Deep Reasoning in General Purpose Agents via Structured Meta-Cognition
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The paper argues that large language models need adaptive reasoning rather than fixed reasoning budgets. It shows that over‑reasoning leads to high computational cost without accuracy gains, while under‑reasoning results in incorrect or incomplete solutions. The authors evaluate these failure modes on MATH‑500 and the GAIA benchmark, highlighting the need for dynamic reasoning allocation in agentic AI systems.
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The paper introduces MIRA, a hierarchical architecture that separates meta-reasoning from execution for long-horizon research agents. An outer-loop meta-reasoner selects the next investigation based on a persistent research record, while an inner-loop executor carries out the chosen work order. The authors demonstrate that MIRA improves inference and compute allocation in theorem proving and neural-architecture research, and that a generative actor-critic variant (MIRA-AC) can be trained efficiently without directly optimizing long execution traces.