Autonomous agents, tool use, and agentic systems
I’ve been seeing more people talk about TeraBox lately, especially around storage for AI-related workflows. Curious if anyone here has us...
We have been exploring a project around post-training infrastructure, a minimalist tool that does one thing really well: Make post-traini...
Unlike static, rules-based systems, AI agents can learn, adapt, and optimize processes dynamically. As they interact with data, systems, ...
![[2602.17221] From Labor to Collaboration: A Methodological Experiment Using AI Agents to Augment Research Perspectives in Taiwan's Humanities and Social Sciences](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This article explores a methodological experiment using AI agents to enhance research in Taiwan's humanities and social sciences, proposi...
This paper presents a framework for online construction of symbolic causal world models, enhancing agents' decision-making through contin...
The paper presents Texo, a compact formula recognition model with 20 million parameters, achieving high performance comparable to larger ...
The paper introduces JEPA-DNA, a novel framework for genomic foundation models that enhances predictive capabilities by integrating joint...
This article presents a new parallel algorithm designed to decompose complex CircuitSAT instances, enhancing efficiency in solving SAT pr...
This article presents InstructKG, a framework for creating instructor-aligned knowledge graphs that enhance personalized learning by mapp...
The paper explores the evolution of 6G wireless communication, emphasizing the shift towards intent-aware, autonomous systems that adapt ...
This study analyzes how AI coding agents create pull request descriptions and how human reviewers respond, revealing distinct styles and ...
This paper presents Successive Sub-value Q-learning (S2Q), a novel approach in multi-agent reinforcement learning (MARL) that retains sub...
The paper presents IntentCUA, a framework for multi-agent planning in computer-use agents, focusing on intent-level representations to en...
The paper presents Instruction-Tool Retrieval (ITR), a method that optimizes the operation of Large Language Model (LLM) agents by dynami...
The paper presents a novel Phase-Aware Mixture of Experts (PA-MoE) architecture for reinforcement learning, addressing the limitations of...
The paper presents M2F, an innovative framework for the automated formalization of mathematical literature, enabling project-scale conver...
The paper presents the Sales Research Agent, an AI tool in Microsoft Dynamics 365 Sales, designed to provide insights from live CRM data....
The paper introduces Cinder, a two-stage matchmaking system designed to enhance fairness and speed in multiplayer online games by utilizi...
Sonar-TS introduces a neuro-symbolic framework for natural language querying of time series databases, addressing limitations of existing...
The paper introduces Conv-FinRe, a benchmark for evaluating financial recommendation systems that emphasizes utility-grounded decision-ma...
This paper presents Phantom, an automated framework for agent hijacking via Structural Template Injection, enhancing attack success rates...
This paper explores the limitations of black-box safety evaluations in AI systems, highlighting the challenges posed by latent context co...
The paper presents LLM4Cov, an offline learning framework for high-coverage testbench generation, addressing challenges in hardware verif...
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