Virtual Cell

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8 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

Jul 6Week of Sep 21

Latest papers 20

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How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM-Inferred Prompt Structure
Sep 24, 2026Dipankar SarkarLarge Language Model Evaluation Reproducibility
Skelf Research
Discover, Falsify, Revise: Auditing Input-Use Claims from Source Code to Predictive Contribution in Agent-Discovered Cell Models
Sep 23, 2026Mengran Li, Bo Li, Chengyang Zhang +3Virtual Cell Model Auditing
Sun Yat-sen University · University of Macau · Sichuan University +3
Feed the Panel Dimensions, Not Verdicts: Rubric-Decomposed Fusion of Vision-Language Aesthetic Judges
Sep 22, 2026Amit Jadhav, Shaurya Beriwala, Beomjin KimAesthetics Multimodal Evaluation
Purdue University Fort Wayne
Math Reasoning in LLMs is Organized by Approach, Not Topic
Sep 22, 2026Sajad Goudarzi, Samaneh Zamanifard, Moloud Nasiri +1Mathematical Reasoning Benchmarks Large Language Model Tool Use
Clemson University
Recognized but Not Produced: A Generation Benchmark for Culturally Specific Kinship Terms
Sep 22, 2026Sahil Pardasani, Madhusudan SinghMultilingual Multiple-Choice Questions
Blockchain Data Intelligence Lab The Pennsylvania State University University Park, PA, USA
What You Can't See Is Still What You Learn: A Preregistered Sixty-Society Confirmation That Evidence Masking Drives Compositional Generalization
Sep 15, 2026Narcis MarincatCompositional Generalization Ablation
Independent researcher.
Portable Semantics, Private Dialects: Reuse and Negative Transfer in Latent Communication Between Language-Model Cells
Sep 12, 2026Narcis MarincatLanguage Pairs Language Modeling
Independent researcher.
Consistency Without Alignment: Item-Sensitive Language Models Indistinguishable From Random
Sep 1, 2026Cris HuynhLarge Language Model Alignment Item Response Theory
Independent researcher
Human-Guided Causal Knowledge Injection for Virtual Cells
Aug 9, 2026Pengcheng Wang, Changjian Chen, Zhuo Tang +4Causal Graph Virtual Cell
From Pixels to PCells: A Neurosymbolic Approach to Photonic Component Creation
Jul 29, 2026Aadarsh Agarwal, Kenaish Al Qubaisi, Dirk EnglundNanophotonic Pixelloop
Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
Scaling Laws for Classical Machine Learning on Tabular Data: A Benchmark Study
Jul 23, 2026Kaihua DingTabular Learning Scaling Laws
University of Pennsylvania
Score Distributions, Not Cells: Evaluating Single-Cell Perturbations Under Class Overlap
Jul 6, 2026Youssef Marrakchi, Davide D'Ascenzo, Sebastiano Cultrera di MontesanoSingle-Cell Perturbation Prediction Cells
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. · Department of Computer Science, University of Milan, Milan, Italy. · Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy. +1
Geometry of Ordinal Representations in Language Models
Jul 5, 2026Saksham Bassi, Sharvi TomarVirtual Cell Counting
*Equal contribution
CellDETR: A Detection-Guided Framework for Scalable Cell Representation Learning from Histopathology Images
Jun 28, 2026Shikang Zhang, Guojun Li, Yicong Mao +1Digital Pathology Whole-Slide Images
Zhejiang University of Technology · Tianjin University · Hangzhou Institute of Medicine, Chinese Academy of Sciences
OCOO-T : A Simple and Scalable Virtual Cell Model for Transcriptional Perturbation Response Prediction
Jun 11, 2026Danning Jiang, Zheming An, Yalong Zhao +1Single-Cell Perturbation Prediction Single-Cell Rna
INFevo
Vision-Guided Dual-Arm Humanoid Robotic Disassembly of End-of-Life 18650 Lithium-ion Battery Packs
Jun 6, 2026Yile Chen, Zhihao Liu, Xi Vincent Wang +1Lithium-Ion Batteries Waste
KTH Royal Institute of Technology, Brinellvägen 8, 114 28, Stockholm, Sweden
Towards World Models in Biomedical Research
Jun 4, 2026Guangyu Wang, Jingkun Yue, Siqi Zhang +19Biomedical World Models
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China · Department of Engineering Science, University of Oxford, Oxford, United Kingdom · Institute of Medical Artificial Intelligence, South China Hospital, Medical School, Shenzhen University, Shenzhen, Guangdong, China +8
AssayBench: An Assay-Level Virtual Cell Benchmark for LLMs and Agents
May 11, 2026Edward De Brouwer, Carl Edwards, Alexander Wu +9Phenotypes Virtual Cell
Genentech
CellScientist: From Execution Feedback to Auditable Model-Revision Trajectories for Cellular Perturbation Prediction
May 8, 2026Mengran Li, Bo Li, Jiaying Wang +7Virtual Cell Single-Cell Perturbation Prediction
Center for Artificial Intelligence and Robotics (CAIR), Hong Kong Institute of Science and Innovation (HKISI)
AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories
Apr 21, 2026Xue Xia, Chengkai Yao, Mingyu Tsoi +10Virtual Cell Cells
Shanghai Artificial Intelligence Laboratory · The Hong Kong University of Science and Technology (Guangzhou) · University of California San Diego +4

Sep 24, 2026, cs.CLJ/K move · Enter open · S save

How Reproducible Are Evaluation Conclusions? A Self-Audit of LLM-Inferred Prompt Structure

Dipankar Sarkar

Skelf Research

Large Language Model Evaluation Reproducibility Prompt Engineering Model Auditing Virtual Cell Model-Based Bootstrap

Open paper AskPDF arXiv

Evaluations of LLM systems routinely average over small prompt sets and report models as a ranked table. We ask how much confidence such a table deserves, using LLM-based prompt-structure inference as the case study: eight open model variants across five families and 8B to 675B parameters, caching disabled, 293 raw intermediate representations persisted. The measured phenomenon is unstable to begin with. Identical calls do not reliably recover identical structure, with mean node-set Jaccard from 0.39 to 0.96 and 72% of prompt-model cells never node-set-perfect. Auditing the evaluation weakens its conclusions further, and this is our main contribution. Under a joint cluster bootstrap over prompts, only the bottom of the ranking is firm: the two least reproducible models hold rank in 99% and 86% of replicates, the middle four in 27% to 48%, and the top two in 68% each, so the table identifies the worst model reliably but does not reliably identify the best. Two equally defensible rules for merging repeated campaigns change four of eight rows and move the study-wide headline by 7 percentage points. Checking the inferred structure against ground-truth annotations shows reproducibility cannot be read as accuracy. And four of the eight endpoints were withdrawn within ten weeks of measurement, so the study as specified can no longer be run. Small-sample LLM evaluations can therefore look far more definitive than their evidence supports. We recommend reporting rank stability, per-cell provenance, executed sensitivity comparisons, raw per-run outputs, and a measurement date alongside any ranking.

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