Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation

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:2604.18789v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is central to aligning Large Language Models (LLMs), yet it introduces a critical

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

arXiv:2603.03686v2 Announce Type: replace Abstract: Automated design of chemical formulations is a cornerstone of materials science, yet it requires navigating a high-dimensional

arXiv:2602.20558v2 Announce Type: replace Abstract: Large language models (LLMs) are promising backbones for generative recommender systems, yet a key challenge remains

arXiv:2603.29871v1 Announce Type: new Abstract: In user-agent interaction scenarios such as recommendation, brainstorming, and code suggestion, Large Language Models (LLMs) often

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

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