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 10464 publications
A Recipe for Improving Remote Sensing Zero Shot Generalization
Aviad Barzilai
Yotam Gigi
Vered Silverman
Yehonathan Refael
Bolous Jaber
Amr Helmy
3rd ML4RS Workshop at ICLR 2025
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Foundation models have had a significant impact across various AI applications, enabling applications for use cases that were previously impossible. Visual language models (VLMs), in particular, have outperformed other techniques in many tasks. In remote sensing (RS), foundation models have shown improvements across various applications. However, unlike other fields, the use of VLMs with large-scale remote sensing image-text datasets remains limited.
In this work, we first introduce two novel image-caption datasets for training of remote sensing foundation models. The first dataset pairs aerial and satellite imagery, aligned with Google-Maps data, with high-quality captions generated using Gemini. The second utilizes public web images and their corresponding alt-text, filtered for only remote sensing domain, resulting in a highly diverse dataset.
We show that using these datasets to pre-train the Mammut [], a VLM architecture, results in state-of-the-art generalization performance in a zero-shot classification and cross-modal retrieval on well-known public benchmarks. Secondly, we leverage this newly pre-trained VLM to generate inference attention maps for a novel class query (i.e., a class unseen during training). We subsequently propose an iterative self-supervised fine-tuning approach where samples aligned with these attention maps are iteratively pseudo-labeled and utilized for model training.
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TOKENFORMER: Rethinking Transformers Scaling with Tokenized Model Parameters
Haiyang Wang
Fan Yue
Jan Eric Lenssen
Liwei Wang
Bernt Schiele
2025
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Transformers have become the predominant architecture in foundation models due to their excellent performance across various domains. However, the substantial cost of scaling these models remains a significant concern. This problem arises primarily from their dependence on fixed parameters within linear projections, especially when architectural modifications (e.g., channel dimensions) are introduced. Each scaling iteration typically requires retraining the entire model from the beginning, leading to suboptimal utilization of computational resources. To overcome this limitation, we introduce TokenFormer, a naturally scalable architecture that leverages the attention mechanism exclusively for computations among input tokens and interactions between input tokens and model parameters, thereby enhancing architectural flexibility. By treating model parameters as tokens, we replace all the linear projections in Transformer with our token-parameter attention layer, where input tokens act as queries and model parameters as keys and values. This innovative approach allows for progressive and efficient scaling without necessitating retraining from scratch. Our model scales from 124 million to 1.4 billion parameters by incrementally adding new key-value parameters, achieving performance comparable to models trained from scratch while greatly reducing training costs. Code and models will be publicly available.
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Silent Data Corruption by 10× Test Escapes Threatens Reliable Computing
Rama Govindaraju
Eric Liu
Subhasish Mitra
Mike Fuller
IEEE (2025) (to appear)
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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.
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Explainable Artificial Intelligence Techniques for Software Development Lifecycle: A phase-specific survey
Shashank Kapoor
Aman Raj
IEEE Compsac (2025)
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Artificial Intelligence (AI) is rapidly expanding and integrating more into daily life to automate tasks, guide decision-making and enhance efficiency. However, complex AI models, which make decisions without providing clear explanations (known as the "black-box problem"), currently restrict trust and widespread adoption of AI.
Explainable Artificial intelligence (XAI) has emerged to address the black-box problem of making AI systems more interpretable and transparent so stakeholders can trust, verify, and act upon AI-based outcomes. Researcher have come up with various techniques to foster XAI in Software Development Lifecycle. However, there are gaps in the application of XAI in Software Engineering phases. Literature shows that 68% of XAI in Software Engineering research focused on maintenance as opposed to 8% on software management and requirements [7].
In this paper we present a comprehensive survey of the applications of XAI methods (e.g., concept-based explanations, LIME/SHAP, rule extraction, attention mechanisms, counterfactual explanations, example-based explanations) to the different phases of Software Development Lifecycles (SDLC) mainly requirements elicitation, design and development, testing and deployment, and evolution.
To the best of our knowledge, this paper presents the first comprehensive survey of XAI techniques for every phase of the Software Development Life Cycle (SDLC). In doing so, we aim to promote explainable AI in Software Engineering and facilitate the use of complex AI models in AI-driven software development.
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Ransomware over Modern Web Browsers: A Novel Strain and A New Defense Mechanism
Harun Oz
Ahmet Aris
Leonardo Babun
Selcuk Uluagac
Abbas Acar
ACM Transactions on the Web (2025)
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Ransomware is an increasingly prevalent form of malware targeting end-users, governments, and businesses. As it has evolved,
adversaries added new capabilities to their arsenal. Throughout the ransomware evolution, the adversaries propose a next-generation
browser-based ransomware, RøB, that performs its malicious actions via emerging web technologies, File System Access API (FSA) and
WebAssembly (Wasm). RøB uses this API through the victims’ browsers; hence, it does not require the victims to download and install
malicious binaries. We performed extensive evaluations with 3 different OSs, 23 file formats, 29 distinct directories, 5 cloud providers,
and 4 antivirus solutions. Our evaluations show that RøB can encrypt various types of files in the local and cloud-integrated directories,
external storage devices, and network-shared folders of victims. Our experiments also reveal that popular cloud solutions, Box
Individual and Apple iCloud can be severely affected by RøB. Moreover, we conducted tests with commercial antivirus software such
as AVG, Avast, Kaspersky, Malware Bytes that perform sensitive directory and suspicious behavior monitoring against ransomware.
We verified that RøB can evade these antivirus software and encrypt victim files. Moreover, existing ransomware detection solutions
in the literature also cannot be a remedy against RøB due to its distinct features. Therefore, in this paper, we also propose broguard,
a new detection system for RøB-like attacks. broguard monitors the web applications that use the FSA API via function hooking and
uses a machine learning classifier to detect RøB-like attacks in real-time without any file loss. Performance evaluations of broguard
on a comprehensive dataset show that broguard can detect RøB-like browser-based ransomware attacks with over 99% accuracy and
minimal overhead.
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Silent Data Corruption by 10× Test Escapes Threatens Reliable Computing
Subhasish Mitra
Rama Govindaraju
Eric Liu
IEEE Design & Test (2025) (to appear)
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Too many defective compute chips are escaping today’s manufacturing tests – at least an order of magnitude more than industrial targets across all compute chip types in data centers. Silent data corruptions (SDCs) caused by test escapes, when left unaddressed, pose a major threat to reliable computing. We present a three-pronged approach outlining future directions for overcoming test escapes: (a) Quick diagnosis of defective chips directly from system-level incorrect behaviors. Such diagnosis is critical for gaining insights into why so many defective chips escape existing manufacturing testing. (b) In-field detection of defective chips. (c) New test experiments to understand the effectiveness of new techniques for detecting defective chips. These experiments must overcome the drawbacks and pitfalls of previous industrial test experiments and case studies.
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Rapid Initial-State Preparation for the Quantum Simulation of Strongly Correlated Molecules
Dominic Berry
Yu Tong
Alec White
Tae In Kim
Lin Lin
Seunghoon Lee
Garnet Chan
PRX Quantum, 6 (2025), pp. 020327
Preview abstract
Studies on quantum algorithms for ground-state energy estimation often assume perfect ground-state preparation; however, in reality the initial state will have imperfect overlap with the true ground state. Here, we address that problem in two ways: by faster preparation of matrix-product-state (MPS) approximations and by more efficient filtering of the prepared state to find the ground-state energy. We show how to achieve unitary synthesis with a Toffoli complexity about 7 × lower than that in prior work and use that to derive a more efficient MPS-preparation method. For filtering, we present two different approaches: sampling and binary search. For both, we use the theory of window functions to avoid large phase errors and minimize the complexity. We find that the binary-search approach provides better scaling with the overlap at the cost of a larger constant factor, such that it will be preferred for overlaps less than about 0.003. Finally, we estimate the total resources to perform ground-state energy estimation of Fe-S cluster systems, including the FeMo cofactor by estimating the overlap of different MPS initial states with potential ground states of the FeMo cofactor using an extrapolation procedure. With a modest MPS bond dimension of 4000, our procedure produces an estimate of approximately 0.9 overlap squared with a candidate ground state of the FeMo cofactor, producing a total resource estimate of 7.3e10 Toffoli gates; neglecting the search over candidates and assuming the accuracy of the extrapolation, this validates prior estimates that have used perfect ground-state overlap. This presents an example of a practical path to prepare states of high overlap in a challenging-to-compute chemical system.
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Zoom in, Zoom out, Reframe: Domain Experts’ Strategies for Addressing Non-Experts’ Complex Questions
Beverly Freeman
Roma Ruparel
Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI) (2025)
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Consumers rely on the Internet for expert information in domains such as healthcare and law. Large Language Models (LLMs) have the potential to increase access to expert knowledge. However, past research has not addressed how to handle certain aspects of complex questions that commonly occur in expert-layperson interactions. We conducted in-depth interviews with 26 experts across multiple domains to understand how they experience and respond to challenges associated with non-experts’ questions. Results from a thematic analysis reveal three recurring strategies that experts across domains employ when fielding complex questions. Experts zoom in to clarify details of a broad information request, zoom out to address overly narrow questions or assumptions, and reframe when the underlying need is unstated or poorly represented. We discuss implications for the design of LLM-based experiences that facilitate access to expert information.
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I know what I don't know: improving model cascades through confidence tuning
Stephan Rabanser
Nathalie Rauschmayr
Petra Poklukar
Congchao Wang
2025
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Large-scale machine learning models deliver strong performance across a wide range of tasks but come with significant computational and resource constraints. To mitigate these challenges, local smaller models are often deployed alongside larger models, relying on routing and deferral mechanisms to offload complex tasks. However, existing approaches inadequately balance the capabilities of these models, often resulting in unnecessary deferrals or sub-optimal resource usage. In this work we introduce a novel loss function called Gatekeeper for calibrating smaller models in cascade setups. Our approach fine-tunes the smaller model to confidently handle tasks it can perform correctly while deferring complex tasks to the larger model. Moreover, it incorporates a mechanism for managing the trade-off between model performance and deferral accuracy, and is broadly applicable across various tasks and domains without any architectural changes. We evaluated our method on encoder-only, decoder-only, and encoder-decoder architectures. Experiments across image classification, language modeling, and vision-language tasks show that our approach substantially improves deferral performance.
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Validation of Quantum Elliptic Curve Point Addition Circuits
(2025) (to appear)
Preview abstract
Specific quantum algorithms exist to—in theory—
break elliptic curve cryptographic protocols. Implementing these
algorithms requires designing quantum circuits that perform elliptic curve arithmetic. To accurately judge a cryptographic protocol’s resistance against future quantum computers, researchers
figure out minimal resource-count circuits for performing these
operations while still being correct. To assure the correctness of
a circuit, it is integral to restore all ancilla qubits used to their
original states. Failure to do so could result in decoherence of the
computation’s final result. Through rigorous classical simulation
and unit testing, I surfaced four inconsistencies in the state-ofthe-art quantum circuit for elliptic curve point addition where
the circuit diagram states the qubits are returned in the original
(|0⟩) state, but the intermediate values are not uncomputed. I
provide fixes to the circuit without increasing the leading-order
gate cost.
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Confidence Improves Self-Consistency in LLMs
Tom Sheffer
Eran Ofek
Ariel Goldstein
Zorik Gekhman
ACL 2025, Findings (2025)
Preview abstract
Self-consistency decoding enhances LLMs’ performance on reasoning tasks by sampling diverse reasoning paths and selecting the most frequent answer. However, it is computationally expensive, as sampling many of these (lengthy) paths is required to increase the chances that the correct answer emerges as the most frequent one. To address this, we introduce Confidence-Informed Self-Consistency (CISC). CISC performs a weighted majority vote based on confidence scores obtained directly from the model. By prioritizing high-confidence paths, it can identify the correct answer with a significantly smaller sample size. When tested on nine models and four datasets, CISC outperforms self-consistency in nearly all configurations, reducing the required number of reasoning paths by over 40% on average. In addition, we introduce the notion of within-question confidence evaluation, after showing that standard evaluation methods are poor predictors of success in distinguishing correct and incorrect answers to the same question. In fact, the most calibrated confidence method proved to be the least effective for CISC. Lastly, beyond these practical implications, our results and analyses show that LLMs can effectively judge the correctness of their own outputs, contributing to the ongoing debate on this topic.
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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.
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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.
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Linear Elastic Caching via Ski Rental
Todd Lipcon
The biennial Conference on Innovative Data Systems Research (2025)
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In this work we study the Linear Elastic Caching problem, where the goal is to minimize the total cost of a cache inclusive of not just its misses, but also its memory footprint integrated over time. We demonstrate a theoretical connection to the classic ski rental problem and propose a practical algorithm that combines online caching algorithms with ski rental policies. We also introduce a lightweight machine learning-based algorithm for ski rental that is optimized for production workloads and is easy to integrate within existing database systems. Evaluations on both production workloads in Google Spanner and publicly available traces show that the proposed elastic caching approach can significantly reduce the total cache cost compared to traditional fixed-size cache policies.
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GOALIE (GOAL oriented IntErventions) Proactive Multimodal Agent to Assist Augmented Reality
Saptarashmi Bandyopadhyay
Vikas Bahirwani
Lavisha Aggarwal
Bhanu Guda
Lin Li
Qin Liu
Tom Goldstein
John Dickerson
Andrea Colaco
2025
Preview abstract
Multimodal AI Agents are helpful to assist and guide users in completing real-time tasks like cooking, robotics, manufacturing. An emerging form of multimodal communication is Augmented Reality (AR), where an AI Agent can enhance user experience with step-by-step guidance of tasks by observing the user's vision and language inputs. Current LLM or VLM based agents are reactive, waiting for an user query before responding. Proactive AI Agents in AR focus on detecting when the AI Agent should autonomously intervene to fix mistakes or followup any instruction. Our GOALIE (GOAL-oriented IntErvention) Agent is the first multimodal proactive AR agent which guides the user step-by-step on its own. We build an innovative Zero-Shot Prompting framework PSoS (Proactive Sequence of Steps) with the context of abstract past user actions, the agent's previous responses, and the user's granular goals and actions before it is detected that the AI Agent should intervene. We use PSoS for Supervised Finetuning (SFT), Direct Preference Optimization (DPO) and Group-Relative Policy Optimization (GRPO) finetuning of our AI agent to improve the quality of the agent's proactive intervention. We also propose a new algorithmic framework, Bagged group Relative Policy Optimization (BRPO), to reduce the variance in rewards of generation groups, to adapt the finetuning algorithm for multimodal proactive interventions by the AI Agent and to enable real-time finetuning of the AI model. We compare the step-by-step intervention quality and efficiency of the GOALIE Agent with Gemma-3 models along with other VLMs for task execution with human expert labels. We conduct human evaluation of the proactive interventions, demonstrating user satisfaction with the GOALIE Agent's proactive interventions. We will release the code, model and human evaluation data.
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