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Machine Learning

Built an open-source runtime layer to stop AI agents before they overspend or take risky actions — looking for feedback

If you’re experimenting with AI agents, you’ve probably run into this problem: once an agent starts calling tools, APIs, models, email sy...

Reddit - Artificial Intelligence · 1 min ·
Llms

Claude mythos preview GameJam contestant

Claude was able to create this Indie Game Jam Challenge with simple user guided prompts in the Godong engine with Mythos Preview with Zer...

Reddit - Artificial Intelligence · 1 min ·
Llms

I implemented meta paper [P]

github link : genji970/Scaling-Test-Time-Compute-for-Agentic-Coding-: paper implementation of Meta Ai paper link : https://arxiv.org/abs/...

Reddit - Machine Learning · 1 min ·

All Content

[2412.11308] From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detection
Machine Learning

[2412.11308] From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detection

Abstract page for arXiv paper 2412.11308: From XAI to MLOps: Explainable Concept Drift Detection with Profile Drift Detection

arXiv - Machine Learning · 4 min ·
[2411.02225] Sparse Max-Affine Regression
Machine Learning

[2411.02225] Sparse Max-Affine Regression

Abstract page for arXiv paper 2411.02225: Sparse Max-Affine Regression

arXiv - Machine Learning · 4 min ·
[2410.14826] SPRIG: Improving Large Language Model Performance by System Prompt Optimization
Llms

[2410.14826] SPRIG: Improving Large Language Model Performance by System Prompt Optimization

Abstract page for arXiv paper 2410.14826: SPRIG: Improving Large Language Model Performance by System Prompt Optimization

arXiv - AI · 4 min ·
[2403.12072] Floralens: a Deep Learning Model for the Portuguese Native Flora
Machine Learning

[2403.12072] Floralens: a Deep Learning Model for the Portuguese Native Flora

Abstract page for arXiv paper 2403.12072: Floralens: a Deep Learning Model for the Portuguese Native Flora

arXiv - Machine Learning · 4 min ·
[2302.08724] Piecewise Deterministic Markov Processes for Bayesian Neural Networks
Machine Learning

[2302.08724] Piecewise Deterministic Markov Processes for Bayesian Neural Networks

Abstract page for arXiv paper 2302.08724: Piecewise Deterministic Markov Processes for Bayesian Neural Networks

arXiv - Machine Learning · 3 min ·
[2302.00797] Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
Machine Learning

[2302.00797] Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning

Abstract page for arXiv paper 2302.00797: Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinfo...

arXiv - AI · 4 min ·
[2006.12024] Bayesian Neural Networks: An Introduction and Survey
Machine Learning

[2006.12024] Bayesian Neural Networks: An Introduction and Survey

Abstract page for arXiv paper 2006.12024: Bayesian Neural Networks: An Introduction and Survey

arXiv - Machine Learning · 3 min ·
[2603.29086] Realistic Market Impact Modeling for Reinforcement Learning Trading Environments
Machine Learning

[2603.29086] Realistic Market Impact Modeling for Reinforcement Learning Trading Environments

Abstract page for arXiv paper 2603.29086: Realistic Market Impact Modeling for Reinforcement Learning Trading Environments

arXiv - Machine Learning · 4 min ·
[2603.28942] ReproMIA: A Comprehensive Analysis of Model Reprogramming for Proactive Membership Inference Attacks
Machine Learning

[2603.28942] ReproMIA: A Comprehensive Analysis of Model Reprogramming for Proactive Membership Inference Attacks

Abstract page for arXiv paper 2603.28942: ReproMIA: A Comprehensive Analysis of Model Reprogramming for Proactive Membership Inference At...

arXiv - Machine Learning · 4 min ·
[2603.13285] Brittlebench: Quantifying LLM robustness via prompt sensitivity
Llms

[2603.13285] Brittlebench: Quantifying LLM robustness via prompt sensitivity

Abstract page for arXiv paper 2603.13285: Brittlebench: Quantifying LLM robustness via prompt sensitivity

arXiv - AI · 4 min ·
[2603.11321] Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings
Machine Learning

[2603.11321] Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings

Abstract page for arXiv paper 2603.11321: Hindsight-Anchored Policy Optimization: Turning Failure into Feedback in Sparse Reward Settings

arXiv - AI · 3 min ·
[2603.10742] A Grammar of Machine Learning Workflows
Machine Learning

[2603.10742] A Grammar of Machine Learning Workflows

Abstract page for arXiv paper 2603.10742: A Grammar of Machine Learning Workflows

arXiv - Machine Learning · 3 min ·
[2603.06977] NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning
Machine Learning

[2603.06977] NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning

Abstract page for arXiv paper 2603.06977: NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning

arXiv - AI · 4 min ·
[2602.04448] RASA: Routing-Aware Safety Alignment for Mixture-of-Experts Models
Llms

[2602.04448] RASA: Routing-Aware Safety Alignment for Mixture-of-Experts Models

Abstract page for arXiv paper 2602.04448: RASA: Routing-Aware Safety Alignment for Mixture-of-Experts Models

arXiv - AI · 3 min ·
[2602.01554] InfoTok: Information-Theoretic Regularization for Capacity-Constrained Shared Visual Tokenization in Unified MLLMs
Llms

[2602.01554] InfoTok: Information-Theoretic Regularization for Capacity-Constrained Shared Visual Tokenization in Unified MLLMs

Abstract page for arXiv paper 2602.01554: InfoTok: Information-Theoretic Regularization for Capacity-Constrained Shared Visual Tokenizati...

arXiv - AI · 4 min ·
[2601.11609] Auxiliary-predicted Compress Memory Model(ApCM Model): A Neural Memory Storage Model Based on Invertible Compression and Learnable Prediction
Llms

[2601.11609] Auxiliary-predicted Compress Memory Model(ApCM Model): A Neural Memory Storage Model Based on Invertible Compression and Learnable Prediction

Abstract page for arXiv paper 2601.11609: Auxiliary-predicted Compress Memory Model(ApCM Model): A Neural Memory Storage Model Based on I...

arXiv - Machine Learning · 3 min ·
[2601.10940] HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Training
Llms

[2601.10940] HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Training

Abstract page for arXiv paper 2601.10940: HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Training

arXiv - Machine Learning · 4 min ·
[2601.06597] Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective
Machine Learning

[2601.06597] Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective

Abstract page for arXiv paper 2601.06597: Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective

arXiv - Machine Learning · 4 min ·
[2601.03484] From Bits to Chips: An LLM-based Hardware-Aware Quantization Agent for Streamlined Deployment of LLMs
Llms

[2601.03484] From Bits to Chips: An LLM-based Hardware-Aware Quantization Agent for Streamlined Deployment of LLMs

Abstract page for arXiv paper 2601.03484: From Bits to Chips: An LLM-based Hardware-Aware Quantization Agent for Streamlined Deployment o...

arXiv - Machine Learning · 4 min ·
[2601.01162] Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models
Llms

[2601.01162] Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models

Abstract page for arXiv paper 2601.01162: Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models

arXiv - AI · 4 min ·
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