Adobe Unveils DISCO: MapReduce-Style Grounding-Reasoning Disaggregation Eradicates 1M-Token Context Rot, Slashing Costs by 80%
Despite modern LLMs touting million-token context windows, multi-hop reasoning degrades steeply as inputs grow—a pervasive phenomenon termed 'context rot.' This collapse stems from architectural entanglement, where the immense search burden of contextual grounding exhausts the representational capacity required for complex reasoning. Adobe Research and Penn State introduce DISCO (Grounding-Reasoning Disaggregation via Distributed Long Context Scaling). Inspired by MapReduce and Apache Spark, DISCO partitions long documents across parallel Worker LLMs for localized grounding, coordinated by a central Driver LLM trained via GRPO to formulate extraction plans and synthesize evidence. On the 1M-token RULER-QA benchmark, DISCO maintains 78.4% accuracy where monolithic models collapse, boosts LongBench v2 scores by up to 9.8 points, matches Gemini-3-Pro-Preview performance, and slashes serving costs by over 80%.
Key Takeaways
- Eliminating Context Rot at Scale: Systematically decouples contextual grounding from reasoning, eliminating attention dispersion and KV cache noise that exhaust model capacity over 1M+ tokens.
- MapReduce Distributed Agent Topology: A central Driver LLM (trained with GRPO) dynamically generates atomic extraction plans, directing a fleet of parallel Worker LLMs to harvest localized evidence before synthesizing final answers.
- 78.4% Accuracy on 1M Tokens & 80% Cost Reduction: Maintains 78.4% accuracy on 1M RULER-QA where monolithic models collapse, gains up to 9.8 points on LongBench v2, and matches frontier models at over 80% lower inference cost.