Adaptive-granularity post-retrieval evidence compressor for multimodal RAG that selects variable-sized regions from retrieved text, tables, images, and videos to retain necessary context while cutting irrelevant content. Key features include a hierarchy-based node encoder with parent-relative refinement and a critic that issues targeted follow-up retrievals; yields higher QA accuracy across five benchmarks and reduces reader-input tokens by ~14–28%.
Xin Wang, Hao Yu +10Affiliation: Alibaba Token Hub, Alibaba Group, Affiliation: Ohio State University
Proposes TRACE, an FP4 quantization framework for RL of MoE LLMs that uses rollout-side FP4 outcomes to guide training-side rounding and caches mantissa/scale from deeper layers to limit overhead. Preserves BF16-level RL performance while enabling up to 5.4× rollout speedup.