1 citations · 1 across the 9 of their papers we have counts for
9 papers
Audio-Visual Exchange-Aware Token Pruning for Efficient Audio-Visual Captioning
Zihan Meng, Dexiang Hong, Weidong Chen +3
Audio-visual captioning generates natural language descriptions from video and audio content. Multimodal LLMs have advanced this task, but both modalities contribute many tokens to…
FinCom: A Financial Multi-Agent Demo with Disagree-or-Commit Deliberation
Chao Peter Yang, Zixiao Tan, Kaisen Yao +3
Multi-agent systems powered by large language models (LLMs) are increasingly used for financial analysis and decision support. However, existing coordination schemes, especially th…
FlyRoute: Self-Evolving Agent Profiling via Data Flywheel for Adaptive Task Routing
Rongjun Li, Ziyu Zhou, Yihang Wu
Enterprise routers assign queries to expert agents, yet deployed profiles stay static while agents evolve (prompts, tools, models), and developers rarely keep descriptions or exemp…
Evaluating the Effectiveness of Black-Box Prompt Optimization as the Scale of LLMs Continues to Grow
Ziyu Zhou, Yihang Wu, Jingyuan Yang +2
Black-Box prompt optimization methods have emerged as a promising strategy for refining input prompts to better align large language models (LLMs), thereby enhancing their task per…
Neural Approaches to SAT Solving: Design Choices and Interpretability
David Mojžíšek, Jan Hůla, Ziwei Li +2
In this contribution, we provide a comprehensive evaluation of graph neural networks applied to Boolean satisfiability problems, accompanied by an intuitive explanation of the mech…
WorkTeam: Constructing Workflows from Natural Language with Multi-Agents
Hanchao Liu, Rongjun Li, Weimin Xiong +2
Workflows play a crucial role in enhancing enterprise efficiency by orchestrating complex processes with multiple tools or components. However, hand-crafted workflow construction r…
Gradient Co-occurrence Analysis for Detecting Unsafe Prompts in Large Language Models
Jingyuan Yang, Bowen Yan, Rongjun Li +4
Unsafe prompts pose significant safety risks to large language models (LLMs). Existing methods for detecting unsafe prompts rely on data-driven fine-tuning to train guardrail model…
LF-Steering: Latent Feature Activation Steering for Enhancing Semantic Consistency in Large Language Models
Jingyuan Yang, Rongjun Li, Weixuan Wang +3
Large Language Models (LLMs) often generate inconsistent responses when prompted with semantically equivalent paraphrased inputs. Recently, activation steering, a technique that mo…
HYBRIDMIND: Meta Selection of Natural Language and Symbolic Language for Enhanced LLM Reasoning
Simeng Han, Tianyu Liu, Chuhan Li +2
LLMs approach logical and mathematical reasoning through natural or symbolic languages. While natural language offers human-accessible flexibility but suffers from ambiguity, symbo…