Recursive–Structured-State–Termination (RST) - Improving the performance of RLM for long-context reasoning with the help of structured graph state

Transformer-based language models achieve strong performance across NLP tasks, but their effectiveness degrades in very long-context settings due to context rot. Although recursive language models mitigate this limitation by processing inputs in chunks, they introduce a control challenge in determining when to continue reasoning, consolidate evidence, or terminate. WeproposetheRecursive-Structured-State-Termination (RST) Reasoning Engine, an adaptive recursive framework that incrementally extracts knowledge, maintains a structured reasoning graph of intermediate states, and employs convergence-based termination to improve reasoning efficiency, transparency, and scalability. We evaluate RST on OOLONG, OOLONG-Pairs, LongBench-v2 CodeQA and MuSiQue. RST consistently improves over RLM across model scales and domains. On OOLONG, RST achieves mean gains of 44.2% over RLM with Qwen3-8B and 33.9% over the base Qwen3-235B-A22B model. On OOLONG-Pairs, RST outperforms both base models and RLM without helper functions, reaching 14.2% and 41.9% F1 on the 32K setting with Qwen3-8B and Qwen3-235B, respectively, and 12.1% F1 on the challenging 131K setting. With helper functions, RST further improves to 76.6% F1 on 32K and 72.1% F1 on 131K using Qwen3-235B, while running approximately 2–3×faster than RLM. RST also transfers effectively to LongBench-v2 CodeQA, yielding large gains across code-capable models. Across the MuSiQue benchmarks, the RST framework consistently achieves the best performance, attaining F1 scores of 40.8% on MuSiQue_ans and 19.3% on MuSiQue_128K, respectively. Finally, integrating Mamba2-1.3B for long-context extraction and compaction further improves effectiveness and efficiency, highlighting the complementarity of structured state-space modeling and adaptive recursive reasoning for long-context tasks.