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 11421 publications
Differential Sensitivity of Impedance Plethysmography and Photoplethysmography Sensors to Temperature-Induced Peripheral Vasoconstriction
Seobin Jung
Seamus Thomson
Alexandros Pantelopoulos
Lindsey Sunden
Pete Richards
Shwetak Patel
Sam Sheng
Scientific Reports (2026)
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Impedance plethysmography (IPG) and photoplethysmography (PPG) are non-invasive techniques for measuring blood volume changes. This study investigated the differential responses of IPG and PPG to temperature-mediated vasoconstriction induced by localized cooling. Twenty-one participants underwent control and treatment conditions, with fake or real ice cubes applied to the forearm. Blood pressure remained stable, while heart rate decreased. PPG signal amplitude significantly decreased with cooling (p_adj = 0.004), indicating sensitivity to superficial blood flow changes. In contrast, IPG signal amplitude remained stable (p_adj = 1.0). No statistically significant differences were observed in timing-derived metrics. These findings suggest IPG is less sensitive to superficial changes in blood flow than PPG, and may be more suitable for monitoring deeper blood flow. This study provides insights into the distinct sensitivities of IPG and PPG, with implications for wearable device development and cardiovascular monitoring.
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Beyond Tsybakov: Model Margin Noise and H-Consistency Bounds
The Nineteenth International Symposium on Artificial Intelligence and Mathematics (ISAIM 2026)
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We introduce a new low-noise condition for classification, the *Model Margin Noise (MM noise)* assumption, and derive enhanced $H$-consistency bounds under this condition. MM noise is *weaker* than Tsybakov noise condition: it is implied by Tsybakov noise condition but can hold even when Tsybakov fails, because it depends on the discrepancy between a given hypothesis and the Bayes-classifier rather than on the intrinsic distributional minimal margin (see Figure 1 for an illustration of an explicit example). This hypothesis-dependent assumption yields enhanced $H$-consistency bounds for both binary and multi-class classification. Our results extend the enhanced $H$-consistency bounds of Mao, Mohri, and Zhong (2025a) with the same favorable exponents but under a weaker assumption than the Tsybakov noise condition; they interpolate smoothly between linear and square-root regimes for intermediate noise levels. We also instantiate these bounds for common surrogate loss families and provide illustrative tables.
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The Ontic-Epistemic Distinction: Implications for General Intelligence
Master's Thesis (2026) (to appear)
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The current pursuit of robust machine intelligence is largely predicated on a substrate independent, computational functionalist view of cognition, where sufficiently complex computational processing is expected to eventually yield generalized reasoning. This paper explores the ontological distinctions between these computational frameworks and biological cognition, specifically how these differences impact the capacity for semantic understanding. By analyzing phenomena such as the "reversal curse" where models fail to generalize the symmetry in identity relations (A=B implies B=A), and performance on novel reasoning benchmarks (e.g., ARC-AGI), this paper examines whether current model limitations are transient artifacts of scale or indicative of a distinct architectural category. Integrating Stevan Harnad’s “symbol grounding problem” with Evan Thompson’s biological model of “intrinsic normativity,” I investigate whether robust general intelligence might require sense-making: a process distinct from information processing, whereby an agent’s internal states are causally coupled with its environment via survival or system-wide stakes which grounds symbols in meaning. Current Large Language Models (LLMs) appear to lack this intrinsic normativity, and consequently may operate primarily as epistemic instruments rather than ontic agents. By introducing the concept of “ontic grounding”, this paper presents a potential framework for distinguishing between the simulation of reasoning and true understanding, which could have implications for AI safety and governance.
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We study the d-dimensional knapsack problem. We are given a set of items, each with a d-dimensional cost vector and a profit, along with a d-dimensional budget vector. The goal is to select a set of items that do not exceed the budget in all dimensions and maximize the total profit. A polynomial-time approximation scheme (PTAS) with running time n^{Θ(d/{ε})} has long been known for this problem, where {ε} is the error parameter and n is the encoding size. Despite decades of active research, the best running time of a PTAS has remained O(n^{⌈ d/{ε} ⌉ - d}). Unfortunately, existing lower bounds only cover the special case with two dimensions d = 2, and do not answer whether there is a n^{o(d/({ε)})}-time PTAS for larger values of d.
In this work, we show that the running times of the best-known PTAS cannot be improved up to a polylogarithmic factor assuming the Exponential Time Hypothesis (ETH). Our techniques are based on a robust reduction from 2-CSP, which embeds 2-CSP constraints into a desired number of dimensions. Then, using a recent result of [Bafna Karthik and Minzer, STOC'25], we succeed in exhibiting tight trade-off between d and {ε} for all regimes of the parameters assuming d is sufficiently large. Informally, our result also shows that under ETH, for any function f there is no f(d/({ε)}) ⋅ n^{õ(d/({ε)})}-time (1-{ε})-approximation for d-dimensional knapsack, where n is the number of items and õ hides polylogarithmic factors in d/({ε)}.
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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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While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning and iterative error correction. Furthermore, standard single-stream prompting proves brittle when models encounter novel abstractions or rigorous domain constraints. We introduce PoTRE (Poly-Topological Reasoning Ensembles), a heterogeneous framework that decouples inference into four agents: (1) Adversarial Refinement Agent, (2) Hierarchical strategic Planning Agent, (3) Spectrum Search Agent, and (4) Direct Chain Agent. A final Task-Adaptive Aggregation Layer dynamically reconciles these perspectives -- via final candidate selection, semantic synthesis, or neuro-symbolic verification -- to produce a robust global solution. We evaluate PoTRE on three frontier benchmarks: ARC-AGI-2, Humanity's Last Exam (HLE), and PRBench Finance. PoTRE achieves state-of-the-art accuracy of 49.92% on HLE, surpassing the previous best official score. We demonstrate that this architectural heterogeneity achieves improved reasoning performance using similar or fewer inference tokens compared to heavily scaled homogeneous baselines.
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An AI system to help scientists write expert-level empirical software
Eser Aygün
Anastasiya Belyaeva
Gheorghe Comanici
Hao Cui
Renee Johnston
Zahra Shamsi
David Smalling
James Thompson
Sarah Martinson
Lai Wei
Yuchen Zhou
Qian-Ze Zhu
Matthew Abraham
Erica Brand
Anna Bulanova
Jeffrey Cardille
Chris Co
Scott Ellsworth
Grace Joseph
Malcolm Kane
Ryan Krueger
Johan Kartiwa
Jackson Cui
Paul Raccuglia
Julie Wang
Kat Chou
James Manyika
Lizzie Dorfman
Shibl Mourad
Nature (2026)
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The cycle of scientific discovery is frequently bottlenecked by the slow, manual creation of software to support computational experiments. To address this, we present Empirical Research Assistance (ERA), an AI system that creates expert-level scientific software whose goal is to maximize a quality metric. The system uses a Large Language Model (LLM) and Tree Search (TS) to systematically improve the quality metric and intelligently navigate the large space of possible solutions. ERA achieves expert-level results when it explores and integrates complex research ideas from external sources. The effectiveness of tree search is demonstrated across a diverse range of tasks. In bioinformatics, ERA discovered 40 novel methods for single-cell data analysis that outperformed the top human-developed methods on a public leaderboard. In epidemiology, ERA generated 14 models that outperformed the CDC ensemble and all other individual models for forecasting COVID-19 hospitalizations. ERA also produced expert-level software for geospatial analysis, neural activity prediction in zebrafish, and numerical solution of integrals, and a novel rule-based construction for time series forecasting. By devising and implementing novel solutions to diverse tasks, ERA represents a significant step towards accelerating scientific progress.
Keywords: Tree Search, Generative AI, Scorable Scientific Tasks, Empirical Software
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Online Advertising with Spatial Interactions
Yifan Wang
Mingfei Zhao
Proceedings of the ACM Web Conference 2026, Association for Computing Machinery, New York, NY, USA, 213–224
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Online advertising platforms must decide how to allocate multiple ads across limited screen real estate, where each ad's effectiveness depends not only on its own placement but also on nearby ads competing for user attention. Such spatial externalities — arising from proximity, clutter, or crowding — can significantly alter welfare and revenue outcomes, yet existing auction and allocation models typically treat ad slots as independent or ordered along a single dimension. We introduce a new framework for spatial externalities in online advertising, in which the value of an ad depends on both its slot and the configuration of surrounding ads. We model ad slots as points in a metric space, and model an advertiser's value as a function of both their bid and a discount factor determined by the configuration of other displayed ads.
Within this framework, we analyze two natural models. For the Nearest-Neighbor model, where the value suppression depends only on the closest neighboring ad, we present a polynomial-time algorithm that achieves a constant approximation for the general case. We show that the allocation rule is monotone and can be implemented as a truthful mechanism. For a structured setting of 2D Euclidean space, we provide a PTAS. In contrast, for the Product-Distance model, where interference is aggregated multiplicatively across all neighbors, we establish a strong (and nearly-tight) hardness of approximation -- no polynomial-time algorithm can achieve any polynomial-factor approximation unless P=NP, via a reduction from Max-Independent-Set. Our results provide a foundation for reasoning about spatial externalities in ad allocation and for designing efficient, truthful mechanisms under such interactions.
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Conformal prediction provides rigorous uncertainty guarantees for model outputs but can produce prohibitively large prediction sets in structured domains such as routing, planning, or sequential recommendation. We introduce \emph{graph-based conformal compression}, a framework for constructing compact subgraphs that preserve the statistical validity of conformal prediction while reducing structural complexity. We study a formulation that selects a smallest subgraph capturing a prescribed fraction of conditional probability mass, and reduce to a weighted version of densest $k$-subgraphs in hypergraphs, in the regime where the subgraph has a large fraction of edges. We design efficient approximation algorithms that achieve constant factor coverage and size trade-offs. Our results highlight an algorithmic regime, distinct from classical densest-$k$-subgraph hardness settings, where the problem can be approximated efficiently, bridging conformal prediction with combinatorial graph compression. We finally validate our algorithmic approach on synthetic and real-world instances of trip planning and navigation, showing in each case that our approach handily beats natural baselines.
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Understanding U.S. Users' Security and Privacy Transparency Needs for Consumer-Facing Generative AI
Jiaxun Cao
Yu Dong
Chunxi Zhan
Rithvik Neti
Pardis Emami-Naeini
USENIX Symposium on Usable Privacy and Security (SOUPS) (2026)
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Users increasingly rely on consumer-facing generative AI (GenAI) for tasks ranging from everyday needs to sensitive use cases. Yet, it remains unclear whether and how existing security and privacy (S&P) communications in GenAI tools shape users’ adoption decisions and experiences. Understanding how users seek, interpret, and evaluate S&P information is critical for designing usable transparency that users can trust and act on. We conducted semi-structured interviews and design sessions with 21 U.S. GenAI users. Our findings suggest that available S&P information rarely drove initial adoption in practice, as participants often perceived it as incomplete, ineffective, or not credible. Instead, they relied on rough proxies (e.g., popularity) to infer S&P practices. After adoption, S&P uncertainty constrained participants’ willingness to use GenAI tools, especially for high-stakes purposes, and, in some cases, contributed to discontinued use. Participants therefore called for transparency that supports decisions and actions through trustworthy information (e.g., independent evaluations) and usable interfaces (e.g., on-demand disclosure). We categorize participants’ desired design practices into five dimensions to facilitate systematic future investigation into best practices. We conclude with recommendations for researchers, designers, and policymakers to improve S&P transparency in consumer-facing GenAI.
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An Empirical Study of Tablet Ergonomics: The Interplay of Temperature, Orientation, and Use Behaviors
Carmen Van Ommen
Mikki Phan
Arun Raghupathy
Daniel Huynh
Barbara Chaparro
Ergonomics in Design: The Quarterly of Human Factors Applications Journal (2026)
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To balance computational performance with thermal comfort, this study explores a consolidated hotspot architecture at the top center of a tablet. We tested hotspot (39°C, 43°C, 45°C, 47°C) and ambient temperatures (25°C, 35°C) with 60 participants, measuring perception, action likelihood, and expectation. The hotspot was observed away from high contact areas, with 43°C identified as the threshold for significant discomfort. Discomfort increased with portrait mode use and higher device and ambient temperatures, while active use duration influenced acceptability. The findings underscore the importance of thermal mapping and contextual sensing, with direct applications for software throttling thresholds of coated aluminum enclosures.
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What does your wearable know about the festive season?
Justin Phillips
Abram Schönfeldt
YongSuk Cho
Conor Heneghan
Robert Harle
(2026)
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As we reach the end of the year and people look forward to spending quality time with loved ones, here at Fitbit, we wonder what our Pixel watches and Fitbit trackers can tell us about how we are spending the festive season. We looked at the data of 11.8 million of our users all over the world between January 2022 and July 2025. Here are the key stats we wanted to share with you!
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Can AI systems be aligned to human values? The popularization of large language models (LLMs) and multi-modal foundation models
has seen a commensurate rise in ways these models cause harm, spanning areas from toxic speech and hallucinations to AI agents
executing unauthorized actions. Given that these models are probabilistic and general-purpose by nature, it is impossible to enumerate
all possible uses and outputs of the model to reach a fully aligned end state. Within the field of AI safety, these harmful instances are
often framed as “the alignment problem,” of models being “misaligned” with human values. Researchers have responded by pursuing
applied and theoretical AI “value alignment” efforts, often without specifying what they mean by human values. How does the field of
AI value alignment conceive of human values? How are these conceptions of values technically operationalized and evaluated? What
does the emergent theory of value from this field signify for the future of AI?
The study of human values has long been part of many academic disciplines outside of computer science, yet these disciplines
are seldom consulted in AI alignment. Building on the theoretical insights of Zhi-Xuan’s (2024) "preferentist paradigm" critique, we
conduct a review of influential AI alignment literature. We also draw from conceptions of human values from philosophy, anthropology,
and sociology, to create an analytical schema. We annotated 94 AI value alignment research papers to discern their implicit theory of
values in AI. The majority do not define values, relying heavily on “preferences” as a stand-in that runs the risk of reducing complex,
culturally situated concepts down to binary choices. As researchers dispense with using human annotators for model training and
evaluation, turning instead to synthetic data and LLM-as-a-judge approaches to aligning and evaluating models, we identify the
potential to close off alternative methods for contesting and enacting values in foundation models. Overall, value alignment is often
reduced to an exercise in utility maximization, which we argue abstracts human values away from their lived context. In making AI
value alignment’s philosophical commitments explicit, we seek to bring greater specificity and under-explored perspectives into the
debate on whether and how AI can address human values
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Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion
Patrick Jiang
Judith Li
Moonkyung Ryu
Lily Hu
Kun Su
Liam Hebert
Hao Peng
Jiawei Han
Dima Kuzmin
Proceedings of the 43rd International Conference on Machine Learning (ICML-26), Seoul, South Korea (2026)
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Many modern retrieval problems are set-valued: given a broad intent, the system must return a collection of results that optimizes higher-order properties (e.g., diversity, coverage, complementarity, coherence) while staying grounded to a fixed database. These objectives are inherently non-decomposable, creating a training bottleneck because property-aligned (query, content) supervision is scarce. Reinforcement learning (RL) can optimize set-level objectives via interaction, but deploying an RL-tuned LLM for fan-out retrieval is expensive at query time. Diffusion-based generative retrieval enables efficient single-pass fan-out in embedding space, but requires objective-aligned training targets. We propose R4T (Retrieve-for-Train), which uses RL once as an objective transducer: (i) train a fan-out LLM with composite set-level rewards, (ii) synthesize objective-consistent training pairs, and (iii) train a lightweight diffusion retriever to model the conditional distribution of set-valued outputs. Across Polyvore and a large-scale music playlist dataset, R4T improves retrieval quality over strong baselines while reducing query-time fan-out latency by an order of magnitude.
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Every team building Large Language Models (LLMs) faces a core challenge: offline benchmarks show performance gains and user engagement rises post deployment, but isolating cause from effect remains difficult. Simultaneous marketing, media coverage, and seasonal demand obscure whether model updates truly drive engagement gains. This paper presents a novel causal estimation approach that leverages non uniform quality improvements across capabilities within a single model version. Because capabilities improve unevenly (e.g., strong gains in coding versus modest gains in writing), users experience varied quality depending on their task distribution. This variation in experienced quality provides causal signal for estimation. We validate this approach on synthetic data with known ground truth. The raw estimator recovers 81% to 88% of the true effect, with the remainder lost to measurement error in usage estimates. Applying an errors in variables disattenuation adjustment (using a test retest reliability ratio mean correlation of 0.861) corrects the estimate to 1.017 (bootstrapped 95% CI: 0.915 to 1.109). By contrast, naive methods fail significantly, recovering only 63% without version controls and 62% using real time rather than frozen usage patterns. Permutation tests confirm the framework distinguishes true causal effects from noise. Sensitivity analysis indicates recovery improves monotonically from 78% to 87% as pre period length increases from 3 to 7 weeks, highlighting a clear tradeoff between sample duration and estimator precision. Seed sensitivity tests further confirm stability across random draws.
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