Hamid Palangi
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Authored Publications
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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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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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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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Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies
Han Zhou
Shariq Iqbal
Ivan Vulić
Anna Korhonen
International Conference on Learning Representations (ICLR) (2026)
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Large language models (LLMs), employed as multiple agents that interact and collaborate with each other, have excelled at solving complex tasks. The agents are programmed with {prompts} that declare their functionality, along with the {workflows} that orchestrate interactions within a structured flow. Designing prompts and workflows for multi-agent systems is inherently complex, especially when addressing a new task. It often demands expert-level knowledge and involves significant trial and error. Gaining a deep understanding of the factors that contribute to effective multi-agent systems is essential for automating the entire process. Motivated by this, we first conduct an in-depth analysis of the design spaces for multi-agent systems, focusing on the impact of prompts, scaling the number of agents, and common types of agentic modules. Our findings reveal that top-performing systems often emerge from simpler design spaces, where prompts play a critical role in enhancing agent functionality and enabling more effective scaling. Based on the insights, we propose Multi-Agent System Search (MASS), a multi-stage optimization framework that performs the optimization in a pruned design space, with prompts and an influential subset of modules. We show that MASS-optimized multi-agent systems outperform existing alterntives by a substantial margin. Based on the MASS-found systems, we finally propose design principles behind building effective multi-agent systems.
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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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CoDaS: AI Co-Data-Scientist for Biomarker Discovery via Wearable Sensors
Juro Gottweis
CJ Park
Salman Rahman
Ahmed Metwally
Hong Yu
Ivor Rendulic
Yuzhe Yang
Petar Sirkovic
Daniel McDuff
Shwetak Patel
Nicolas Stroppa
Yubin Kim
Mark Malhotra
Orson Xu
Sam Schmidgall
Tim Althoff
Elahe Vedadi
Cynthia Breazeal
Hae Won Park
(2026)
The Anatomy of a Personal Health Agent
Ahmed Metwally
Ken Gu
Jiening Zhan
Kumar Ayush
Hong Yu
Akshay Paruchuri
Amy Lee
Qian He
Zhihan Zhang
Isaac Galatzer-Levy
Xavi Prieto
Andrew Barakat
Ben Graef
Yuzhe Yang
Daniel McDuff
Brent Winslow
Shwetak Patel
Girish Narayanswamy
Conor Heneghan
Max Xu
Jacqueline Shreibati
Mark Malhotra
Orson Xu
Tim Althoff
Tony Faranesh
Nova Hammerquist
Vidya Srinivas
arXiv (2025)
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Health is a fundamental pillar of human wellness, and the rapid advancements in large language models (LLMs) have driven the development of a new generation of health agents. However, the solution to fulfill diverse needs from individuals in daily non-clinical settings is underexplored. In this work, we aim to build a comprehensive personal health assistant that is able to reason about multimodal data from everyday consumer devices and personal health records. To understand end users’ needs when interacting with such an assistant, we conducted an in-depth analysis of query data from users, alongside qualitative insights from users and experts gathered through a user-centered design process. Based on these findings, we identified three major categories of consumer health needs, each of which is supported by a specialist subagent: (1) a data science agent that analyzes both personal and population-level time-series wearable and health record data to provide numerical health insights, (2) a health domain expert agent that integrates users’ health and contextual data to generate accurate, personalized insights based on medical and contextual user knowledge, and (3) a health coach agent that synthesizes data insights, drives multi-turn user interactions and interactive goal setting, guiding users using a specified psychological strategy and tracking users’ progress. Furthermore, we propose and develop a multi-agent framework, Personal Health Insight Agent Team (PHIAT), that enables dynamic, personalized interactions to address individual health needs. To evaluate these individual agents and the multi-agent system, we develop a set of N benchmark tasks and conduct both automated and human evaluations, involving 100’s of hours of evaluation from health experts, and 100’s of hours of evaluation from end-users. Our work establishes a strong foundation towards the vision of a personal health assistant accessible to everyone in the future and represents the most comprehensive evaluation of a consumer AI health agent to date.
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Multi-turn Function-calling via Graph-based Execution and Translation
Kai-Wei Chang
I-Hung Hsu
Jindong Gu
Fan Yin
2025
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We propose a principled method to synthesize high-quality multi-turn function calling trajectories to align large language model (LLM)-based agents. We start with iteratively building function calling graph and defining node operations to increase its complexity. This enables us to construct reliable reference. Then, based on the synthesized function calling graph, we adopt back-and-forth translation to first construct multi-turn user queries and then, fill in the function arguments with information in the query. We sample positive trajectories that distill the function graph reference and negative trajectories that contrast with the positive trajectories in targeted loss patterns in multi-turn scenarios. Training with the positive trajectories with supervised fine-tuning and preference optimization against negative trajectories, we obtain 67.42 on BFCL and 71.7 on ToolQuery with an open-sourced model with 14B parameters, surpassing the performance of strong proprietary models like o1.
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Model Swarms: Collaborative Search of Adapted LLM Experts via Swarm Intelligence
Shangbin Feng
Yike Wang
Nathalie Rauschmayr
Yejin Choi
Yulia Tsvetkov
ICML 2025
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We propose Model Swarms, a collaborative search algorithm to adapt LLM experts via swarm intelligence. Specifically, Model Swarms starts with a pool of LLM experts and a utility function. Guided by the best-found checkpoints across models, diverse LLM experts collaboratively move in the weight space and search for adapted models that optimize the utility function. Compared to existing model composition approaches, Model Swarms offers modularity, works in low-data regimes, and doesn't need assumptions about existing experts and how they should be composed. Extensive experiments demonstrate that Model Swarms could flexibly adapt LLM experts to a single dataset, multi-dataset domains, reward models, as well as diverse human preferences. Further analysis reveals that LLM experts discover previously unseen capabilities in the search process and that Model Swarms enable the weak-to-strong transition of experts through the collaborative search process.
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PlanGEN: A Framework Utilizing Inference-Time Algorithms with LLM Agents for Planning and Reasoning
Hootan Nakhost
Swaroop Mishra
Chitta Baral
Jindong Gu
2025
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Scaling inference-time computation in Large Language Models (LLMs) dramatically improves their capabilities for solving complex problems. While test-time scaling has shown promise in many tasks such as code generation and mathematical reasoning, integration of inference-time algorithms into multi-agent frameworks for planning and reasoning remains under-explored. To this end, we explore popular inference-time algorithms—Best of N, Tree of Thought (ToT), and REward BAlanced SEarch (REBASE)—with proposed feedback-driven refinement. Our feedback-driven refinement employs specialized agents: a constraint agent to enforce task instance-specific constraints, and a verifier agent to evaluate plan quality. Furthermore, we hypothesize that test-time scaling can be proportional to instance-level complexity. Thus, we propose an additional selection agent to dynamically optimize algorithm choice. We evaluate our proposed approaches on four different benchmarks, i.e., NATURAL PLAN, GPQA, OlympiadBench, and DocFinQA. Experimental results show that our methods outperform strong baselines, achieving state-of-the-art results in NATURAL PLAN, OlympiadBench , and DocFinQA. Our key findings demonstrate that constraint-guided iterative refinement and algorithm selection improves both planning and downstream reasoning in LLMs
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