🚀 Introducing DR-DCI: Scaling Direct Corpus Interaction via Dynamic Workspace Expansion Search agents face a basic tradeoff: Retrieval scales to massive corpora, but often exposes evidence only as ranked snippets or bounded document views. Direct Corpus Interaction (DCI) gives agents precise terminal-style control with tools like rg, grep, find, and read, but raw full-corpus search becomes slow and unstable as the corpus grows. DR-DCI bridges the two. The agent dynamically calls pull(query, topK) to expand a local workspace, then performs DCI inside that bounded workspace for evidence search, comparison, and verification. Retriever = corpus-level discovery DCI = workspace-level investigation Result: on BrowseComp-Plus, DR-DCI improves Raw DCI from 62.9% to 71.2%, while reducing wall time and estimated cost. With workspace-preserving context reset, it reaches 73.25%. HF paper: Code: More details below 👇