反事实推理优化LLM Agent记忆管理，创新性地解决工具使用中的上下文选择与压缩难题。
arXiv:2606.08151v1 Announce Type: new Abstract: Tool-using LLM agents often fail not because relevant text is absent, but because decisive evidence is…
在固定LLM预算下，提出智能体搜索方法实现高效反事实解释，平衡推理成本与可解释性。
arXiv:2606.08696v1 Announce Type: new Abstract: Counterfactual recourse aims to provide actionable feature changes that would alter an unfavorable dec…
6G智能新范式：摒弃预测式AI，通过反事实世界模型实现近实时状态空间推理与自主决策
arXiv:2511.02748v2 Announce Type: replace-cross Abstract: We argue that sixth-generation (6G) intelligence is not fluent token prediction but the capa…
KDD 2026论文提出针对纵向数据中罕见事件的可扩展反事实风险估计方法，解决因果推断与高维数据挑战。
arXiv:2606.01539v1 Announce Type: cross Abstract: Estimating the causal effect of time-varying treatments on survival outcomes in large observational …
反事实追踪审计方法，可揭示LLM智能体技能的可解释性与可靠性缺陷，提升AI安全。
arXiv:2605.11946v2 Announce Type: replace Abstract: Large Language Model agents are increasingly augmented with agent skills. Current evaluation metho…
LLM在政策评估中的反事实推理受直觉影响，40个实证案例揭示可靠性短板
arXiv:2604.10511v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for causal and counterfactual reasoning, yet th…
用反事实推理构建1型糖尿病数字孪生，通过患者行为优化实现精准血糖控制。
arXiv:2504.09846v2 Announce Type: replace-cross Abstract: Frequent and long-term exposure to hyperglycemia increases the risk of chronic complications…
提出反事实推理路径方法降低信用分配方差，强化学习领域新思路。
arXiv:2605.16302v1 Announce Type: new Abstract: Reinforcement learning for multi-step reasoning with large language models (LLMs) often relies on spar…