Recent test-time training (TTT) frameworks have enabled open-weight large language models (LLMs) to outperform inference-only frameworks powered by frontier models on scientific discovery tasks. Howev
Test-Time Memory Evolution updates external memory without changing model weights to guide scientific discovery more efficiently than test-time training.
Recent test-time training (TTT) frameworks have enabled open-weight large language models (LLMs) to outperform inference-only frameworks powered by frontier models on scientific discovery tasks. However, TTT introduces additional computational cost for online weight updates during the discovery process, making computational efficiency an important consideration when scaling to larger models. This motivates us to explore an efficient test-time adaptation method for scientific discovery. We introduce Test-Time Memory Evolution (TTME), a plug-and-play test-time adaptation method, as an alternative to TTT. TTME continually updates the external memory by distilling the experience to guide…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Can Test-Time Memory Evolution Outperform Test-Time Training for Scientific Discovery? was first seen on Hugging Face · Daily papers on Oct 27, 2026. Since then it has appeared in 19 checks on 3 different days. Its best position was #6 of 30, reached on Oct 28, 23:28 UTC. It dropped off the list after Oct 29, 13:28 UTC.
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It was first seen on Oct 27, 2026 and has been on the list on 3 days (19 checks) since.