Communication and Verification in LLM Agents towards Collaboration under Information Asymmetry

FutureWeaver has been introduced as a framework designed to optimize test-time compute allocation in multi-agent systems, addressing the challenges of collaboration among agents under fixed budget constraints. This framework aims to enhance the performance of large language models (LLMs) by enabling more effective use of inference-time compute through modularized collaboration.

A new study introduces a method for long video summarization through key moment extraction, utilizing Vision-Language Models (VLMs) to identify and select the most relevant clips from lengthy video content. This approach aims to enhance the efficiency of video analysis by generating compact visual descriptions and leveraging large language models (LLMs) for summarization. The evaluation is based on reference clips derived from the MovieSum dataset.

A new framework integrating ontologies with large language models (LLMs) has been developed for chemical engineering, enhancing control systems by combining structured domain knowledge with generative reasoning. This approach utilizes the COPE ontology to guide model training and inference through a series of data processing steps, resulting in improved question-answer pairs and a focus on syntactic and factual accuracy.