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ARXIV:2605.11388 · AGENTS · SUBMITTED 13 MAY · 20:57 UTC · FRESHNESS STALE
ARXIV:2605.11388AGENTSSUBMITTED 13 MAY · 20:57 UTCFRESHNESS STALEDean Light · Michael Theologitis · Kshitish Ghate · Shuyue Stella Li · Benjamin Newman · Chirag Shah · +4 at arXiv
A meta-reasoning approach for LLM agents that dynamically constructs reasoning scaffolds to outperform state-of-the-art methods on complex tasks.
Opportunity summary
Pain A meta-reasoning approach for LLM agents that dynamically constructs reasoning scaffolds to outperform state-of-the-art methods on complex tasks.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
A meta-reasoning approach for LLM agents that dynamically constructs reasoning scaffolds to outperform state-of-the-art methods on complex tasks. Current LLM agents lack this flexibility, as their scaffolds hard-code such reasoning decisions in advance.
Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambiguity through associative judgment, and apply formal procedures to well-specified subproblems. Current LLM agents lack this…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. These results point toward future agentic systems that treat scaffolding as adaptive reasoning, constructing the structure each task requires just-in-time. Code availability is flagged…
Agents moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A meta-reasoning approach for LLM agents that dynamically constructs reasoning scaffolds to outperform state-of-the-art methods on complex tasks.
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10.48550/arXiv.2605.11388A meta-reasoning approach for LLM agents that dynamically constructs reasoning scaffolds to outperform state-of-the-art methods on complex tasks.
Abstract
Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambiguity through associative judgment, and apply formal procedures to well-specified subproblems. Current LLM agents lack this flexibility, as their scaffolds hard-code such reasoning decisions in advance. These scaffolds are effective when their prescribed structure matches the task, but brittle when solving the task requires adapting the structure of reasoning itself. We introduce Deep Reasoning -- an inference-time approach for constructing task-specific scaffolds through structured meta-reasoning. Deep Reasoning uses a formal language that represents meta-reasoning as executable decompositions over associative inference, formal computation, and recursive subproblem solving, enabling decomposition principles to be encoded as in-context examples that guide test-time scaffold construction. We instantiate this approach in a general-purpose agent (DOLORES) that distributes complex tasks across more controlled reasoning threads. We evaluate it against state-of-the-art scaffolding methods across four hard benchmarks: multi-hop reasoning, long-chain question answering, long-context aggregation, and deep research-style information seeking. DOLORES outperforms all evaluated scaffolds across three model sizes and two model families, improving over the strongest evaluated scaffold baseline by 24.8% on average. DOLORES distributes cognition across structured, lower-load reasoning threads, thereby reducing premature termination and hallucinations. This advantage can even bridge the scaling gap, with an 8B version surpassing all evaluated 32B baselines from the same family in more than half the settings. These results point toward future agentic systems that treat scaffolding as adaptive reasoning, constructing the structure each task requires just-in-time.
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PROBLEM
A meta-reasoning approach for LLM agents that dynamically constructs reasoning scaffolds to outperform state-of-the-art methods on complex tasks. Current LLM agents lack this flexibility, as their scaffolds hard-code such reasoning decisions in advance.
METHOD
Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambiguity through associative judgment, and apply formal procedures to well-specified subproblems. Current LLM agents lack this flexibilit...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. These results point toward future agentic systems that treat scaffolding as adaptive reasoning, constructing the structure each task requires just-in-time. Code availability is flagged in the production r...
WHY NOW
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 meta-reasoning approach for LLM agents that dynamically constructs reasoning scaffolds to outperform state-of-the-art methods on complex tasks. Current LLM agents lack this flexibility, as their scaffolds hard-code such reasoning decisions in advance.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambiguity through associative judgment, and apply formal procedures to well-specified subproblems. Current LLM agents lack this flexibility, as their scaffolds hard-code such reasoning decisions in advance.
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. These results point toward future agentic systems that treat scaffolding as adaptive reasoning, constructing the structure each task requires just-in-time. 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 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 meta-reasoning approach for LLM agents that dynamically constructs reasoning scaffolds to outperform state-of-the-art methods on complex tasks.
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