arXiv AI
Jun 24
LemonHarness Technical Report
arXiv:2606. 24311v1 Announce Type: new Abstract: As large language model (LLM) agents are applied to longer tasks, they increasingly modify workspace state across multiple rounds of iteration.
AI agents
Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv Machine Learning
Jun 24
Managing Task Execution for Unknown Workloads in Batteryless IoT: A Hardware-Agnostic Evaluation
arXiv:2606. 24340v1 Announce Type: new Abstract: In recent years, the Internet of Things (IoT) paradigm has been shifting toward batteryless, energy-harvesting architectures.
arXiv AI
Jun 24
Governed Shared Memory for Multi-Agent LLM Systems
arXiv:2606. 24535v1 Announce Type: new Abstract: Multi-agent LLM environments require robust mechanisms for shared knowledge management.
arXiv AI
Jun 24
Grading the Grader: Lessons from Evaluating an Agentic Data Analysis System
arXiv:2606. 24839v1 Announce Type: new Abstract: Agentic data analysis systems produce rich outputs, including code, numerical results, and verbal diagnostics.
arXiv Machine Learning
Jun 24
Forget Without Compromise: Nexus Sampling for Streaming KV-Cache Eviction Under Fixed Budgets
arXiv:2606. 23961v1 Announce Type: new Abstract: Long-context and agentic LLM workloads push the KV cache past any fixed memory budget, forcing the inference stack to permanently evict tokens at every step of a continuous-inference stream.
arXiv AI
Jun 24
Beyond Bayer: Task-Optimal Sensor Co-Design for Robust Autonomous-Driving Segmentation
arXiv:2606. 24096v1 Announce Type: cross Abstract: Robust perception underpins autonomous driving, and most recent progress comes from scaling the model-larger backbones, foundation models, and cooperative multi-agent fusion.
arXiv AI
Jun 24
When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments
arXiv:2407. 18957v5 Announce Type: replace-cross Abstract: Can AI Agents simulate real-world trading environments to investigate the impact of external factors on stock trading activities (e.
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
Red-Teaming the Agentic Red-Team
arXiv:2606. 24496v1 Announce Type: cross Abstract: The use of agentic systems to perform offensive security operations has moved from a theoretical possibility to a commoditized capability.
arXiv Machine Learning
Jun 24
Debate2Create: Robot Co-design via Multi-Agent LLM Debate
arXiv:2510. 25850v3 Announce Type: replace-cross Abstract: We introduce Debate2Create (D2C), a multi-agent LLM framework that formulates robot co-design as structured, iterative debate grounded in physics-based evaluation.
arXiv AI
Jun 24
When Helpfulness Overrides Causal Caution: Context-Dependent Suppression and Recovery in LLMs
arXiv:2606. 24370v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into decision-support roles in business and policy contexts.
arXiv AI
Jun 24
NoContactNoWorries: Estimating Contact through Vision and Proprioception for In-Hand Dexterous Manipulation
arXiv:2606. 24450v1 Announce Type: cross Abstract: Perceiving physical contact is fundamental to dexterous manipulation.
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.