Autonomous agents, tool use, and agentic systems
Measured the actual token waste on a local Qwen 3.5 122B setup. The numbers are unreal. Found a compile-time approach that cuts query con...
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...
![[2306.00554] ShaRP: Shape-Regularized Multidimensional Projections](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper introduces ShaRP, a novel projection technique for dimensionality reduction that allows users to control the visual signature o...
The paper presents Aletheia, an autonomous mathematics research agent that successfully solved 6 out of 10 problems in the FirstProof cha...
The paper presents NoRD, a data-efficient Vision-Language-Action model that enhances autonomous driving without requiring extensive datas...
The paper introduces DEEPSYNTH, a benchmark for evaluating large language models on complex tasks requiring deep information synthesis an...
This paper introduces the Initial Exploration Problem (IEP) in Knowledge Graphs, highlighting barriers faced by users during their first ...
The paper presents a novel training paradigm for AI that integrates concepts from affective neuroscience, focusing on a dual-model framew...
LogicGraph introduces a benchmark for evaluating multi-path logical reasoning in large language models, highlighting their limitations in...
The paper explores how Large Language Models (LLMs) can achieve superintelligence through the Diligent Learner framework, emphasizing the...
The paper introduces AgentOS, a conceptual framework that transitions Large Language Models from static inference engines to dynamic cogn...
This article presents the HELP framework, which enhances Retrieval-Augmented Generation (RAG) by addressing knowledge boundaries and hall...
This paper presents a novel evaluation framework for assessing the alignment of language models under realistic pressure, revealing behav...
POMDPPlanners is an open-source Python package designed for the empirical evaluation of POMDP planning algorithms, integrating advanced f...
The paper introduces PyVision-RL, a reinforcement learning framework designed to enhance agentic multimodal models by preventing interact...
This paper explores the use of reinforcement learning from AI feedback (RLAIF) to balance multiple objectives in urban traffic control, a...
The paper presents MAGNET, a novel multimodal recommendation framework that utilizes a mixture of adaptive graph experts and entropy-trig...
This paper introduces Batch Adaptation Policy Optimization (BAPO), an off-policy reinforcement learning framework designed to enhance dat...
The paper introduces ICON, a novel framework designed to defend Large Language Model (LLM) agents against Indirect Prompt Injection (IPI)...
This paper presents a novel model for online decision-making called Online Algorithms with Unreliable Guidance (OAG), which separates pre...
This article discusses the limitations of current benchmarks for vision-language model (VLM)-driven embodied agents and introduces Native...
The paper presents EmbodiedAct, a framework that enhances Large Language Models (LLMs) by grounding them in embodied actions for scientif...
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