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ARXIV:2603.25342 · AGENTS · SUBMITTED 27 MAR · 20:30 UTC · FRESHNESS STALE
ARXIV:2603.25342AGENTSSUBMITTED 27 MAR · 20:30 UTCFRESHNESS STALEShuoling Liu · Zhiquan Tan · Kun Yi · Hui Wu · Yihan Li · Jiangpeng Yan · +3 at arXiv
A new benchmark and theoretical framework for evaluating deep research agents, revealing significant gaps in their ability to perform complex structural synthesis.
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Pain A new benchmark and theoretical framework for evaluating deep research agents, revealing significant gaps in their ability to perform complex structural synthesis.
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A new benchmark and theoretical framework for evaluating deep research agents, revealing significant gaps in their ability to perform complex structural synthesis. These heuristic approaches do not rigorously model agent behavior or adequately stress-test…
Although deep research agents (DRAs) have emerged as a promising paradigm for complex information synthesis, their evaluation remains constrained by ad hoc empirical benchmarks. These heuristic approaches do not rigorously model agent behavior or…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Ultimately, this work demonstrates that while top-tier autonomous agents can now organically unify search and reasoning, achieving a generalized mastery over complex structural information…
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A new benchmark and theoretical framework for evaluating deep research agents, revealing significant gaps in their ability to perform complex structural synthesis.
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10.48550/arXiv.2603.25342A new benchmark and theoretical framework for evaluating deep research agents, revealing significant gaps in their ability to perform complex structural synthesis.
Abstract
Although deep research agents (DRAs) have emerged as a promising paradigm for complex information synthesis, their evaluation remains constrained by ad hoc empirical benchmarks. These heuristic approaches do not rigorously model agent behavior or adequately stress-test long-horizon synthesis and ambiguity resolution. To bridge this gap, we formalize DRA behavior through the lens of category theory, modeling deep research workflow as a composition of structure-preserving maps (functors). Grounded in this theoretical framework, we introduce a novel mechanism-aware benchmark with 296 questions designed to stress-test agents along four interpretable axes: traversing sequential connectivity chains, verifying intersections within V-structure pullbacks, imposing topological ordering on retrieved substructures, and performing ontological falsification via the Yoneda Probe. Our rigorous evaluation of 11 leading models establishes a persistently low baseline, with the state-of-the-art achieving only a 19.9\% average accuracy, exposing the difficulty of formal structural stress-testing. Furthermore, our findings reveal a stark dichotomy in the current AI capabilities. While advanced deep research pipelines successfully redefine dynamic topological re-ordering and exhibit robust ontological verification -- matching pure reasoning models in falsifying hallucinated premises -- they almost universally collapse on multi-hop structural synthesis. Crucially, massive performance variance across tasks exposes a lingering reliance on brittle heuristics rather than a systemic understanding. Ultimately, this work demonstrates that while top-tier autonomous agents can now organically unify search and reasoning, achieving a generalized mastery over complex structural information remains a formidable open challenge.\footnote{Our implementation will be available at https://github.com/tzq1999/CDR.
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PROBLEM
A new benchmark and theoretical framework for evaluating deep research agents, revealing significant gaps in their ability to perform complex structural synthesis. These heuristic approaches do not rigorously model agent behavior or adequately stress-test long-horizon synthesis...
METHOD
Although deep research agents (DRAs) have emerged as a promising paradigm for complex information synthesis, their evaluation remains constrained by ad hoc empirical benchmarks. These heuristic approaches do not rigorously model agent behavior or adequately stress-test long-hori...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Ultimately, this work demonstrates that while top-tier autonomous agents can now organically unify search and reasoning, achieving a generalized mastery over complex structural information remains a formi...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A new benchmark and theoretical framework for evaluating deep research agents, revealing significant gaps in their ability to perform complex structural synthesis. These heuristic approaches do not rigorously model agent behavior or adequately stress-test long-horizon synthesis and ambiguity resolution.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Although deep research agents (DRAs) have emerged as a promising paradigm for complex information synthesis, their evaluation remains constrained by ad hoc empirical benchmarks. These heuristic approaches do not rigorously model agent behavior or adequately stress-test long-horizon synthesis and ambiguity resolution.
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. Ultimately, this work demonstrates that while top-tier autonomous agents can now organically unify search and reasoning, achieving a generalized mastery over complex structural information remains a formidable open challenge.\footnote{Our implementation will be available at https://github.com/tzq1999/CDR. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A new benchmark and theoretical framework for evaluating deep research agents, revealing significant gaps in their ability to perform complex structural synthesis.
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