LLM chain-of-thought crosses a commitment boundary early; subsequent steps are epiphenomenal, enabling early-exit that shortens traces 55% with negligible performance change.
M o N a C o: More Natural and Complex Questions for Reasoning Across Dozens of Documents
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
SEMJ is a self-evolving multilingual LLM judge that turns cross-lingual inconsistency into iterative self-reflection, outperforming voting and reflection baselines on accuracy and consistency.
A graph-augmented RAG system with vector and graph query tools halves hallucinations and raises factual correctness scores on the MoNaCo complex QA benchmark.
citing papers explorer
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Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models
LLM chain-of-thought crosses a commitment boundary early; subsequent steps are epiphenomenal, enabling early-exit that shortens traces 55% with negligible performance change.
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When Languages Disagree: Self-Evolving Multilingual LLM Judges
SEMJ is a self-evolving multilingual LLM judge that turns cross-lingual inconsistency into iterative self-reflection, outperforming voting and reflection baselines on accuracy and consistency.
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Reducing Hallucinations in Complex Question Answering using Simple Graph-based Retrieval-Augmented Generation (long version)
A graph-augmented RAG system with vector and graph query tools halves hallucinations and raises factual correctness scores on the MoNaCo complex QA benchmark.