We introduce SIMIT, a test-time self-improvement framework in which a vision-language model creates its own query-specific training data before answering an unlabeled test query. Our goal is to improv
SIMIT is a test-time framework that lets vision-language models generate their own practice data to improve answers on unlabeled queries without external annotations.
We introduce SIMIT, a test-time self-improvement framework in which a vision-language model creates its own query-specific training data before answering an unlabeled test query. Our goal is to improve performance on a given target test set without annotations or external models. To pursue this goal, some test-time methods improve answers through longer reasoning or repeated sampling but leave the model unchanged. Others update the model using rewards based on majority agreement among multiple candidate answers or the model's own judgments of correctness, but such rewards can reinforce existing mistakes. SIMIT instead imagines similar problems whose solutions it controls: a model need not…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
SIMIT: Self-Improving Vision-Language Models via Imagination at Test-Time was first seen on Hugging Face · Daily papers on Nov 4, 2026. Since then it has appeared in 1 check on 1 different day. Its best position was #30 of 30, reached on Nov 4, 05:28 UTC. It dropped off the list after Nov 4, 05:28 UTC.
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It was first seen on Nov 4, 2026 and has been on the list on 1 day (1 check) since.