AI News

As large language models increasingly use tools to act, they must reconcile user instructions, knowledge stored in model parameters, and observations from changing environments. KC-Bench is a controlled, multi-turn benchmark designed to measure that behavior across three settings: conflicts with world knowledge, inconsistent inputs, and temporal conflicts involving multiple sources. The benchmark contains 238 manually screened tasks selected from more than 1,000 generated candidates. Each task combines a user simulator, stateful tools, deterministic assertions about the environment, an open-source natural-language evaluator, and human verification of agent trajectories. The authors evaluate nine models, including DeepSeek-V4-Flash, GLM-5.2, and MiniMax-M3. Results vary substantially by domain, and no evaluated model reliably handles factual correction, identity-consistency checking, and temporal-conflict resolution across all settings. In simulated environments, failing to detect a conflict can carry through to tool calls or synthetic protected-data flows. KC-Bench is intended to isolate model-level conflict handling rather than rank complete agent frameworks. The authors present it as a reproducible diagnostic for developing reasoning and execution safeguards that are aware of conflicting information.