EquiMem calibrates shared memory in multi-agent debate by computing a game-theoretic equilibrium from agent queries and paths, outperforming heuristics and LLM validators across benchmarks while remaining robust to adversarial agents.
Resolving knowledge conflicts in large language models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
LLMs prefer document assertions over user assertions, are impressionable to external information, and gain better discrimination after fine-tuning on diverse source-interaction data.
DCRD uses attention-map analysis to detect context-memory conflicts in LLMs and conditionally applies either greedy or fidelity-based dynamic decoding, achieving SOTA results on QA tasks across four models and six datasets.
citing papers explorer
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EquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium
EquiMem calibrates shared memory in multi-agent debate by computing a game-theoretic equilibrium from agent queries and paths, outperforming heuristics and LLM validators across benchmarks while remaining robust to adversarial agents.
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How Large Language Models Balance Internal Knowledge with User and Document Assertions
LLMs prefer document assertions over user assertions, are impressionable to external information, and gain better discrimination after fine-tuning on diverse source-interaction data.
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Mitigating Context-Memory Conflicts in LLMs through Dynamic Cognitive Reconciliation Decoding
DCRD uses attention-map analysis to detect context-memory conflicts in LLMs and conditionally applies either greedy or fidelity-based dynamic decoding, achieving SOTA results on QA tasks across four models and six datasets.