MACHINE LEARNING

–

11 January 2026 2h31

What if the key to spotting fake audio isn't a better algorithm, but smarter data? Researchers from SJTU present a data-centric breakthrough. Instead of just building a bigger model, they focus on optimizing which training data to use, prioritizing diversity of sources and AI

→ View original post on X — @terence_mills

11 January 2026

Intelligence saturation and the future of work implications

By

@terence_mills

–

11 January 2026 2h00

(Artificial) Intelligence saturation and the future of work
#AI #AIio #AIInnovation #ML #DataScience #Futureofwork @timnitgebru @oriolvinyalsml @ceobillionaire @soumithchintala @waitin4agi_ @sallyeaves @bernardmarr

11 January 2026

RoboTracer: Vision-Language Model for Precise Robot Task Execution

By

–

10 January 2026 20h28

What if a robot could understand "water the flowers from left to right" and precisely measure the height to hover above each one? A team from Beihang University, Peking University, and the Beijing Academy of AI presents RoboTracer. It's a new vision-language model that gives

→ View original post on X — @miketamir

MiroThinker: Scaling Open-Source Research Agents Performance

By

@miketamir

–

10 January 2026 18h08

MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling https://
buff.ly/jXip8ln
#AI #MachineLearning #DeepLearning #LLMs #DataScience

→ View original post on X — @askalphaxiv

Epiplexity: Measuring Learnable Structure for Bounded Intelligence

By

@askalphaxiv

–

10 January 2026 15h45

Now Trending on alphaXiv From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence This paper introduces epiplexity, a way to measure how much learnable & reusable structure a computationally limited model can extract from data This

Jacobi Forcing: Generate Multiple Tokens in Parallel Without Quality Loss

By

–

10 January 2026 15h04

What if you could generate 4-5 tokens at once without losing quality? Researchers from UC San Diego, SJTU, and Snowflake introduce "Jacobi Forcing." It smoothly converts standard autoregressive models into parallel decoders by training them on their own predicted token

→ View original post on X — @ingliguori

9 Essential AI Skills for 2026: From Prompting to System Design

By

@ingliguori

–

10 January 2026 14h17

9 AI skills that will matter in 2026 Prompting • Agents • RAG • Automation • Tool stacking • Multimodal • Fine-tuning • Evaluation Using AI is table stakes.
Designing AI systems is the edge. Credit: @rathanuday #AI #GenAI #FutureOfWork #AIAgents

UniCardio AI Model Cleans Noisy Heart Signal Data

By

–

10 January 2026 11h01

This AI could fix messy heart data and even generate missing signals. UniCardio is a unified model that uses the relationships between heart signals to clean up noisy wearables, fill in gaps, and translate between signal types. It beats specialized models in denoising &

Digital Red Queen: AIs Evolve Through Competitive Self-Play

By

–

10 January 2026 3h49

Sakana makes AIs "hunt" each other, and they begin to converge in evolution

Researchers from MIT & Sakana AI present "Digital Red Queen."

They use LLMs in a self-play loop to evolve "warrior" programs that compete in Core War, a classic programming game. Each new warrior must… https://t.co/n9Los852dU
— 机器之心 JIQIZHIXIN (@jiqizhixin) 10 janvier 2026

Sakana makes AIs "hunt" each other, and they begin to converge in evolution Researchers from MIT & Sakana AI present "Digital Red Queen." They use LLMs in a self-play loop to evolve "warrior" programs that compete in Core War, a classic programming game. Each new warrior must