Mathias MJ Bellaiche
Authored Publications
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Semantic Representation for Agentic Self-Play in Automated Laboratories
Yves Delley
John Coller
Zachary Cutts
Alexander Daryin
Jun Kim
Oscar Donosa
Srinidhi B Seshadri
Tanner Clary
Sandy Klemm
Petar Sirkovic
arXiv (2026)
Preview abstract
This technical report introduces a lab controller system designed as the foundational infrastructure for agentic self-driving laboratories. The core challenge in modern biological research is the immense scale of the search spaces, which are too vast for traditional experimental methods. Our solution is a lab controller that bridges the semantic gap between AI reasoning and the physical wet lab by managing a high-fidelity semantic model, or digital twin, of the laboratory workcell. This live, queryable model provides four crucial services to AI agents:
Introspection: Agents can query the lab's real-time state (e.g., reagent volumes, instrument status) to inform their planning and decision-making, grounding their logic in physical reality. Simulation: Agents can safely test experimental protocols in a time-compressed, virtual environment before physical execution. This is achieved via a discrete accounting engine that validates logistical and scientific feasibility, preventing errors, resource waste, and hardware damage. Trace Persistence: The controller records a complete, multimodal history of every experiment into a research dossier. This dataset is processed by a Wet2Dry pipeline into structured trajectories for training agents via imitation learning and offline reinforcement learning, using advanced techniques to compute rewards from noisy data. Safe Execution: The controller translates agent commands into hardware actions within a multi-layered safety architecture inspired by frameworks like Safe-SDL. This uses Verified Operating Domains (ODDs) to enforce pre-verified boundaries and Control Barrier Functions (CBFs) as real-time safety filters to prevent failures and ensure safe operation.
We have validated these services through case studies demonstrating that the controller is essential for: Robust Debugging: The detailed traces allowed us to diagnose subtle physical-world failures (metamerism) that would have corrupted an AI agent's learning process. Pre-emptive Protocol Verification: The simulation service caught critical scientific and logical errors in agent-generated code before they could cause an experiment to fail. End-to-End Automation: The controller successfully orchestrated a complex, multi-instrument workflow driven entirely by a high-level AI agent command.
Looking forward, this lab controller infrastructure enables a new paradigm of research. It allows for large-scale combinatorial assays that can explore thousands of experimental conditions simultaneously. The resulting data closes the loop on agentic ideation, allowing our AI systems to learn and improve with each experimental cycle, moving beyond simple hill-climbing to sophisticated, multi-variable optimization. By providing the tools to navigate these vast search spaces, the lab controller is a foundational step toward solving grand challenges in biology. Our work is guided by a strong commitment to safety and responsible innovation, incorporating a Human-in-the-Loop (HITL) review portal that uses the digital twin for transparent oversight and an automated biosecurity gateway with state-of-the-art, function-based screening to ensure all synthesized materials are safe by design. This report outlines the what of our system, its capabilities and strategic importance, with further technical details on the how reserved for future publications.
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Aptamers as a sandbox for lab-in-the-loop agentic molecular discovery
Zach Cutts
John Coller
Rachel Chen
Hua Tang
Ryder Easterlin
Dina Zverinski
Petar Sirkovic
arXiv preprint arXiv:2610.24806 (2026)
Preview abstract
Despite breakthroughs in AI-driven molecular generation, existing discovery pipelines operate largely in silico, with wet-lab feedback reserved for validation rather than iterative improvement. Whether large language model (LLM)-based reasoning agents can accelerate de novo discovery by iterating with lab data remains unproven.
Here we use protein-binding aptamer design as a sandbox (1-2 week cycles, $1-$3 per candidate) to demonstrate a wet lab in a closed loop with AI systems. We tasked agentic pipelines with developing aptamer binders against α-thrombin and myoglobin across three design-test-improve cycles (∼1,000 candidates per round; ∼4,400 constructs). Statistically significant, bead-corrected on-target hit rates among new candidates rose across rounds from 0.0% in Round 1 to 26.0% in Round 2 and 42.1% in Round 3 on α-thrombin (37.1% excluding literature motif descendants), and from 10.2% in Round 1 to 3.7% in Round 2 and 33.5% in Round 3 on myoglobin. Single-clone bead-binding titrations of eight full-length α-thrombin candidates confirmed concordance with pooled sequencing enrichment (Pearson r=0.90). In these assays, literature-derived motifs engineered by the agents to reduce off-target reactivity in the pooled screen reached apparent affinities of KD=24.6--133 nM, whereas de novo scaffolds showed low-micromolar binding (KD>1 μM).
We also report how our pipelines used myoglobin binding data to exploit the G-quadruplex binder space, diagnose cross-reactivity as on-target specificity dropped from 75.0% to 19.1%, and recover by developing strategies that restored specificity to 72.4%. We demonstrate that empirical lab data is more valuable than existing computational proxies, showing how iterative experimental feedback enables AI agents to diagnose assay artifacts and refine molecular designs across rounds.
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LLM-based Lossless Text Simplification and its Effect on User Comprehension and Cognitive Load
Theo Guidroz
Diego Ardila
Jimmy Li
Adam Mansour
Paul Jhun
Nina Gonzalez
Xiang Ji
Mike Sanchez
Sujay Kakarmath
Miguel Ángel Garrido
Faruk Ahmed
Divyansh Choudhary
Jay Hartford
Georgina Xu
Henry Serrano
Yifan Wang
Jeff Shaffer
Eric (Yifan) Cao
Sho Fujiwara
Peggy Bui
arXiv (2025)
Preview abstract
Information on the web, such as scientific publications and Wikipedia, often surpasses users' reading level. To help address this, we used a self-refinement approach to develop a LLM capability for minimally lossy text simplification. To validate our approach, we conducted a randomized study involving 4563 participants and 31 texts spanning 6 broad subject areas: PubMed (biomedical scientific articles), biology, law, finance, literature/philosophy, and aerospace/computer science. Participants were randomized to viewing original or simplified texts in a subject area, and answered multiple-choice questions (MCQs) that tested their comprehension of the text. The participants were also asked to provide qualitative feedback such as task difficulty. Our results indicate that participants who read the simplified text answered more MCQs correctly than their counterparts who read the original text (3.9% absolute increase, p<0.05). This gain was most striking with PubMed (14.6%), while more moderate gains were observed for finance (5.5%), aerospace/computer science (3.8%) domains, and legal (3.5%). Notably, the results were robust to whether participants could refer back to the text while answering MCQs. The absolute accuracy decreased by up to ~9% for both original and simplified setups where participants could not refer back to the text, but the ~4% overall improvement persisted. Finally, participants' self-reported perceived ease based on a simplified NASA Task Load Index was greater for those who read the simplified text (absolute change on a 5-point scale 0.33, p<0.05). This randomized study, involving an order of magnitude more participants than prior works, demonstrates the potential of LLMs to make complex information easier to understand. Our work aims to enable a broader audience to better learn and make use of expert knowledge available on the web, improving information accessibility.
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LLM-based Lossless Text Simplification and its Effect on User Comprehension and Mental Load
Theo Guidroz
Diego Ardila
Jimmy Li
Adam Mansour
Paul Jhun
Nina Gonzalez
Xiang Ji
Mike Sanchez
Sujay Kakarmath
Miguel Ángel Garrido
Faruk Ahmed
Divyansh Choudhary
Jay Hartford
Georgina Xu
Henry Serrano
Yifan Wang
Jeff Shaffer
Eric (Yifan) Cao
Sho Fujiwara
Peggy Bui
arXiv (2025)
Preview abstract
Information on the web, such as scientific publications and Wikipedia, often surpasses users' reading level. To help address this, we used a self-refinement approach to develop a LLM capability for minimally lossy text simplification. To validate our approach, we conducted a randomized study involving 4563 participants and 31 texts spanning 6 broad subject areas: PubMed (biomedical scientific articles), biology, law, finance, literature/philosophy, and aerospace/computer science. Participants were randomized to viewing original or simplified texts in a subject area, and answered multiple-choice questions (MCQs) that tested their comprehension of the text. The participants were also asked to provide qualitative feedback such as task difficulty. Our results indicate that participants who read the simplified text answered more MCQs correctly than their counterparts who read the original text (3.9% absolute increase, p<0.05). This gain was most striking with PubMed (14.6%), while more moderate gains were observed for finance (5.5%), aerospace/computer science (3.8%) domains, and legal (3.5%). Notably, the results were robust to whether participants could refer back to the text while answering MCQs. The absolute accuracy decreased by up to ~9% for both original and simplified setups where participants could not refer back to the text, but the ~4% overall improvement persisted. Finally, participants' self-reported perceived ease based on a simplified NASA Task Load Index was greater for those who read the simplified text (absolute change on a 5-point scale 0.33, p<0.05). This randomized study, involving an order of magnitude more participants than prior works, demonstrates the potential of LLMs to make complex information easier to understand. Our work aims to enable a broader audience to better learn and make use of expert knowledge available on the web, improving information accessibility.
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UWB Radar-based Heart Rate Monitoring: A Transfer Learning Approach
Elzbieta Gruzewska
Sebastien Baur
Matthew Baugh
Sharanya Srinivas
Matthew Thompson
Pramod Rudrapatna
Michael A. Sanchez
Lawrence Z. Cai
Timothy JA Chico
Robert F Storey
Emily Maz
Umesh Telang
Shravya Shetty
Mayank Daswani
arXiv (2025)
Preview abstract
Radar technology presents untapped potential for continuous, contactless, and passive heart rate monitoring via consumer electronics like mobile phones. However the variety of available radar systems and lack of standardization means that a large new paired dataset collection is required for each radar system. This study demonstrates transfer learning between frequency-modulated continuous wave (FMCW) and impulse-radio ultra-wideband (IR-UWB) radar systems, both increasingly integrated into consumer devices. FMCW radar utilizes a continuous chirp, while IR-UWB radar employs short pulses. Our mm-wave FMCW radar operated at 60 GHz with a 5.5 GHz bandwidth (2.7 cm resolution, 3 receiving antennas [Rx]), and our IR-UWB radar at 8 GHz with a 500 MHz bandwidth (30 cm resolution, 2 Rx). Using a novel 2D+1D ResNet architecture we achieved a mean absolute error (MAE) of 0.85 bpm and a mean absolute percentage error (MAPE) of 1.42% for heart rate monitoring with FMCW radar (N=119 participants, an average of 8 hours per participant). This model maintained performance (under 5 MAE/10% MAPE) across various body positions and heart rate ranges, with a 98.9% recall. We then fine-tuned a variant of this model, trained on single-antenna and single-range bin FMCW data, using a small (N=376, avg 6 minutes per participant) IR-UWB dataset. This transfer learning approach yielded a model with MAE 4.1 bpm and MAPE 6.3% (97.5% recall), a 25% MAE reduction over the IR-UWB baseline. This demonstration of transfer learning between radar systems for heart rate monitoring has the potential to accelerate its introduction into existing consumer devices.
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Automated LOINC Standardization Using Pre-trained Large Language Models
Eric Loreaux
Emma Chesley
Paul Gamble
Martin Seneviratne
Ming-Jun Chen
PMLR (2022), pp. 343-355
Preview abstract
Harmonization of local source concepts to standard clinical terminologies is a prerequisite for multi-center data aggregation and sharing. Challenges in automating the mapping process stem from the idiosyncratic source encoding schemes adopted by different health systems and the lack of large publicly available training data. In this study, we aim to develop a scalable and generalizable machine learning tool to facilitate standardizing laboratory observations to the Logical Observation Identifiers Names and Codes (LOINC). Specifically, we leverage the contextual embedding from pre-trained T5 models and propose a two-stage fine-tuning strategy based on contrastive learning to enable learning in a few-shot setting without manual feature engineering. Our method utilizes unlabeled general LOINC ontology and data augmentation to achieve impressive performance on retrieving the most relevant LOINC targets when limited amount of labeled data are available. We further show that our model generalizes well to unseen targets. Taken together, our approach shows great potential to reduce manual effort in LOINC standardization and can be easily extended to mapping other terminologies.
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