Autonomous agents, tool use, and agentic systems
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We're in the DNS era of agent infrastructure. Before agents can find and trust each other at scale, you need identity, attestation, reput...
In addition to their efforts around the Lemonade SDK itself, AMD software engineers working on their AI initiatives continue to be invest...
![[2602.08216] Thermodynamic Isomorphism of Transformers: A Lagrangian Approach to Attention Dynamics](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper presents a thermodynamic framework for analyzing Transformer attention dynamics, linking it to statistical mechanics through a...
This paper explores imitation learning for combinatorial optimization under uncertainty, introducing a taxonomy of expert types and a new...
This paper presents a framework for context-specific causal graph discovery that addresses non-stationarity and spatio-temporal patterns,...
This paper presents MA-SCLUCB, an algorithm for multi-agent linear bandit problems, focusing on balancing exploration and exploitation wh...
This paper presents an innovative online reinforcement learning framework using sparse Gaussian mixture model Q-functions, enhancing expl...
The paper presents MissionHD, a novel approach for video anomaly detection using hyperdimensional refinement of reasoning graphs, address...
This article presents a novel approach to computing finite-width neural tangent kernels (NTKs) using Feynman diagrams, enhancing the unde...
The paper presents DART, a novel algorithm for non-linear top-K subset identification in bandit problems, achieving efficient performance...
The paper presents B3C, a novel approach to offline multi-agent reinforcement learning that addresses overestimation issues by integratin...
This article presents a novel framework for generating physically realistic dynamics in data-driven contexts by incorporating physical pr...
The paper presents MASAR, a novel framework for joint 3D detection and trajectory forecasting that enhances performance by integrating mo...
This paper explores nonparametric contextual online bilateral trade, presenting an algorithm that optimizes trade pricing based on contex...
The paper discusses 'Reliable Thinking with Images,' a method to enhance reasoning in Multi-modal Large Language Models (MLLMs) by addres...
This paper explores contextual online bilateral trade, focusing on how agents' valuations depend on context vectors. It presents algorith...
This paper explores the multi-objective linear bandit problem, revealing that multiple good arms can lead to implicit exploration, enhanc...
This paper presents a novel composable model-free reinforcement learning approach for navigation in dynamic environments, focusing on rea...
The paper presents ARMOR, a self-refining vision language model designed for robotic failure detection and reasoning, achieving significa...
This paper presents a novel Actor-Critic algorithm for risk-averse Multi-Agent Reinforcement Learning (MARL), demonstrating global conver...
This paper explores quantization-aware collaborative inference for large embodied AI models, addressing challenges in resource-limited en...
The paper introduces the Hierarchical Successor Representation (HSR), addressing limitations of classical successor representation in dyn...
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