DISCO splits long context into parallel Worker grounding plus Driver reasoning, holding 78.4% on 1M-token RULER-QA while cutting inference cost by over 80%
The work proposes DISCO, which separates long-context processing into local evidence grounding executed in parallel by lightweight Worker LLMs and global reasoning handled by a central Driver LLM whose dynamic DAG planning is optimized with GRPO reinforcement learning; with Qwen3-8B it holds 78.4% accuracy on RULER-QA at 1M tokens (where the RAG baseline collapses to 10.9%), improves up to 9.8 points over full-context baselines on LongBench v2, and matches Gemini-3-Pro-Preview while reducing inference cost by over 80%.