AI Agents

Boston's CIO wants the public — and other city governments — to use his open-source agentic AI tools

[P] If you're building AI agents, logs aren't enough. You need evidence.

I have built a programmable governance layer for AI agents. I am considering to open source completely. Looking for feedback. Agent demos...

[2602.00185] QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities

Abstract page for arXiv paper 2602.00185: QUASAR: A Universal Autonomous System for Atomistic Simulation and a Benchmark of Its Capabilities

[2602.16113] Evolutionary Context Search for Automated Skill Acquisition

The paper presents Evolutionary Context Search (ECS), a novel method for automated skill acquisition in large language models, enhancing ...

[2602.16069] The Limits of Long-Context Reasoning in Automated Bug Fixing

This paper evaluates the limitations of long-context reasoning in automated bug fixing using large language models (LLMs), revealing sign...

[2602.16063] MARLEM: A Multi-Agent Reinforcement Learning Simulation Framework for Implicit Cooperation in Decentralized Local Energy Markets

The paper presents MARLEM, a novel multi-agent reinforcement learning framework designed for studying implicit cooperation in decentraliz...

[2602.16194] Temporal Panel Selection in Ongoing Citizens' Assemblies

This paper presents a framework for temporal panel selection in ongoing citizens' assemblies, ensuring proportional representation and in...

[2602.16062] Harnessing Implicit Cooperation: A Multi-Agent Reinforcement Learning Approach Towards Decentralized Local Energy Markets

This paper presents a framework for decentralized local energy markets using implicit cooperation among agents, optimizing coordination w...

[2602.16189] Beyond Learning: A Training-Free Alternative to Model Adaptation

The paper presents a novel approach to model adaptation in language models, introducing a training-free method that utilizes internal mod...

[2602.16061] Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models

This paper presents a novel framework for partial identification of population quantities under missing data, utilizing weak shadow varia...

[2602.16177] Conjugate Learning Theory: Uncovering the Mechanisms of Trainability and Generalization in Deep Neural Networks

This paper introduces Conjugate Learning Theory, exploring trainability and generalization in deep neural networks through a novel theore...

[2602.16038] Heuristic Search as Language-Guided Program Optimization

The paper presents a structured framework for Language-Guided Program Optimization, improving Automated Heuristic Design in combinatorial...

[2602.16140] Human-AI Collaboration in Large Language Model-Integrated Building Energy Management Systems: The Role of User Domain Knowledge and AI Literacy

This study explores how user domain knowledge and AI literacy influence the effectiveness of human-AI interactions in building energy man...

[2602.15951] MadEvolve: Evolutionary Optimization of Cosmological Algorithms with Large Language Models

The paper presents MadEvolve, a framework for optimizing cosmological algorithms using large language models, demonstrating significant i...

[2602.16111] Surrogate-Based Prevalence Measurement for Large-Scale A/B Testing

The paper presents a scalable framework for measuring content prevalence in large-scale A/B testing, decoupling expensive labeling from e...

[2602.15950] Can Vision-Language Models See Squares? Text-Recognition Mediates Spatial Reasoning Across Three Model Families

This article investigates the limitations of vision-language models (VLMs) in spatial reasoning, particularly their struggle to localize ...

[2602.16110] OmniCT: Towards a Unified Slice-Volume LVLM for Comprehensive CT Analysis

The paper presents OmniCT, a unified slice-volume large vision-language model (LVLM) designed for comprehensive CT analysis, addressing l...

[2602.16109] Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

The paper presents FedGraph-AGI, a federated learning framework designed to enhance cross-border insider threat detection in government f...

[2602.15926] A Study on Real-time Object Detection using Deep Learning

This article explores real-time object detection using deep learning, detailing various algorithms, applications, and future research dir...

[2602.16093] Updating Parametric Knowledge with Context Distillation Retains Post-Training Capabilities

The paper introduces a novel approach called Distillation via Split Contexts (DiSC) for continual knowledge adaptation in large language ...

[2602.15922] World Action Models are Zero-shot Policies

The paper introduces DreamZero, a World Action Model (WAM) that enhances zero-shot policy learning for robotic tasks by predicting future...

[2602.15914] Steering Dynamical Regimes of Diffusion Models by Breaking Detailed Balance

This paper explores how breaking detailed balance in generative diffusion processes can enhance reverse processes while maintaining stati...

[2602.15893] Statistical-Geometric Degeneracy in UAV Search: A Physics-Aware Asymmetric Filtering Approach

This article presents a novel approach to UAV search operations in post-disaster scenarios, addressing the challenges posed by Non-Line-o...