AI Agents

OpenClaw security checklist: practical safeguards for AI agents

Here is one of the better quality guides on the ensuring safety when deploying OpenClaw: https://chatgptguide.ai/openclaw-security-checkl...

Auto agent - Self improving domain expertise agent

someone opensource an ai agent that autonomously upgraded itself to #1 across multiple domains in < 24 hours…. then open sourced the e...

Walmart CEO reportedly brags that company's in-app AI agent is making people spend 35% more money

[2602.19022] An interpretable framework using foundation models for fish sex identification

The paper presents FishProtoNet, a non-invasive computer vision framework for accurately identifying the sex of delta smelt, an endangere...

[2602.18452] RA-QA: Towards Respiratory Audio-based Health Question Answering

The paper presents RA-QA, a novel dataset and benchmark for respiratory audio-based health question answering, addressing a critical gap ...

[2602.18450] Asymptotic Semantic Collapse in Hierarchical Optimization

The paper explores 'Asymptotic Semantic Collapse' in multi-agent language systems, where agents converge to a uniform behavior due to a d...

[2602.18962] NeuroWise: A Multi-Agent LLM "Glass-Box" System for Practicing Double-Empathy Communication with Autistic Partners

NeuroWise is a multi-agent LLM system designed to enhance double-empathy communication between neurotypical and autistic individuals, dem...

[2602.18929] Give Users the Wheel: Towards Promptable Recommendation Paradigm

This paper introduces the Decoupled Promptable Sequential Recommendation (DPR) framework, which enhances traditional recommendation syste...

[2602.18922] Why Agent Caching Fails and How to Fix It: Structured Intent Canonicalization with Few-Shot Learning

The paper discusses the limitations of current agent caching methods in AI, proposing a new framework, W5H2, that improves efficiency and...

[2602.20152] Behavior Learning (BL): Learning Hierarchical Optimization Structures from Data

The paper introduces Behavior Learning (BL), a machine learning framework that learns interpretable optimization structures from data, en...

[2602.18920] DeepInnovator: Triggering the Innovative Capabilities of LLMs

DeepInnovator proposes a novel training framework to enhance the innovative capabilities of Large Language Models (LLMs) for scientific r...

[2602.20132] LAD: Learning Advantage Distribution for Reasoning

The paper introduces Learning Advantage Distribution (LAD), a novel framework for improving reasoning in reinforcement learning by focusi...

[2602.18916] Adaptive Collaboration of Arena-Based Argumentative LLMs for Explainable and Contestable Legal Reasoning

The paper presents Adaptive Collaboration of Arena-Based Argumentative LLMs (ACAL), a framework designed for explainable and contestable ...

[2602.18915] AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting

The article presents AAVGen, a generative AI framework designed for the precise engineering of adeno-associated viral capsids, enhancing ...

[2602.20062] A Theory of How Pretraining Shapes Inductive Bias in Fine-Tuning

This paper presents a theoretical framework explaining how pretraining influences inductive bias during fine-tuning in machine learning, ...

[2602.18891] Orchestrating LLM Agents for Scientific Research: A Pilot Study of Multiple Choice Question (MCQ) Generation and Evaluation

This pilot study explores the orchestration of LLM agents in scientific research, focusing on the generation and evaluation of multiple-c...

[2602.19987] Counterfactual Understanding via Retrieval-aware Multimodal Modeling for Time-to-Event Survival Prediction

The paper presents CURE, a novel framework for counterfactual survival prediction that integrates multimodal data to enhance individualiz...

[2602.19980] Discrete Diffusion Models Exploit Asymmetry to Solve Lookahead Planning Tasks

This paper explores how Discrete Diffusion Models (NAR) outperform Autoregressive models (AR) in lookahead planning tasks by leveraging a...

[2602.18873] BiMotion: B-spline Motion for Text-guided Dynamic 3D Character Generation

BiMotion introduces a novel approach to dynamic 3D character generation using B-spline curves, enhancing motion quality and alignment wit...

[2602.19967] Unlearning Noise in PINNs: A Selective Pruning Framework for PDE Inverse Problems

The paper presents P-PINN, a selective pruning framework for enhancing the robustness of physics-informed neural networks (PINNs) against...

[2602.19956] Sparse Masked Attention Policies for Reliable Generalization

This paper presents a novel method for improving policy generalization in reinforcement learning by using a learned masking function inte...

[2602.18850] When the Inference Meets the Explicitness or Why Multimodality Can Make Us Forget About the Perfect Predictor

This paper explores the effectiveness of multimodal communication systems in human-robot collaboration, analyzing how explicit communicat...

[2602.18844] When Agda met Vampire

The paper discusses integrating proof assistants like Agda with automated theorem provers (ATPs) to enhance automation in mechanized math...