arXiv AI
Jun 24
Engineering Reliable Autonomous Systems: Challenges and Solutions
arXiv:2606. 23760v1 Announce Type: cross Abstract: Engineering reliable autonomous systems is an important and growing topic in computer science.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv AI
Jun 24
OpenThoughts-Agent: Data Recipes for Agentic Models
arXiv:2606. 24855v1 Announce Type: new Abstract: Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents.
arXiv AI
Jun 24
AutoSpec: Safety Rule Evolution for LLM Agents via Inductive Logic Programming
arXiv:2606. 24245v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly automate complex tasks by integrating language models with external tools and environments.
arXiv AI
Jun 24
BioMedArena: An Open-source Toolkit for Building and Evaluating Biomedical Deep Research Agents
arXiv:2605. 06177v2 Announce Type: replace Abstract: Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies across papers because the harness and tool registry differ, and integrating a new model into a comparable evaluation surface costs weeks of model-specific engineering.
Hugging Face Trending Papers
Jun 24
Lifelong In-Context Learning with Transformers Requires Parametric Forms of Attention
Lifelong continual learning remains an obstacle on the path to human-like intelligence. Modern transformers show sparks of intelligence with in-context learning.
Hugging Face Trending Papers
Jun 24
Stagnant Neuron: Towards Understanding the Plasticity Loss in Multi-Agent Reinforcement Learning Value Factorization Methods
Multi-Agent Reinforcement Learning (MARL) value factorization methods can suffer from a loss of plasticity, gradually failing to adapt when transferring to new task instances. We trace this issue to stagnant neurons, units whose gradient updates become negligibly small relative to their weights, thereby hindering learning.
Hugging Face Trending Papers
Jun 24
Decoupling Reconnaissance and Exploitation: Measuring the Capability Boundaries of LLM-Based Web Penetration Testing
Large Language Models (LLMs) have shown promise for automated penetration testing, yet existing end-to-end black-box evaluations are highly susceptible to error cascading: failures in early reconnaissance can mask an agent's actual ability to exploit vulnerabilities. To more accurately characterize these capabilities, we propose a two-stage decoupled evaluation framework that separates exploit execution from reconnaissance.
Towards Data Science
Jun 23
How to Create Powerful Loops in Claude Code
Learn about the concept of loops to power your coding agents. The post How to Create Powerful Loops in Claude Code appeared first on Towards Data Science .
Hugging Face Trending Papers
Jun 23
InSight: Self-Guided Skill Acquisition via Steerable VLAs
Vision-language-action (VLA) models can learn manipulation skills from demonstrations, but their capabilities are bounded by the skills in the training data. We present InSight, a framework that unlocks autonomous skill acquisition by rendering VLAs steerable at the primitive-action level (e.
Hugging Face Trending Papers
Jun 23
Paying to Know: Micro-Transaction Markets for Verified Product Information in Agentic E-Commerce
Commercial NLP treats the shopping chatbot as a recommender or a conversion tool: its job is to match a user to a catalogue entry and close a sale. We argue that the arrival of agent-native micro-payment rails (e.
Hugging Face Trending Papers
Jun 23
UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving
Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial precision. Models that rely on single-frame or low-resolution inputs often miss small, distant, or partially occluded hazards, while language-centric driving models frequently provide limited grounded evidence for their explanations.
Hugging Face Trending Papers
Jun 23
AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning
Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grounded reasoning: locating sparse evidence across a large, messy collection of workplace files, reconciling inconsistent terminology, units, and time conventions, and computing an answer.
Hugging Face Trending Papers
Jun 23
Red-Teaming the Agentic Red-Team
The use of agentic systems to perform offensive security operations has moved from a theoretical possibility to a commoditized capability. However, while the community has focused on creating more and more capable agents, less attention has been allocated to assessing the security of those systems.
Towards Data Science
Jun 23
Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode
From installing Ollama to launching OpenCode with a local model, step by step. The post Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode appeared first on Towards Data Science .
Hugging Face Trending Papers
Jun 23
ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling
Mixture-of-Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM agents into layered reasoning pipelines. However, existing MoA variants fail to sustain gains as depth increases, exhibiting degradation, early plateauing, or saturation.
Hugging Face Trending Papers
Jun 23
Detecting AI Coding Agents in Open Source: A Validated Multi-Method Census of 180 Million Repositories
Generative AI coding agents are entering the open-source supply chain, yet their diverse and often invisible traces leave their prevalence poorly understood. We introduce a multi-layered detection framework that integrates configuration-file scanning, commit-message analysis, author-identity matching, and bot-signature lookup across World of Code (180M+ Git repositories), classifying agent traces into four behavioral types.
Hugging Face Trending Papers
Jun 23
Escaping the Self-Confirmation Trap: An Execute-Distill-Verify Paradigm for Agentic Experience Learning
Experience-driven self-evolution is critical for large language model (LLM) agents to improve through open-world interaction. However, existing experience learning methods mostly rely on single-agent loops, where the same agent executes tasks, summarizes outcomes, and determines memory content.
Hugging Face Trending Papers
Jun 23
Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation
In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures. Sustaining reliable operation in these systems requires intelligent management of highly volatile stored energy.
Hugging Face Trending Papers
Jun 23
Deep Learning Approaches for 3D Medical Scene Completion: From Geometric Modeling to Generative Paradigms
Three-dimensional scene completion has evolved as a major problem in computer vision and robotics, and its applications are diverse, including autonomous navigation and augmented reality. In this study, a systematic review has been conducted to compile the research contributions made in the last ten years, i.
Hugging Face Trending Papers
Jun 23
An Introduction to Causal Reinforcement Learning
Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i. e.