knowledge-conflict

该论文提出以“认知谦逊”（Epistemic Humility）评估 LLM 智能体在知识冲突下的表现，通过 Identify、Solve、Escalate 三个轨迹级维度进行衡量，发现高任务准确率并不意味着更高的认知谦逊，且模型级干预往往以牺牲准确率为代价。

This paper proposes TRACE, a fine-tuning framework for Retrieval-Augmented Generation (RAG) that uses multi-agent debate traces and answer completeness regularization to handle knowledge conflicts, improving robustness against misleading context and reducing incomplete answers.

This paper identifies a failure mode where language models are persuaded by assertions from incentive-misaligned witnesses in CRM records, leading to incorrect decisions, and proposes a diagnostic method to analyze this issue.

This paper provides a mechanistic comparison of knowledge-conflict circuits in LLMs under instruction tuning, finding that tuning gates rather than rewires these circuits across multiple model families, with implications for interpretability.

KC-Bench is a dynamic interactive benchmark for evaluating how LLM agents detect and resolve knowledge conflicts between user instructions, parametric knowledge, and environmental observations, with evaluations showing no model handles all conflict types reliably.

Introduces SHIFT, a framework that uses learnable gate modulation to adaptively steer internal activations in LLMs, mitigating knowledge conflicts in RAG with fewer than 0.01% trainable parameters.

The paper generalizes contrastive decoding to a conflict-aware paradigm that dynamically allocates authority between external context and parametric priors, proposes the TriState-Bench evaluation protocol, and introduces Adaptive Regime Routing (ARR) to resolve asymmetry between correction and resistance.

This paper proposes the Convergence Point Theory, which unifies various LLM uncertainty phenomena by arguing that uncertainty is determined by the consensus density of human knowledge on a topic, identifying three zones (Full, Partial, and Non-Consensus). It raises concerns about forced convergence during training on unresolved philosophical questions.

This paper introduces Context-Driven Decomposition (CDD), a probe to diagnose when RAG systems comply with retrieved context despite conflicting parametric knowledge, and releases the Epi-Scale benchmark for systematic study across model families.