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April 18, 2026Open Access

Reasoning Topology Evolution: Automated Discovery of Effective Reasoning DAG Structures for LLM Agents

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RNRaviteja Nekkalapu

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Overview

Framework discovers effective reasoning topologies in large language model agents, suggesting potential for improved reasoning strategies.

Key Points

  • This research aims to automate the discovery of effective reasoning structures for large language model agents using evolutionary algorithms.
  • Introduced Reasoning Topology Evolution framework using evolutionary algorithms.
  • Encoded reasoning strategies as directed acyclic graphs with nodes and edges.
  • Evolved topologies starting from linear chains and random DAGs.
  • Conducted five independent runs on the Qwen-2.5-1.5B-Instruct model.
  • Achieved 0.720 accuracy on a 50-problem held-out set.
  • Outperformed linear Chain-of-Thought (0.420) and random DAGs (0.360) significantly.
  • Matched performance of hand-designed Tree-of-Thought (0.720).
  • Discovered structurally distinct yet comparably effective topologies across runs.

Cite This Study

Raviteja Nekkalapu (2026) studied this question.

synapsesocial.com/papers/69e3216540886becb6540b14https://doi.org/10.5281/zenodo.19614078
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