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There Will Be a Scientific Theory of Deep Learning
High-fidelity collisional quantum gates with fermionic atoms
The Last Human-Written Paper: Agent-Native Research Artifacts
Co-Evolving Policy Distillation
Representation Fréchet Loss for Visual Generation
Recursive Multi-Agent Systems
« De nos jours, ce sont les résultats qui comptent » : création et étude diachronique d'un corpus de revendications issues d'articles de TAL
Toward Scalable Terminal Task Synthesis via Skill Graphs
DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training
HVI-CIDNet+: Beyond Extreme Darkness for Low-Light Image Enhancement
BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
GPU Memory Prediction for Multimodal Model Training
Human-LLM Coevolution: Evidence from Academic Writing
High-fidelity collisional quantum gates with fermionic atoms
High-fidelity collisional quantum gates with fermionic atoms
A Theory of Generalization in Deep Learning
Uni-OPD: Unifying On-Policy Distillation with a Dual-Perspective Recipe
The Last Harness You'll Ever Build
Distribution-Guided Policy Optimization for Fine-Grained Credit Assignment
Shape anisotropy governs organization of active rods: Swarming, turbulence, flocking, and jamming.
Can Coding Agents Reproduce Findings in Computational Materials Science?
Model Spec Midtraining: Improving How Alignment Training Generalizes
From food waste to value‐added surplus products (VASP): Consumer acceptance of a novel food product category
GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents GLM-5V-Turbo Team
Model Spec Midtraining: Improving How Alignment Training Generalizes
Shape anisotropy governs organization of active rods: Swarming, turbulence, flocking, and jamming.
Action Reasoning Models for Real-World Deployment Haoquan Fang
On Training Large Language Models for Long-Horizon Tasks: An Empirical Study of Horizon Length
AI and Machine Learning in Geospatial Analysis: Advances, Biases, and Future Directions (2020–2026)