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This paper proposes DASO, a tree-aware post-training method for generative recommendation that addresses difficulty mismatch in GRPO by profiling rollout groups and reallocating based on prefix-match depth, improving performance on public benchmarks.
A new paper proposes VDAR-Router, a difficulty-aware retrieval-based routing framework for LLMs that adaptively selects models based on query difficulty, achieving better cost-performance trade-offs.
This paper presents the NLPCC 2026 Shared Task 1: Difficulty-Aware Multilingual and Multimodal Medical Instructional Video Understanding Evaluation (DA-MIVQA), which extends previous benchmarks with difficulty-aware annotations and three tracks: temporal answer grounding, video corpus retrieval, and grounding in corpus. The dataset includes medical instructional videos from public channels and aims to evaluate systems under varying reasoning requirements.