CuSearch: Curriculum Rollout Sampling via Search Depth for Agentic RAG

arXiv:2607.10463v1 Announce Type: new Abstract: Agentic retrieval-augmented generation (RAG) extends static RAG by allowing language models to iteratively reason, generate search

arXiv:2607.22002v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a highly effective framework for improving LLM reasoning, with

arXiv:2605.05538v1 Announce Type: new Abstract: We present AgenticRAG, a practical agentic harness for retrieval and analysis over enterprise knowledge bases. Standard RAG

arXiv:2605.15871v1 Announce Type: new Abstract: Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers.

arXiv:2603.18859v1 Announce Type: new Abstract: Reinforcement learning (RL) holds significant promise for enhancing the agentic reasoning capabilities of large language models

arXiv:2603.27195v1 Announce Type: new Abstract: Designing microstructures that satisfy coupled cross-physics objectives is a fundamental challenge in material science. This

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

arXiv:2604.05336v1 Announce Type: new Abstract: Large Language Models (LLMs) deployed in agentic environments must exercise multiple capabilities across different task instances,