Deep Reasoning in General Purpose Agents via Structured Meta-Cognition

• URL: http://arxiv.org/abs/2605.11388v1
• Date: Tue, 12 May 2026 01:21:37 GMT
• Title: Deep Reasoning in General Purpose Agents via Structured Meta-Cognition
• Abstract summary: We introduce an inference-time approach for constructing task-specific scaffolds through structured meta-reasoning.<n>We instantiate this approach in a general-purpose agent that distributes complex tasks across more controlled reasoning threads.
• Score: 58.185853639335896
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• RoBoSR: Structured Scene Representations for Embodied Robotic Reasoning [14.955041656490849]
  We introduce RoBoSR, an intermediate structural representation that formulates manipulation as step-wise state transitions.<n>By modeling object states and their spatial relations at the perception-action interface, RoBoSR disentangles high-level task reasoning from raw inputs.<n>We construct Manip-Cognition-1.6M, an open-world dataset that jointly supervises scene understanding, instruction interpretation, and subtask planning.
  arXiv  Detail & Related papers  (2026-06-23T09:24:52Z)
• Self-Awareness before Action: Mitigating Logical Inertia via Proactive Cognitive Awareness [7.845857636481423]
  Large language models often lack awareness of whether their current knowledge or reasoning state is complete.<n>In non-interactive puzzle settings, the narrative is fixed and the underlying structure is hidden.<n>We propose SABA, a reasoning framework that explicitly introduces self-awareness of missing premises.
  arXiv  Detail & Related papers  (2026-04-22T10:33:24Z)
• STRIDE: Strategic Iterative Decision-Making for Retrieval-Augmented Multi-Hop Question Answering [9.69666629029613]
  Multi-hop question answering (MHQA) enables accurate answers to complex queries by retrieving and reasoning over evidence dispersed across multiple documents.<n>Existing MHQA approaches mainly rely on iterative retrieval-augmented generation, which suffer from the following two major issues.<n>We propose STRIDE, a framework that separates strategic planning, dynamic control, and grounded execution.
  arXiv  Detail & Related papers  (2026-04-19T12:19:43Z)
• Breaking the Chain: A Causal Analysis of LLM Faithfulness to Intermediate Structures [58.54426802984356]
  We introduce a causal evaluation protocol that makes explicit intermediate structures causally determine the output.<n>Across eight models and three benchmarks, models appear self-consistent with their own intermediate structures but fail to update predictions after intervention in up to 60% of cases.<n>Overall, intermediate structures in schema-guided pipelines function as influential context rather than stable causal mediators.
  arXiv  Detail & Related papers  (2026-03-17T13:01:44Z)
• Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structure [58.89643769707751]
  We study latent chain-of-thought as a manipulable causal process in representation space.<n>We find that latent-step budgets behave less like homogeneous extra depth and more like staged functionality with non-local routing.<n>These results motivate mode-conditional and stability-aware analyses as more reliable tools for interpreting and improving latent reasoning systems.
  arXiv  Detail & Related papers  (2026-02-09T15:25:12Z)
• Structured Reasoning for Large Language Models [59.215789462977206]
  We propose Structured Reasoning (SCR), a framework that decouples reasoning trajectories into explicit, evaluable, and trainable components.<n>SCR substantially improves reasoning efficiency and self-verification.<n>Compared with existing reasoning paradigms, it reduces output token length by up to 50%.
  arXiv  Detail & Related papers  (2026-01-12T04:04:01Z)
• Selection, Reflection and Self-Refinement: Revisit Reasoning Tasks via a Causal Lens [19.316594303998667]
  Reasoning tasks have long been regarded as rigorous benchmarks for assessing the capabilities of machine learning models.<n>We revisit reasoning tasks from a causal perspective, seeking to understand their behavior in latent space.<n>We introduce a framework, called SR$2$, that incorporates the estimated latent variables as feedback into the selection mechanism.
  arXiv  Detail & Related papers  (2025-10-09T13:45:31Z)
• Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoning [53.45095336430027]
  We develop a unified framework that combines implicit retrieval and structured collaboration.<n>On Humanity's Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy.<n>Results on SuperGPQA and TRQA confirm robustness across domains.
  arXiv  Detail & Related papers  (2025-09-25T14:05:55Z)
• Implicit Reasoning in Large Language Models: A Comprehensive Survey [67.53966514728383]
  Large Language Models (LLMs) have demonstrated strong generalization across a wide range of tasks.<n>Recent studies have shifted attention from explicit chain-of-thought prompting toward implicit reasoning.<n>This survey introduces a taxonomy centered on execution paradigms, shifting the focus from representational forms to computational strategies.
  arXiv  Detail & Related papers  (2025-09-02T14:16:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.