Tomas Pfister
Tomas Pfister is the Head of Cloud AI Research. He came to Google from Apple where he cofounded Apple's central AI research group and published Apple’s first research paper that won the Best Paper Award at CVPR’17. Tomas’ key scientific achievements have been proposing a method to improve the realism of synthetic images; developing the first automated method to detect facial micro-expressions; and inventing a new way for neural networks to exploit spatiotemporal structure. He is currently exploring learning from small amount of labeled data (using techniques such as generative models, few-shot learning, transfer learning) and explainability/interpretability of deep learning models, and is particularly excited about the potential of AI in healthcare & education. His research has laid the foundation for several applications such as Face ID in iPhone X, autonomous driving, human pose estimation, detecting facial micro-expressions & translating sign language. Tomas did his PhD in deep learning with Prof Andrew Zisserman at Oxford University and bachelor’s degree in computer science at Cambridge University. He is the recipient of the Forbes 30 Under 30 award, and has received over 40 research awards, including 3 best paper awards, with numerous publications in top AI research venues. His work has been frequently featured in mainstream media, including Forbes, BusinessInsider & Wired.
Authored Publications
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Causal Armor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution
Minbeom Kim
Phil Wallis
Kyomin Jung
Dj Dvijotham
2026
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AI agents equipped with tool-calling capabilities are susceptible to \emph{Indirect Prompt Injection} (IPI) attacks. In this attack scenario, malicious commands hidden within \emph{untrusted} content trick the agent into performing unauthorized actions. Existing defenses can reduce attack success but often suffer from the \emph{over-defense dilemma}: they deploy expensive, \emph{always-on} sanitization that degrades utility and latency even in benign scenarios. We revisit IPI through an operational causal lens: a successful injection manifests as a \emph{grounding collapse} where the user request no longer provides decisive support for the agent's privileged action, while a particular untrusted segment provides disproportionate marginal support. Based on this signature, we propose \texttt{CausalArmor}, a selective defense framework that (i) computes lightweight, normalized leave-one-out attributions at privileged decision points, and (ii) triggers targeted sanitization only when an untrusted segment dominates the user intent. Additionally, CausalArmor employs \emph{retroactive Chain-of-Thought masking} to prevent the agent from acting on ``poisoned" reasoning traces. Experiments on AgentDojo and DoomArena demonstrate that CausalArmor matches the security of aggressive defenses with explainability while preserving utility and latency of AI agents.
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Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents
Pengfei He
Ash Fox
Stefan Friedli
Daniel Fabian
Burak Gokturk
Jiliang Tang
ICML 2026
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Large language models (LLMs) have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation due to limited interaction, weak execution grounding, and a lack of experience reuse. We propose Code-RedTeam, a security-aware multi-agent framework designed to mirror real-world red-teaming workflows by integrating security-domain knowledge, code-aware analysis, execution-grounded iterative reasoning, and long-term memory. Code-RedTeam decomposes vulnerability analysis into coordinated discovery and exploitation stages, enabling agents to plan, execute, validate, and refine actions based on real execution feedback while learning from prior trajectories. Extensive evaluations on challenging security benchmarks demonstrate that Code-RedTeam consistently outperforms strong baselines across diverse backbone models, achieving over 60% attack success rate in vulnerability exploitation and up to 10% absolute improvement in vulnerability detection. Ablation and iteration studies further confirm the critical role of execution feedback, structured interaction, and memory for building robust and generalizable cybersecurity agents.
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The exponential growth of machine learning submissions has strained the traditional peer review process, resulting in slow feedback loops for authors and an immense burden on reviewers to rigorously audit technical soundness and verify literature. To address this, we introduce ScholarPeer, a multi-agent framework designed to operationalize the rigorous auditing workflow of a senior researcher. Rather than attempting to replace human judgment, ScholarPeer serves as a co-scientist: acting as a mentor for rapid author iteration prior to submission, and as an active verification assistant that augments human reviewers. The framework structurally decouples contextualization from critique by deploying a sub-domain historian to synthesize the field's trajectory, a baseline scout to proactively hunt for omitted state-of-the-art comparisons, and a multi-aspect Q&A engine that deeply audits technical soundness-scrutinizing internal logical consistency, experimental validity, and mathematical rigor-while cross-referencing claims against top-tier academic venues. We comprehensively evaluate ScholarPeer on ~1,800 ICLR submissions spanning 2020 through 2025. Our results show that ScholarPeer achieves significant win-rates against state-of-the-art fine-tuned models and search-augmented agentic baselines.
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Synthesizing unstructured research materials into manuscripts is an essential yet under-explored challenge in AI-driven scientific discovery. Existing autonomous writers are rigidly coupled to specific experimental pipelines, and produce superficial literature reviews. We introduce PaperOrchestra, a multi-agent framework for automated AI research paper writing. It flexibly transforms unconstrained pre-writing materials into publication-ready LaTeX manuscripts, including comprehensive literature synthesis and generated visuals, such as plots and conceptual diagrams. To evaluate performance, we present PaperWriting-Eval200 , the first standardized benchmark of reverse-engineered raw materials from 200 top-tier AI conference papers, alongside a suite of automated peer-review evaluators. PaperOrchestra significantly improves literature review depth and citation coverage, outperforming autonomous baselines with 90%–99% win rates and reaching human-level parity. It also outperforms autonomous baselines on overall manuscript quality by 14%–38%, as measured by side-by-side human evaluation.
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Watch and Learn: Learning to use computers from online videos
Luke Song
Yu Su
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2026), pp. 5421-5431
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Computer-using agents (CUAs) must plan task workflows across diverse and evolving applications, yet progress is limited by the lack of large-scale, high-quality training data. Existing datasets are narrow, static, and costly to annotate, while synthetic data often yields oversimplified or misaligned behaviors.
We present Watch & Learn (W&L), a framework that converts readily available Internet videos of human computer use into executable UI trajectories at scale. Instead of directly generating actions or relying on handcrafted heuristics, we cast trajectory annotation as an inverse dynamics problem that predicts user actions from consecutive screen states, which simplifies learning and generalizes across domains.
Through a task-aware retrieval and labeling pipeline, W&L yields over 53K high-quality trajectories that enhance CUAs both as in-context exemplars and as supervised training data. On OSWorld, it consistently improves general-purpose and specialized CUAs, while on WindowsAgentArena it achieves state-of-the-art performance among 7B-scale models under the 15-step limit. These results show that web-scale human demonstration videos can serve as a practical and scalable foundation for advancing real-world CUAs.
Project page: https://chanh.ee/wandl/
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LiSA: Lifelong Safety Adaptation via Conservative Policy Induction
Minbeom Kim
Phil Wallis
Bharath Chandrasekhar
Kyomin Jung
NeurIPS (2026)
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Large language model agents increasingly act in deployment environments where failures are contextual, user-specific, and costly. In such settings, a \emph{static general-purpose guardrail is often insufficient}: whether an action should be allowed may depend on local privacy norms, organizational rules, or evolving user expectations that are difficult to enumerate fully in advance. We study \emph{lifelong deployment-time guardrail adaptation}, where a fixed base guardrail improves over time from sparse, noisy user-reported failures without repeated fine-tuning. We propose a conservative policy induction framework organized as an online--offline loop. Online, the deployed guardrail uses structured policy memory to guide runtime decisions. Offline, newly accumulated reports are converted into reusable policy items and folded back into memory through periodic refresh. The method combines three ingredients: \emph{broad policy abstraction} for sparse failure generalization, \emph{conflict-aware local policies} for mixed-label regions where broad reuse becomes too coarse, and \emph{confidence-gated reuse} based on conservative posterior lower bounds so that weakly supported memory does not influence inference too early. Across PrivacyLens+, ConFaide+, and AgentHarm, the resulting system consistently improves over a lightweight base guardrail and strong memory-based baselines in sparse-feedback regimes, remains robust to noisy feedback, traces a better cost--performance frontier than scaling the base model alone, and jointly reduces over-refusal and over-acceptance without an explicit balance knob.
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Temporally consistent long video generation remains a fundamental challenge. Existing methods suffer from feature drift, where entities and environments gradually change unintentionally, or content collapse, where narratives fail to progress meaningfully. We introduce A${^2}$RD, an agentic autoregressive video generation architecture that decouples creative synthesis from consistency by modeling consistency as a test-time objective. A${^2}$RD features segment-by-segment generation augmented with a novel Multimodal Video Memory (\memory{}) that tracks segment contexts and dynamics and Test-Time Scaling algorithms that verify and refine generation. For each segment, it operates in a Retrieve--Synthesize--Refine--Update (RSRU) loop: the agent retrieves relevant contexts, determines the segment generation mode (extrapolation or interpolation) adaptively, synthesizes boundary frames then video segment with refinements applied at both frame and video levels, and updates \memory{} for subsequent generation. We further develop LVbench-C, a challenging benchmark measuring long-horizon entities and environments evolving in non-linear transitions. Extensive experiments on public and LVbench-C benchmarks across one-, three-, and five-minute video generation demonstrate that A${^2}$RD generates significantly more consistent, meaningful videos than existing baselines. Human evaluations confirm strong consistency in characters, objects, and environments, with smooth motion and meaningful narrative progression.
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PI-Hunter: Automated Red-Teaming for Exposing and Localizing Prompt Injections
Pengfei He
Vishesh Sharma
Ash Fox
George Lee
Jiliang Tang
2026
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Large Language Models (LLMs) are rapidly evolving into agentic systems that interact with external tools and dynamic environments, but this also introduces severe security risks. In particular, indirect prompt injection attacks can compromise agents through malicious instructions hidden in external sources such as web pages, emails, and retrieved documents. Existing defenses are largely reactive, while current automated red-teaming methods mainly optimize attack success rather than systematically uncovering hidden vulnerabilities within the agent pipeline. In this work, we propose PI-Hunter, an automated agentic red-teaming framework that shifts the focus from attack optimization to vulnerability exposure. By combining static attack-surface analysis, source-aware seeding, trajectory evaluation, and feedback-guided exploration, PI-Hunter proactively discovers vulnerable ingestion paths and localizes how malicious instructions propagate through agent reasoning. Extensive experiments across multiple benchmarks, agent architectures, attacks, and defenses show that \method~substantially improves vulnerability exposure and attack-surface coverage compared with existing automated red-teaming baselines, while remaining effective even under strong prompt injection defenses.
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TFRBench: A Reasoning Benchmark for Evaluating Forecasting Systems
MD ATIK AHAMED
Qiang Cheng
Forty-third International Conference on Machine Learning (2026)
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Time-series forecasting has traditionally been evaluated solely on numerical accuracy, treating models as "black boxes'' that fail to capture the underlying reasoning. To address this gap, we introduce TFRBench, a novel benchmark designed to evaluate the reasoning capabilities of forecasting systems alongside their numerical accuracy. Unlike existing benchmarks, TFRBench requires models to generate verifiable natural language reasoning by analyzing cross-channel dependencies, identifying strategic trends, and justifying significant events using external context. To construct this benchmark, we propose a systematic multi-agent framework comprising Reasoning, Search, Verifier, Forecasting, and Summary agents. Our benchmark spans five diverse domains including Energy, Sales, Web/CloudOps, Transportation, and Finance, covering 10 distinct datasets. Qualitative evaluation confirms that our generated reasoning is highly faithful and effective; specifically, Large Language Models (LLMs) prompted with our generated reasoning demonstrate significantly improved forecasting accuracy compared to direct forecasting with LLMs. Conversely, benchmarking experiments reveal that off-the-shelf LLMs consistently struggle with both reasoning (shows lower LLM-as-Judge scores) and direct numerical forecasting (MAE and MASE), frequently failing to capture domain-specific dynamics. TFRBench thus establishes a new standard for interpretable, reasoning-based evaluation in time-series forecasting.
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VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation
Dj Dvijotham
Mirko Montanari
ACL 2026
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Artificial intelligence is rapidly evolving, marked by the emergence of Large Language Model (LLM) agents – systems capable of complex reasoning, planning, and interaction with digital and physical environments. These agents, powered by advancements in LLMs, demonstrate remarkable capabilities across diverse domains, including finance, healthcare, web navigation, software development, and daily task assistance. Unlike traditional AI systems, LLM agents can perceive their surroundings, formulate multi-step plans, utilize external tools and APIs, access memory or knowledge bases, and execute actions to achieve specified goals. This ability to act upon the world, however, introduces significant safety and security challenges.
The safety paradigms developed for traditional LLMs, primarily focused on mitigating harmful textual outputs (e.g., toxicity, bias), are insufficient for safeguarding LLM agents. Agents interacting with dynamic environments and executing actions present a broader attack surface and new categories of risk. These include performing unsafe operations, violating privacy constraints through improper data handling or access control failures, deviating from user objectives (task misalignment), and susceptibility to novel manipulation techniques like indirect prompt injection and memory poisoning. Ensuring the trustworthy operation of these powerful agents is paramount, especially as they are integrated into high-stakes applications. To address this critical challenge, we introduce VeriGuard, a novel framework designed to enhance the safety and reliability of LLM agents by interactively verifying their policies and the actions. VeriGuard integrates a verification module that intercepts code-based actions proposed by the agent. In the first step, VeriGuard will generates and verifies the policies. The policies are rigorously checked against a set of predefined safety and security specifications Then each action will be verified to make sure it will align with the agent specification. This interactive verification loop ensures that the agent's behavior remains within safe operational bounds, effectively preventing the execution of harmful or unintended operations. By verifying each step, VeriGuard provides a robust safeguard, substantially improving the trustworthiness of LLM agents in complex, real-world environments.
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