arXiv:2608. 07527v1 Announce Type: cross Abstract: Long-document understanding requires models to find and combine evidence across many pages, layouts, tables, figures, and charts.
By Hongchen Wei, Yuanzhe Wang, Bei Liu, Yifan Yang, Qi Dai, Kai Qiu, Yunsheng Li, Dongdong Chen, Chong Luo, Zhenzhong Chen, Baining Guo
arXiv:2607. 18358v1 Announce Type: cross Abstract: Document classification is a solved problem in the laboratory and an unsolved one in the enterprise.
By Bogdan Raduta, Horia Velicu, Alexandru Preda, Serban Chiricescu
PaperScout is an autonomous agent that treats academic paper search as a sequential decision-making process, dynamically deciding when and how to use search and expansion tools based on accumulated context. The authors identify a granularity mismatch in standard reinforcement learning for multi-turn tasks and propose Proximal Sequence Policy Optimization (PSPO), a sequence-level policy optimization method that aligns learning with agent–environment interactions. Experiments on synthetic and real-world benchmarks show that PaperScout outperforms structured retrieval and RL baselines in recall and relevance, demonstrating the effectiveness of its adaptive agentic framework and optimization strategy.
By Tingyue Pan, Jie Ouyang, Mingyue Cheng, Qingchuan Li, Zirui Liu, Daoyu Wang, Mingfan Pan, Shuo Yu, Qi Liu, Enhong Chen
arXiv:2602. 10238v2 Announce Type: replace-cross Abstract: The growing size of Large Language Models (LLMs) makes efficient inference challenging, primarily due to the memory demands of the autoregressive Key-Value (KV) cache.
By Luca Moschella, Laura Manduchi, Ozan Sener
arXiv:2607. 20497v1 Announce Type: new Abstract: Prompt optimization for text classification spans diverse approaches, from demonstration selection to exploration-based search to error-driven diagnosis, each with known but incompletely characterized strengths and limitations.
By Yueying Cui, Renhao Xue, Yi Zhang, Mukul Prasad
arXiv:2605.29307v2 Announce Type: replace-cross
Abstract: Large Language Model (LLM) search agents have shown strong promise on knowledge-intensive tasks through iterative reasoning and retrieval. Mo...
By Alireza Salemi, Chang Zeng, Atharva Nijasure, Jui-Hui Chung, Razieh Rahimi, Fernando Diaz, Hamed Zamani