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ARXIV:2605.07042 · AGENTS · SUBMITTED 11 MAY · 20:49 UTC · FRESHNESS STALE
ARXIV:2605.07042AGENTSSUBMITTED 11 MAY · 20:49 UTCFRESHNESS STALEChinmaya Kausik · Adith Swaminathan · Nathan Kallus · arXiv
A framework to improve LLM agent search by formalizing context gathering as a decision process and introducing modular interventions for belief state and search termination.
Opportunity summary
Pain A framework to improve LLM agent search by formalizing context gathering as a decision process and introducing modular interventions for belief state and search termination.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A framework to improve LLM agent search by formalizing context gathering as a decision process and introducing modular interventions for belief state and search termination. To navigate these spaces, an agent must iteratively explore…
Large Language Model (LLM) agents are deployed in complex environments -- such as massive codebases, enterprise databases, and conversational histories -- where the relevant state far exceeds their context windows. To navigate these spaces,…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Across four methods and three question-answering domains, we empirically validate that replacing an LLM's implicit state with our CGDP-motivated belief state improves multi-hop reasoning…
Agents moved forward this cycle; last verified May 2026. Public score 4.0/10.
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A framework to improve LLM agent search by formalizing context gathering as a decision process and introducing modular interventions for belief state and search termination.
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10.48550/arXiv.2605.07042A framework to improve LLM agent search by formalizing context gathering as a decision process and introducing modular interventions for belief state and search termination.
Abstract
Large Language Model (LLM) agents are deployed in complex environments -- such as massive codebases, enterprise databases, and conversational histories -- where the relevant state far exceeds their context windows. To navigate these spaces, an agent must iteratively explore the environment to find relevant information. However, without explicit infrastructure, an agent's working memory can degrade into lossy representations of the search state, resulting in redundant work (e.g. repetitive looping) and premature stopping. In this work, we formalize this challenge as the Context Gathering Decision Process (CGDP), a specialized Partially Observable Markov Decision Process, where an agent's objective is to adaptively refine its belief state to isolate the necessary information for a task. We model an LLM's behavior as approximate Thompson Sampling within this CGDP, and introduce a predicate-based method that decomposes an LLM's implicit search into explicit and modular operations. We then derive two plug-and-play interventions for iterative LLM agents: a persistent, predicate-based belief state that bounds context while preserving multi-hop reasoning, and a programmatic exhaustion gate that halts unproductive search without premature stopping. Across four methods and three question-answering domains, we empirically validate that replacing an LLM's implicit state with our CGDP-motivated belief state improves multi-hop reasoning by up to $11.4\%$; while the modular programmatic exhaustion detection saves up to $39\%$ of tokens without any degradation in agent performance. Ultimately, we argue that framing the LLM agent loop as a CGDP can guide the design of modular, non-interfering improvements to agentic search harnesses.
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Dimensions overall score 4.0
PROBLEM
A framework to improve LLM agent search by formalizing context gathering as a decision process and introducing modular interventions for belief state and search termination. To navigate these spaces, an agent must iteratively explore the environment to find relevant information.
METHOD
Large Language Model (LLM) agents are deployed in complex environments -- such as massive codebases, enterprise databases, and conversational histories -- where the relevant state far exceeds their context windows. To navigate these spaces, an agent must iteratively explore the...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Across four methods and three question-answering domains, we empirically validate that replacing an LLM's implicit state with our CGDP-motivated belief state improves multi-hop reasoning by up to $11.4\%$...
WHY NOW
Agents moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A framework to improve LLM agent search by formalizing context gathering as a decision process and introducing modular interventions for belief state and search termination. To navigate these spaces, an agent must iteratively explore the environment to find relevant information.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large Language Model (LLM) agents are deployed in complex environments -- such as massive codebases, enterprise databases, and conversational histories -- where the relevant state far exceeds their context windows. To navigate these spaces, an agent must iteratively explore the environment to find relevant information.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. Across four methods and three question-answering domains, we empirically validate that replacing an LLM's implicit state with our CGDP-motivated belief state improves multi-hop reasoning by up to $11.4\%$; while the modular programmatic exhaustion detection saves up to $39\%$ of tokens without any degradation in agent performance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework to improve LLM agent search by formalizing context gathering as a decision process and introducing modular interventions for belief state and search termination.
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4.0/10 public viability
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