ArbGraph resolves conflicts in RAG evidence by constructing a conflict-aware graph of atomic claims and applying intensity-driven iterative arbitration to suppress unreliable claims prior to generation.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Topological features of reasoning-trace embeddings correlate with Smith-Waterman alignment to expert AIME solutions more than graph metrics do, but the paper does not validate this out of sample.
SOM uses a Structural Causal Model to create an explicit graph of opponent observation-to-action links, allowing LLMs to reason along those paths for more accurate and stable predictions in multi-agent settings.
citing papers explorer
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ArbGraph: Conflict-Aware Evidence Arbitration for Reliable Long-Form Retrieval-Augmented Generation
ArbGraph resolves conflicts in RAG evidence by constructing a conflict-aware graph of atomic claims and applying intensity-driven iterative arbitration to suppress unreliable claims prior to generation.
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The Shape of Reasoning: Topological Analysis of Reasoning Traces in Large Language Models
Topological features of reasoning-trace embeddings correlate with Smith-Waterman alignment to expert AIME solutions more than graph metrics do, but the paper does not validate this out of sample.
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SOM: Structured Opponent Modeling for LLM-based Agents via Structural Causal Model
SOM uses a Structural Causal Model to create an explicit graph of opponent observation-to-action links, allowing LLMs to reason along those paths for more accurate and stable predictions in multi-agent settings.