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Llms

[R] GPT-5.4-mini regressed 22pp on vanilla prompting vs GPT-5-mini. Nobody noticed because benchmarks don't test this. Recursive Language Models solved it.

GPT-5.4-mini produces shorter, terser outputs by default. Vanilla accuracy dropped from 69.5% to 47.2% across 12 tasks (1,800 evals). The...

Reddit - Machine Learning · 1 min ·
Top 10 AI certifications and courses for 2026
Ai Startups

Top 10 AI certifications and courses for 2026

This article reviews the top 10 AI certifications and courses for 2026, highlighting their significance in a rapidly evolving field and t...

AI Events · 15 min ·
Hub Group Using AI, Machine Learning for Real-Time Visibility of Shipments
Machine Learning

Hub Group Using AI, Machine Learning for Real-Time Visibility of Shipments

Hub Group says it’s using artificial intelligence and machine learning to leverage data from its GPS-equipped container fleet to give cus...

AI Events · 4 min ·

All Content

[2603.25730] PackForcing: Short Video Training Suffices for Long Video Sampling and Long Context Inference
Machine Learning

[2603.25730] PackForcing: Short Video Training Suffices for Long Video Sampling and Long Context Inference

Abstract page for arXiv paper 2603.25730: PackForcing: Short Video Training Suffices for Long Video Sampling and Long Context Inference

arXiv - AI · 4 min ·
[2510.12453] Time-Correlated Video Bridge Matching
Machine Learning

[2510.12453] Time-Correlated Video Bridge Matching

Abstract page for arXiv paper 2510.12453: Time-Correlated Video Bridge Matching

arXiv - Machine Learning · 3 min ·
[2510.06790] Get RICH or Die Scaling: Profitably Trading Inference Compute for Robustness
Llms

[2510.06790] Get RICH or Die Scaling: Profitably Trading Inference Compute for Robustness

Abstract page for arXiv paper 2510.06790: Get RICH or Die Scaling: Profitably Trading Inference Compute for Robustness

arXiv - Machine Learning · 4 min ·
[2510.04900] Benchmarking M-LTSF: Frequency and Noise-Based Evaluation of Multivariate Long Time Series Forecasting Models
Machine Learning

[2510.04900] Benchmarking M-LTSF: Frequency and Noise-Based Evaluation of Multivariate Long Time Series Forecasting Models

Abstract page for arXiv paper 2510.04900: Benchmarking M-LTSF: Frequency and Noise-Based Evaluation of Multivariate Long Time Series Fore...

arXiv - Machine Learning · 4 min ·
[2603.25716] Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models
Machine Learning

[2603.25716] Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models

Abstract page for arXiv paper 2603.25716: Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models

arXiv - AI · 4 min ·
[2509.15199] CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairness
Machine Learning

[2509.15199] CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairness

Abstract page for arXiv paper 2509.15199: CausalPre: Scalable and Effective Data Pre-Processing for Causal Fairness

arXiv - Machine Learning · 4 min ·
[2508.09223] Hierarchical Adaptive networks with Task vectors for Test-Time Adaptation
Machine Learning

[2508.09223] Hierarchical Adaptive networks with Task vectors for Test-Time Adaptation

Abstract page for arXiv paper 2508.09223: Hierarchical Adaptive networks with Task vectors for Test-Time Adaptation

arXiv - AI · 4 min ·
[2509.08617] Towards Interpretable Deep Neural Networks for Tabular Data
Machine Learning

[2509.08617] Towards Interpretable Deep Neural Networks for Tabular Data

Abstract page for arXiv paper 2509.08617: Towards Interpretable Deep Neural Networks for Tabular Data

arXiv - Machine Learning · 3 min ·
[2603.25697] The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase
Llms

[2603.25697] The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase

Abstract page for arXiv paper 2603.25697: The Kitchen Loop: User-Spec-Driven Development for a Self-Evolving Codebase

arXiv - AI · 3 min ·
[2507.19737] Predicting Human Mobility during Extreme Events via LLM-Enhanced Cross-City Learning
Llms

[2507.19737] Predicting Human Mobility during Extreme Events via LLM-Enhanced Cross-City Learning

Abstract page for arXiv paper 2507.19737: Predicting Human Mobility during Extreme Events via LLM-Enhanced Cross-City Learning

arXiv - AI · 4 min ·
[2505.23004] QLIP: A Dynamic Quadtree Vision Prior Enhances MLLM Performance Without Retraining
Llms

[2505.23004] QLIP: A Dynamic Quadtree Vision Prior Enhances MLLM Performance Without Retraining

Abstract page for arXiv paper 2505.23004: QLIP: A Dynamic Quadtree Vision Prior Enhances MLLM Performance Without Retraining

arXiv - Machine Learning · 4 min ·
[2603.25646] A Mentalistic Interface for Probing Folk-Psychological Attribution to Non-Humanoid Robots
Llms

[2603.25646] A Mentalistic Interface for Probing Folk-Psychological Attribution to Non-Humanoid Robots

Abstract page for arXiv paper 2603.25646: A Mentalistic Interface for Probing Folk-Psychological Attribution to Non-Humanoid Robots

arXiv - AI · 3 min ·
[2411.17501] The Limits of Inference Scaling Through Resampling
Machine Learning

[2411.17501] The Limits of Inference Scaling Through Resampling

Abstract page for arXiv paper 2411.17501: The Limits of Inference Scaling Through Resampling

arXiv - AI · 4 min ·
[2603.25613] Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verification
Llms

[2603.25613] Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verification

Abstract page for arXiv paper 2603.25613: Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verif...

arXiv - AI · 4 min ·
[2410.21764] Adaptive Online Mirror Descent for Tchebycheff Scalarization in Multi-Objective Learning
Machine Learning

[2410.21764] Adaptive Online Mirror Descent for Tchebycheff Scalarization in Multi-Objective Learning

Abstract page for arXiv paper 2410.21764: Adaptive Online Mirror Descent for Tchebycheff Scalarization in Multi-Objective Learning

arXiv - AI · 4 min ·
[2603.25607] DeepFAN, a transformer-based deep learning model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multi-reader, multi-case trial
Machine Learning

[2603.25607] DeepFAN, a transformer-based deep learning model for human-artificial intelligence collaborative assessment of incidental pulmonary nodules in CT scans: a multi-reader, multi-case trial

Abstract page for arXiv paper 2603.25607: DeepFAN, a transformer-based deep learning model for human-artificial intelligence collaborativ...

arXiv - AI · 4 min ·
[2408.05696] SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction
Llms

[2408.05696] SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction

Abstract page for arXiv paper 2408.05696: SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction

arXiv - Machine Learning · 4 min ·
[2403.04545] Branch Scaling Manifests as Implicit Architectural Regularization for Improving Generalization in Overparameterized ResNets
Machine Learning

[2403.04545] Branch Scaling Manifests as Implicit Architectural Regularization for Improving Generalization in Overparameterized ResNets

Abstract page for arXiv paper 2403.04545: Branch Scaling Manifests as Implicit Architectural Regularization for Improving Generalization ...

arXiv - Machine Learning · 3 min ·
[2401.12546] On Building Myopic MPC Policies using Supervised Learning
Machine Learning

[2401.12546] On Building Myopic MPC Policies using Supervised Learning

Abstract page for arXiv paper 2401.12546: On Building Myopic MPC Policies using Supervised Learning

arXiv - Machine Learning · 4 min ·
[2603.25740] Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving
Machine Learning

[2603.25740] Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving

Abstract page for arXiv paper 2603.25740: Drive My Way: Preference Alignment of Vision-Language-Action Model for Personalized Driving

arXiv - AI · 4 min ·
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