Tootfinder

Teaching LLMs to Speak Spectroscopy
Nesar Ramachandra, Yuan-Sen Ting, Zechang Sun, Azton Wells, Salman Habib
https://arxiv.org/abs/2508.10075 https://arxiv…

Teaching LLMs to Speak Spectroscopy
Pre-trained Large Language Models (LLMs) have revolutionized text processing, yet adapting Transformer-based neural networks to non-textual scientific modalities typically requires specialized architectures and extensive computational resources. We demonstrate that LLaMA-3.1-8B can be efficiently repurposed to predict galaxy redshifts from spectroscopic data through Low-Rank Adaptation (LoRA), achieving competitive performance while preserving its linguistic capabilities. Using only 16 GPU-hour…

ImportSnare: Directed "Code Manual" Hijacking in Retrieval-Augmented Code Generation
Kai Ye, Liangcai Su, Chenxiong Qian
https://arxiv.org/abs/2509.07941 https://

ImportSnare: Directed "Code Manual" Hijacking in Retrieval-Augmented Code Generation
Code generation has emerged as a pivotal capability of Large Language Models(LLMs), revolutionizing development efficiency for programmers of all skill levels. However, the complexity of data structures and algorithmic logic often results in functional deficiencies and security vulnerabilities in generated code, reducing it to a prototype requiring extensive manual debugging. While Retrieval-Augmented Generation (RAG) can enhance correctness and security by leveraging external code manuals, it …

Reinforcement Learning for Clinical Reasoning: Aligning LLMs with ACR Imaging Appropriateness Criteria
Anni Tziakouri, Filippo Menolascina
https://arxiv.org/abs/2510.05194 https…

Reinforcement Learning for Clinical Reasoning: Aligning LLMs with ACR Imaging Appropriateness Criteria
Medical imaging has revolutionized diagnosis, yet unnecessary procedures are rising, exposing patients to radiation and stress, limiting equitable access, and straining healthcare systems. The American College of Radiology Appropriateness Criteria, developed through extensive multidisciplinary review, provide evidence-based guidance but remain underutilized. Leveraging advances in LLM reasoning, we introduce a Reasoning Agent trained with Reinforcement Learning (RL), specifically Group Relative…