PARSER: parallel chunk subagents with an RL-trained lead agent for long-context QA
omarsar0 · x · 2026-09-11
Elvis Saravia highlights PARSER, a long-context agent design that decouples reasoning depth from document traversal. A bank of lightweight frozen subagents, each bound to one chunk, reads the document in parallel, while an RL-trained lead agent runs iterative scatter-gather rounds: broadcasting queries, aggregating evidence, and forming deeper follow-ups. This improves multi-hop QA from 7K to 896K tokens while avoiding the linear latency growth of sequential memory agents.
Related event: PARSER Parallelizes Long-Document Reading for Deeper Reasoning(2 posts)→
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