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
7 Pith papers cite this work. Polarity classification is still indexing.
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MACR adaptively assesses LLM confidence via semantic entropy then applies inductive multi-agent reasoning with rule-induction, conflict-analysis, and resolution agents to handle unreliable parametric and contextual knowledge.
LLMs prefer document assertions over user assertions, are impressionable to external information, and gain better discrimination after fine-tuning on diverse source-interaction data.
Caesar improves creative synthesis by 13-23% over prior deep research agents by using context-aware web traversal to build a knowledge graph and adversarial refinement to seek novel perspectives.
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.
CRVA-TGRAG combines parent-document segmentation, ensemble retrieval, and teacher-guided fine-tuning to mitigate knowledge conflicts and improve accuracy in LLM-based CVE vulnerability analysis.
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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Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference
MACR adaptively assesses LLM confidence via semantic entropy then applies inductive multi-agent reasoning with rule-induction, conflict-analysis, and resolution agents to handle unreliable parametric and contextual knowledge.
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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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Caesar: Deep Agentic Web Exploration for Creative Answer Synthesis
Caesar improves creative synthesis by 13-23% over prior deep research agents by using context-aware web traversal to build a knowledge graph and adversarial refinement to seek novel perspectives.
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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.
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Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generations
CRVA-TGRAG combines parent-document segmentation, ensemble retrieval, and teacher-guided fine-tuning to mitigate knowledge conflicts and improve accuracy in LLM-based CVE vulnerability analysis.
- ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation