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...
![[2602.20480] VINA: Variational Invertible Neural Architectures](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper presents VINA, a framework for Variational Invertible Neural Architectures, addressing theoretical gaps in normalizing flows an...
This article explores a hybrid dialogue system that integrates Large Language Models (LLMs) within a rule-based framework to enhance lear...
This article explores the differences between protein language models (PLMs) and natural language models, highlighting how these distinct...
This article explores the limitations of diversity in ideas generated by large language models (LLMs) compared to human creativity, ident...
The paper presents a case-aware evaluation framework for enterprise-scale Retrieval-Augmented Generation (RAG) systems, addressing the li...
This article presents GraSPNet, a novel hierarchical self-supervised learning framework for molecular representation that enhances graph ...
This article presents PhysMem, a memory framework that allows vision-language model planners to learn physical principles through interac...
The paper presents InterviewSim, a framework for simulating personalities using large language models grounded in real interview data, en...
This article examines the expectation-realisation gap in agentic AI systems, revealing discrepancies between anticipated productivity gai...
This paper explores design choices that enhance online reinforcement learning (RL) on physical robots, presenting findings from 100 train...
This paper proposes the 'Right to History,' a principle ensuring individuals have a verifiable record of AI agent actions on personal har...
CodeHacker is an automated framework designed to generate test cases that identify vulnerabilities in competitive programming solutions, ...
This paper explores the concept of 'Epistemic Debt' in novice programming using generative AI, proposing metacognitive scripts to enhance...
The paper presents OptimusVLA, a dual-memory framework for robotic manipulation that enhances efficiency and robustness in action generat...
The paper presents CalibRL, a hybrid-policy RLVR framework that enhances exploration in multi-modal reasoning tasks by balancing explorat...
The OpenPort Protocol introduces a governance-first approach for AI agents, ensuring secure access to application tools while addressing ...
This article presents a domain-specific large language model (LLM) designed to assist homeowners in making informed decisions about resid...
This study explores the use of Physics Informed Neural Networks (PINNs) to optimize coolant velocity for enhancing heat sink efficiency i...
This article explores the ownership rules surrounding AI-generated outputs, examining how they are linked to their creators and the impli...
This paper presents JurisMMA, a novel framework for Legal Judgment Prediction (LJP) that utilizes multimodal data to enhance the accuracy...
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