---
title: "CellCanon: An Agentic Cellular World Model for Prediction, Explanation, and Observation-Driven Updating"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.05890v1"
permalink: "https://feed.astralor.com/items/itm_79f5f7231e57aeb1cca620c1"
published: "2026-11-04T03:29:21.000Z"
score: 59
channel: "ai"
tags: ["AI Agent","多模态","CellCanon","Agentic Cellular World","Model","Prediction","Explanation","Observation-Driven Updating Biological"]
---

# CellCanon: An Agentic Cellular World Model for Prediction, Explanation, and Observation-Driven Updating

## 速览

Biological discovery requires using molecular knowledge and new measurements to infer responses beyond the conditions already observed. We introduce CellCanon, the first agentic cellular world model to unify single-cell…

## 关键点

- Biological discovery requires using molecular knowledge and new measurements to infer responses beyond the conditions already observed.
- We introduce CellCanon, the first agentic cellular world model to unify single-cell expression prediction, evidence-grounded explanation, and observation-driven updating.
- Its predictive core reuses condition-level biological evidence across gene-specific numerical readouts to guide single-cell expression generation.

## 全文

Biological discovery requires using molecular knowledge and new measurements to infer responses beyond the conditions already observed. We introduce CellCanon, the first agentic cellular world model to unify single-cell expression prediction, evidence-grounded explanation, and observation-driven updating. Its predictive core reuses condition-level biological evidence across gene-specific numerical readouts to guide single-cell expression generation. A shared world state links predictions, acquired measurements, and source-linked claims, supporting explanation and revision. On 339 held-out SciPlex3 conditions across three cell lines, CellCanon improves differential-expression AUPRC and Macro-F1 over the best baseline for each metric by 63.3% and 17.3%, respectively, and direction Macro-F1 by 7.2%. Relative to gene-wise reasoning on matched drug-cell cohorts, measured language-model token use falls by 99.77% for Predict and 98.41% for Explain. On the source-grounded benchmark, Explain correctly answers all answerable questions with supporting proofs and identifies all insufficient-evidence cases. In simulated experimental feedback, Update uses one measured source context per drug to exceed the best fine-tuned baselines in differential-expression AUPRC and Macro-F1 without changing model weights. Together, these capabilities support a cumulative account of cellular responses, with predictions and biological judgments that can be revised as evidence accumulates.

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