The NSF-NIST Institute for Trustworthy AI in Law & Society focuses on broad participation in AI, deep technical research, and informed governance of AI systems.

Joined May 2023
TRAILS is launching 11 Broader Impact Awards to expand access to trustworthy AI. These grants—up to $25K apiece—support projects @UofMaryland, @MorganStateU and @GWtweets to empower diverse stakeholder communities to engage with and influence the future of AI. trails.umd.edu/news/trails-a…
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Helping students learn to evaluate AI. Making autonomous drones safer. Measuring how people express trust in AI. Helping local governments navigate AI policy. Understanding how to reestablish trust after AI failures. These ideas are driving five new research projects selected by @trails_ai for its latest round of seed funding. Ranging from $40K to $160K, the grants are designed to launch new collaborations, generate preliminary results and position investigators to compete for larger external funding. trails.umd.edu/news/trails-a…
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Is AI hiring software playing favorites? A new study by researcher Jiannan Xu of @SmithSchool and @trails_ai, shows how AI screening systems exhibit a self-preference bias, favoring resumes generated by their own model over human or competing AI writers. This disparity was especially pronounced in business-related roles, where standardized language may cause the AI to reward its own writing style independent of a candidate's actual qualifications. The findings have attracted coverage from outlets including @nypost and @BusinessInsider. go.umd.edu/2c1l
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I posted this when it came out, but it didn't have a nice online readable form (only a PDF). If this sounds fun to you can get in on the action in person (DC area) or online *THIS WEEKEND* (1/4)
Can AI and people work better together? @UofMaryland CS professor Jordan Boyd-Graber (@boydgraber) uses quizbowl to study when people trust #AI, when they rely on their own judgment and what makes human-AI collaboration effective. Read more: go.umd.edu/Boyd-Graber-6-202…
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Excited to be at #CVPR 2026 in Denver this week for events around trustworthy AI, embodied reasoning, watermarking, and world models. I will only be around on Thursday, June 4, so please come say hi tomorrow! On June 4, I will be speaking at several CVPR workshops and tutorials: 1⃣ CVPR 2026 Workshop on Trustworthy AI / TRUE-V 🔗 trustworthy-ai-workshop.gith… 📍 9:10–9:40 AM | Room 705/707 Talk Title: A Few Early Steps Away: Building Self-Correcting Vision-Language Systems I will discuss how we can move beyond static vision-language models toward systems that can recognize, reason about, and correct their own failures. 2⃣ The first CVPR Workshop on Embodied Reasoning in Action (ERA) 🔗 embodied-reasoning.github.io… 📍11:45 AM–12:20 PM | Room 605 Talk Title: From Perception to Action: From Latent World Models to State-Aware Scene Graphs for Physical Intelligence This talk will focus on representations and learning systems that connect perception, reasoning, and action for physical intelligence. Later in the day, I will also be part of the CVPR tutorial: 3⃣Foundations and Frontiers of Watermarking 🔗 vishal3477.github.io/waterma… 📍3:30–4:10 PM | Room: Mile High 2B Session Title: Benchmarking & Robustness Evaluation I will cover how to evaluate watermarking systems under distortions, regeneration, and adaptive attacks—an increasingly important direction for trustworthy generative AI. Our team will also present TraceGen at the CVPR main conference. I will not be there on June 6, but my students will be presenting the work — please stop by and talk with them! 4⃣TraceGen: World Modeling in 3D Trace Space Enables Learning from Cross-Embodiment Videos Project 🔗: tracegen.github.io/ YouTube 📽️: youtu.be/JCXnK2tHE_I Poster 📍: Saturday, June 6, 2026 | 11:45 AM–1:45 PM MDT | ExHall F 605 TraceGen introduces a world-modeling framework that predicts future motion in a compact 3D trace space, rather than directly in pixel space. This abstraction preserves the geometry needed for manipulation while reducing dependence on embodiment-specific appearance, enabling learning from heterogeneous human and robot videos and improving transfer to real-world robotic tasks. Fresh out of oven new research: We have also pushed this direction to the next level. Stay tuned for our upcoming release of μ₀, a symbolic world model pretrained only from video data that reaches π₀.₅-level performance. 🔥 Looking forward to seeing friends, collaborators, and new colleagues tomorrow at CVPR! #CVPR2026 #TrustworthyAI #EmbodiedAI #VisionLanguageModels #Robotics #WorldModels #Watermarking #GenerativeAI #PhysicalIntelligence
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Can we make AI forget harmful data without erasing its entire intelligence? Researchers @UofMaryland and the Max Planck Institute for Software Systems (@mpi_sws_) have developed a new AI “unlearning” method to address this hurdle in LLMs. The method can selectively remove sensitive data—like private information, copyrighted text or misinformation—without damaging the model’s reasoning abilities. Led by @FeiziSoheil and @RezaeiKeivan, the team’s method works like a precision rollback tool for AI memory, allowing developers to reverse the influence of specific data instead of retraining massive models from scratch. The research was presented at #ICLR2026 (@iclr_conf) in Rio de Janeiro. Read more: go.umd.edu/2bjx
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Can you spot when AI bluffs? Can you outguess AI or work with one to dominate trivia? Can you make an AI that can figure out tricky visual questions?
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The Institute for Trustworthy AI in Law & Society (@trails_ai) is supporting three camps this summer to engage and educate high schoolers in trustworthy AI: 💻The TRAILS Summer Camp @GWtweets will run July 20–August 7 and focus on modeling, ethics, and real-world decisions. 💻The TRAILS AI Camp @UofMaryland will run July 20–August 7 for rising 10th and 11th graders to explore what trust in AI looks like from social and technical viewpoints. 💻The AI & Analytics Camp @MorganStateU is a FREE week of technology focused skill development workshops from June 22-26 for high schoolers in the Greater Baltimore Metropolitan Area. Learn more and apply: trails.umd.edu/2026-summer-c…
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The first deadline for submitting a paper to our workshop is today, but there are lots of opportunities to participate: submit a trivia playing robot, play yourself, or write (multimodal) questions. Please share the word! (More details/links in the first comment.)
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#TRAILSCon2026 was packed with presentations and panels on real-world approaches to evaluating AI performance, risk, and impact.
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Tom Goldstein (@tomgoldsteincs) was honored at the 2026 Maryland Research Excellence Celebration for his influential work in machine learning and AI security. With more than 9,000 Google Scholar citations annually and an h-index exceeding 90, he ranks among the most highly cited scholars in his field. Read more about the event: today.umd.edu/new-presidents…
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It's not too late to register for #TRAILSCon2026 at George Washington University on Wednesday, March 4. This year, TRAILSCon will explore real-world approaches to evaluating AI performance, risk, and impact. Learn more: trails.umd.edu/trailscon-202…
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Come join @trails_ai as a postdoc at @UofMaryland (and work w folks at GW, MSU & Cornell) to conduct research and scholarship focused on approaches to AI that advance trust and trustworthiness with a great group of colleagues! 🌐 go.umd.edu/trails-postdoc-sp… 🗓️ Summer/Fall 2026 start
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How do we measure AI’s performance, risk & real-world impact? 🤖 Find out at TRAILSCon 2026, March 4–5 at GW. Panels, roundtables & workshops focused on responsible AI design & governance. 🔗 Register: go.gwu.edu/trailscon #TRAILSCon2026 #AI #ResponsibleAI #FutureOfTech
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We just posted a new paper on arXiv about a blind spot in today’s LLM agents: temporal blindness. Many agents behave as if time has not passed. In multi-turn settings, this shows up as relying on stale context with too much confidence and skipping a needed refresh, or repeatedly re-checking information that likely has not changed. We study this through a concrete decision agents face constantly: whether to call a tool to verify or update, or answer directly. To measure the problem, we introduce TicToc, a benchmark of 1.8K+ multi-turn trajectories across 76 scenarios, spanning low, medium, and high time sensitivity, with aggregated human preference labels for tool use. Across many proprietary and open models, alignment is close to chance without timestamps and improves only modestly even when timestamps are provided, suggesting that having time metadata is not the same as reasoning with it. Prompting helps a bit in some cases while post-training with DPO and a dynamic margin helps substantially. Paper: arxiv.org/abs/2510.23853 More details 👇
⏳ Your LLM Agent is “Temporally Blind”! (1/n) We study “temporal blindness” in multi-turn LLM agents making tool-call decisions—where models either over-rely on prior context and skip tool calls, or under-rely on it and repeat unnecessary calls. Paper: arxiv.org/abs/2510.23853
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AI is everywhere—but how we evaluate these systems is still evolving. Join AI practitioners, researchers, and thought leaders March 4-5 at #TRAILSCon2026 to discuss the latest in AI evaluation. The conference will explore real-world approaches to evaluating AI performance, risk, and impact through dynamic panels, interactive roundtables, and deep-dive workshops. Learn more and register: trails.umd.edu/trailscon-202…
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Join us this Friday, Feb. 13 for student lightning talks to share research, ideas or potential collaborations about trustworthy AI in a supportive, low-stakes environment. This event will take place on the UMD and GW campuses, and Zoom. Food provided! Learn more: trails.umd.edu/student-light…
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