Autonomous agents, tool use, and agentic systems
The viral AI agentic tool let attackers silently gain admin unauthenticated access.
Ran an experiment — gave AI agents full control over writing, character creation, and performing a sitcom. Left it running nonstop for ov...
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![[2511.23055] MindPower: Enabling Theory-of-Mind Reasoning in VLM-based Embodied Agents](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper presents MindPower, a framework that enhances embodied agents' decision-making by integrating Theory of Mind (ToM) reasoning, o...
The article introduces NewtonBench, a new benchmark for evaluating large language models (LLMs) in scientific law discovery, addressing k...
The paper introduces BioX-Bridge, a framework for unsupervised cross-modal knowledge transfer in biosignals, enhancing model efficiency w...
This article presents a framework for evaluating AI agent behavior through consumer choice experiments, highlighting biases in decision-m...
The paper introduces DS-STAR, a data science agent designed to automate complex workflows by integrating diverse data formats and generat...
The paper presents HybridDeepSearcher, a novel approach that enhances search reasoning by integrating parallel query expansion with struc...
The paper presents TASER, a system designed for schema-guided extraction and recommendation from complex financial tables, improving data...
The article presents AutoEDA, a framework that utilizes microservice-based LLM agents to automate Electronic Design Automation (EDA) proc...
This paper presents a novel method for enabling large language models (LLMs) to control embodied agents through iterative policy refineme...
This survey reviews optimization techniques for Large Language Model (LLM)-based agents, categorizing methods into parameter-driven and p...
This paper explores the concept of Test-Time Training (TTT) with KV binding, revealing that it functions as learned linear attention rath...
This article presents a novel approach called Reflective Test-Time Planning for embodied LLMs, enabling robots to learn from mistakes thr...
This paper presents an efficient hierarchical approach for any-angle path planning on multi-resolution 3D grids, addressing scalability i...
The paper presents SparkMe, a multi-agent LLM system designed for adaptive semi-structured interviewing, enhancing qualitative data colle...
This study investigates human vulnerability to deception by large language model (LLM) agents, revealing significant trust issues in high...
The paper explores advancements in multi-agent reinforcement learning for training cooperative and competitive robots, introducing a nove...
The paper presents Localized Dynamics-Aware Domain Adaptation (LoDADA) for off-dynamics offline reinforcement learning, enhancing data se...
This article explores the relationship between graph topology and activation patterns in Graph Neural Networks (GNNs), revealing insights...
The paper presents a novel kernelized self-attention mechanism designed to enhance next-item recommendations by improving the representat...
The paper presents CrystaL, a novel framework for Multimodal Large Language Models (MLLMs) that enhances visual understanding by crystall...
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