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 11422 publications
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We introduce AASE (Activation-based AI Safety Enforcement), a framework for post-perception safety monitoring in large language models. Unlike pre-perception approaches that analyze input or output text, AASE monitors the model's internal activation patterns—what the model "understands" rather than what text it processes or generates—enabling detection of safety-relevant states before harmful outputs are produced. The framework comprises three techniques: Activation Fingerprinting (AF) for harmful content detection, Agent Action Gating (AAG) for prompt injection defense, and Activation Policy Compliance (APC) for enterprise policy enforcement. We introduce paired contrastive training to isolate safety-relevant signals from confounding factors such as topic and style, addressing signal entanglement in polysemantic activations. Validation across 7 models from 3 architecture families shows strong class separation: Gemma-2-9B achieves AUC 1.00 with 7.2σ separation across all probes; AAG achieves AUC ≥0.88 across all models on the InjecAgent benchmark; APC achieves 0.97-1.00 AUC across three enterprise policies. Model size correlates with probe quality—Gemma-2-9B (7.2σ separation) outperforms Gemma-2-2B (4.3σ). All techniques survive INT4 quantization with minimal separation degradation. AASE is 9× faster than Llama Guard 3 (33ms vs 306ms) with higher TPR (88% vs 50%) at a tunable threshold that trades FPR for detection sensitivity, adding only 0.002ms probe overhead to existing inference.
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We study contextual bandits in the stochastic i.i.d.\ setting, where a learner observes contexts drawn from an unknown distribution, selects actions from a finite set $\cA$, and aims to identify an approximately optimal policy from a given class based on bandit feedback.
Motivated by the important special case of bandit multiclass classification with zero-one rewards, we focus on the \emph{$s$-sparse} setting in which, for every context, the underlying reward vector has $L_1$-norm at most $s \ll |\cA|$. Our main result is the design of algorithms that, with probability at least~$1-\delta$, output an $\eps$-optimal policy compared to policy class $\Pi$ using
\begin{align*}
\wt{O} \brk*{\brk*{\frac{s}{\eps^2} + \frac{|\cA|}{\eps}} \log \frac{|\Pi|}{\delta}}
\end{align*}
samples.
We further extend this bound to general Natarajan classes and complement it with a matching lower bound (up to logarithmic factors), thereby closing a substantial gap left by prior work~\citep{erez2024real,erez2024fast,erez2025bandit}, which incurred an additional $\Theta(|\cA|^9)$ dependence.
We obtain these results via two complementary approaches. First, we analyze contextual bandits through the lens of contextual decision making with structured observations, designing an exploration-by-optimization algorithm whose sample complexity is governed by the \emph{decision-estimation coefficient} (DEC; \citealp{foster2021statistical,foster2022complexity}). We show that, with $s$-sparse rewards, the induced model class admits a sharp DEC bound that scales with $s$ and directly yields the optimal rate. Since this approach is largely information-theoretic and involves solving complex min-max optimization problems, we also develop a second, more specialized algorithmic method based on a low-variance exploration technique. This approach leads to concrete, tractable algorithms and naturally extends to contextual combinatorial semi-bandits, leading to improved sample complexity guarantees for bandit multiclass list classification.
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Mining Attribute Subspaces for Efficient Fine-tuning of 3D Foundation Models
Yu Jiang
Hanwen Jiang
Vincent Chu
Brandon Y. Feng
Zhangyang Wang
Qixing Huang
IEEE/CVF Conference on Computer Vision and Pattern Recognition (2026)
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With the emergence of 3D foundation models, there is growing interest in fine-tuning them for downstream tasks, where LoRA is the dominant fine-tuning paradigm. As 3D datasets exhibit distinct variations in texture, geometry, camera motion, and lighting, there are interesting fundamental questions: 1) Are there LoRA subspaces associated with each type of variation? 2) Are these subspaces disentangled (i.e., orthogonal to each other)? 3) How do we compute them effectively? This paper provides answers to all these questions. We introduce a robust approach that generates synthetic datasets with controlled variations, fine-tunes a LoRA adapter on each dataset, and extracts a LoRA sub-space associated with each type of variation. We show that these subspaces are approximately disentangled. Integrating them leads to a reduced LoRA subspace that enables efficient LoRA fine-tuning with improved prediction accuracy for downstream tasks. In particular, we show that such a reduced LoRA subspace, despite being derived entirely from synthetic data, generalizes to real datasets. An ablation study validates the effectiveness of the choices in our approach.
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We introduce a new context-enriched time series forecasting benchmark TimesX. TimesX contains a wide selection of high-quality real-world time series and diverse textual contexts from an automated generating pipeline, which helps address three main issues of existing benchmarks: (1) poor generalization due to low data volume and data being synthetic, (2) restricted forms of context, and (3) an inability to mitigate data leakage. We conduct a thorough empirical study of current multimodal solutions on TimesX. Our results suggest that most multimodal solutions that work well on existing benchmarks may fail on TimesX. In contrast, simple ensemble methods that leverage the rich textual context can outperform strong unimodal baselines and other multimodal baselines.
** Below this is what was submitted to ITP. **
We create a real world multimodal time-series forecasting benchmark that encompasses diverse domains and regions. Each time-series is annotated by various kinds of contexts like metadata, date and holiday information, dynamic events related to the time-series. This is sufficiently more advanced than other available benchmarks which rely wither on static metadata alone or synthetic examples. This forms a test bed for multimodal forecasting. We also present some baseline results showing that ensembles of publicly available LLMs and time-series foundation models can demonstrate non-trivial performance on this bechmark.
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Enterprise service delivery platforms, while vital for HR operations, create significant challenges in managing the risks of Personally Identifiable Information (PII) exposure. The integration of Generative AI offers new efficiencies but also amplifies these risks. Existing solutions—ranging from manual redaction and rule-based Data Loss Prevention (DLP) to inflexible data masking—fail to provide a nuanced, integrated approach. This paper introduces the Dual-Mode Privacy Guard (DMPG), a conceptual framework that establishes a model for Augmented Compliance. The framework provides a "defense-in-depth" strategy built on three pillars: (1) a Zero-Trust AI Foundation leveraging a verifiable, non-retention API gateway to ensure data privacy; (2) a proactive "Guardrail" that uses AI to detect and flag potential PII for human-in-the-loop review; and (3) an on-demand "Tool" that allows users to create securely anonymized data assets. By differentiating between proactive monitoring and reactive utility, the DMPG shifts the compliance paradigm from a manual burden to an AI-assisted process that enhances, rather than replaces, human oversight. This paper details the framework’s platform-agnostic architecture, using Salesforce as a reference implementation, and argues for its novelty as a model for operationalizing privacy principles within modern enterprise systems.
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Learning Conditional Averages
Marco Bressan
Nataly Brukhim
Nicolo Cesa-Bianchi
Emmanuel Esposito
Shay Moran
Maximilian Thiessen
COLT (2026)
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We introduce the problem of learning \emph{conditional averages} in the PAC framework.
The learner receives a sample labeled by an unknown target concept from a known concept class, as in standard PAC learning. However, instead of learning the target concept itself, the goal is to predict, for each instance, the average label over its \emph{neighborhood}---an arbitrary subset of points that contains the instance.
In the degenerate case where all neighborhoods are singletons, the problem reduces exactly to classic PAC learning. More generally, it extends PAC learning to a setting that captures learning tasks arising in several domains, including explainability, fairness, and recommendation systems. %including explainability, fairness, and recommendation systems.
Our main contribution is a complete characterization of when conditional averages are learnable, together with sample complexity bounds that are tight up to logarithmic factors. The characterization hinges on the joint finiteness of two novel combinatorial parameters, which depend on both the concept class and the neighborhood system, and are closely related to the independence number of the associated neighborhood graph.
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Despite significant strides in factual reliability, errors -- often termed hallucinations -- remain a major concern for generative AI, especially as LLMs are increasingly expected to be helpful in more complex or nuanced setups. Yet even in the simplest setting -- factoid question-answering with clear ground truth-frontier models without external tools continue to hallucinate. We argue that most factuality gains in this domain have come from expanding the model's knowledge boundary (encoding more facts) rather than improving awareness of that boundary (distinguishing known from unknown). We conjecture that the latter is inherently difficult: models may lack the discriminative power to perfectly separate truths from errors, creating an unavoidable tradeoff between eliminating hallucinations and preserving utility.
This tradeoff dissolves under a different framing. If we understand hallucinations as confident errors -- incorrect information delivered without appropriate qualification -- a third path emerges beyond the answer-or-abstain dichotomy: expressing uncertainty. We propose faithful uncertainty: aligning linguistic uncertainty with intrinsic uncertainty. This is one facet of metacognition -- the ability to be aware of one's own uncertainty and to act on it. For direct interaction, acting on uncertainty means communicating it honestly; for agentic systems, it becomes the control layer governing when to search and what to trust. Metacognition is thus essential for LLMs to be both trustworthy and capable; we conclude by highlighting open problems for progress towards this objective.
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Usability Hasn’t Peaked: Exploring How Expressive Design Overcomes the Usability Plateau
Alyssa Sheehan
Bianca Gallardo
Ying Wang
Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI ’26), April 13–17, 2026, Barcelona, Spain (2026)
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Critics have argued that mobile usability has largely been optimized, and that only incremental gains are possible. We set out to explore if the newest generation of design systems, which promote greater flexibility and a return to design basics, could produce substantially more usable designs while maintaining or increasing aesthetic judgments. Through a study with 48 diverse participants completing tasks in 10 different applications, we found that in designs created following Material 3 Expressive guidelines, users fixated on the correct screen element for a task 33% faster, completed tasks 20% faster, and rated experiences more positively compared to versions designed using the previous Material design system. These improvements in performance and aesthetic ratings challenge the premise of a usability plateau and show that mobile usability has not peaked. We illustrate specific opportunities to make mobile experiences more usable by returning to design fundamentals while highlighting risks of added flexibility.
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ALF: Advertiser Large Foundation Model for Multi-Modal Advertiser Understanding
Sunny Rajagopalan
Alireza Golestaneh
Shubhra Chandra
Min Zhou
Jonathan Vronsky
Songbai Yan
2026
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We present ALF (Advertiser Large Foundation model), a multi-modal transformer architecture for understanding advertiser behavior and intent across text, image, video and structured data modalities. Through contrastive learning and multi-task optimization, ALF creates unified advertiser representations that capture both content and behavioral patterns. Our model achieves state-of-the-art performance on critical tasks including fraud detection, policy violation identification, and advertiser similarity matching. In production deployment, ALF reduces false positives by 90\% while maintaining 99.8\% precision on abuse detection tasks. The architecture's effectiveness stems from its novel combination of multi-modal transformations, intersample attention mechanism, spectrally normalized projections, and calibrated probabilistic outputs.
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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’s on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale
Rameen Mahmood
Danny Yuxing Huang
Proceedings of ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT), Association for Computing Machinery (2026)
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Understanding long visual documents, where information is distributed across extensive pages of text and graphics, remains a critical challenge for modern Vision-Language Models (VLMs). This difficulty is rooted in two fundamental obstacles: poor evidence localization and a high tendency for model hallucination. To address these issues, we propose DocLens, a multi-agent framework that decomposes the task into two specialized stages.
First, a Lens Module leverages document parsing tools for fine-grained, hierarchical evidence localization at both the page and element level. Second, a Reasoning Module employs a sampling-adjudication mechanism to systematically analyze the localized evidence, mitigating hallucination and synthesizing reliable answers.
Paired with Gemini-2.5-Pro, DocLens achieves state-of-the-art performance on MMLongBench-Doc and FinRAGBench-V, even surpassing human experts. Furthermore, our framework offers a highly cost-effective variant that delivers comparable performance to strong baselines at a five-fold reduction in cost.
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"It didn’t feel right but I needed a job so desperately": Understanding People's Emotions & Help Needs During Financial Scams
G. Jake Chanenson
Tara Matthews
Jessica McClearn
Sarah Meiklejohn
Mia Hassoun
2026
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Online financial scams represent a long-standing and serious threat for which people seek help. We present a study to understand people’s in situ motivations for engaging with scams and the help needs they express before, during, and after encountering a scam. We identify the main emotions scammers exploited (e.g., fear, hope) and characterize how they did so. We examine factors—such as financial insecurity and legal precarity—which elevate people’s risk of engaging with specific scams and experiencing harm. We indicate when people sought help and describe their help-seeking needs and emotions at different stages of the scam. We discuss how these needs could be met through the design of contextually-specific prevention, diagnostic, mitigation, and recovery interventions.
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This disclosure describes systems and methods for a multi-agent framework that can automate and scale cognitive work. The framework can, for example, use a cognitive assembly line of specialized computational agents to perform tasks such as research and drafting. A beneficial component could be an adversarial review panel (ARP), which is a multi-agent review system where distinct agent personas critique a generated draft from varied perspectives. The structured feedback from the ARP can be used to automatically iterate on and refine the work product. This approach can improve the intellectual rigor of generated content and reduce the time required for production, which may allow human operators to focus on activities such as strategic oversight and final validation.
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Rank aggregation is a task of combining the rankings of items from multiple users into a single ranking that best represents the users' rankings. Alabi et al. (AAAI'22) presents differentially-private (DP) polynomial-time approximation schemes (PTASes) and 5-approximation algorithms with certain additive errors for the Kemeny rank aggregation problem in both central and local models. In this paper, we present improved DP PTASes with smaller additive error in the central model. Furthermore, we are first to study the footrule rank aggregation problem under DP. We give a near-optimal algorithm for this problem; as a corollary, this leads to 2-approximation algorithms with the same additive error as the 5-approximation algorithms of Alabi et al. for the Kemeny rank aggregation problem in both central and local models.
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