The Natural Language Processing Reading Group at Stony Brook University meets weekly to discuss recent research papers in NLP and related fields.
Join the Google Group here.

IdeaLens: Detecting AI Ideas in Long-form Writing

Abstract: While modern AI detectors identify who wrote the words, emerging policies on AI use increasingly hinge on a different question: who came up with the ideas? We introduce IdeaLens, a detector that identifies whether a document's ideas came from a human or AI (idea provenance), regardless of who wrote its words. To focus IdeaLens on ideas rather than prose, we represent documents as outlines: lists of items that each pair a discourse role with a brief, paraphrased description of the content, minimizing word-level overlap with the raw text. We train IdeaLens on 1M FineWeb documents with silver labels from Pangram, a prose provenance detector. Since the outlines are largely stripped of surface-level information, the labels must be fit mainly through the ideas. In a controlled study, IdeaLens's AI flag rate drops from 95% to 7% as models write from increasingly detailed human plans, while Pangram 4 still flags 92%; from AI-derived plans, IdeaLens stays above 96%. Conversely, on a new dataset of 50 stories that human authors wrote from AI-generated plans, IdeaLens flags 68% of the stories as AI, compared to 8% for Pangram 4. On a comprehensive suite of 19 existing detection benchmarks, we show that IdeaLens maintains strong detection rates at low false positive rates, suggesting that ideas themselves provide a powerful discriminative signal, and its performance holds across domains, formats, and languages. Finally, we examine 90K predictions from IdeaLens to characterize systematic differences between human and AI ideation. We release our models and labeled datasets to facilitate future research on idea provenance detection.

Location: NCS 220

Zoom Link: https://stonybrook.zoom.us/j/92942833294?pwd=927683
CSE 600 Seminar Series | Fall 2025

Abstract: Imagine machines that can see the invisible: drones locating wildfire survivors, cameras predicting building failures, and smartphones detecting skin tumors. These applications lie beyond today's vision systems, which focus only on human-visible information. In this talk, I argue that a wealth of scene information is hidden in light properties invisible to the human eye, such as the travel time of photons and polarization of light waves. I will present how co- designing camera hardware, graphics models, and learning algorithms unlocks these invisible properties to create superhuman vision systems. I will present three superhuman vision capabilities: seeing around blind corners, turning objects into cameras, and extracting internal stress fields. By analyzing faint light reflections on diffuse walls and shiny objects, we create virtual cameras that reveal scenes hidden from the line of sight - enabling autonomous systems to navigate safely. Using the polarization of light, we recover mechanical stress fields hidden inside objects - opening new possibilities for non-destructive material characterization. These capabilities point toward a future where machines can see the invisible: around us, beneath our bodies, and beyond our scientific understanding.

Bio:
Akshat Dave is an Assistant Professor in the Department of Computer Science at Stony Brook
University, USA. His research lies at the intersection of applied optics, computer vision, and
machine learning. His work has been recognized by Rice University's Best Thesis Award, Optica Best Paper Prize, SIGGRAPH Asia Doctoral Consortium, and fellowships by Qualcomm, Texas Instruments, and INK Global Foundation. Prior to Stony Brook, he was a Postdoctoral Associate at MIT Media Lab. He holds a Ph.D. from Rice University and a Masters and a Bachelors from Indian Institute of Technology Madras.
Kate Armstrong, a Vancouver-based artist, writer, and independent curator, will explore the role of AI in art and creativity through three AI-driven projects: KEKE Terminal, Botto, and Sasha Stiles' AI collaborator Technelegy. She will compare these projects to historical artistic movements and investigate AI's role as an autonomous creative agent, the function of community participation, and the shifting dynamics of authorship.

Location: Humanities Institute Room 1008

Yihui Ren

AI Department, Brookhaven National Laboratory

Abstract: Artificial intelligence is rapidly changing how we approach scientific discovery and engineering design. However, realizing the potential of modern AI in scientific domains requires going beyond simply applying models developed for natural language: scientific data have distinct structures, and scientific workflows often require tight integration among learned representations, domain knowledge, simulation, and numerical optimization.
In this talk, I will present two recent efforts that explore complementary directions for building AI systems for science and engineering. First, FM4NPP investigates whether the foundation-model paradigm can scale to experimental nuclear and particle physics. Using more than 11 million particle-collision events, we develop a self-supervised learning framework for sparse detector data and demonstrate scaling up to 188 million parameters. The resulting representations generalize across diverse downstream tasks and enable data-efficient adaptation with lightweight task-specific components.
Second, I will present AutoSizer, an LLM-driven framework for automatic sizing of analog and mixed-signal circuits. Rather than asking an LLM to perform numerical optimization directly, AutoSizer uses the LLM as a reasoning and orchestration layer that interprets circuit behavior, analyzes simulation feedback, and adaptively refines the optimization process.
Together, these studies illustrate two complementary roles for modern AI in scientific workflows: learning reusable representations from complex scientific data and reasoning over computational tools to guide challenging design and optimization problems. I will discuss the lessons from these efforts and opportunities toward more general, scalable, and reusable AI systems for science and engineering.

Short bio: Dr. Yihui (Ray) Ren is a Senior Computational Scientist in AI/ML and the AI-Codesign Group Lead in the Artificial Intelligence Department within Brookhaven National Laboratory's Computing and Data Sciences Directorate. His research focuses on AI for Science, AI-hardware codesign, real-time AI systems, and the evaluation of emerging AI hardware architectures. He also develops advanced AI methodologies for nuclear safeguards applications. Dr. Ren's key contributions include unpaired domain mapping techniques to mitigate domain shift, generative surrogate modeling, object detection and video synthesis using event-based cameras, and the development of AI foundation models such as the Foundation Model for Nuclear and Particle Physics (FM4NPP). Prior to joining Brookhaven National Laboratory in 2018, he was a postdoctoral researcher at Virginia Tech. He received his Ph.D. in Physics from the University of Notre Dame in 2015.

Location: NCS 120

Zoom: https://stonybrook.zoom.us/j/92507585369?pwd=9bw2hsQmLbladVpPRiaWK94gYlwCGQ.1&jst=2

Abstract: Monocular surface normal estimators trained on large-scale RGB-normal data often perform poorly in the edge cases of reflective, textureless, and dark surfaces. Polarization encodes surface orientation independently of texture and albedo, offering a physics-based complement for these cases. Existing polarization methods, however, require multi-view capture or specialized training data, limiting generalization. We introduce Poppy, a training-free framework that refines normals from any frozen RGB backbone using single-shot polarization measurements at test time. Keeping backbone weights frozen, Poppy optimizes per-pixel offsets to the input RGB and output normal along with a learned reflectance decomposition. A differentiable rendering layer converts the refined normals into polarization predictions and penalizes mismatches with the observed signal. Across seven benchmarks and three backbone architectures (diffusion, flow, and feed-forward), Poppy reduces mean angular error by 23-26% on synthetic data and 6-16% on real data. These results show that guiding learned RGB-based normal estimators with polarization cues at test time refines normals on challenging surfaces without retraining.

Speaker: Irene Kim

Location: NCS 115

Abstract: This talk is about the two ends of LLM training: pre-training and in-deployment learning. I will present an approach to disentangle knowledge from skill in model pre-training. This brings about a new class of LLMs that externalize knowledge, with dramatically different characteristics from common LLMs along dimensions of scale, factuality, and updateability. On the other end, I will discuss two in-deployment learning methods. I will describe how in-context learning abilities extend beyond supervised settings, showing that LLMs display in-context reinforcement learning from rewards. Finally, if time allows, I will describe continual learning from implicit interaction signals, demonstrating that LLMs can retrospectively decode latent interaction cues by observing how humans respond to their outputs.

Bio: Yoav Artzi is an Associate Professor in the Department of Computer Science and Cornell Tech at Cornell University, a visiting faculty researcher at Google DeepMind, and arXiv's associate faculty director. His research focuses on language modeling and learning in interactive and situated scenarios. His work was acknowledged by awards and honorable mentions at ACL, EMNLP, NAACL, and IROS, as well as a TACL test-of-time award. Yoav holds a B.Sc. from Tel Aviv University and a Ph.D. from the University of Washington.

Location: NCS 120
Predictable Autonomy for Cyber-Physical Systems by Stanley Bak, Safe Sky Analytics

ABSTRACT: Cyber-physical systems combine complex physics with complex software. Although these systems offer significant potential in fields such as smart grid design, autonomous robotics and medical systems, verification of CPS designs remains challenging. Model-based design permits simulations to be used to explore potential system behaviors, but individual simulations do not provide full coverage of what the system can do. In particular, simulations cannot guarantee the absence of unsafe behaviors, which is unsettling as many CPS are safety-critical systems.

The goal of set-based analysis methods is to explore a system's behaviors using sets of states, rather than individual states. The usual downside of this approach is that set-based analysis methods are limited in scalability, working only for very small models. This talk describes our recent process on improving the scalability of set-based reachability computation for LTI hybrid automaton models, some of which can apply to very large systems (up to one billion continuous state variables!). Lastly, we'll discuss the significant overlap of techniques used for our scalable reachability analysis methods with set-based input/output analysis of neural networks.

BIO: Stanley Bak is a computer scientist investigating the predictable design of autonomous cyber-physical systems. He strives to develop practical formal methods that are both scalable and useful, which demands developing new theory, programming efficient tools and building experimental systems. He received a Bachelor's degree in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007 (summa cum laude), and a Master's degree in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009. He completed his PhD from the Department of Computer Science at UIUC in 2013. He received the Founders Award of Excellence for his undergraduate research at RPI in 2004, the Debra and Ira Cohen Graduate Fellowship from UIUC twice, in 2008 and 2009, and was awarded the Science, Mathematics and Research for Transformation (SMART) Scholarship from 2009 to 2013. From 2013 to 2018, Stanley was a Research Computer Scientist at the US Air Force Research Lab (AFRL), both in the Information Directorate in Rome, NY, and in the Aerospace Systems Directorate in Dayton, OH. He currently helps run Safe Sky Analytics, a research consulting company investigating verification and autonomous systems, and performs teaching as an Adjunct Professor at Georgetown University.

Google Docs has been updated to integration Gemini AI. Learn about the integrations that gives you more features within Google Docs. There are also AI Automations that will keep you updated when things happen with documents.

In this session, you will:

  1. Learn about the AI features within Google Docs
  2. Learn about workflow to be notified of changes to Docs
  3. Understand the features that allow AI to work

Register here.
The New York Academy of Sciences Presents AI for Materials: From Discovery to Production - A Virtual Symposium

Event Description: This interdisciplinary symposium covers the application of artificial intelligence (AI) throughout the entire life cycle of new materials -- from materials simulations and synthesis to translating research into high-volume industrial production.

Event Link & Registration: nyas.org/AI4Materials2020
Educational objectives:
  1. Techniques involving data driven inferences from multi-omics data
  2. Interdisciplinary approaches to uncover aspects of tumor biology
  3. Computational genomics method development, benchmarking, and validation


Speaker: Dr. Subhajyoti De, PhDProfessor, Cancer SystemsBiology and Pathology,Rutgers Cancer Institute,The State University of New Jersey

Location: MART Building, Room 7M-0602 (7th Floor)

Remote Access: https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09
Meeting ID: 95617197636 | Passcode: 924293