefa Lia Stoisser · Marc Boubnovski Martell · Sidsel Boldsen · Kaspar Märtens · Robert Kitchen · arXiv
A benchmark suite to evaluate and improve task-framing accuracy in data-science agents.
Opportunity summary
Pain A benchmark suite to evaluate and improve task-framing accuracy in data-science agents.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A benchmark suite to evaluate and improve task-framing accuracy in data-science agents. Agents quietly commit to plausible but unintended task framings, producing clean, executable artifacts that hide their incorrect assessment of the task.
As data-science agents shift from co-pilots to auto-pilots, silent misframing becomes a critical failure mode. Agents quietly commit to plausible but unintended task framings, producing clean, executable artifacts that hide their incorrect assessment of…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Recognizing target and objective underspecification, not pipeline execution, is the bottleneck missing from standard DS-agent evaluations. Code availability is flagged in the production record;…
Data Science Agents moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A benchmark suite to evaluate and improve task-framing accuracy in data-science agents.
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10.48550/arXiv.2605.09698A benchmark suite to evaluate and improve task-framing accuracy in data-science agents.
Abstract
As data-science agents shift from co-pilots to auto-pilots, silent misframing becomes a critical failure mode. Agents quietly commit to plausible but unintended task framings, producing clean, executable artifacts that hide their incorrect assessment of the task. Existing benchmarks score whether the pipeline runs, ignoring whether the agent recognized the task was underspecified. We introduce Ambig-DS, two diagnostic suites: one for prediction-target ambiguity (Ambig-DS-Target, 51 tasks built on DSBench, a tabular modeling benchmark) and one for evaluation-objective ambiguity (Ambig-DS-Objective, 61 tasks built on MLE-bench, a Kaggle-style ML competition benchmark), constructed so that scoring uses each source benchmark's original evaluator. For every task we pair the original, fully specified version with an ambiguous variant produced by controlled edits; a human-and-LLM verification pipeline confirms each variant admits multiple plausible interpretations with decision-relevant consequences. The suites are analyzed independently and ambiguity lowers performance in both. Across five agents spanning efficient to frontier-class models, we find in our controlled diagnostic setting: (i) failures are silent commitments: wrong-target submissions on Target, wrong-metric or non-committal baseline submissions on Objective, rather than execution errors; (ii) allowing the agent to ask one clarifying question recovers much of the loss under idealized conditions, suggesting missing framing information drives a substantial part of the observed degradation; but (iii) agents cannot reliably tell when to use it: permissive prompts induce over-asking on clear tasks, while conservative prompts induce silent defaulting on ambiguous ones. Recognizing target and objective underspecification, not pipeline execution, is the bottleneck missing from standard DS-agent evaluations.
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Dimensions overall score 7.0
PROBLEM
A benchmark suite to evaluate and improve task-framing accuracy in data-science agents. Agents quietly commit to plausible but unintended task framings, producing clean, executable artifacts that hide their incorrect assessment of the task.
METHOD
As data-science agents shift from co-pilots to auto-pilots, silent misframing becomes a critical failure mode. Agents quietly commit to plausible but unintended task framings, producing clean, executable artifacts that hide their incorrect assessment of the task.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Recognizing target and objective underspecification, not pipeline execution, is the bottleneck missing from standard DS-agent evaluations. Code availability is flagged in the production record; the public...
WHY NOW
Data Science Agents moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A benchmark suite to evaluate and improve task-framing accuracy in data-science agents. Agents quietly commit to plausible but unintended task framings, producing clean, executable artifacts that hide their incorrect assessment of the task.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
As data-science agents shift from co-pilots to auto-pilots, silent misframing becomes a critical failure mode. Agents quietly commit to plausible but unintended task framings, producing clean, executable artifacts that hide their incorrect assessment of the task.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Recognizing target and objective underspecification, not pipeline execution, is the bottleneck missing from standard DS-agent evaluations. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Data Science Agents moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A benchmark suite to evaluate and improve task-framing accuracy in data-science agents.
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Data Science Agents
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