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Open-ended discovery requires learning from repeated attempts while continuing to explore directions whose value is not yet apparent. Search with a frozen large language model (LLM) can reuse previous solutions in context, but cannot update the model from its successes and failures on the test problem. Reinforcement le...
This report presents PolyOCR, a family of unified OCR foundation models of varying scales that combines a shared instruction-following framework with a large-scale data engine that converts heterogeneous visual resources into quality-verified OCR supervision.
UniAR leverages a large multimodal model to generate hierarchical diagnostic descriptions at the word, phrase, and sentence levels, compensating for the lack of paired clinical reports, and demonstrates that it offers a robust and interpretable framework for ASD screening under semantic scarcity.
HAE-GEO is introduced, a benchmark that tracks the full trajectory from exposure to recovery under progressively more persuasive Web poisoning and finds three recurring patterns: evidence recognition degrades under the corroboration trap, agentic search improves final resistance without improving evidence recognition o...
UniMoMo, a post-training compression framework formulated as a constrained graph coarsening problem, is introduced, and a layer-adaptive protection mechanism that restricts the merging of high-traffic experts based on their routing exposure is introduced.
Think with Structured Grounding (TwSG), a novel fine-grained image perception framework designed to internalize complex images's tool-use capabilities within the model, is proposed, endowing models with native fine-grained region description and flexible reasoning capabilities.
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