Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 11251 publications
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Generative AI is reshaping software development, yet its psychological impact remains under-researched. During May and August 2025 we conducted reflexive thematic analysis of interviews with 12 senior engineers (≥5 years experience) recruited from Western technology hubs to explore shifts in professional identity. We identify a central transition from "coder to conductor," where AI acts as a cognitive partner. Key findings include: (1) a re-architecting of focus from implementation to strategy; (2) a shift in productivity metrics from output to impact; and (3) a dual-impact on agency, where AI empowers autonomy but threatens competence through de-skilling anxieties. These findings suggest that as implementation becomes commoditised, organisational training and career progression must prioritise architectural mastery and metacognitive oversight to ensure sustained developer motivation and system integrity.
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System for a Secure, Outcome-Based Synthetic Labor Market Using Trusted Execution Environments
Patent (2026)
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Some artificial intelligence provisioning models that function as tools for human users or rely on labor arbitrage can present challenges for organizations, such as managing personnel rather than task outcomes and introducing data security risks. An architecture is described for an outcome-based synthetic labor market in which autonomous computational agents can be compensated based on verified task completion. The framework can leverage trusted execution environments to create secure hardware enclaves for processing sensitive data, which can render the data cryptographically inaccessible to a host system or agent provider. This approach can facilitate a secure, transactional market for autonomous professional execution, which may enable a shift from managing labor resources to procuring verified outcomes from a pool of specialized agents.
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Managing compiler build errors that can arise during infrastructure upgrades in large, polyglot codebases may be challenging, as manual remediation can be slow and some automated tools may not support modern language syntax. A system can provide automated error remediation by ingesting compiler diagnostics and analyzing source code using an Abstract Syntax Tree (AST). A recursive scope resolution algorithm, for example, can traverse the AST to identify a specific and narrowly-scoped code block at which to apply an error suppression. Conversely, this algorithmic complexity can be bypassed when lexical scope resolution is not required, and the system can identify the specific location of error suppressions directly from the error's exact coordinates. The system may then generate and apply language-specific patches, such as structured comments for JavaScript source files or line-scoped comments for TypeScript source files, for example, by using a transactional rewrite engine. This approach can provide a scalable method for managing automated code remediation, which may facilitate infrastructure upgrades by reducing the need for manual intervention.
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Object-Counting for remote-sensing (RS) imagery is raising increasing research interest due to its crucial role in a wide and diverse set of applications. While several promising methods for RS object-counting have been proposed, existing methods focus on a closed, pre-defined set of object classes. This limitation necessitates costly re-annotation and model re-training to adapt current approaches for counting of novel objects that have not been seen during training, and severely inhibits their application in dynamic, real-world monitoring scenarios.
To address this gap, in this work we propose RS-OVC - an adaptation of existing work for Open Vocabulary Counting (OVC) approach from general computer vision to the RS domain. We show that our model is capable of accurate counting of novel object classes, that are unseen during training, based solely on textual and/or visual conditioning.
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Automating AI research differs from general software engineering due to computationally expensive evaluation (e.g., model training) and opaque performance attribution. Current LLM-based agents struggle here, often generating monolithic scripts that ignore execution costs and causal factors. We introduce MARS (Modular Agent with Reflective Search), a framework optimized for autonomous AI research. MARS relies on three pillars: (1) Budget-Aware Planning via cost-constrained Monte Carlo Tree Search (MCTS) to explicitly balance performance with execution expense; (2) Modular Construction, employing a "Design-Decompose-Implement" pipeline to manage complex research repositories; and (3) Comparative Reflective Memory, which addresses credit assignment by analyzing solution differences to distill high-signal insights. MARS achieves state-of-the-art performance among open-source frameworks on MLE-Bench under comparable settings, maintaining competitiveness with the global leaderboard's top methods. Furthermore, the system exhibits qualitative "Aha!" moments, where 63% of all utilized lessons originate from cross-branch transfer, demonstrating that the agent effectively generalizes insights across search paths.
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Exponential quantum advantage in processing massive classical data
Haimeng Zhao
Alexander Zlokapa
John Preskill
Hsin-Yuan (Robert) Huang
arXiv:2604.07639 (2026)
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Broadly applicable quantum advantage, particularly in classical data processing and machine learning, has been a fundamental open problem. In this work, we prove that a small quantum computer of polylogarithmic size can perform large-scale classification and dimension reduction on massive classical data by processing samples on the fly, whereas any classical machine achieving the same prediction performance requires exponentially larger size. Furthermore, classical machines that are exponentially larger yet below the required size need superpolynomially more samples and time. We validate these quantum advantages in real-world applications, including single-cell RNA sequencing and movie review sentiment analysis, demonstrating four to six orders of magnitude reduction in size with fewer than 60 logical qubits. These quantum advantages are enabled by quantum oracle sketching, an algorithm for accessing the classical world in quantum superposition using only random classical data samples. Combined with classical shadows, our algorithm circumvents the data loading and readout bottleneck to construct succinct classical models from massive classical data, a task provably impossible for any classical machine that is not exponentially larger than the quantum machine. These quantum advantages persist even when classical machines are granted unlimited time or if BPP=BQP, and rely only on the correctness of quantum mechanics. Together, our results establish machine learning on classical data as a broad and natural domain of quantum advantage and a fundamental test of quantum mechanics at the complexity frontier.
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Peeking Ahead of the Field Study: Exploring VLM Personas as Support Tools for Embodied Studies in HCI
Xinyue Gui
Ding Xia
Mark Colley
Yuan Li
Vishal Chauhan
Anubhav Anubhav
Ehsan Javanmardi
Stela Hanbyeol Seo
Chia-Ming Chang
Manabu Tsukada
Takeo Igarashi
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI 26)
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Field studies are irreplaceable but costly, time-consuming, and error-prone, which need careful preparation. Inspired by rapid-prototyping in manufacturing, we propose a fast, low-cost evaluation method using Vision-Language Model (VLM) personas to simulate outcomes comparable to field results. While LLMs show human-like reasoning and language capabilities, autonomous vehicle (AV)-pedestrian interaction requires spatial awareness, emotional empathy, and behavioral generation. This raises our research question: To what extent can VLM personas mimic human responses in field studies? We conducted parallel studies: 1) one real-world study with 20 participants, and 2) one video-study using 20 VLM personas, both on a street-crossing task. We compared their responses and interviewed five HCI researchers on potential applications. Results show that VLM personas mimic human response patterns (e.g., average crossing times of 5.25 s vs. 5.07 s) lack the behavioral variability and depth. They show promise for formative studies, field study preparation, and human data augmentation.
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ConvApparel: A Benchmark Dataset and Validation Framework for User Simulators in Conversational Recommenders
Jihwan Jeong
The 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL-26), Rabat, Morocco (2026)
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LLM-based user simulators are a scalable solution for improving conversational AI, but a critical realism gap undermines their effectiveness. To close this gap, we introduce a framework for building and validating high-fidelity simulators. We present a novel dataset of human-AI shopping conversations designed to capture a wide spectrum of user experiences. To measure fidelity, we propose a hybrid evaluation protocol that combines statistical alignment with a learned, discriminator-based Human-Likeness Score. Our most sophisticated simulator, trained via reinforcement learning with iterative critique, achieves a significant leap in realism. Critically, we demonstrate through counterfactual validation that our simulator—trained exclusively on optimal interactions—realistically adapts its behavior to suboptimal system responses, mirroring real user reactions and marking a key advance in creating reliable simulators for robust AI development.
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An experimental evaluation of an AI-powered interactive learning platform
Nicole Miller
Yael Haramaty
Lidan Hackmon
Lior Belinsky
Abraham Oritz Tapia
Lucy Tootill
Scott Siebert
Frontiers in Artificial Intelligence (2026) (to appear)
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Generative AI, which is capable of transforming static content into dynamic learning experiences, holds the potential to revolutionize student engagement in educational contexts. However, questions still remain around whether or not these tools are effective at facilitating student learning. In this research, we test the effectiveness of an AI-powered platform incorporating multiple representations and assessment through Learn Your Way, an experimental research platform that transforms textbook chapters into dynamic visual and audio representations. Through a between-subjects, mixed methods experiment with 60 US-based students, we demonstrate that students who used Learn Your Way had a more positive learning experience and had better learning outcomes compared to students learning the same content through a digital textbook. These findings indicate that AI-driven tools, capable of providing choice among interactive representations of content, constitute an effective and promising method for enhancing student learning.
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Bridging the Dimensionality Gap: A Taxonomy and Survey of 2D Vision Model Adaptation for 3D Analysis
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The remarkable success of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in 2D computer vision has catalyzed significant research into their adaptation for the complex domain of 3D analysis. However, a fundamental dichotomy exists between the regular, dense grid of 2D images and the irregular, sparse nature of 3D data formats such as point clouds and meshes. This paper provides a comprehensive survey and a novel intellectual framework for navigating this burgeoning field. Our core contribution is a new taxonomy that organizes adaptation strategies into three distinct families: (1) Data-centric methods, which project 3D data into 2D formats to leverage off-the-shelf 2D models; (2) Architecture-centric methods, which design intrinsic network modules to directly process 3D data; and (3) Hybrid methods, which synergistically combine pre-trained 2D features with 3D modeling processing pipelines to benefit from both rich visual priors and explicit geometric reasoning. Through this taxonomic lens, we conduct a systematic review and qualitative synthesis of the field. We illuminate the fundamental trade-offs between these families concerning computational complexity, reliance on large-scale pre-training, and the preservation of geometric inductive biases. Based on this analysis, we identify and discuss critical open challenges and chart promising future research directions, including the development of 3D foundation models, advancements in self-supervised learning for geometric data, and the deeper integration of multi-modal signals. This survey serves as an essential resource and roadmap for researchers seeking to understand and advance the state-of-the-art in 3D computer vision.
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Reasoning-Driven Synthetic Data Generation and Evaluation
Tim R. Davidson
Benoit Seguin
Transactions on Machine Learning Research (2026)
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Although many AI applications of interest require specialized multi-modal models, relevant data to train such models is inherently scarce or inaccessible. Filling these gaps with human annotators is prohibitively expensive, error-prone, and time-consuming, leading model builders to increasingly consider synthetic data as a scalable alternative. However, existing synthetic data generation methods often rely on manual prompts, evolutionary algorithms, or extensive seed data from the target distribution — limiting their scalability, explainability, and control. In this paper, we introduce Simula: a novel reasoning-driven framework for data generation and evaluation. It employs a seedless, agentic approach to generate synthetic datasets at scale, allowing users to define desired dataset characteristics through an explainable and controllable process that enables fine-grained resource allocation. We show the efficacy of our approach on a variety of datasets, rigorously testing both intrinsic and downstream properties. Our work (1) offers guidelines for synthetic data mechanism design, (2) provides insights into generating and evaluating synthetic data at scale, and (3) unlocks new opportunities for developing and deploying AI in domains where data scarcity or privacy concerns are paramount.
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This framework manages AI agents by establishing behavioral boundaries and a persistent identity. It uses a multi-layered stack, combining safety rules with brand guidelines, to shape an agent's reasoning. Features include authority decay to limit power if confidence drops and memory segmentation to prevent data tampering. Centralized oversight ensures these digital representatives remain aligned with company policies through continuous monitoring and testing.
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We introduce ALPS (Activation-based Length Prediction for Scheduling), a method for predicting LLM generation length from prefill activations before any tokens are generated. Unlike existing approaches that require model fine-tuning or complex entropy-weighted pooling, ALPS uses a simple linear probe on the last-token activation at intermediate layers. We discover that generation length is encoded in prefill representations: a ridge regression probe achieves R-squared > 0.85 across three model families. Validation across Llama-3.1-8B, Gemma-2-9B, and Qwen-2.5-7B demonstrates: (1) intermediate layers generally perform well, with some architectural variation; (2) simple last-token extraction outperforms complex pooling strategies; (3) activations improve substantially over surface-feature baselines (24 percentage points over input length plus lexical features). The best models achieve R-squared = 0.943 (Gemma), R-squared = 0.880 (Llama), and R-squared = 0.857 (Qwen) with MAE of 38-80 tokens. All test prompts terminated naturally (100% EOS), eliminating truncation confounds. While our evaluation uses 200 curated prompts—sufficient for demonstrating the phenomenon but requiring broader validation—cross-validation confirms generalization beyond training data. ALPS enables practical applications including budget-constrained inference, request scheduling, and resource allocation. The probe adds negligible overhead (~16KB direction vector, single dot product), making ALPS practical for production deployment.
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VISTA: A Test-Time Self-Improving Video Generation Agent
Xuan Long Do
Hootan Nakhost
The IEEE/CVF Conference on Computer Vision and Pattern Recognition (to appear) (2026)
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Despite rapid advances in text-to-video (T2V) synthesis, generated video quality remains critically dependent on precise user prompts. Existing test-time optimization methods, successful in other domains, struggle with the multi-faceted nature of video. To address this, we introduce VISTA, a novel multi-agent system that autonomously refines prompts to improve video generation. VISTA operates in an iterative loop, first decomposing a user's idea into a structured temporal plan. After generation, the best video is identified through a robust pairwise tournament. This winning video is then critiqued by a trio of specialized agents focusing on visual, audio, and contextual fidelity. Finally, a reasoning agent synthesizes this feedback to introspectively rewrite and enhance the prompt for the next generation cycle. To rigorously evaluate our proposed approach, we introduce MovieGen-Bench, a new benchmark of diverse single- and multi-scene video generation tasks. Experiments show that while prior methods yield inconsistent gains, VISTA consistently improves video quality, achieving up to 60% pairwise win rate against state-of-the-art baselines. Human evaluators concur, preferring VISTA's outputs in 68% of comparisons.
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A Computer Vision Problem in Flatland
Erin Connelly
Annalisa Crannell
Timothy Duff
Rekha R. Thomas
SIAM Journal on Applied Algebra and Geometry, 10 (2026), pp. 14-45
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When is it possible to project two sets of labeled points of equal cardinality lying in a pair of projective planes to the same image on a projective line? We give a complete answer to this question, obtaining the following results. We first show that such a pair of projections exist if and only if the two point sets are themselves images of a common point set in projective space. Moreover, we find that for generic pairs of point sets, a common projection exists if and only if their cardinality is at most seven. In these cases, we give an explicit description of the loci of projection centers that enable a common image.
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