GraphEcho Reveals Agent Overlooking Evidence
A new study, 'GraphEcho,' explores how large language model (LLM) agents can traverse more paths in graphs without acquiring additional evidence. The research uses synthetic experiments to test whether these repeated encounters are seen as corroboration by the models. Results show that while some improvements exist with provenance-aware post-training (PAPT), agents still revisit paths and…
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Key points
- GraphEcho tests LLM agents on path traversal without additional evidence
- Agents often repeat paths despite having redundant support
- Provenance-aware training reduces revisits but doesn't cover all sources
Read the original at arXiv cs.AI · by Sikun Wang, Yixi Zhou, Lei Fan, Fan Zhang primary sourceOpen source ↗
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GraphEcho
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