Apple just proposed the first unified visual tokenizer! They proposed ATOKEN which is the first tokenizer that jointly cover images, videos, and 3D assets in a single shared 4D latent/token space, matching performance with any other specialized tokenizers.
@askalphaxiv
-
Scaling Language Models with Infinite Compute via Ensemble Distillation
By
–
As data and compute scales differently, what do we do when we have infinite compute? To answer this, this paper shows you can still scale LMs by cranking up regularization, training many small clones on the same corpus, then ensemble → distill, netting 5x data-efficiency
-
Chrome Extension Enables Direct alphaXiv Access from arXiv Papers
By
–
Check out our Chrome extension here! It'll automatically allow you to navigate to alphaXiv for every arXiv paper.
-
Google Breakthrough in Fluid Dynamics Using Neural Networks
By
–
Google just made a huge fluid dynamics breakthrough They discovered new solutions to century-old problems where fluid motion would break unexpectedly creating “singularities” Their framework combines neural nets & high-precision optimization to uncover unstable singularities
-
Reverse-Engineering Thought Chains Improves LLM Reasoning Capabilities
By
–
Open-ended reasoning is one of the hardest problems in reasoning LLMs rn. So in this paper, they aim to solve this by reverse-engineering plausible thought chains from good answers via a gradient-free search With DeepWriter-8B trained on this data outperforming top OS models!
-
LLM Planning-Execution Hierarchy Emerges Through Credit Assignment
By
–
This new paper suggests that LLM ‘aha moments’ arise from an emergent planning-vs-execution hierarchy, similar to HRM’s slow-planner/fast-executor idea So they proposed HICRA which amplifies per-token credit on scarce planning tokens, focusing strategy & often beating GRPO!
-
ArXiv Research Paper on Machine Learning or Scientific Computing
By
–
https://
alphaxiv.org/pdf/2509.03646 -
Open-Ended Quality Diversity AI-Generating Algorithms Foundation Models
By
–
Excited to host @jeffclune this Friday in our AI4Science Community! Open-Ended, Quality Diversity, and AI-Generating Algorithms in the Era of Foundation Models Join in the link below!
-
Meta FAIR Releases New Test-Time Compute Scaling Method for LLMs
By
–
Meta FAIR just released a brand new test-time compute scaling method! With their new LLM solution aggregator trained with RLVR, they showed that majority voting or reward model ranking aren’t actually the most efficient. This simple yet robust method outperforms RM baselines.
-
StepWiser: Stepwise Generative Judges for Wiser Reasoning
By
–
What if we apply RL+Reasoning into PRMs? StepWiser: Stepwise Generative Judges for Wiser Reasoning STEPWISER trains a CoT-based judge that checks whether each “chunk-of-thought” helps solve the problem, producing better step labels and stronger search than discriminative PRMs