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arXiv:2603.09268v1 Announce Type: new Abstract: The discovery and design of functional molecules remain central challenges across chemistry,biology, and materials science. While recent advances in machine …
arXiv:2603.09185v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) have enabled diverse retrieval methods. However, existing retrieval methods …
arXiv:2603.09481v1 Announce Type: new Abstract: We present GenePlan (GENeralized Evolutionary Planner), a novel framework that leverages large language model (LLM) assisted evolutionary algorithms to generate …
arXiv:2603.09706v1 Announce Type: new Abstract: While safety alignment for Multimodal Large Language Models (MLLMs) has gained significant attention, current paradigms primarily target malicious intent or …
arXiv:2603.08899v1 Announce Type: new Abstract: Speculative decoding has emerged as a powerful approach to accelerate large language model (LLM) inference by employing lightweight draft models …
arXiv:2603.09715v1 Announce Type: new Abstract: Visual instruction tuning is crucial for improving vision-language large models (VLLMs). However, many samples can be solved via linguistic patterns …
arXiv:2603.09533v1 Announce Type: new Abstract: This study proposes a novel methodology for generating personalized fake news debunking messages by prompting Large Language Models (LLMs) with …
arXiv:2603.09222v1 Announce Type: new Abstract: Efficient context compression is crucial for improving the accuracy and scalability of question answering. For the efficiency of Retrieval Augmented …
arXiv:2603.09313v1 Announce Type: new Abstract: Activation steering is a widely used approach for controlling large language model (LLM) behavior by intervening on internal representations. Existing …
arXiv:2603.09344v1 Announce Type: new Abstract: Offline reinforcement learning (RL) enables data-efficient and safe policy learning without online exploration, but its performance often degrades under distribution …
arXiv:2603.09095v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can process text presented as images, yet they often perform worse than when the same …
arXiv:2603.09641v1 Announce Type: new Abstract: LLM agents that store knowledge as natural language suffer steep retrieval degradation as condition count grows, often struggle to compose …