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20222026
most citedFew-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion Prompts

32 citations · 48 across the 47 of their papers we have counts for

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Showing 2026 · cs.CLShow all

13 papers · 2 filters

cs.CL2026

Self-Evolving Embodied Agents via Skill-Harness Evolution

Peidong Wang, Zhiming Ma, Ying Chang +7

Embodied agents are increasingly built as systems around foundation models, where performance depends not only on model weights but also on the skills, context, action interfaces,…

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

Boosting Deepresearch and LongContext Ability with Self-Generated Deepresearch Rollouts Traces

Zihan Wang, Hao Wang, Boyuan Jiang +8

Deepresearch (DR) agents interact with real-world web environments through multi-turn search and visit, causing their contexts to grow rapidly over time. We observe that, even afte…

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

CrossLex: A Source-Grounded Benchmark for Cross-Jurisdictional Legal Reasoning in Large Language Models

Xiaocui Yang, Xican Tan, Shoujie Chen +3

Legal reasoning is inherently jurisdiction-dependent: the same facts can call for different legal rules and yield different conclusions across legal systems. Yet existing benchmark…

cs.CL2026

What are Key Factors for Updates in RL for LLM Reasoning?

Peidong Wang, Demi Wang, Xufang Luo +5

Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a promising framework for enhancing the reasoning ability of large language models. However, much of the existi…

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…

cs.CL2026

GRASP: Grounded CoT Reasoning with Dual-Stage Optimization for Multimodal Sarcasm Target Identification

Faxian Wan, Xiaocui Yang, Yifan Cao +3

Moving beyond the traditional binary classification paradigm of Multimodal Sarcasm Detection, Multimodal Sarcasm Target Identification (MSTI) presents a more formidable challenge,…

cs.CL2026

NEAT: Neuron-Based Early Exit for Large Reasoning Models

Kang Liu, Yongkang Liu, Xiaocui Yang +5

Large Reasoning Models (LRMs) often suffer from \emph{overthinking}, a phenomenon in which redundant reasoning steps are generated after a correct solution has already been reached…

cs.CL2026

PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs

Zijing Wang, Yongkang Liu, Mingyang Wang +8

Multimodal Large Language Models (MLLMs) rely on strong linguistic reasoning inherited from their base language models. However, multimodal instruction fine-tuning paradoxically de…

cs.CL2026

CIRAG: Construction-Integration Retrieval and Adaptive Generation for Multi-hop Question Answering

Zili Wei, Xiaocui Yang, Yilin Wang +5

Triple-based Iterative Retrieval-Augmented Generation (iRAG) mitigates document-level noise for multi-hop question answering. However, existing methods still face limitations: (i)…