Autonomous agents, tool use, and agentic systems
NeuBird AI, a San Francisco-based artificial intelligence company, has raised $19.3 million in funding to scale its agentic AI technology...
CodeGraphContext- the go to solution for graph-code indexing 🎉🎉... It's an MCP server that understands a codebase as a graph, not chunks ...
And I know some of yall doubt - so I’ll follow up. submitted by /u/Snoo-76697 [link] [comments]
![[2602.17312] LexiSafe: Offline Safe Reinforcement Learning with Lexicographic Safety-Reward Hierarchy](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper presents LexiSafe, a novel offline safe reinforcement learning framework that employs a lexicographic safety-reward hierarchy t...
The paper introduces RLGT, a novel reinforcement learning framework designed for extremal graph theory, enhancing the application of RL i...
This article discusses the challenges of multi-expert learning in machine learning, highlighting how underfitting can occur when multiple...
This paper explores online learning models using improving agents, focusing on multiclass setups, budgeted agents, and bandit learners, e...
This paper introduces STDSH-MARL, a novel framework for human-centric traffic signal control that enhances multimodal transportation effi...
The paper introduces WS-GRPO, a method for improving rollout efficiency in language model training by providing correctness-aware guidanc...
This paper establishes a connection between pathwise and score-function gradient estimators in stochastic backpropagation, introducing a ...
The paper introduces Action Graph Policies (AGP) for multi-agent reinforcement learning, emphasizing the importance of action co-dependen...
The paper presents a novel approach to the multi-agent Lipschitz bandit problem, proposing a communication-free policy that maximizes col...
The paper presents Neuro-Symbolic Graph Generative Modeling (NSGGM), a framework that enhances molecule generation by integrating symboli...
This paper analyzes how two-layer neural networks learn to solve the modular addition task, providing insights into feature learning, tra...
This paper presents a residual-aware theory explaining the position bias in Transformers, revealing how residual connections prevent atte...
This article presents a novel approach to automated circuit discovery in neural networks, emphasizing provable guarantees for robustness ...
The paper presents a novel inference pipeline that leverages off-the-shelf models to solve International Mathematical Olympiad problems e...
This paper introduces Double Counterfactual Consistency (DCC), a method for evaluating and enhancing causal reasoning in large language m...
MolmoSpaces introduces a large-scale open ecosystem designed for benchmarking robot navigation and manipulation, featuring over 230k dive...
This paper analyzes DARPA's AI Cyber Challenge (AIxCC), focusing on competition design, architectural approaches of finalists, and key le...
The paper presents Di3PO, a novel method for improving image generation in text-to-image diffusion models by efficiently creating targete...
This paper explores the relationship between fixed-budget and fixed-confidence settings in best-arm identification, demonstrating that th...
This study audits the collaboration between online graduate CS students and AI, exploring preferences for automation in academic tasks an...
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