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Diffusion language models (DLMs) enable fast generation by predicting multiple tokens in parallel, but their practical adoption remains limited by a persistent quality gap relative to comparably sized autoregressive (AR) models. We attribute this gap to a computation-difficulty mismatch: within a partially observed seq...
DynGraphAgentBench, an executable benchmark for agentic lifecycle control under delayed feedback, is introduced, an executable benchmark for agentic lifecycle control under delayed feedback that measures detection utility with average precision and capture at fixed review depth, and characterize adaptation through mode...
This work introduces EAVer, an End-to-end Agentic Verifier that learns to control the complete response-level verification workflow as a unified policy, and construct 794 bidirectional same-trajectory preference pairs that keep claim grouping, search, and evidence fixed, enabling decision-focused Direct Preference Opti...
This survey investigates the current research landscape of multimodality modeling from three perspectives: the first group of multimodal models adopts a heterogeneous architecture to bridge different modality data, the second leverages LLM for multimodality modeling via a unified language modeling objective, and the th...
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