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 12, 2026 • arXiv • Nicholas Lee, Lutfi Eren Erdogan, Chris Joseph John, Surya Krishnapillai, Kurt Keutzer, Amir Gholami
Current WebAgents struggle with long-horizon tasks and complex navigation. We propose an agentic scaling framework that increases compute at test-time through iterative trajectory pruning and rewar...
Paper • Oct 29, 2025 • arXiv • Mohamad Abou Ali, Fadi Dornaika
Agentic AI represents a transformative shift in artificial intelligence, but its rapid advancement has led to a fragmented understanding, often conflating modern neural systems with outdated symbol...
Paper • May 15, 2025 • arXiv • Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
This review critically distinguishes between traditional 'AI Agents' and the broader paradigm of 'Agentic AI', offering a structured conceptual taxonomy and application mapping. The authors argue t...
Paper • Jun 2, 2025 • arXiv • Peter Belcak, Greg Heinrich, Shizhe Diao, Yonggan Fu, Xin Dong, Saurav Muralidharan, Yingyan Celine Lin, Pavlo Molchanov
The rise of agentic AI systems has ushered in a mass of applications where language models perform specialized tasks repetitively with little variation. This position paper argues that Small Langua...
Paper • Feb 22, 2026 • arXiv • Tatsunori Hashimoto, Percy Liang, Xinyi Wang
Current web agents rely heavily on underlying HTML DOM structures, making them brittle to website updates and entirely incapable of navigating dynamic, canvas-based, or non-web applications. We pro...
Paper • Feb 19, 2026 • arXiv • Sergey Levine, Chelsea Finn, Marcus Sterling
While digital agents have mastered software-based orchestration, integrating these cognitive loops with physical robotics remains a challenge. We present a unified framework that closes the 'Agenti...
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...
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