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ResComEmb is proposed, a trainable framework for effective and efficient universal multi-vector multimodal embedding that produces higher-quality universal multimodal embeddings than VLM2Vec-V2, and outperforms ColQwen2.5 in visual document retrieval.
PAWS provides an auditable substrate for evaluating agent influence, policy-response cascades, and action-outcome alignment in historically grounded financial simulations, and shows that high accuracy can mask failure to detect rare stakeholder actions.
By systematically characterizing the search behavior and failure modes of long-horizon search agents, this work points to practical directions for building better deep research systems, including stronger query formulation, more effective evidence selection and context management, and stopping criteria based on whether...
Fetch-then-Explore is proposed, which separates page selection from evidence extraction and keeps what it selects: pages are recorded in a per-question workspace on the filesystem rather than the context window or a transient session, and evidence is pulled from them on demand later.
FinDeepIndicator is proposed, the first benchmark dedicated to evaluating Deep Research agents in end-to-end financial indicator construction, and covers fundamental, technical, and macroeconomic indicators organized into 21 fine-grained sub-categories.
GenRubric is introduced, a self-evolving framework that improves rubric generation from unlabeled queries without requiring additional human annotations during self-evolution, and experiments show that self-evolution improves the agreement between evaluations induced by generated rubrics and those induced by expert-wri...
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