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ARXIV:2604.15709 · AGENTS · SUBMITTED 20 APR · 20:24 UTC · FRESHNESS STALE
ARXIV:2604.15709AGENTSSUBMITTED 20 APR · 20:24 UTCFRESHNESS STALEChenyi Huang · Haoting Zhang · Jingxu Xu · Zeyu Zheng · Yunduan Lin · arXiv
A bilevel optimization framework using Monte Carlo Tree Search and LLMs to systematically optimize agent skills for improved task performance.
Opportunity summary
Pain A bilevel optimization framework using Monte Carlo Tree Search and LLMs to systematically optimize agent skills for improved task performance.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A bilevel optimization framework using Monte Carlo Tree Search and LLMs to systematically optimize agent skills for improved task performance. Empirical evidence shows that the design of \texttt{skills} can materially affect agent task performance,…
Agent \texttt{skills} are structured collections of instructions, tools, and supporting resources that help large language model (LLM) agents perform particular classes of tasks. Empirical evidence shows that the design of \texttt{skills} can materially affect…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Empirical evidence shows that the design of \texttt{skills} can materially affect agent task performance, yet systematically optimizing \texttt{skills} remains challenging. Code availability is flagged…
Agents moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
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A bilevel optimization framework using Monte Carlo Tree Search and LLMs to systematically optimize agent skills for improved task performance.
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10.48550/arXiv.2604.15709A bilevel optimization framework using Monte Carlo Tree Search and LLMs to systematically optimize agent skills for improved task performance.
Abstract
Agent \texttt{skills} are structured collections of instructions, tools, and supporting resources that help large language model (LLM) agents perform particular classes of tasks. Empirical evidence shows that the design of \texttt{skills} can materially affect agent task performance, yet systematically optimizing \texttt{skills} remains challenging. Since a \texttt{skill} comprises instructions, tools, and supporting resources in a structured way, optimizing it requires jointly determining both the structure of these components and the content each component contains. This gives rise to a complex decision space with strong interdependence across structure and components. We therefore represent these two coupled decisions as \texttt{skill} structure and component content, and formulate \texttt{skill} optimization as a bilevel optimization problem. We propose a bilevel optimization framework in which an outer loop employs Monte Carlo Tree Search to determine the \texttt{skill} structure, while an inner loop refines the component content within the structure selected by the outer loop. In both loops, we employ LLMs to assist the optimization procedure. We evaluate the proposed framework on an open-source Operations Research Question Answering dataset, and the experimental results suggest that the bilevel optimization framework improves the performance of the agents with the optimized \texttt{skill}.
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PROBLEM
A bilevel optimization framework using Monte Carlo Tree Search and LLMs to systematically optimize agent skills for improved task performance. Empirical evidence shows that the design of \texttt{skills} can materially affect agent task performance, yet systematically optimizing...
METHOD
Agent \texttt{skills} are structured collections of instructions, tools, and supporting resources that help large language model (LLM) agents perform particular classes of tasks. Empirical evidence shows that the design of \texttt{skills} can materially affect agent task perform...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Empirical evidence shows that the design of \texttt{skills} can materially affect agent task performance, yet systematically optimizing \texttt{skills} remains challenging. Code availability is flagged in...
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A bilevel optimization framework using Monte Carlo Tree Search and LLMs to systematically optimize agent skills for improved task performance. Empirical evidence shows that the design of \texttt{skills} can materially affect agent task performance, yet systematically optimizing \texttt{skills} remains challenging.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agent \texttt{skills} are structured collections of instructions, tools, and supporting resources that help large language model (LLM) agents perform particular classes of tasks. Empirical evidence shows that the design of \texttt{skills} can materially affect agent task performance, yet systematically optimizing \texttt{skills} remains challenging.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. Empirical evidence shows that the design of \texttt{skills} can materially affect agent task performance, yet systematically optimizing \texttt{skills} remains challenging. 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
Agents moved forward this cycle; last verified April 2026. Public score 6.0/10. 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 bilevel optimization framework using Monte Carlo Tree Search and LLMs to systematically optimize agent skills for improved task performance.
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