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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![[2510.23587] A Survey of Data Agents: Emerging Paradigm or Overstated Hype?](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This survey explores the concept of data agents, autonomous systems that manage complex data tasks. It introduces a hierarchical taxonomy...
The paper presents E-GRPO, a novel framework for training search agents using synthetic data, enhancing their ability to learn from near-...
This article evaluates the security of large language models (LLMs) used in AI agents, introducing a framework for identifying vulnerabil...
The paper proposes a scalable oversight framework for AI systems using partitioned human supervision, addressing challenges in obtaining ...
The paper presents RHYTHM, a framework utilizing hierarchical temporal tokenization to enhance human mobility predictions by leveraging l...
This paper presents an unsupervised method for compressing the policy parameter space in Deep Reinforcement Learning, enhancing sample ef...
The paper presents MCTD-ME, a novel approach combining Monte Carlo Tree Search and masked diffusion models for efficient protein design, ...
The K-Function framework enhances children's language evaluation by integrating precise phoneme transcription with LLM-driven scoring, im...
This paper presents a minimax-optimal algorithm for the multinomial logistic bandit problem, enhancing existing regret guarantees by leve...
This article presents a novel approach to active view selection (AVS) for 3D reconstruction using neural uncertainty maps, significantly ...
This paper presents a multimodal denoising diffusion model for synthesizing discrete-continuous quantum circuits, improving efficiency in...
The paper explores performance asymmetry in Model-Based Reinforcement Learning (MBRL), highlighting significant disparities in agent perf...
The paper presents MARVEL, a multi-agent framework utilizing Large Language Models for extracting vulnerabilities in RTL hardware designs...
HoloLLM introduces a Multimodal Large Language Model that enhances human sensing and reasoning by integrating diverse sensory inputs, out...
The paper presents a novel LiDAR-camera fusion framework for real-time 3D dynamic object detection and trajectory prediction, enhancing s...
The paper presents MoEMba, a novel framework utilizing Mamba-based Mixture of Experts for enhancing high-density EMG-based hand gesture r...
This article presents a statistical learning perspective on semi-dual adversarial neural optimal transport solvers, addressing theoretica...
This paper explores the use of Large Language Models (LLMs) for code optimization, proposing a problem-oriented approach and an anchor ve...
The paper presents STAR, a novel framework for transferring capabilities from large language models to super-tiny function calling models...
This article explores the potential of large language models (LLMs) to act as mediators in online conflicts, moving beyond moderation to ...
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