The Reasoning Paradox in Scientific Sandboxes: Why Extended Reasoning Fails in Autonomous Bioinformatics and How to Fix It.

Dual Track Decoupled Reasoning (DTDR) rescues the 14B reasoning model DeepSeek-R1-Distill-Qwen-14B in containerized bioinformatics sandboxes, raising accuracy from 0.0% to 9.09% (2/22).

• Why it matters: Extended-reasoning LLMs underperform in multi-turn empirical bioinformatics settings, creating a paradox that led BixBench authors to exclude them from primary agentic evaluations.
• What they did: The study audits internal monologues of DeepSeek-R1-Distill-Qwen-14B versus Qwen2.5-Coder-14B in Nvidia H100 container sandboxes, identifying the PubMed Prior Trap and Tag Language Nesting Collisions. DTDR then evicts monologues from multi-turn history, adds empirical grounding guardrails, and intercepts tool calls.
• The result: Across a stratified diagnostic benchmark (n=22 per arm) covering single-cell RNA-seq, DESeq2 bulk differential expression, and genomic variant prioritization, DTDR cuts code execution errors by 81% (13.6% vs. 72.7%) and reduces steps to solution by 66% (1.77 vs. 5.18).

AI summary of the abstract · may contain errors · not medical advice · read the paper.