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PertReason: A Knowledge-Grounded Benchmark and Framework for Cell-State-Conditioned Mechanistic Reasoning of Perturbation Effects

Research Bioinformatics AI

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TL;DR - PertReason is a benchmark and framework for evaluating whether models explain single-cell perturbation effects with mechanisms grounded in cell-specific states. It exposes failures that predictive accuracy alone misses, including correct answers derived from flawed or context-insensitive reasoning.

  • PertReasonQA combines genetic and chemical perturbation data across cellular contexts with knowledge graphs.
  • Dynamic pathway conditioning tests robustness to unseen perturbations, new cells, and differing basal states.
  • Evaluated models sometimes ignore cellular context or produce directionally inconsistent mechanisms.
  • PertReasonLM aligns outcome predictions with context-specific, pathway-grounded rationales as a reference approach.

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PertReason: A Knowledge-Grounded Benchmark and Framework for Cell-State-Conditioned Mechanistic Reasoning of Perturbation Effects

arXiv cs.LG Dongkwan Kim, Yiming Gao, Yining Yang, Yang Shen 2026-07-21 arXiv:2607.18777
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-18 14:38:22.991810 UTC

TL;DR - PertReason is a benchmark and framework for evaluating whether models explain single-cell perturbation effects with mechanisms grounded in cell-specific states. It exposes failures that predictive accuracy alone misses, including correct answers derived from flawed or context-insensitive reasoning.

  • PertReasonQA combines genetic and chemical perturbation data across cellular contexts with knowledge graphs.
  • Dynamic pathway conditioning tests robustness to unseen perturbations, new cells, and differing basal states.
  • Evaluated models sometimes ignore cellular context or produce directionally inconsistent mechanisms.
  • PertReasonLM aligns outcome predictions with context-specific, pathway-grounded rationales as a reference approach.
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