Autonomous agents, tool use, and agentic systems
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]
It is frustrating to use the Wandb CLI and MCP tools with my agents. For one, the MCP tool basically floods the context window and freque...
![[2407.01566] A Parametric Contextual Online Learning Theory of Brokerage](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper presents a parametric contextual online learning theory focused on brokerage, where brokers suggest trading prices based on tr...
This paper presents an online robust reinforcement learning (DR-RL) algorithm that utilizes general function approximation, enabling robu...
The paper presents a novel method, Entropy After </Think> (EAT), to optimize reasoning in LLMs by reducing unnecessary computations while...
This paper presents a Continuous-Time Multi-Agent Reinforcement Learning (CT-MARL) framework that enhances value iteration using physics-...
This paper explores two-player zero-sum games using bandit feedback, proposing algorithms that adapt existing frameworks to optimize acti...
This paper presents algorithms for nearly-optimal bandit learning in Stackelberg games, achieving improved regret rates and extending app...
The paper presents a novel approach to continual learning in machine learning models, introducing a parameter-efficient fine-tuning modul...
The paper presents algorithms for gradient testing and estimation using a comparison oracle, optimizing query efficiency for smooth funct...
The paper presents a two-stage framework called 'Mine and Refine' for optimizing graded relevance in e-commerce search retrieval, enhanci...
This paper explores dynamic decision-making under model misspecification, focusing on Thompson Sampling (TS) in Bayesian reinforcement le...
This paper presents a simplified transformer architecture tailored for small longitudinal cohort data, enhancing predictive performance w...
The paper presents Hybrid-Gym, a training environment designed to enhance coding agents' ability to generalize across various software en...
The paper presents SEMAS, a self-evolving multi-agent network designed for predictive maintenance in Industrial IoT, enhancing real-time ...
The paper discusses a framework for multi-round human-AI collaboration, emphasizing user-specified requirements to enhance decision quali...
This article explores the geometric relationships between independently trained multimodal contrastive models, revealing that an orthogon...
The paper presents a novel approach called Retrospective In-Context Learning (RICL) for enhancing temporal credit assignment in reinforce...
This paper explores the linear convergence of the Weighted Optimistic Gradient Descent-Ascent algorithm in multi-agent games with delayed...
The paper presents a novel grey-box method that integrates incomplete physics models into deep generative models, enabling the learning o...
This article presents a novel approach to episodic Markov decision process (MDP) planning by framing it as Bayesian inference over polici...
The paper presents LexiSafe, a novel offline safe reinforcement learning framework that employs a lexicographic safety-reward hierarchy t...
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