GraphEvoResearcher: Graph-Aware Training-Free Self-Reflective Reasoning for Cost-Bounded Multi-Hop Question Answering

Key points

• Multi-hop question answering (QA) requires models to chain intermediate conclusions while remaining cost-aware.1
• Existing training-free self-reflective protocols (generate -> critique -> revise) improve accuracy but operate on a flat token stream: revisions cannot identify which prior step is unreliable, and early stopping cannot exploit structural convergence.1
• A graph-aware meta-reward combines correctness, efficiency, reflection, diversity, and four structural signals (connectivity, acyclicity, branch coverage, path convergence) to decide whether to revise, backtrack, or stop.1
• A graph-convergence early-stop terminates when evidence branches converge on a single conclusion.1

Sources (1)

• [1]GraphEvoResearcher: Graph-Aware Training-Free Self-Reflective Reasoning for Cost-Bounded Multi-Hop Question Answering
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 08:06 AM

  We introduce GraphEvoResearcher, a training-free protocol that lifts EvoResearcher's self-reflective meta-reward onto an explicit reasoning graph - nodes are intermediate conclusions/evidence and edges encode support, contradict, and depends_on.

  Multi-hop question answering (QA) requires models to chain intermediate conclusions while remaining cost-aware.

Extractive summary: sentences quoted from the sources.