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DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval

Intermediate

Maojun Sun, Yue Wu et al.Mar 5arXiv

DARE is a new way for AI assistants to find the right R functions by also looking at what the data looks like, not just the words in the question.

#distribution-aware retrieval#RPKB#RCodingAgent

ManCAR: Manifold-Constrained Latent Reasoning with Adaptive Test-Time Computation for Sequential Recommendation

Intermediate

Kun Yang, Yuxuan Zhu et al.Feb 23arXiv

ManCAR helps recommendation systems think step by step but keeps their thoughts on realistic paths using a map of how items connect.

#sequential recommendation#latent reasoning#interaction graph

HyTRec: A Hybrid Temporal-Aware Attention Architecture for Long Behavior Sequential Recommendation

Intermediate

Lei Xin, Yuhao Zheng et al.Feb 20arXiv

The paper proposes HyTRec, a recommender system that reads very long histories fast while still paying sharp attention to the latest clicks and purchases.

#Hybrid Attention#Linear Attention#Softmax Attention

RealMem: Benchmarking LLMs in Real-World Memory-Driven Interaction

Beginner

Haonan Bian, Zhiyuan Yao et al.Jan 11arXiv

RealMem is a new benchmark that tests how well AI assistants remember and manage long, ongoing projects across many conversations.

#RealMem#long-term memory#project-oriented interactions