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The paper measures dynamic evaluation (taking gradient steps on text being read) across softmax-attention, hybrid linear-attention, and masked-diffusion language models, finding 5–20% next-token loss reductions but showing the adaptation captures style/vocabulary rather than factual retrieval, while low-rank distillation of a model's own conclusions reaches in-context performance without degrading GSM8K.
论文提出 Test-Time Memory Evolution (TTME)，一种即插即用的测试时适应方法，通过持续更新外部记忆来指导科学发现而无需更新模型权重。实验表明 TTME 在五个数学任务上全面优于测试时训练 (TTT)，同时将计算成本降低 82.8–84.6% FLOPs 和 47.7–58.4% 运行时间。
论文提出 Test-Time Memory Evolution（TTME），一种通过持续更新外部记忆而非模型权重来进行测试时自适应的方法，在五个数学任务上优于测试时训练（TTT），同时降低 82.8-84.6% 的 FLOPs 和 47.7-58.4% 的运行时间。
Titan-Llama 提出一种后训练方法，通过分层记忆架构和测试时参数更新，将预训练注意力模型改造为具备外部神经记忆模块的长上下文高效推理模型，在 QuALITY 和 LongHealth 上显著提升准确率并降低推理延迟与显存占用。
论文提出 Latent Compaction，通过测试时训练将 KV 缓存压缩为紧凑的文档特定表示，在冻结模型权重的前提下实现 32 倍压缩下 96.7% 的 RULER 准确率，显著优于 KVZip+ 等基线，并可跨模型规模与系列迁移。
NandakishorM 开源了基于物理的类型化决策模型 Vega（800M 及 4B 参数版本），采用 Test-Time Training 引擎+适配器架构，支持 73k 上下文和图像输入，通过在隐藏层构建"结果山谷"并用带摩擦的小球物理模拟来判定输出意图，单次推理超越多项 JEV 基准。
This paper shows that models adapting on their own generated text during inference destabilize over long (128K-token) horizons, and proposes a causal decomposition plus a 'Settlement' validation mechanism that tests candidate updates on independent text before retaining them, substantially reducing damage while preserving genuine adaptation.
The paper introduces ASCENT, a method for online agentic test-time training that lets an LLM agent improve its own weights across tasks by self-distilling verified execution trajectories into persistent LoRA updates, evaluated on ALFWorld, WebShop, and AppWorld.
JEPA-TTT adapts the latent dynamics predictor of a pretrained JEPA world model during test time using self-supervised dense replay, improving planning under dynamics shifts with 83% lower latent prediction error and 153% better planning performance over the frozen model across four continuous-control environments.
The paper proposes SMART, a self-evolving multi-agent system for long-form subtitle translation that uses test-time training with a dynamic router, Mixture-of-Agents layer, and judge-refiner loop to build series-level memory and adapt agent prompts without retraining the underlying LLMs. It also introduces the SubtitleArena benchmark (14 genres, 15 target locales) and the SubMQM evaluation framework, achieving a 6.9% average MQM penalty reduction over competing agent systems and top results on the MuSC benchmark.
The paper introduces Embed-TTT, a two-step test-time training protocol that improves rule induction in ARC-like tasks by first finetuning task embeddings and then the backbone, leading to better alignment with underlying rules and enhanced performance on benchmarks like ARC-AGI-1 and ConceptARC.
The article discusses Test Time Training as a potential new scaling axis in AI development, analyzing a paper that frames it as a form of linear attention and exploring its implications for model training and continual learning.
This paper introduces In-Place Test-Time Training, a framework that updates MLP weights in real-time during inference, allowing LLMs to dynamically adapt and handle long contexts up to 128k tokens.
TTPO introduces a label-free method for test-time training in mathematical reasoning that matches supervised performance by asymmetrically handling agreeing and disagreeing rollouts.
This article explains test-time training, where AI models adapt during inference to improve personalization and reduce memory usage, but at the cost of increased per-user compute. It discusses implications for serving models at scale, balancing long context and user concurrency.
TTT-Discover is a framework that trains LLMs at test time using reinforcement learning on individual problems, setting new records in GPU kernel engineering and improving mathematical bounds.
This paper introduces Modular TTT, a framework that represents test-time training inner learners as directed acyclic graphs, enabling systematic ablation and composition of components. The authors train 410M and 1.45B parameter models on 100B tokens, achieving performance comparable to GatedDeltaNet.
RoboTTT scales visuomotor context to 8K timesteps for robot policies, enabling one-shot imitation from human video demonstrations, on-the-fly policy improvement, and robustness to perturbations. It achieves an 87% improvement over baselines and completes a five-minute, ten-stage assembly task that no baseline could.
Fei-Fei Li highlights a new test-time training approach for robotic learning, developed in collaboration between Stanford SVL and NVIDIA Robotics, which scales robot model context to 8000 timesteps with constant inference cost.
Proposes Self-Guided Test-Time Training (S-TTT), where the model identifies relevant evidence spans in long contexts for adaptation, achieving up to 15% relative improvement on long-context reasoning benchmarks.