Agentic Recommender System with Hierarchical Belief-State Memory · DeepSignal
Agentic Recommender System with Hierarchical Belief-State Memory arXiv cs.CL · Xiang Shen, Yuhang Zhou, Yifan Wu, Zhuokai Zhao, Siyu Lin, Lei Huang, Qianqian Zhong, Lizhu Zhang, Benyu Zhang, Xiangjun Fan, Hong Yan 2d ago · · · · MARS introduces a hierarchical memory framework for personalized recommendations, enhancing user preference modeling.
Key Points MARS organizes memory into event, preference, and profile tiers. Adaptive lifecycle operations optimize memory evolution. Achieves state-of-the-art performance on InstructRec benchmarks. Reader Mode unavailable (could not extract clean content).
arXiv cs.CL · Luis Lara, Aristides Milios, Zhi Hao Luo, Aditya Sharma, Ge Ya Luo, Christopher Beckham, Florian Golemo, Christopher Pal 2d ago Generative Floor Plan Design with LLMs via Reinforcement Learning with Verifiable Rewards AI Summary
A new LLM-based approach generates floor plans while adhering to numerical and topological constraints using reinforcement learning.
📰 Read Original Signal Score
Moderate signal — interesting but narrower impact.
Weight Score
Source authority 20% 80
Community heat 20% 0
Technical impact 30%
📰 Read Original arXiv cs.CL · Mokshit Surana, Archit Rathod, Akshaj Satishkumar 2d ago Measuring and Mitigating Toxicity in Large Language Models: A Comprehensive Replication Study AI Summary
This study evaluates DExperts for mitigating toxicity in LLMs, revealing strengths and weaknesses in safety and latency.
arXiv cs.CL · Chengzhi Liu, Yichen Guo, Yepeng Liu, Yuzhe Yang, Qianqi Yan, Xuandong Zhao, Wenyue Hua, Sheng Liu, Sharon Li, Yuheng Bu, Xin Eric Wang 2d ago Auditing Agent Harness Safety AI Summary
HarnessAudit framework evaluates safety in LLM agent execution, revealing risks in multi-agent systems.
Invisible Orchestrators Suppress Protective Behavior and Dissociate Power-Holders: Safety Risks in Multi-Agent LLM Systems AI Summary
Invisible orchestrators in multi-agent LLM systems pose significant safety risks and affect behavior dynamics.
Enhanced and Efficient Reasoning in Large Learning Models AI Summary
The paper proposes an efficient reasoning method for large language models, enhancing trust in generated content.
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≥75 high · 50–74 medium · <50 low
Why Featured
MARS's hierarchical memory framework improves user preference modeling, signaling a shift towards more sophisticated AI-driven personalization, crucial for developers, PMs, and investors in enhancing user engagement and retention.