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From the 1 of 56 linked papers with an AI index.

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20242026
most citedT-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems

1 citations · 1 across the 28 of their papers we have counts for

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cs.CL2026

Do AI Personas Grow? Analyzing and Benchmarking Personality Evolution in LLM Agents After Life Events

Ming Wang, Peidong Wang, Xiaocui Yang +4

Personality-conditioned LLM agents (PC-Agents) are increasingly used in emotional support, social simulation, and role-playing, motivating the development of lifelong agents that r…

cs.CL2026

TRAM: Enhancing Multimodal Reasoning with Trajectory-Derived Auxiliary Memory

Kang Liu, Zijing Wang, Yongkang Liu +5

Multimodal Large Reasoning Models (MLRMs) have achieved strong performance on tasks requiring visual understanding and multi-step inference. However, as reasoning trajectories grow…

cs.CL2026

Beyond Feeling Better: Capability-Sustaining Emotional Dialogue as a Longitudinal Research Paradigm

Ming Wang, Jiaqi Wu Young, Wenfang Wu +2

The paper introduces capability‑sustaining emotional dialogue (CSED) as a longitudinal research paradigm that aims to support users' emotional regulation, coping, and autonomy over…

cs.CL2026

PRISM: Prosody-Integrated Multi-Agent Reasoning Framework for Empathetic Spoken Dialogue

Wen Zhang, Xiaocui Yang, Zhuoyue Gao +3

Empathetic spoken dialogue systems require not only semantically appropriate responses but also emotionally aligned prosodic expression. However, cascade pipelines often discard ac…

cs.CL2026

DiM\textsuperscript{3}: Bridging Multilingual and Multimodal Models via Direction- and Magnitude-Aware Merging

Zijing Wang, Mingyang Wang, Ercong Nie +6

Towards more general and human-like intelligence, large language models should seamlessly integrate both multilingual and multimodal capabilities; however, extending an existing mu…

cs.CL2026

MTRouter: Cost-Aware Multi-Turn LLM Routing with History-Model Joint Embeddings

Yiqun Zhang, Hao Li, Zihan Wang +6

Multi-turn, long-horizon tasks are increasingly common for large language models (LLMs), but solving them typically requires many sequential model invocations, accumulating substan…