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.
Sort By
1 - 15 of 10464 publications
Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques
Shashank Kapoor
Aman Raj
IEEE Compsac 2025 (2025)
Preview abstract
Large Language Models (LLMs) are revolutionizing many areas of AI, but their substantial resource requirements limit their deployment on mobile and edge devices. This survey paper provides a comprehensive overview of techniques for compressing LLMs to enable efficient inference in resource-constrained environments. We examine three primary approaches: knowledge distillation, model quantization and model pruning. For each technique, we discuss the underlying principles, present different forms, and provide examples of successful applications. We also briefly discuss complementary techniques like mixture-of-experts and early exit strategies and highlight the promising future directions. We aim to provide a valuable resource for both researchers and practitioners seeking to optimize LLMs for edge deployment. To the best of our knowledge, this is the first paper that provides a focused survey of LLM compression techniques from the lens of resource-constrained environments.
View details
Fine-grained Measurement of Vehicle Delay Fairness
Eliav Buchnik
Tom Kalvari
Jack Haddad
Dan Karliner
Danny Veikherman
Ron Tsibulsky
Shai Ferster
Ori Rottenstreich
2025
Preview abstract
Optimizing signal timing in traffic lights helps to improve traffic flow and reduce emissions through reducing delays. At intersections, vehicles from different movements observe different delays impacted by the traffic light plan. This paper analyzes delay fairness among various vehicles at intersections. We refer to three cities: Rio de Janeiro, Hamburg and Seattle with a total number of over 5100 intersections. We present an intuitive methodology to compute delay fairness based on Gini index, a common fairness measure in economics. We evaluate the fairness based on real traffic data and provide insights on the relationship of fairness with day hours and traffic demand. We also examine real changes in traffic light plans that occurred in practice to check whether improving delay is often aligned with increasing fairness.
View details
Silent Data Corruption by 10× Test Escapes Threatens Reliable Computing
Rama Govindaraju
Eric Liu
Subhasish Mitra
Mike Fuller
IEEE (2025) (to appear)
Preview abstract
Summary:
Silent Data Corruption by 10x Test Escapes Threatens Reliable Computing" highlights a critical issue: manufacturing defects, dubbed "test escapes," are evading current testing methods at an alarming rate, ten times higher than industry targets. These defects lead to Silent Data Corruption (SDC), where applications produce incorrect outputs without error indications, costing companies significantly in debugging, data recovery, and service disruptions. The paper proposes a three-pronged approach: quick diagnosis of defective chips directly from system-level behaviors, in-field detection using advanced testing and error detection techniques like CASP, and new, rigorous test experiments to validate these solutions and improve manufacturing testing practices.
View details
Preview abstract
This paper explores the integration of model-based and data-driven approaches within the realm of neural speech and audio coding systems. It highlights the challenges posed by the subjective evaluation processes of speech and audio codecs and discusses the limitations of purely data-driven approaches, which often require inefficiently large architectures to match the performance of model-based methods. The study presents hybrid systems as a viable solution, offering significant improvements to the performance of conventional codecs through meticulously chosen design enhancements. Specifically, it introduces a neural network-based signal enhancer designed to post-process existing codecs’ output, along with the autoencoder-based end-to-end models and LPCNet—hybrid systems that combine linear predictive coding (LPC) with neural networks. Furthermore, the paper delves into predictive models operating within custom feature spaces (TF-Codec) or predefined transform domains (MDCTNet) and examines the use of psychoacoustically calibrated loss functions to train end-to-end neural audio codecs. Through these investigations, the paper demonstrates the potential of hybrid systems to advance the field of speech and audio coding by bridging the gap between traditional model-based approaches and modern data-driven techniques.
View details
Context is Key for Agent Security
Lillian Tsai
Eugene Bagdasaryan
arXiv (2025)
Preview abstract
Judging the safety of an action, whether taken by a human or a system, must take into account the context in which the action takes place. For example, deleting an email from a user's mailbox may or may not be appropriate depending on the email's content, the user's goals, or even available space. Systems today that make these judgements---providing security against harmful or inappropriate actions---rely on manually-crafted policies or user confirmation for each relevant context. With the upcoming deployment of systems like generalist agents, we argue that we must rethink security designs to adapt to the scale of contexts and capabilities of these systems. As a first step, this paper explores contextual security in the domain of agents and proposes contextual security for agents (Conseca), a framework to generate just-in-time, contextual, and human-verifiable security policies.
View details
On the relationship of speed limit and CO2 emissions in urban traffic
Tamás Tettamanti
Balázs Varga
Ori Rottenstreich
Transportation Research Interdisciplinary Perspectives, 32 (2025)
Preview abstract
The paper analyzes the relationship between urban speed limits and vehicle emissions. There is an ongoing trend of reducing speed limits from to for the sake of increasing road safety. However, the impact of this policy on emissions is still unclear. It can be mixed depending on the proportion of dynamic and steady-state driving. While cruising emissions are higher at lower speeds, lower speeds entail less acceleration in urban traffic. Based on our investigation, one network topology feature (road length) and two traffic-related parameters (traffic volume and turning ratio) have been suggested for analysis being the most relevant to affect vehicle emission. Their correlation with potential emission reduction was evaluated using high-fidelity traffic simulation based on traffic scenarios validated with real traffic data. Random forest regression was used to support the optimal selection of zones for speed limit reduction. Traffic simulations on large urban networks prove that emission reductions of over 10% can be achieved in the case of a well-chosen speed limit policy.
View details
Preview abstract
Several systems rely on traceroute to track a large number of Internet paths as they change over time. Monitoring systems perform this task by remapping paths periodically or whenever a change is detected. This paper shows that such complete remapping is inefficient, because most path changes are localized to a few hops of a path. We develop RemapRoute, a tool to remap a path locally given the previously known path and a change point. RemapRoute sends targeted probes to locate and remap the often few hops that have changed. Our evaluation with trace-driven simulations and in a real deployment shows that local remapping reduces the average number of probes issued during remapping by 63% and 79%, respectively, when compared with complete remapping. At the same time, our results show that local remapping has little impact on the accuracy of inferred paths.
View details
A Scalable Framework for Evaluating Health Language Models
Neil Mallinar
Tony Faranesh
Brent Winslow
Nova Hammerquist
Ben Graef
Cathy Speed
Mark Malhotra
Shwetak Patel
Xavi Prieto
Daniel McDuff
Ahmed Metwally
(2025)
Preview abstract
Large language models (LLMs) have emerged as powerful tools for analyzing complex datasets. Recent studies demonstrate their potential to generate useful, personalized responses when provided with patient-specific health information that encompasses lifestyle, biomarkers, and context. As LLM-driven health applications are increasingly adopted, rigorous and efficient one-sided evaluation methodologies are crucial to ensure response quality across multiple dimensions, including accuracy, personalization and safety. Current evaluation practices for open-ended text responses heavily rely on human experts. This approach introduces human factors and is often cost-prohibitive, labor-intensive, and hinders scalability, especially in complex domains like healthcare where response assessment necessitates domain expertise and considers multifaceted patient data. In this work, we introduce Adaptive Precise Boolean rubrics: an evaluation framework that streamlines human and automated evaluation of open-ended questions by identifying gaps in model responses using a minimal set of targeted rubrics questions. Our approach is based on recent work in more general evaluation settings that contrasts a smaller set of complex evaluation targets with a larger set of more precise, granular targets answerable with simple boolean responses. We validate this approach in metabolic health, a domain encompassing diabetes, cardiovascular disease, and obesity. Our results demonstrate that Adaptive Precise Boolean rubrics yield higher inter-rater agreement among expert and non-expert human evaluators, and in automated assessments, compared to traditional Likert scales, while requiring approximately half the evaluation time of Likert-based methods. This enhanced efficiency, particularly in automated evaluation and non-expert contributions, paves the way for more extensive and cost-effective evaluation of LLMs in health.
View details
Perch 2.0: The Bittern Lesson for Bioacoustics
Bart van Merriënboer
Tom Denton
arXiv (2025)
Preview abstract
Perch is a performant pre-trained model for bioacoustics. It was trained in supervised fashion, providing both off-the-shelf classification scores for thousands of vocalizing species as well as strong embeddings for transfer learning. In this new release, Perch 2.0, we expand from training exclusively on avian species to a large multi-taxa dataset. The model is trained with self-distillation using a prototype-learning classifier as well as a new source-prediction training criterion. Perch 2.0 obtains state-of-the-art performance on the BirdSet and BEANS benchmarks. It also outperforms specialized marine models on marine transfer learning tasks, despite having almost no marine training data. We present hypotheses as to why fine-grained species classification is a particularly robust pre-training task for bioacoustics.
View details
An Empirical Study of Time of Day Breakpoints in Traffic Light Plans
Eliav Buchnik
Tom Kalvari
Jack Haddad
Dan Karliner
Danny Veikherman
Shai Ferster
Ori Rottenstreich
2025
Preview abstract
Fixed time strategy is a common approach in signal traffic control in which signal plans are simple and periodic, enjoying easy implementation without detection mechanisms. A traffic light is associated with several daily plans, each applied to several consecutive hours. Time-of-day breakpoints (TODs) refer to the times over the day in which the plan is changed. TODs are often selected based on traffic, aiming to divide the day into groups of consecutive hours with similar traffic characteristics within each group of hours. We present a methodology to study time-of-day breakpoints in practice. We use this methodology to estimate and analyze time-of-day breakpoints in the city of Rio de Janeiro, Brazil based on traffic properties derived from traffic trajectories. Our study examines over 900 of the city intersections. We refer to properties such as the number of daily plans and the times by which plans start. We also provide traffic-aware insights on the potential improvement in the selection of TODs and identify key intersections where adjusting TODs could reduce average delay times. We identify potential improvements in over 8% of the examined intersections. These findings provide valuable insights for traffic engineers seeking to optimize signal timing.
View details
Preview abstract
Storage on Android has evolved significantly over the years, with each new Android version introducing changes aimed at enhancing usability, security, and privacy. While these updates typically help with restricting app access to storage through various mechanisms, they may occasionally introduce new complexities and vulnerabilities. A prime example is the introduction of scoped storage in Android 10, which fundamentally changed how apps interact with files. While intended to enhance user privacy by limiting broad access to shared storage, scoped storage has also presented developers with new challenges and potential vulnerabilities to address. However, despite its significance for user privacy and app functionality, no systematic studies have been performed to study Android’s scoped storage at depth from a security perspective. In this paper, we present the first systematic security analysis of the scoped storage mechanism. To this end, we design and implement a testing tool, named ScopeVerif, that relies on differential analysis to uncover security issues and implementation inconsistencies in Android’s storage. Specifically, ScopeVerif takes a list of security properties and checks if there are any file operations that violate any security properties defined in the official Android documentation. Additionally, we conduct a comprehensive analysis across different Android versions as well as a cross-OEM analysis to identify discrepancies in different implementations and their security implications. Our study identifies both known and unknown issues of scoped storage. Our cross-version analysis highlights undocumented changes as well as partially fixed security loopholes across versions. Additionally, we discovered several vulnerabilities in scoped storage implementations by different OEMs. These vulnerabilities stem from deviations from the documented and correct behavior, which potentially poses security risks. The affected OEMs and Google have acknowledged our findings and offered us bug bounties in response.
View details
Circadian rhythm of heart rate and activity: a cross-sectional study
Maryam Khalid
Logan Schneider
Aravind Natarajan
Conor Heneghan
Karla Gleichauf
Chronobiology International (2025)
Preview abstract
ABSTRACT
Background: Circadian rhythms are commonly observed in a number of physiological processes. Consumer wearable devices have made it possible to obtain continuous time series data from a large number of individuals. We study circadian rhythms from measurements of heart rate, movement, and sleep, from a cohort of nearly 20,000 participants over the course of 30 days.
Methods: Participation was restricted to Fitbit users of age 21 years or older residing in the United States or Canada. Participants were enrolled through a recruitment banner shown on the Fitbit App. The advertisement was shown to 531,359 Fitbit users, and 23,239 enrolled in the program. Of these, we obtained heart rate data from 19,350 participants. We obtain the underlying circadian rhythm from time series heart rate by modeling the circadian rhythm as a sum over the first two Fourier harmonics. The first Fourier harmonic accounts for the 24-hour rhythmicity, while the second harmonic accounts for non-sinusoidal perturbations.
Findings: We observe a circadian rhythm in both heart rate and acceleration. From the diurnal modulation, we obtain the following circadian parameters: (i) amplitude of modulation, (ii) bathyphase, (iii) acrophase, (iv) non-sinusoidal fraction, and (v) fraction of day when the heart rate is greater than the mean. The amplitude, bathyphase, and acrophase depend on sex, and decrease with age. The waketime on average, follows the bathyphase by 2.4 hours. In most individuals, the circadian rhythm of heart rate lags the circadian rhythm of activity.
Interpretation: Circadian metrics for heart rate and activity can be reliably obtained from commercially available wearable devices. Distributions of circadian metrics can be valuable tools for individual-level interpretation.
View details
CountQA: How Well Do MLLMs Count in the Wild?
Jayant Tamarapalli
Rynaa Grover
Nilay Pande
Sahiti Yerramilli
(2025)
Preview abstract
While Multimodal Large Language Models (MLLMs) display a remarkable fluency in describing visual scenes, their ability to perform the fundamental task of object counting remains poorly understood. This paper confronts this issue by introducing CountQA, a challenging new benchmark composed of over 1,500 question-answer pairs centered on images of everyday, real-world objects, often in cluttered and occluded arrangements. Our evaluation of 15 prominent MLLMs on CountQA systematically investigates this weakness, revealing a critical failure of numerical grounding: the models consistently struggle to translate raw visual information into an accurate quantity. By providing a dedicated tool to probe this foundational weakness, CountQA paves the way for the development of more robust and truly capable MLLMs that are spatially aware and numerically grounded.
View details
Preview abstract
We describe an efficient quantum algorithm for solving the linear matrix equation AX+XB=C, where A, B and C are given complex matrices and X is unknown. This is known as the Sylvester equation, a fundamental equation with applications in control theory and physics. Rather than encoding the solution in a quantum state in a fashion analogous to prior quantum linear algebra solvers, our approach constructs the solution matrix X in a block-encoding, rescaled by some factor. This allows us to obtain certain properties of the entries of X exponentially faster than would be possible from preparing X as a quantum state. The query and gate complexities of the quantum circuit that implements this block-encoding are almost linear in a condition number that depends on A and B, and depend logarithmically in the dimension and inverse error. We show how our quantum circuits can solve BQP-complete problems efficiently, discuss potential applications and extensions of our approach, its connection to Riccati equation, and comment on open problems.
View details
Calibration Properties of Time-Series Foundation Models: An Empirical Analysis
Coen Adler
Samar Abdi
Yuxin Chang
Padhraic Smyth
2025
Preview abstract
Recent development of foundation models for time series data has generated considerable interest in using such models across a variety of applications. Although they achieve state-of-the-art predictive performance, the ability to produce well-calibrated probabilistic distributions is critical for practical applications and is relatively underexplored. In this paper, we investigate the calibration-related properties of five recent time series foundation models and two competitive baselines. We perform systematic evaluations and identify significant variation in calibration performances across models.
View details
