A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Yi Yu, Liuyi Yao, Yuexiang Xie, et al.
Proposes Agentic Memory (AgeMem), a unified framework integrating long-term and short-term memory management into LLM agent policy via tool-based actions and progressive reinforcement learning.
Adam Kostka, J. Chudziak
This paper introduces a novel multi-agent architecture integrating Theory of Mind, BDI-style internal beliefs, and symbolic solvers to improve collaborative decision-making in LLM-based systems.
Nuo Chen, Yicheng Tong, Yuzhe Yang, et al.
This paper systematically studies diversity collapse in multi-agent LLMs, showing that interaction structures, not model insufficiency, primarily cause reduced exploration diversity.
Unknown
A generative PRM trained on small synthetic data outperforms discriminative PRMs and LLM-as-a-Judge baselines in step-by-step reasoning verification.
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, et al.
Code Llama is a family of open-source LLMs for code based on Llama 2, achieving state-of-the-art performance in code generation, infilling, and long-context tasks.
Roberto Navigli, Simone Conia, Björn Roß
This paper surveys bias in LLMs from data selection to social biases like gender, ethnicity, and religion, and outlines measurement and mitigation directions.
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.
A comprehensive survey of bias evaluation and mitigation techniques for LLMs, proposing taxonomies for metrics, datasets, and mitigation methods.
Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn, et al.
Proposes semantic entropy to detect LLM confabulations by measuring uncertainty at the meaning level, enabling task-agnostic hallucination detection.
Zifan Zheng, Yezhaohui Wang, Yuxin Huang, et al.
This review demystifies LLM reasoning by categorizing attention head functions within a four-stage framework inspired by human thought.
Jialong Li, Mingyue Zhang, Nianyu Li, et al.
This paper surveys the potential benefits and challenges of using generative AI, particularly LLMs, to enhance self-adaptive systems, and outlines a research roadmap.
Jakub Swacha, Michał Gracel
A survey of 47 papers on RAG chatbots in education, analyzing their character, target support, knowledge scope, LLM, and evaluation.
Yuren Mao, Yuhang Ge, Yijiang Fan, et al.
A comprehensive survey categorizing LoRA variants for LLMs into downstream adaptation, cross-task generalization, efficiency, privacy, and applications.