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20232026
most citedFedFed: Feature Distillation against Data Heterogeneity in Federated Learning

28 citations · 92 across the 52 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

Beyond the Shadows of Plato's Cave: Evaluating False Memory in Autonomous Agents via Counterfactual Reasoning

Quan M. Tran, Zhuo Huang, Zhen Fang +3

Autonomous agents increasingly rely on memory to generalize beyond their training environments. However, agents are bounded by what they have seen and believed, and leveraging such…

cs.AI2026

Taming CoT Obfuscation in VLMs: From Mechanistic Evidence to Activation-Level Enforcement

Xutao Mao, Jianing Zhu, Jinman Zhao +4

Reinforcement learning (RL) has substantially improved the reasoning capabilities of vision-language models (VLMs), yet it can induce chain-of-thought (CoT) obfuscation, an operati…

cs.AI2026

Are Human-Aligned Models Models of Humans? A Turing-Test Gap in Preference Alignment

Suqin Yuan, Runqi Lin, Muyang Li +5

Human-feedback alignment has made language models useful assistants and is commonly described as aligning them with humans. However, the responses people prefer from an AI need not…

cs.AI2026

MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs

He Li, Haoang Chi, Qizhou Wang +6

Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific co…

cs.AI2026

AgentHijack: Benchmarking Computer Use Agent Robustness to Common Environment Corruptions

Jingwei Sun, Jianing Zhu, Yuanyi Li +3

Autonomous computer use agents that powered by multimodal large language models (MLLMs) are emerging as capable assistants for completing complex digital workflows. However, real-w…

cs.AI2025★ 1 cited

Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?

Haoang Chi, He Li, Wenjing Yang +5

Causal reasoning capability is critical in advancing large language models (LLMs) toward strong artificial intelligence. While versatile LLMs appear to have demonstrated capabiliti…