This page shows the most relevant public items for cs.AI, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Feb 18, 2026 • arXiv • Song Han, William J. Dally
The computational and bandwidth requirements for massive multi-agent swarms present a critical bottleneck for cloud-centric AI infrastructure. We introduce SwarmLLM, a framework for distributed inf...
Paper • Oct 30, 2019 • Nature • Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, Michaël Mathieu, Andrew Dudzik, David Silver
The game of StarCraft II has emerged as a grand challenge for artificial intelligence research owing to its complex, multi-agent, and partially observable environment. Here we introduce AlphaStar, ...
Paper • Feb 21, 2026 • arXiv • Liu et al., Wang et al.
Current agents often utilize third-party tools (APIs, web browsers, databases) with full authority, creating a 'Tools-as-Attack-Vector' problem. We introduce 'Agency Sandboxing,' a software enginee...
Paper • Feb 12, 2026 • arXiv • Nenad Tomašev, Kevin R. McKee, Jack Rae, Iason Gabriel, Vukosi Marivate, Milind Tambe, Demis Hassabis, Charles Blundell
As advanced AI agents evolve beyond query-response models, their utility is increasingly defined by how effectively they can decompose complex objectives and delegate sub-tasks. We propose an adapt...
Paper • Feb 11, 2026 • arXiv • Sangyoon Bae, Miruna Oprescu, David Keetae Park, Shinjae Yoo, Jiook Cha
Recovering the true causal connectivity of the human brain from indirect observations like EEG and fMRI is a fundamental but ill-posed problem due to the presence of unobserved confounders and the ...
Paper • Jan 19, 2026 • arxiv.org • Chen et al.
Despite recent advancements in Large Language Models (LLMs), complex Software Engineering (SE) tasks require more collaborative and specialized approaches. This concept paper systematically reviews...
Paper • Feb 16, 2026 • arXiv • MiniMax Research Team
Training agents for industrial-scale deployment requires extreme stability and data throughput. We present Minimax M2.5, a model trained using a novel asynchronous RL architecture designed to proce...
Paper • Feb 19, 2026 • arXiv • Xiaoliang Fu, Jiaye Lin, Yangyi Fang
Policy optimization for Large Language Models often suffers from gradient instability and reward signal unreliability, particularly in mathematical and verifiable reasoning tasks. We introduce MASP...
Paper • Feb 18, 2026 • arXiv • Adam Zweiger, Xinghong Fu, Han Guo, MIT Team
Large Language Models struggle with memory overhead during long-context inference due to the linear growth of the Key-Value (KV) cache. We propose Attention Matching (AM), a framework for high-qual...
Paper • Feb 19, 2026 • arXiv • Yue Liu, Zhiyuan Hu, Flood Sung
Current LLM agents frequently fail in tasks requiring hundreds of steps due to error accumulation and context overflow. We introduce KLong, an agentic framework that utilizes 'Trajectory-Splitting ...
Paper • Feb 15, 2026 • arXiv • Zheng et al.
Existing multi-agent frameworks often rely on static or task-level workflows, which either over-process simple queries or underperform on complex ones, while neglecting the efficiency-performance t...
Paper • Feb 18, 2026 • arXiv • Jianhao Ruan, et al.
Language agents have shown strong promise for task automation, but realizing this promise for increasingly complex, long-horizon tasks has driven the rise of a sub-agent-as-tools paradigm. However,...
Paper • Feb 17, 2026 • arXiv • Moritz Miller, Florent Draye, Bernhard Schölkopf
This paper addresses the fundamental challenge of 'feature disentanglement' in modern deep learning. We propose an Orthogonality Regularization technique to identify features that are both interpre...
Paper • Feb 15, 2026 • arXiv • DeepMind Research Team
We explore the boundary between simple heuristics and complex neural-cognitive models in combinatorial optimization. This paper demonstrates how hybrid architectures can leverage memory to shape re...
Paper • Feb 18, 2026 • arXiv • Moritz Miller, Florent Draye, Bernhard Schölkopf
This paper distinguishes between two paradigms in AI explanation: static prediction and agentic trajectories. In agentic systems, behavior emerges as a sequence of observations, reasoning steps, an...
Paper • Sep 12, 2024 • OpenAI • OpenAI
We introduce OpenAI o1, a new series of large language models trained with reinforcement learning to perform complex reasoning. o1 models are designed to spend more time thinking before they respon...
Paper • Mar 15, 2023 • arXiv • OpenAI
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT...
Paper • Mar 4, 2022 • arXiv • Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, Ryan Lowe
Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not he...
Paper • May 12, 2022 • arXiv • Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Nando de Freitas
Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato,...
Paper • Feb 20, 2026 • arXiv • Armando Solar-Lezama, Sumit Gulwani, Elena Rossi
The rapid adoption of natural language programming ('vibe coding') has democratized software creation but introduced unprecedented levels of technical debt and architectural instability. We propose...
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