1 citations · 1 across the 9 of their papers we have counts for
9 papers
Ambig-DS: A Benchmark for Task-Framing Ambiguity in Data-Science Agents
Josefa Lia Stoisser, Marc Boubnovski Martell, Sidsel Boldsen +2
As data-science agents shift from co-pilots to auto-pilots, silent misframing becomes a critical failure mode. Agents quietly commit to plausible but unintended task framings, prod…
Measuring Black-Box Confidence via Reasoning Trajectories: Geometry, Coverage, and Verbalization
Marc Boubnovski Martell, Josefa Lia Stoisser, Kaspar Märtens +4
Reliable confidence estimation enables safe deployment of chain-of-thought (CoT) reasoning through text-only APIs. Yet the dominant black-box baseline, self-consistency over K samp…
MechPert: Mechanistic Consensus as an Inductive Bias for Unseen Perturbation Prediction
Marc Boubnovski Martell, Josefa Lia Stoisser, Lawrence Phillips +6
Predicting transcriptional responses to unseen genetic perturbations is essential for understanding gene regulation and prioritizing large-scale perturbation experiments. Existing…
Towards Label-Free Biological Reasoning Synthetic Dataset Creation via Uncertainty Filtering
Josefa Lia Stoisser, Lawrence Phillips, Aditya Misra +5
Synthetic chain-of-thought (CoT) traces are widely used to train large reasoning models (LRMs), improving generalization by providing step-level supervision. Yet most approaches re…
Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoning
Josefa Lia Stoisser, Marc Boubnovski Martell, Lawrence Phillips +6
Large language model (LLM) agents are increasingly deployed in structured biomedical data environments, yet they often produce fluent but overconfident outputs when reasoning over…
SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction
Lawrence Phillips, Marc Boubnovski Martell, Aditya Misra +4
Predicting cellular responses to genetic perturbations represents a fundamental challenge in systems biology, critical for advancing therapeutic discovery and virtual cell modeling…
STRuCT-LLM: Unifying Tabular and Graph Reasoning with Reinforcement Learning for Semantic Parsing
Josefa Lia Stoisser, Marc Boubnovski Martell, Lawrence Phillips +2
We propose STRuCT-LLM, a unified framework for training large language models (LLMs) to perform structured reasoning over both relational and graph-structured data. Our approach jo…
Sparks of Tabular Reasoning via Text2SQL Reinforcement Learning
Josefa Lia Stoisser, Marc Boubnovski Martell, Julien Fauqueur
This work reframes the Text-to-SQL task as a pathway for teaching large language models (LLMs) to reason over and manipulate tabular data--moving beyond the traditional focus on qu…
Query, Don't Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries
Josefa Lia Stoisser, Marc Boubnovski Martell, Kaspar Märtens +4
Electronic health records (EHRs) contain richly structured, longitudinal data essential for predictive modeling, yet stringent privacy regulations (e.g., HIPAA, GDPR) often restric…