CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models
- URL: http://arxiv.org/abs/2605.07335v1
- Date: Fri, 08 May 2026 06:40:24 GMT
- Title: CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models
- Abstract summary: Virtual Cell Modeling (VCM) requires models that not only predict perturbation responses, but also support targeted revision when predictions fail.<n>Current LLM-assisted modeling face a refinement-routing problem: prediction discrepancies are observed through executable implementations.<n>We propose CellScientist, a dual-space hierarchical framework that couples a high-level hypothesis space with a low-level executable implementation space.
- Score: 62.281480231694
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Virtual Cell Modeling (VCM) requires models that not only predict perturbation responses, but also support targeted revision when predictions fail. Current LLM-assisted modeling workflows face a refinement-routing problem: prediction discrepancies are observed through executable implementations, but the relevant revision may involve the modeling assumption, representation design, implementation, or task constraint. Without structured feedback propagation across these levels, iterative refinement may repair code while failing to revise the assumption responsible for the discrepancy. We propose CellScientist, a dual-space hierarchical framework that couples a high-level hypothesis space with a low-level executable implementation space. CellScientist represents modeling decisions as structured states, realizes them as admissible programs under task and interface constraints, and routes execution discrepancies back to targeted hypothesis or implementation updates. This enables a closed Hypothesis -> Implementation -> Hypothesis loop where failures become structured signals for model refinement rather than debugging events. Across morphology and transcriptomic benchmarks, with additional single-cell perturbation evaluations, the final executable models selected by CellScientist improve over reference baselines under fixed split and evaluation protocols, while the workflow produces auditable refinement traces.
Related papers
- Reality Is the Final Verifier: On Two Key Gaps in Agentic Software Engineering [78.51705689800838]
We propose an assurance-revision loop that uses deployment evidence to revise the requirements, model, or evaluator.<n>We then cast assured agentic development as a resource-allocation problem over human judgment, agent capability, and compute.
arXiv Detail & Related papers (2026-09-10T17:58:48Z) - Gatekeepers and Hallucinations: A Layered Evaluation Framework for LLM-Driven Quantum Circuit Generation [0.0]
We present a layered evaluation framework for materials-informed Variational Quantum Eigensolver (VQE) circuit generation.<n>We surface a taxonomy of five distinct LLM failure modes (geometry hallucination, nonexistent API usage, integration failures, constraint violations, and plausible-but-unverifiable output)<n>A forensic audit of the evaluation platform's own source code further establishes that two apparent model failures originated in the harness through silent fallback-template substitution.
arXiv Detail & Related papers (2026-06-16T19:17:54Z) - HoloRec: Holistic Encoding and Interleaved Reasoning for Generative Recommendation [57.802158017741824]
HoloRec is an endogenous chain-of-thought recommendation mechanism that unifies representation, reasoning, and generation.<n> Experiments on multiple public recommendation datasets demonstrate that HoloRec consistently outperforms baselines.
arXiv Detail & Related papers (2026-06-13T14:52:03Z) - One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data [6.704099492625706]
We introduce a two-stage, model-agnostic framework that explicitly decouples forecasting and residual learning into distinct stages of representation learning.<n>By formalizing this pipeline as a hypothesis space expansion, our framework addresses approximation limitations inherent in single-stage architectures.
arXiv Detail & Related papers (2026-06-09T10:31:54Z) - CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials [61.546562658308005]
We propose QE-Catalytic-V2, a unified graph--text multimodal large language model for catalytic materials.<n>It integrates property prediction and inverse design within the same model and shared representation space.<n>Under this unified framework, QE-Catalytic-V2 can not only perform reliable property prediction but also generate and screen physically feasible CIF candidates.
arXiv Detail & Related papers (2026-05-17T04:31:46Z) - On Predicting the Post-training Potential of Pre-trained LLMs [60.07459271263409]
We introduce a new task of predicting post-training potential - forecasting a base model's performance before post-training.<n>We propose RuDE, a unified framework that bypasses the generation gap of base models by leveraging response discrimination.<n>Experiments demonstrate a correlation greater than 90% with post-training performance.
arXiv Detail & Related papers (2026-05-12T11:33:49Z) - LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation [75.05397479715576]
Large language models (LLMs) and agents have shown promising progress, but their true capabilities and failure modes remain unclear.<n>We present the first systematic and contamination-aware study of LLM- and agent-based formal specification generation for C programs.
arXiv Detail & Related papers (2026-05-02T11:31:33Z) - Decoding the Critique Mechanism in Large Reasoning Models [50.821607345799386]
Large Reasoning Models (LRMs) exhibit backtracking and self-verification mechanisms that enable them to revise intermediate steps and reach correct solutions.<n>We investigate how current LRMs recover from errors by inserting arithmetic mistakes in their intermediate reasoning steps.<n>We find that despite the error propagating through the chain-of-thought, the model still reaches the correct final answer.
arXiv Detail & Related papers (2026-03-17T10:03:30Z) - X-RAY: Mapping LLM Reasoning Capability via Formalized and Calibrated Probes [11.988348978958376]
Large language models (LLMs) achieve promising performance, yet their ability to reason remains poorly understood.<n>We present X-Ray, an explainable reasoning analysis system that maps the LLM reasoning capability using calibrated, formally verified probes.<n>We evaluate state-of-the-art LLMs on problems ranging from junior-level to advanced in mathematics, physics, and chemistry.
arXiv Detail & Related papers (2026-03-05T15:34:22Z) - Agentic Scientific Simulation: Execution-Grounded Model Construction and Reconstruction [0.0]
This paper investigates agentic scientific simulation, where model construction is organized as an execution-grounded interpret-act-validate loop.<n>We present JutulGPT, a reference implementation built on the fully differentiable Julia-based reservoir simulator JutulDarcy.
arXiv Detail & Related papers (2026-02-27T15:42:05Z) - Rethinking Scientific Modeling: Toward Physically Consistent and Simulation-Executable Programmatic Generation [8.067859101380389]
Non-executable or physically inconsistent outputs remain prevalent under stringent engineering constraints.<n>A framework for physics-consistent automatic building modeling is proposed.<n>CivilInstruct is introduced as a domain-specific dataset that formalizes structural engineering knowledge and constraint reasoning.<n> MBEval is presented as a verification-driven benchmark that evaluates executability and structural dynamics consistency.
arXiv Detail & Related papers (2026-02-06T06:57:04Z) - Reshaping Action Error Distributions for Reliable Vision-Language-Action Models [69.38615670891038]
In robotic manipulation, vision-language-action (VLA) models have emerged as a promising paradigm for learning generalizable and scalable robot policies.<n>We focus on continuous-action VLA models and move beyond conventional MSE-based regression by reshaping action error distributions during training.<n>We evaluate our approaches across standard, few-shot, and noisy settings on multiple representative VLA architectures.
arXiv Detail & Related papers (2026-02-04T05:37:09Z) - Partial Transportability for Domain Generalization [56.37032680901525]
Building on the theory of partial identification and transportability, this paper introduces new results for bounding the value of a functional of the target distribution.<n>Our contribution is to provide the first general estimation technique for transportability problems.<n>We propose a gradient-based optimization scheme for making scalable inferences in practice.
arXiv Detail & Related papers (2025-03-30T22:06:37Z) - Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory [9.771997770574947]
We analyze how model reconstruction using counterfactuals can be improved.
Our main contribution is to derive novel theoretical relationships between the error in model reconstruction and the number of counterfactual queries.
arXiv Detail & Related papers (2024-05-08T18:52:47Z) - Towards Characterizing Domain Counterfactuals For Invertible Latent Causal Models [15.817239008727789]
In this work, we analyze a specific type of causal query called domain counterfactuals, which hypothesizes what a sample would have looked like if it had been generated in a different domain.
We show that recovering the latent Structural Causal Model (SCM) is unnecessary for estimating domain counterfactuals.
We also develop a theoretically grounded practical algorithm that simplifies the modeling process to generative model estimation.
arXiv Detail & Related papers (2023-06-20T04:19:06Z) - Control as Hybrid Inference [62.997667081978825]
We present an implementation of CHI which naturally mediates the balance between iterative and amortised inference.
We verify the scalability of our algorithm on a continuous control benchmark, demonstrating that it outperforms strong model-free and model-based baselines.
arXiv Detail & Related papers (2020-07-11T19:44:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.