The Reasoning Paradox in Scientific Sandboxes: Why Extended Reasoning Fails in Autonomous Bioinformatics and How to Fix It.
- 1University of Ghana
Abstract
Frontier large language models (LLMs) trained with extended reasoning, such as DeepSeek-R1 and OpenAI o1, have established state-of-the-art benchmarks in formal logic, competitive programming, and mathematics. In interactive, empirical scientific sandboxes, however, their efficacy remains counter-intuitive. In the Bioinformatics Benchmark (BixBench), extended reasoning models paradoxically degraded in multi-turn environments, leading benchmark authors to exclude them from primary agentic evaluations. In this paper, we conduct the first systematic forensic audit of the internal cognitive monologue ( ... ) of open-weight reasoning models (DeepSeek-R1-Distill-Qwen-14B) and matched instruction-tuned controls (Qwen2.5-Coder-14B) deployed inside containerized computational biology sandboxes on Nvidia H100 GPUs. Our forensic analysis uncovers two distinct systemic failure modes: (1) The PubMed Prior Trap, in which reasoning models engage in epistemic rationalization within their cognitive stream, falsely convincing themselves that empirical datasets are missing and substituting empirical computation with speculative literature recall (e.g., estimating adjusted p-values based on "typical experimental outcomes"); and (2) Tag Language Nesting Collisions, in which models conflate agent-level XML action tags with Python syntax, producing terminal interpreter syntax errors. To resolve this paradox, we introduce Dual Track Decoupled Reasoning (DTDR), an agent scaffolding architecture that evicts internal cognitive monologues from multi-turn history, injects strict empirical grounding guardrails, and dynamically intercepts runtime tool invocations. On a stratified diagnostic benchmark spanning single-cell RNA-seq, bulk differential expression (DESeq2), and genomic variant prioritization (n=22 per arm), DTDR rescues the 14B reasoning model from 0.0% to 9.09% accuracy (2/22), doubling the performance of the matched non-reasoning coder control (4.55%, 1/22) while slashing code execution errors by 81% (13.6% vs. 72.7%) and reducing average steps to solution by 66% (1.77 vs. 5.18 steps). All replication pipelines, prompts, and trajectory manifests are openly released.
Related
- From Problems to Solutions: How Self-Questions Drive Strong Scientific Reasoning
- Assessment of LLMs on basic bioinformatics tasks requiring multi-step reasoning and code writing
- BioPhys-Bridge: A Benchmark for Interdisciplinary Scientific Reasoning in Physics-Grounded Biological Research
- Science sandboxes measure the scientific capability of AI agents
- Cell-Lens: Advancing LLM Reasoning at Single-Cell Resolution
BibTeX
@misc{salami2026reasoning,
title = {{The Reasoning Paradox in Scientific Sandboxes: Why Extended Reasoning Fails in Autonomous Bioinformatics and How to Fix It.}},
author = {R. O. Salami and D. Amuna and R. A. Akorful and R. Anjali and C. M. Morang'a and P. K. Quashie and E. A. Asiamah and R. A. Carr and I. Adase and S. M. Kumordzi},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.30.763189},
url = {https://www.biorxiv.org/content/10.64898/2026.10.30.763189v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - The Reasoning Paradox in Scientific Sandboxes: Why Extended Reasoning Fails in Autonomous Bioinformatics and How to Fix It.
AU - R. O. Salami
AU - D. Amuna
AU - R. A. Akorful
AU - R. Anjali
AU - C. M. Morang'a
AU - P. K. Quashie
AU - E. A. Asiamah
AU - R. A. Carr
AU - I. Adase
AU - S. M. Kumordzi
PY - 2026
DA - 2026/10/30
PB - bioRxiv
DO - 10.64898/2026.10.30.763189
UR - https://www.biorxiv.org/content/10.64898/2026.10.30.763189v1
AB - Frontier large language models (LLMs) trained with extended reasoning, such as DeepSeek-R1 and OpenAI o1, have established state-of-the-art benchmarks in formal logic, competitive programming, and mathematics. In interactive, empirical scientific sandboxes, however, their efficacy remains counter-intuitive. In the Bioinformatics Benchmark (BixBench), extended reasoning models paradoxically degraded in multi-turn environments, leading benchmark authors to exclude them from primary agentic evaluations. In this paper, we conduct the first systematic forensic audit of the internal cognitive monologue ( ... ) of open-weight reasoning models (DeepSeek-R1-Distill-Qwen-14B) and matched instruction-tuned controls (Qwen2.5-Coder-14B) deployed inside containerized computational biology sandboxes on Nvidia H100 GPUs. Our forensic analysis uncovers two distinct systemic failure modes: (1) The PubMed Prior Trap, in which reasoning models engage in epistemic rationalization within their cognitive stream, falsely convincing themselves that empirical datasets are missing and substituting empirical computation with speculative literature recall (e.g., estimating adjusted p-values based on "typical experimental outcomes"); and (2) Tag Language Nesting Collisions, in which models conflate agent-level XML action tags with Python syntax, producing terminal interpreter syntax errors. To resolve this paradox, we introduce Dual Track Decoupled Reasoning (DTDR), an agent scaffolding architecture that evicts internal cognitive monologues from multi-turn history, injects strict empirical grounding guardrails, and dynamically intercepts runtime tool invocations. On a stratified diagnostic benchmark spanning single-cell RNA-seq, bulk differential expression (DESeq2), and genomic variant prioritization (n=22 per arm), DTDR rescues the 14B reasoning model from 0.0% to 9.09% accuracy (2/22), doubling the performance of the matched non-reasoning coder control (4.55%, 1/22) while slashing code execution errors by 81% (13.6% vs. 72.7%) and reducing average steps to solution by 66% (1.77 vs. 5.18 steps). All replication pipelines, prompts, and trajectory manifests are openly released.
ER -