Tootfinder

Rethinking Reasoning in Document Ranking: Why Chain-of-Thought Falls Short
Xuan Lu, Haohang Huang, Rui Meng, Yaohui Jin, Wenjun Zeng, Xiaoyu Shen
https://arxiv.org/abs/2510.08985

Rethinking Reasoning in Document Ranking: Why Chain-of-Thought Falls Short
Document reranking is a key component in information retrieval (IR), aimed at refining initial retrieval results to improve ranking quality for downstream tasks. Recent studies--motivated by large reasoning models (LRMs)--have begun incorporating explicit chain-of-thought (CoT) reasoning into LLM-based rerankers. However, the effectiveness of such reasoning for ranking tasks remains underexplored. In this work, we present the first systematic study of reasoning in reranking across both pointwis…

Fundamentals of Building Autonomous LLM Agents
Victor de Lamo Castrillo, Habtom Kahsay Gidey, Alexander Lenz, Alois Knoll
https://arxiv.org/abs/2510.09244 https://

Fundamentals of Building Autonomous LLM Agents
This paper reviews the architecture and implementation methods of agents powered by large language models (LLMs). Motivated by the limitations of traditional LLMs in real-world tasks, the research aims to explore patterns to develop "agentic" LLMs that can automate complex tasks and bridge the performance gap with human capabilities. Key components include a perception system that converts environmental percepts into meaningful representations; a reasoning system that formulates plans, adapts t…