Multimodal RAG retrieves text, tables, images, and videos, but choosing a retrieval granularity does not determine how much context to retain within each item. Coarse units include irrelevant content, while uniformly fine selection can remove context needed to interpret the evidence. Existing compressors address this t...
Hyojeong Yun, Jueun Kim, Wook-Shin Han· 0 citations
Automated harness optimization can substantially improve LLM agents by iteratively updating their prompts, tool interfaces, and control logic from execution feedback. However, existing methods primarily optimize how the harness is updated while largely fixing which training scenarios generate the feedback that drives t...
Sungho Park, Wonjoong Kim, Jue Zhang et al.· 0 citations
Hi-Q is introduced, an evidence-conditioned framework for hierarchical query refinement that grows a query tree whose topology is determined by corpus support signals rather than by a fixed decomposition template or a pre-built graph.
Jueun Kim, Sungho Park, Wook-Shin Han· 0 citations
The proposed AutoSaddler, an automatic harness optimization framework that formulates harness improvement as an offline learning problem and iteratively updates the harness using failure signals from mini-batches, suggests that automatic harness optimization is a promising path toward more performant and reliable agent...
Sungho Park, Wonjoong Kim, Rongyuan Tan et al.· 15 citations· ⚡1
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