What if an LLM could choose its own reasoning path for each question? Enter CogER, a new framework inspired by human problem-solving. It acts like a smart dispatcher, first judging a query's difficulty, then dynamically picking the best reasoning strategy—from fast, internal
@jiqizhixin
-
Echo-N1: AI Learns Human Conversation Through Affective Reinforcement Learning
By
–
What if AI could learn the art of conversation like a human? This research challenges a year of focusing RL on logic, showing it’s possible to optimize AI for personality and emotional depth—and the results outperform leading models. Echo-N1: Affective RL Frontier Paper:
-
Light-X: Generative 4D Video Rendering Framework
By
–
-
LLaVA-UHD v3 Halves Processing Time With Progressive Visual Compression
By
–
Ever wonder why AI vision models are so slow? This research shows the quest for high-resolution image understanding creates a major speed bottleneck. The solution, LLaVA-UHD v3, uses a clever "Progressive Visual Compression" method to cut processing time by over half while
-
Foundation Model Transparency Index 2025 Shows Sharp Decline
By
–
The 2025 Foundation Model Transparency Index is here! This report reveals a sharp drop in openness, with average scores plummeting from 58 to 40. While IBM leads with 95, xAI and Midjourney scored just 14. – Google: 41
– OpenAI: 35
– DeepSeek: 32
– Qwen: 26 The 2025 -
Apple Research Advances Score Distillation for Flow Matching
By
–
Score Distillation of Flow Matching Models – Apple Machine Learning Research The University of Texas at Austin, Apple
Paper: https://
machinelearning.apple.com/research/score
-distillation
…
Page:
https://
yigu1008.github.io/SiD-DiT/ -
Apple Unifies Diffusion and Flow Matching for Faster Image Generation
By
–
What if you could speed up a cutting-edge AI image generator without retraining it from scratch? Apple presents a new method that unifies two major AI image models (diffusion & flow matching) under a simple statistical rule. This lets them directly apply a powerful
-
Canvas-to-Image: AI Generates Images from Your Imagination
By
–
What if you could paint your imagination directly onto a canvas and have an AI generate the exact image you envisioned?
— 机器之心 JIQIZHIXIN (@jiqizhixin) 17 décembre 2025
Canvas-to-Image does just that.
It unifies text, subject references, poses, and layouts into a single canvas, training a diffusion model to reason across… pic.twitter.com/0fEfJl42jDWhat if you could paint your imagination directly onto a canvas and have an AI generate the exact image you envisioned? Canvas-to-Image does just that. It unifies text, subject references, poses, and layouts into a single canvas, training a diffusion model to reason across
-
Shorter reasoning paths outperform longer ones in AI models
By
–
Ever wondered why large reasoning models sometimes overcomplicate problems? This study finds shorter reasoning paths consistently outperform longer ones across stochastic decodes, but exhaustive exploration of the tree-like reasoning space is impossible due to exponential
-
VQ-Insight: Progressive Learning for AI Video Quality Evaluation
By
–
How do we fix the messy state of AI video quality evaluation? Enter VQ-Insight: it uses progressive learning (starting with image quality, then adding temporal skills, and linking directly to generation models) plus multi-layered rewards to judge everything from frame