Machine learning research with positive real-world impact.
We develop and deploy industry-leading machine learning systems. Our initiatives have the power to uplift large populations, while advancing the field of artificial intelligence.
Causal Inference Enters Its Foundation Model Era
Prediction has become one of the defining strengths of modern AI. Across industries, models are used to forecast what may happen next: whether a patient is at risk, whether a...
Research, /
The Hidden Influence of AI Benchmarks
Research, /
Can Smaller AI Models Solve Text-to-SQL?
Ambitious applied research, positive outcomes
Layer 6 unites research, engineering, and product teams to quickly translate theory into impactful real-world applications.
Research Highlights
Our research is supported by access to massive datasets, close collaboration with world renowned academic faculty, and is deployed in impactful applications.
Our research areas include:
- deep learning and generative AI
- model explainability and trustworthy AI
- time series modelling
- natural language processing
Neural Information Processing Systems
NeurIPS 2026 | Conformal Agent Error Atttribution
Abstract
When multi-agent systems (MAS) fail, identifying where the decisive error occurred is the first step for automated recovery to an earlier state. Error attribution remains a fundamental challenge due to the long interaction traces that large language model-based MAS generate. This paper presents a framework for error attribution based on conformal prediction (CP) which provides finite-sample, distribution-free coverage guarantees. We introduce new algorithms for filtration-based CP designed for sequential data such as agent trajectories. Unlike existing CP algorithms, our approach predicts sets that are contiguous sequences to enable efficient recovery and debugging. We verify our theoretical guarantees on a variety of agents and datasets, show that errors can be precisely isolated, then use prediction sets to rollback MAS to correct their own errors. Our overall approach is model-agnostic, and offers a principled uncertainty layer for MAS error attribution.
Neural Information Processing Systems
NeurIPS 2025 Spotlight | CausalPFN: Amortized Causal Effect Estimation via In-Context Learning
Abstract
Causal effect estimation from observational data is fundamental across various applications. However, selecting an appropriate estimator from dozens of specialized methods demands substantial manual effort and domain expertise. We present CausalPFN, a single transformer that amortizes this workflow: trained once on a large library of simulated data-generating processes that satisfy ignorability, it infers causal effects for new observational datasets out-of-the-box. CausalPFN combines ideas from Bayesian causal inference with the large-scale training protocol of prior-fitted networks (PFNs), learning to map raw observations directly to causal-effects without any task-specific adjustment. Our approach achieves superior average performance on heterogeneous and average treatment effect estimation benchmarks (IHDP, Lalonde, ACIC). Moreover, it shows competitive performance for real-world policy making on uplift modeling tasks. CausalPFN provides calibrated uncertainty estimates to support reliable decision-making based on Bayesian principles. This ready-to-use model does not require any further training or fine-tuning and takes a step toward automated causal inference.
Conference of the European Chapter of the Association for Computational Linguistics
EACL 2026 | Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems
Abstract
Retrieval-augmented generation (RAG) is a prevalent approach for building LLM-based question-answering systems that can take advantage of external knowledge databases. Due to the complexity of real-world RAG systems, there are many potential causes for erroneous outputs. Understanding the range of errors that can occur in practice is crucial for robust deployment. We present a new taxonomy of the error types that can occur in realistic RAG systems, examples of each, and practical advice for addressing them. Additionally, we curate a dataset of erroneous RAG responses annotated by error types. We then propose an auto-evaluation method aligned with our taxonomy that can be used in practice to track and address errors during development.
International Conference on Learning Representations
ICLR 2026 | Textual Bayes: Quantifying Uncertainty in LLM-Based Systems
Abstract
Although large language models (LLMs) are becoming increasingly capable of solving challenging real-world tasks, accurately quantifying their uncertainty remains a critical open problem–one that limits their applicability in high-stakes domains. This challenge is further compounded by the closed-source, black-box nature of many state-of-the-art LLMs. Moreover, LLM-based systems can be highly sensitive to the prompts that bind them together, which often require significant manual tuning (i.e., prompt engineering). In this work, we address these challenges by viewing LLM-based systems through a Bayesian lens. We interpret prompts as textual parameters in a statistical model, allowing us to use a small training dataset to perform Bayesian inference over these prompts. This novel perspective enables principled uncertainty quantification over both the model’s textual parameters and its downstream predictions, while also incorporating prior beliefs about these parameters expressed in free-form text. To perform Bayesian inference–a difficult problem even for well-studied data modalities–we introduce Metropolis-Hastings through LLM Proposals (MHLP), a novel Markov chain Monte Carlo (MCMC) algorithm that combines prompt optimization techniques with standard MCMC methods. MHLP is a turnkey modification to existing LLM pipelines, including those that rely exclusively on closed-source models. Empirically, we demonstrate that our method yields improvements in both predictive accuracy and uncertainty quantification (UQ) on a range of LLM benchmarks and UQ tasks. More broadly, our work demonstrates a viable path for incorporating methods from the rich Bayesian literature into the era of LLMs, paving the way for more reliable and calibrated LLM-based systems.
International Conference on Machine Learning
ICML 2026 | Beyond Procedure: Substantive Fairness in Conformal Prediction
Abstract
Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in downstream decision-making remains underexplored. Moving beyond CP as a standalone operation (procedural fairness), we analyze the holistic decision-making pipeline to evaluate substantive fairness-the equity of downstream outcomes. Theoretically, we derive an upper bound that decomposes prediction-set size disparity into interpretable components, clarifying how label-clustered CP helps control method-driven contributions to unfairness. To facilitate scalable empirical analysis, we introduce an LLM-in-the-loop evaluator that approximates human assessment of substantive fairness across diverse modalities. Our experiments reveal that label-clustered CP variants consistently deliver superior substantive fairness. Finally, we empirically show that equalized set sizes, rather than coverage, strongly correlate with improved substantive fairness, enabling practitioners to design more fair CP systems.
Our research areas include:
- deep learning and generative AI
- model explainability and trustworthy AI
- time series modelling
- natural language processing
Big vision, deep roots
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The founders of Layer 6 are also the founders of the Vector Institute for Artificial Intelligence, and we maintain active research collaborations with Vector faculty. 
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Signal 1 spun out of the research collaboration between Layer 6 and St. Michael’s Hospital, and provides cutting-edge AI platforms for real time monitoring of patient outcomes. 
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Radical Ventures was launched by the founders of Layer 6 to incubate and support leading AI startups in Canada and abroad. With over a billion dollars raised, Radical has become one of the premier AI focused venture funds in the world. 
Impactful partnerships
Originally founded in 2011, Layer 6 now forms the AI centre of excellence of TD Bank Group. Layer 6 impacts the lives of over 27 million customers, helping more people achieve their financial goals and needs through AI systems founded on the responsible use of AI.
Layer 6 embraces opportunities to contribute to the Canadian AI ecosystem. The founders of Layer 6 played pivotal roles in launching the Vector Institute, Radical Ventures, and Signal 1. Together these entities are integral in driving the Canadian AI innovation, from research to product incubation to scale-up. We continue to collaborate with leading academic institutions globally.
Passion to learn, driven to succeed
Our team comes from globally diverse backgrounds and we care deeply about fostering an inclusive culture. We learn from each other and win together. We are united by our passion for deep learning and a desire to apply our skills to have an outsized and positive impact on the future.
Meet some of our team
Develop your career at Layer 6
We’re growing our team with people driven to be at the cutting edge of machine learning in research, engineering, and impactful applications.
