Argus (arXiv 2605.16217, May 15) splits research agents into a Searcher that gathers evidence ReAct-style and an RL-trained Navigator that maintains an evidence graph, identifies missing pieces, and dispatches parallel Searchers purposefully. With 64 parallel Searchers and a 35B-A3B MoE backbone, Argus reaches 86.2 on BrowseComp — highest reported for any agent system — while keeping Navigator context under 21.5K tokens. The separation of search from orchestration turns out to matter more than raw parallelism.
Semble (v0.1.7, May 12) is a code search library for AI agents that uses ~98% fewer tokens than grep+read while matching 99% of the retrieval quality of much heavier transformer-based approaches. It indexes a repository in 263ms and answers queries in 1.5ms on CPU, ships as an MCP server for Claude Code, Cursor, and Codex, and requires no API keys, GPU, or external services. The design bets that static embeddings plus BM25, fused carefully and reranked with code-specific signals, are almost as good as a code-specialized transformer — and orders of magnitude cheaper to operate.
