Meta introduced Omnilingual Automatic Speech Recognition (ASR), a suite of models providing ASR capabilities for over 1,600 languages, including 500 low-coverage languages never before served by any ASR system.

While most ASR systems focus on a limited set of languages that are well-represented on the internet, this release marks a major step toward building a truly universal transcription system.

They’re released a full suite of models and a dataset:

1. Omnilingual ASR: A suite of ASR models ranging from 300M to 7B parameters, supporting 1600+ languages

2. Omnilingual w2v 2.0: a 7B-parameter multilingual speech representation model that can be leveraged for other downstream speech-related tasks

3. Omnilingual ASR Corpus: a unique dataset spanning 350 underserved languages that was curated in collaboration with our global partners

Pleias released a fully synthetic generalist dataset for pretraining, SYNTH and two new SOTA reasoning models exclusively trained on it.

Despite having seen only 200 billion tokens, Baguettotron is currently best-in-class in its size range.

SYNTH is a radical departure from the classic pre-training recipe. At its core it’s an upsampling of Wikipedia 50,000 “vital” articles.

SYNTH is a collection of several synthetic playgrounds: data is not generated through simple prompts but by integrating smaller fine-tuned models into workflows with seeding, constraints, and formal verifications/checks.

Synthetic playgrounds enabled a series of controlled experiments that brought us to favor extreme depth design. Pleias selected a 80-layers architecture for Baguettotron, with improvements across the board on memorization of logical reasoning.

Along with Baguettotron Pleias released the smallest viable language model to date. Monad, a 56M transformer, trained on the English part of SYNTH with non-random performance on MMLU. Desiging Monad an engineering challenge requiring a custom tiny tokenizer.

Google has released a new "Introduction to Agents" guide, which discusses a "self-evolving" agentic system (Level 4).

"At this level, an agentic system can identify gaps in its own capabilities and create new tools or even new agents to fill them."

AELLA is an open-science initiative to make scientific research accessible via structured summaries created by LLMs

Available now:
- Dataset of 100K summaries
- 2 fine-tuned LLMs
- 3d visualizer.

This project spans many disciplines:
- bespoke model-training pipelines
- high-throughput inference systems
- protocols to ensure compute integrity and more.

Models.
Visualizer.

ByteDance launched Doubao-Seed-Code, a model specifically designed for programming tasks.

It supports native 256K long context and has claimed the top spot on the SWE-Bench Verified leaderboard.

A new paper from YANN LECUN. LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics. GitHub.

This could be one of LeCun's last papers at Meta (lol), but it's a really interesting one.

Quick summary:

Yann LeCun's big idea is JEPA, a self-supervised learning method. However, there are various failure modes of this approach, so training strong JEPA models is very brittle, unstable, and quite difficult. So overall JEPA has seen little adoption in practice.

This paper tries to directly address this, making specific design decisions that improve training stability.

The authors identify the isotropic Gaussian as the optimal distribution that JEPA models’ embeddings should follow and design the Sketched Isotropic Gaussian Regularization (SICReg) to constrain embeddings to reach that ideal distribution. This forms the LeJEPA framework, which can be implemented in ~50 lines of code.

On empirical tests, the authors demonstrate stability of training across hyperparameters, architectures, and datasets.

A result particularly interesting to me however is that training a LeJEPA model from scratch directly on the downstream dataset outperforms finetuning a DINOv2/v3 model on the dataset!

Japan’s first yen stablecoin issuer says stablecoin issuers could replace the Bank of Japan as major bond buyers.

Anthropic is investing $50B to build data centers in TX and NY, with sites coming online throughout 2026.

Last year Google’s AlphaProof & AlphaGeometry reached a key landmark in AI by achieving silver medal level performance at the International Math Olympiad.

Today, Nature is publishing the methodology behind agent AlphaProof.

Anthropic’s applied AI team with a great write up on improving Claude’s frontend design via Skills.

Also with a Claude Code plugin that packages up the skill.

New ByteDance + Yale + NYU + Tsinghua paper builds an LLM based agent called AlphaResearch that searches for new algorithms instead of reusing known ones.

For each problem, AlphaResearch first writes a natural language idea for an algorithm and then turns that idea into code.

The big deal is that this setup lets an LLM push actual mathematical records using a simple loop of scoring ideas and executing code, and the same loop could also search for better algorithms in many other domains.

A reward model trained on peer review data scores each idea and filters out the weakest ones before coding.

An execution engine then runs the code, checks all constraints, and reports a numeric performance score.

The agent loops over this process, sampling old attempts, tweaking ideas and programs, and keeping any version that improves the score.

To measure progress, the authors build a benchmark of 8 open ended algorithm problems with strong human baselines.

On this benchmark, AlphaResearch improves steadily and beats the best human constructions on 2 circle packing tasks, while still trailing people on the other 6.

Czech National Bank has announced the establishment of a pilot digital asset portfolio totaling $1 million, comprising Bitcoin, a USD stablecoin, and a tokenized deposit.

Approved on October 30, the initiative plans to share insights within the next 2–3 years.

The central bank reportedly maintains this is the first instance of a central bank including Bitcoin on its balance sheet.

Google introduced SIMA 2: an agent that plays, reasons, and learns with u in vrtual 3D Worlds

Powered by Gemini, it goes beyond following basic instructions to think, understand, and take actions in interactive environments – meaning you can talk to it through text, voice, or even images.

Google trained SIMA 2 to achieve high-level goals in a wide array of games – allowing it to perform complex reasoning and independently plan how to accomplish tasks.

It acts like a collaborative partner that can explain its intentions and answer questions about its behavior.

SIMA 2 is now far better at carrying out detailed instructions, even in worlds it's never seen before.

It can transfer learned concepts like “mining” in one game and apply it to “harvesting” in another – connecting the dots between similar tasks.

It even navigated unseen environments created in real-time by Genie 3 model.

SIMA 2 can teach itself new skills, learning through trial-and-error, based on feedback from Gemini. Getting better the more it plays –without additional human input.

SIMA 2 research offers a path towards applications in robotics and another step towards AGI in the real world.

OpenAI developed a new way to train small AI models with internal mechanisms that are easier for humans to understand.

Language models like the ones behind ChatGPT have complex, sometimes surprising structures, and we don’t yet fully understand how they work.

In a new research, team train “sparse” models—with fewer, simpler connections between neurons—to see whether their computations become easier to understand.

Efficient Self-Improving Agent Systems. AgentEvolver lets AI agents improve themselves instead of requiring manual prompt tuning.

They use three core mechanisms: self-questioning, self-navigating, and self-attributing.

Agents evaluate their own work, spot failures, and write better instructions for themselves.

This leads to a self-improvement loop capable of running without human oversight.

Shows better performance across benchmarks with less manual work.

The framework works by having agents evaluate their own performance on tasks, identify where they failed or underperformed, and then generate improved behavioral instructions for the next iteration.

The results are impressive.

Agents using this approach show measurable performance gains across diverse benchmarks compared to static configurations, all while reducing the overhead of constant manual optimization.

Google is working on multi-agent systems to help you refine ideas with tournament-like evaluation.

Each run takes around 40 minutes and brings you 100 detailed ideas on a given research topic.

2 new multi-agents are being developed for Gemini Enterprise:
- Idea Generation - "Create a multi-agent innovation session"
- Co-Scientist - "Drive novel scientific discovery with Co-Scientist"

Co-Scientist 3-step workflow 👀
- Tell Co-Scientist what you plan to research, point it to relevant data, and set your evaluation criteria.
- A team of agents will generate ideas on your topic using their available data
- The agents will evaluate the ideas against your criteria and rank them, tournament-style

Google is not only automating research but also preparing a product that will enable others to do so.

Android creator Andy Rubin is launching a new humanoid robotics startup, "Genki Robotics," in Tokyo.

The company is operating in stealth mode, tapping Japan's engineering talent to enter an already crowded field.

During his tenure at Google, Rubin spearheaded an ambitious robotics division, leading the acquisition of numerous startups in 2013, including the high-profile Japanese humanoid firm Shaft, a spin-off from the University of Tokyo.

His interest in legged locomotion, a core challenge in humanoid development, is well-documented. At a 2018 tech conference, Rubin, then leading the incubator Playground Global, predicted a future of "legs everywhere." He argued that legged systems are essential for navigating human-centric environments, such as climbing stairs or using elevators for "last-mile delivery"—tasks impossible for wheeled machines.

MIT and Oxford released their $2,500 agentic AI curriculum on GitHub at no cost.

15,000 people already paid for it.

It covers patterns, orchestration, memory, coordination, and deployment.
A strong roadmap to production ready systems.

Google DeepMind introduced WeatherNext 2 is most advanced system yet, able to generate more accurate and higher-resolution global forecasts.

The model’s improved performance is enabled by a new approach called a Functional Generative Network, which can generate the full range of possible forecasts in a single step.

Team added targeted randomness directly into the architecture, allowing it to explore a wide range of sensible weather scenarios.

MIT Introduced JiT (Just image Transformers)

JiTs are simple large-patch Transformers that operate on raw pixels, no tokenizer, pre-training, or extra losses needed.

By predicting clean data on the natural-data manifold, JiT excels in high-dimensional spaces where traditional noise-predicting models can fail.

On ImageNet (256 & 512), JiT achieves competitive generative performance, showing that sometimes going back to basics is the key.

GitHub.

Physical intelligence introduced a new model π*0.6

π*0.6 can more than double throughput over a base model trained without RL, and can perform real-world tasks: making espresso drinks, folding diverse laundry, and assembling boxes.

Team trained a general-purpose value function on all of own data, which tells the π*0.6 VLA which actions are good or bad. By asking π*0.6 to produce only good actions, researchers get better performance. Team call this method Recap.

π*0.6 can then collect more autonomous data, which can be used to further train the value function and further improve π*0.6.

During autonomous data collection, a teleoperator can also intervene and provide corrections for significant mistakes, coaching π*0.6 further.

Quantitatively, training π*0.6 with RL can more than double throughput (number of successful task executions per hour) on the hardest tasks and cut the number of failures by as much as a factor of two.