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While large language models (LLMs) are increasingly deployed in long-context scenarios, lengthy prompts can increase inference costs and latency and exacerbate the ``lost-in-the-middle''phenomenon. Selective prompt compression offers a model-agnostic approach to alleviating these issues. However, methods based on fixed...
OASIS is proposed, an outlier- and sink-aware method that stabilizes dual-normalized attention-residual architectures through explicit null routing and token-to-depth null coupling and offers insight into the low-bit sensitivity observed in AttnResidual.
A VLM-as-a-Judge metric for SVG generation, validated through human correlation studies, and an evaluation of frontier VLMs reveals significant performance gaps, positioning VectorGym as a rigorous framework for advancing visual code generation.
Branch2Skill is introduced, an efficient framework that transforms a single reasoning tree into dense supervision for skill evolution, demonstrating that reasoning trees can support not only more effective trajectory search, but also richer supervision for more efficient skill improvement.
LT-MKT first integrates textual information from questions and their associated concepts to construct a Multi-domain Hierarchical Graph, leveraging the advanced representational capabilities of large language models (LLMs) to bridge isolated domains and proposes a novel method incorporating cognitive Load and knowledge...
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