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Machine Learning

[P] Run Karpathy's Autoresearch for $0.44 instead of $24 — Open-source parallel evolution pipeline on SageMaker Spot

TL;DR: I built an open-source pipeline that runs Karpathy's autoresearch on SageMaker Spot instances — 25 autonomous ML experiments for $...

Reddit - Machine Learning · 1 min ·
ProCap Financial Acquires AI Agent Lab
Ai Agents

ProCap Financial Acquires AI Agent Lab

ProCap Financial, a leading financial services firm, has successfully acquired AI Agent Lab, a pioneering artificial intelligence company...

AI News - General · 4 min ·
When Agentic AI Browsers Outrun Governance
Ai Safety

When Agentic AI Browsers Outrun Governance

Agentic AI browsers introduce new enterprise risk. Learn how AI governance helps leaders assess exposure, oversight gaps, and safe adopti...

AI Tools & Products · 14 min ·

All Content

[2602.23132] From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation
Machine Learning

[2602.23132] From Agnostic to Specific: Latent Preference Diffusion for Multi-Behavior Sequential Recommendation

This paper presents FatsMB, a novel framework for Multi-Behavior Sequential Recommendation (MBSR) that enhances user preference modeling ...

arXiv - Machine Learning · 4 min ·
[2602.22752] Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction
Llms

[2602.22752] Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction

This article presents a study on the operational validity of using Large Language Models (LLMs) to simulate social media user behavior th...

arXiv - AI · 4 min ·
[2602.23061] MoDora: Tree-Based Semi-Structured Document Analysis System
Nlp

[2602.23061] MoDora: Tree-Based Semi-Structured Document Analysis System

MoDora is a novel LLM-powered system designed for analyzing semi-structured documents, addressing challenges in information retrieval and...

arXiv - Machine Learning · 4 min ·
[2602.22740] AMLRIS: Alignment-aware Masked Learning for Referring Image Segmentation
Machine Learning

[2602.22740] AMLRIS: Alignment-aware Masked Learning for Referring Image Segmentation

The paper presents AMLRIS, a novel training strategy for Referring Image Segmentation (RIS) that enhances object segmentation through ali...

arXiv - AI · 3 min ·
[2602.22735] Simulation-based Optimization for Augmented Reading
Machine Learning

[2602.22735] Simulation-based Optimization for Augmented Reading

This article presents a novel approach to augmented reading systems, proposing a simulation-based optimization framework that enhances te...

arXiv - AI · 3 min ·
[2602.22724] AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification
Llms

[2602.22724] AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification

AgentSentry introduces a novel framework to mitigate indirect prompt injection (IPI) in LLM agents, enhancing their security while mainta...

arXiv - AI · 4 min ·
[2602.22710] Same Words, Different Judgments: Modality Effects on Preference Alignment
Ai Safety

[2602.22710] Same Words, Different Judgments: Modality Effects on Preference Alignment

This study explores how modality affects preference alignment in AI systems, comparing human and synthetic evaluations of audio and text ...

arXiv - AI · 3 min ·
[2602.22698] Tokenization, Fusion and Decoupling: Bridging the Granularity Mismatch Between Large Language Models and Knowledge Graphs
Llms

[2602.22698] Tokenization, Fusion and Decoupling: Bridging the Granularity Mismatch Between Large Language Models and Knowledge Graphs

This paper presents KGT, a novel framework addressing the granularity mismatch between large language models (LLMs) and knowledge graphs ...

arXiv - AI · 4 min ·
[2602.22938] pMoE: Prompting Diverse Experts Together Wins More in Visual Adaptation
Machine Learning

[2602.22938] pMoE: Prompting Diverse Experts Together Wins More in Visual Adaptation

The paper presents pMoE, a novel Mixture-of-Experts prompt tuning method that enhances visual adaptation by integrating diverse domain kn...

arXiv - Machine Learning · 4 min ·
[2602.22925] Beyond NNGP: Large Deviations and Feature Learning in Bayesian Neural Networks
Machine Learning

[2602.22925] Beyond NNGP: Large Deviations and Feature Learning in Bayesian Neural Networks

This paper explores the behavior of wide Bayesian neural networks, focusing on rare fluctuations that influence posterior concentration b...

arXiv - Machine Learning · 3 min ·
[2602.22683] SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses
Llms

[2602.22683] SUPERGLASSES: Benchmarking Vision Language Models as Intelligent Agents for AI Smart Glasses

The paper introduces SUPERGLASSES, a benchmark for evaluating Vision Language Models (VLMs) in AI smart glasses, addressing the limitatio...

arXiv - AI · 4 min ·
[2602.22697] Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue
Llms

[2602.22697] Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue

The paper presents InteractCS-RL, a novel framework for enhancing task-oriented dialogue systems by balancing empathetic communication an...

arXiv - AI · 3 min ·
[2602.22903] PSQE: A Theoretical-Practical Approach to Pseudo Seed Quality Enhancement for Unsupervised MMEA
Llms

[2602.22903] PSQE: A Theoretical-Practical Approach to Pseudo Seed Quality Enhancement for Unsupervised MMEA

The paper presents PSQE, a method for enhancing pseudo seed quality in unsupervised multimodal entity alignment, addressing challenges in...

arXiv - Machine Learning · 4 min ·
[2602.22624] Instruction-based Image Editing with Planning, Reasoning, and Generation
Llms

[2602.22624] Instruction-based Image Editing with Planning, Reasoning, and Generation

This paper presents a novel approach to instruction-based image editing by integrating planning, reasoning, and generation through a mult...

arXiv - AI · 4 min ·
[2602.22884] Unsupervised Continual Learning for Amortized Bayesian Inference
Machine Learning

[2602.22884] Unsupervised Continual Learning for Amortized Bayesian Inference

This article presents a novel framework for Unsupervised Continual Learning in Amortized Bayesian Inference, addressing performance issue...

arXiv - Machine Learning · 3 min ·
[2602.22801] Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
Machine Learning

[2602.22801] Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving

This article explores the application of diffusion models in end-to-end autonomous driving, demonstrating their effectiveness through ext...

arXiv - Machine Learning · 4 min ·
[2602.22606] CoLyricist: Enhancing Lyric Writing with AI through Workflow-Aligned Support
Generative Ai

[2602.22606] CoLyricist: Enhancing Lyric Writing with AI through Workflow-Aligned Support

CoLyricist is an AI-assisted tool designed to enhance the lyric writing process by aligning with the common workflows of lyricists, impro...

arXiv - AI · 3 min ·
[2602.22786] QSIM: Mitigating Overestimation in Multi-Agent Reinforcement Learning via Action Similarity Weighted Q-Learning
Ai Agents

[2602.22786] QSIM: Mitigating Overestimation in Multi-Agent Reinforcement Learning via Action Similarity Weighted Q-Learning

The paper introduces QSIM, a novel framework that addresses the issue of Q-value overestimation in multi-agent reinforcement learning (MA...

arXiv - Machine Learning · 4 min ·
[2602.22630] HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioning
Robotics

[2602.22630] HyperKKL: Enabling Non-Autonomous State Estimation through Dynamic Weight Conditioning

The paper presents HyperKKL, a novel approach for designing KKL observers for non-autonomous nonlinear systems, leveraging hypernetwork a...

arXiv - Machine Learning · 3 min ·
[2602.22618] Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems
Machine Learning

[2602.22618] Advancing accelerator virtual beam diagnostics through latent evolution modeling: an integrated solution to forward, inverse, tuning, and UQ problems

This article presents a novel hybrid machine learning framework, Latent Evolution Model (LEM), for advancing virtual beam diagnostics in ...

arXiv - Machine Learning · 4 min ·
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