ViT³: Vision Test‑Time Training Architecture Breaking Transformer Complexity (CVPR 2026 Oral)

The paper systematically studies Test‑Time Training (TTT) for vision, derives six design principles, and introduces ViT³—a pure TTT architecture that uses full‑batch internal training, a learning rate of 1.0, and lightweight SwiGLU‑Depthwise convolution modules, achieving state‑of‑the‑art linear‑complexity performance across classification, detection, segmentation and generation tasks.

Linear ComplexitySequence ModelingTest-Time Training

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ViT³: Vision Test‑Time Training Architecture Breaking Transformer Complexity (CVPR 2026 Oral)

Machine Heart

May 6, 2026 · Artificial Intelligence

Scal3R Enables Stable Kilometer-Scale 3D Reconstruction of Long Videos

Scal3R introduces test‑time training with a global‑context memory and synchronization mechanism that lets models train on and infer over ultra‑long video sequences, achieving accurate camera poses and dense point clouds for kilometer‑scale scenes while outperforming prior SLAM, SfM and streaming baselines on multiple benchmarks.

3D ReconstructionScal3RTest-Time Training

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Scal3R Enables Stable Kilometer-Scale 3D Reconstruction of Long Videos

Machine Learning Algorithms & Natural Language Processing

Apr 28, 2026 · Artificial Intelligence

Can Reasoning Models Keep Improving? TEMPO Uses EM to Stop Reward Drift

The paper introduces TEMPO, a test‑time training framework inspired by the Expectation‑Maximization algorithm, which alternates policy optimization (M‑step) with Critic calibration (E‑step) to prevent reward‑signal drift, and demonstrates on Qwen3 and OLMO3 models that it continuously improves reasoning performance and maintains output diversity beyond the saturation point of existing TTT methods.

EM algorithmReasoningTest-Time Training

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Can Reasoning Models Keep Improving? TEMPO Uses EM to Stop Reward Drift

Data Party THU

Jan 31, 2026 · Artificial Intelligence

Can LLMs Learn While Being Tested? Inside the TTT-Discover Breakthrough

The article examines the Test‑Time Training to Discover (TTT‑Discover) approach, which applies reinforcement learning during inference to let large language models continuously improve on single test problems, and reports strong results across mathematics, GPU kernel optimization, algorithm design, and biology.

AI researchLLMScientific Discovery

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Can LLMs Learn While Being Tested? Inside the TTT-Discover Breakthrough

ShiZhen AI

Dec 5, 2025 · Artificial Intelligence

Can AI Achieve Human‑Like Long‑Term Memory? Inside Google’s Titans Architecture

Google’s newly unveiled Titans architecture tackles AI’s “forgetfulness” by embedding a Neural Long‑Term Memory (LMM) module that updates model weights during inference using a test‑time training approach and a MIRAS surprise metric, enabling over 2 million‑token context with linear O(N) computation and superior benchmark results versus GPT‑4 RAG.

AI ArchitectureGoogle TitansLong-term Memory

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Can AI Achieve Human‑Like Long‑Term Memory? Inside Google’s Titans Architecture

AI Frontier Lectures

Nov 22, 2025 · Artificial Intelligence

Can Vision Transformers Crack the ARC Puzzle? Introducing VARC

MIT researchers argue that the ARC benchmark is essentially a visual problem and present the Vision ARC (VARC) framework, which reformulates ARC as an image‑to‑image translation task using a Vision Transformer, achieving human‑level accuracy through a novel canvas representation and test‑time training.

ARCArtificial IntelligenceImage-to-Image Translation