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 11671 publications
A 3D Scene Graphs Survey: Open Challenges and Future Directions
Dennis Rotondi
Francesco Argenziano
Sebastian Koch
Nathan Hughes
Martin Büchner
Johanna Wald
Lukas Schmid
Daniele Nardi
Abhinav Valada
Liam Paul
Luca Carlone
Kai Arras
Annual Review of Control, Robotics, and Autonomous Systems (ARCRAS), 10 (2027) (to appear)
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3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relational abstractions of the environment. Their expressiveness has made them relevant to a broad range of problems in robotics and computer vision, including mapping, task and motion planning, scene understanding, and many others. However, the field remains fragmented: different communities adopt distinct formulations, construction pipelines, and evaluation protocols, making it difficult to compare methods, identify common assumptions, and assess remaining challenges for robust real- world deployment. This survey provides a unified and critical review of 3DSGs, with particular emphasis on open challenges and future directions. We first formalize 3DSGs under a common definition and analyze the principal modeling choices that characterize existing formulations, including node and edge attributes, hierarchical structure, dynamic scene representations, and affordance-aware extensions. We then review how 3DSGs are constructed from raw sensory observations, covering both learning-oriented and construction-oriented systems. Finally, we examine downstream applications and evaluation strategies, from intrinsic graph quality to task-level performance. To support the community, we also provide a dedicated website that organizes and extends the surveyed works.
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When Android Apps Request Location with a Reason: A Developer Study and User Behavior Experiment
May Smith
Aman Jain
Proceedings on Privacy Enhancing Technologies (2027)
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Recent reports have highlighted how mobile apps share user location data with third parties, risking user privacy and platform trust. Although location data is highly sensitive, when users grant apps location access, they may not know the full extent to which it is used. We study how requiring Android apps to show a reason for location access could impact developers, users, and the platform.
We surveyed 323 Android app developers and found most supported such a requirement. The majority said it would have a positive impact on user privacy, trust for apps, and trust for Android, where impact on user trust for Android correlated most strongly with support. Many developers also said the intervention would increase the number of users granting location access. Yet their open-ended comments also revealed consistent concerns, such as apps providing dishonest reasons and platform verification.
To study the impact on user behavior, we conducted a randomized controlled experiment with 2579 US Android users. We tested how users' decisions to grant location access were impacted by app type, whether reasons were included in the requests, and the content of the reasons, including monetization. We did not find the reasons impacted users' decisions; decisions were instead driven by app type and demographics. Yet we did find the reasons could have a positive impact on user perception for the platform when the reasons did not include using data for ads. Our findings provide insights into developers' willingness to implement privacy-enhancing changes, and expose limits to improving user privacy by simply adding information to user interfaces.
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Modern transformers have been scaled up in size, data, training-time compute, and more recently test-time compute based on Chain-of-Thought (CoT), but the consequences of trading off these parameters are unknown. We construct explicit tradeoffs between two of these aspects: the number of layers in a transformer and the number of CoT tokens. Our first result is general: with mild assumptions and a single-token prompt, for any integer k, a transformer using soft attention and SwiGLU MLP layers with L layers and n CoT tokens can be converted to a transformer with at most ckL layers and ⌈n/k⌉ CoT tokens, and vice versa, although with an increase to the size of other parameters. We then a multi-token prompt path following task, finding that additional preprocessing from the multi-token prompt changes the depth-CoT relationship in this case. We construct an attention-only network with L layers and 2^(L−2) heads that can traverse 2^(L−1) − 1 edges along a path with a single CoT token, demonstrating at least an exponential relationship between increases in depth and reductions in the number of CoT tokens needed to traverse the entire path. This relationship for the path following task holds empirically as well, as demonstrated by training networks with up to 5 layers, with the tradeoff between depth and the maximum number of edge traversals in a single CoT token exactly matching our theoretical constructions.
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The Synthetic Gap: Automating Forensic Investigation of "AI Slop" with the Scaled Abuse Forensics Examiner (SAFE)
Vahid Jalali
Longling Wang
Geethik Narayana Kamineni
Utkarsh Chaudhary
Crystal Zhao
Lucas Liu
2026
Preview abstract
Generative AI capabilities have enabled malicious actors to flood online platforms with "AI slop"—mass-produced, low-quality synthetic media designed to overwhelm traditional integrity systems. These adversarial campaigns often utilize coordinated networks to distribute unique, localized variations of synthetic content, rendering static detection methods ineffective. The signals to detect coordination often have recall gaps. The content is not exactly duplicative to be in the same repetitive video cluster. The abusers however show similar patterns of behavior which need forensics. Manual forensic investigations cannot scale to match the velocity of these generative attacks.
To address this, we present SAFE (Scaled Abuse Forensics Examiner), an automated multi-agent architecture designed for the scalable forensics of adversarial synthetic media. The system decomposes the investigation process into specialized agents: a Cluster Understanding Agent specialized in analyzing the relations between channels in a cluster, a Behavior Understanding Agent that identifies inorganic spatiotemporal patterns, and a Content Understanding Agent that utilizes LoRA-adapted Large Language Models (LLMs) and few-shot learning to detect existing policy violations and spirit of the policy violations respectively . A Root Agent synthesizes these multimodal signals to render a final verdict. Early deployment results indicate that SAFE significantly accelerates the identification of novel synthetic threats, reducing forensic investigation time compared to human-in-the-loop workflows.
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StreamLens: An Open-Source ABR Simulation Environment for Multi-Region Buffering, Seeking, and Prefetching
Bei Zhao
Jinhui Song
Jiayu Zhu
Yih‑Chun Hu
2026
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ABR simulators are useful for studying ABR behavior under different network conditions. SABRE is an open-source simulator for evaluating ABR algorithms with different videos, network traces,
and ABR policies. However, SABRE has several limitations. First, it does not accurately emulate user seeks or their impact on the viewing experience. Second, it provides limited support for nonlinear buffering, where the client buffers non-contiguous video chunks. Furthermore, SABRE does not provide adequate analysis and visualization support for interpreting simulation results. This paper presents StreamLens, an advanced video simulator that supports: 1) pluggable ABR policies, including BOLA, BOLA-E, Dynamic, DynamicDash, and throughput-based policy; 2) configurable user seeks; 3) nonlinear buffering with customizable prefetching policy; 4) browser-based viewer for result visualization, including
stalls, seeks, rebuffering time, QoE, bitrate, buffer, etc. StreamLens allows developers to configure videos, network traces, and seek patterns, and to evaluate ABR performance across different setups, including ABR policies, linear versus nonlinear buffering, and user seek traces. It also provides metrics and visualizations that help developers inspect streaming behavior in a straightforward way. Several predefined scenarios are included as both evaluation examples and configuration templates, covering synthetic seek and prefetch settings, session-replay workflows, generated reports, and regression tests. The software is available publicly at
https://github.com/Slothbetty/StreamLens.
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Gaze Target Estimation Anywhere with Concepts
Xu Cao
Houze Yang
Vipin Gunda
Inki Kim
Jim Rehg
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2026)
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Estimating human gaze targets in-the-wild is a formidable challenge. Existing computer vision algorithms rely on brittle, multi-stage pipelines that require explicit inputs like head bounding boxes and human pose, causing initial detection errors to cascade and lead to system failure. To overcome this, we introduce the \textbf{Promptable Gaze Target Estimation (PGE)} task, a new end-to-end, concept-driven paradigm. PGE conditions gaze prediction on flexible user text or visual prompts (e.g., "the boy in the red shirt" or "person in point [0.52, 0.48]") to identify a specific subject's target, which eliminates the rigid dependency on intermediate localization cues. We develop a scalable data engine to generate \textbf{Gaze-Co}, a dataset and benchmark of 120K high-quality, prompt-annotated image pairs. We also propose \textbf{AnyGaze}, the first model designed for PGE. AnyGaze uses a Transformer-based detector to fuse features from frozen encoders and simultaneously solves subject localization, in/out-of-frame presence, and gaze target heatmap estimation. AnyGaze achieves state-of-the-art performance on standard gaze target estimation benchmarks, setting a strong baseline for this new problem even on a difficult out-of-domain, real-world clinical dataset. We will open-source the AnyGaze model and the Gaze-Co benchmark.
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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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Enhancements in Memory Test for Improved Diagnosability and Comprehensive Multi-Bank Test
Shivam Tulsyan
Veerabhadra Rao Vasa
Prachi Sinha
Mayank Parasrampuria
2026
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The relentless scaling of System-on-Chip (SoC) architectures toward sub-5nm nodes has driven a shift from standard compiled memories to highly optimized custom memory designs to meet aggressive Power, Performance, and Area (PPA) targets. However, these custom layouts introduce unique physical defect mechanisms and layout-dependent coupling faults that are often invisible to industry-standard March tests. This paper proposes a novel custom testing methodology specifically engineered for multi-bank custom memory architectures in modern SoCs. By implementing a programmable, bank-aware Built-In Self-Test (BIST) architecture, the proposed solution enables concurrent multi-bank stressing to detect inter-bank crosstalk while maintaining strict power-density limits through a custom scheduling algorithm. This methodology provides a scalable framework for ensuring high reliability in performance-critical silicon environments. Industry standard memory built in self test does not cover this. This paper presents a novel custom memory testing methodology utilizing Soft Programmable Opset Logic Addition to address potential coupling-related faults during simultaneous multi-bank access in multi-port memories. While the proposed multi-bank BIST identifies the presence of inter-bank coupling, physical localization of these defects in sub-5nm nodes requires non-invasive back-side analysis. Our methodology includes a 'Diagnostic Mode' that allows the BIST to loop specific stress patterns, enabling Laser Voltage Probing (LVP) to capture high-resolution internal waveforms. This synergy between custom BIST and optical probing significantly reduces the Time-to-Yield (TTY) by pinpointing the exact physical origin of marginal delay faults.
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Analyzing Bytes: Pre-Disassembly Static Binary Analysis
Soumyakant Priyadarshan
ChenCheng Jiang
R. Sekar
Proceedings of the ACM on Programming Languages, Association for Computing Machinery (2026), pp. 1127-1151
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Binary code analysis plays a central role in numerous applications in software security, performance optimization, reverse engineering, and so on. Existing techniques need to first disassemble binaries into functions in assembly code before an analysis can be performed. However, disassembly and function identification have proven to be major challenges for complex variable-length instruction sets such as the x86. A recent trend has been to use static analysis to improve the accuracy of these tasks. This raises a chicken-and-egg problem: a disassembly is needed for static analysis, but a static analysis is needed for accurate disassembly! We overcome this problem by developing a novel static analysis approach that can operate before committing to a disassembly. Our analysis operates on the output of exhaustive disassembly that considers each possible offset in a binary as an instruction, and constructs what is known as a super-set control-flow graph (CFG). The central technical challenge in analyzing this CFG is that it mixes legitimate instructions with unintended ones, causing analysis results from invalid code paths to pollute legitimate ones. To overcome this challenge, we begin with a key new insight that if we focus on backward analyses, we can ensure accuracy of analysis results at intended instructions even though we have no idea where these intended instructions are! Moreover, our analysis operates in time that is linear in the size of the binary. Specifically, in O(n) total time, it yields analysis results for every one of the n offsets in an n-byte binary. For this task, it is orders of magnitude faster than previous techniques, as the previous techniques typically need to repeat the analysis many times.
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Profit Maximization in Bilateral Trade against a Smooth Adversary
Simone di Gregorio
Paul Duetting
Federico Fusco
Chris Schwiegelshohn
NeurIPS 2026
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Bilateral trade models the task of intermediating between two strategic agents, a seller and a buyer, who wish to trade a good. We study this problem from the perspective of a profit-maximizing broker within an online learning framework, where the agents' valuations are generated by a smooth adversary.
We devise a learning algorithm that guarantees a $\tilde{O}(\sqrt{T})$ regret bound, which is tight in the time horizon $T$ up to poly-logarithmic factors. This matches the minimax rate for the stochastic i.i.d.~case, and is also well separated from the adversarial setting, where sublinear-regret is unattainable. By extending the strong regret guarantees from the i.i.d.~case to the smooth adversary, we significantly broaden the scope of settings where such fast rate is achievable, while closing an important gap in the regret landscape of this fundamental economic problem.
To overcome the challenges posed by this adversary, we leverage a continuity property of smooth instances and combines this with a hierarchical net-construction of the broker's action space, which is analyzed via algorithmic chaining. We showcase the applicability of these techniques by deriving a similarly tight $\tilde{O}(\sqrt{T})$ regret bound for a related mechanism design model: the joint ads problem.
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A DV Approach to Post-Silicon: Validating SoC Low Power through Synthetic Software Scenarios
Kapish Pandey
Utkarsh Prabhat
Yash Kumar
V Sindhu
2026
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Lightweight execution environments like the Little Kernel (LK) are commonly deployed in post-silicon validation to assess software-hardware interactions. These bare-metal kernels, however, lack the sophisticated power management features present in full operating systems, such as the Generic Power Domain (GenPD) framework. Instead of building complex software abstractions that simulate production-grade power management drivers, this paper applies a Design Verification (DV) approach to post silicon. By discarding standard software paradigms, the introduced framework leverages fundamental bare-metal kernel primitives to intentionally engineer synthetic, non-deterministic stressors. Eliminating intermediate software layers allows us to utilize the bare-metal setup to drive chaotic, aggressive stimuli straight to the hardware execution layer.
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PaperBanana: Automating Academic Illustration for AI Scientists
Dawei Zhu
Sujian Li
International Conference on Machine Learning (2026)
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While AI scientists increasingly automate research tasks through advanced language models, generating publication-ready illustrations remains a labor-intensive bottleneck in the scientific workflow. To lift this burden, we introduce PaperBanana, an agentic framework for automated generation of publication-ready academic diagrams. Powered by Nano-Banana-Pro and Gemini-3-Pro, PaperBanana orchestrates a team of specialized agents to retrieve reference examples, devise detailed plans for content and style, render the image, and perform iterative refinement based on self-critique. To rigorously evaluate our framework and address the absence of dedicated benchmarks for automated academic illustration, we introduce PaperBananaBench, comprising 292 test cases for methodology diagrams curated from NeurIPS 2025 publications.
Comprehensive experiments demonstrate that PaperBanana consistently outperforms vanilla Nano-Banana-Pro across all four dimensions—faithfulness, conciseness, readability, and aesthetics—achieving human-level performance. We further show that PaperBanana seamlessly extends to statistical plots through targeted adaptations. Collectively, PaperBanana enables AI scientists to fully automate the generation of publication-ready academic illustrations.
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In modern Kubernetes environments, eBPF (Extended Berkeley Packet Filter) has become the de facto standard for high-performance dataplane enforcement. However, this architecture introduces a complex distributed state problem: the asynchronous synchronization between the Kubernetes control plane (Intent) and the kernel-space BPF maps (Reality).
A critical failure mode, termed “Silent Divergence,” occurs when the control plane believes a network policy or identity is applied, but the underlying kernel state is missing or corrupted. In this “Gray Failure” state, standard observability tools—including logs, liveness probes, and agent status checks—report health, while the network silently drops traffic.
This paper introduces eBPF-Auditor, a specialized consistency verification framework. Unlike standard agents that rely on event-based reconciliation, eBPF-Auditor performs a periodic “Two-Way State Audit” that mathematically verifies the intersection of Kubernetes Intent and BPF Reality. We demonstrate through fault injection and benchmarks on 5,000 pods that this approach successfully detects state drift with 100% accuracy and negligible sub-millisecond overhead (ms), making it a viable solution for high-frequency runtime verification in production hyperscale clusters.
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Generational differences in anthropomorphism and trust calibration for large language models
Michelle Cohn
Joscelin Cooper
Femi Olanubi
Zion Mengesha
Frontiers in Cognition (2026)
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This paper examines how English-speaking adults in the United States (n=1530) anthropomorphize and calibrate trust for a pseudo-large language model (LLM). We compared four generations: Gen Z (age 18-26), Millennials (age 27-43), Gen X (age 44-57), and Baby Boomers (age 58-78). The LLM varied in cues of anthropomorphism: whether the system responded in text-only or text and speech, as well as whether it used the first-person singular (“I”) or third-person singular (“The system”) in its responses. Across both types of manipulations, results showed that Gen Z, in particular, showed consistently lower anthropomorphism (less competent, human-like, natural, conscious, life-like), reduced trust ratings of the system, and lower ratings of accuracy for responses generated by the LLM. Furthermore, we saw that the likelihood to externally validate the LLM’s response varied across the generations across contexts, where Baby Boomers were more likely to validate information about medication and health disorders and Gen Z about cooking. We did not see an interaction between the generations and the anthropomorphic cues manipulated; rather, there was a consistent increase in anthropomorphism and perceived accuracy for systems that used speech + text, relative to text only. There were no across-the-board effects of the first-person singular. We discuss these findings in terms of their implications for responsible AI.
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We introduce ALPS (Activation-based Length Prediction for Scheduling), a method for predicting LLM generation length from prefill activations before any tokens are generated. Unlike existing approaches that require model fine-tuning or complex entropy-weighted pooling, ALPS uses a simple linear probe on the last-token activation at intermediate layers. We discover that generation length is encoded in prefill representations: a ridge regression probe achieves R-squared > 0.85 across three model families. Validation across Llama-3.1-8B, Gemma-2-9B, and Qwen-2.5-7B demonstrates: (1) intermediate layers generally perform well, with some architectural variation; (2) simple last-token extraction outperforms complex pooling strategies; (3) activations improve substantially over surface-feature baselines (24 percentage points over input length plus lexical features). The best models achieve R-squared = 0.943 (Gemma), R-squared = 0.880 (Llama), and R-squared = 0.857 (Qwen) with MAE of 38-80 tokens. All test prompts terminated naturally (100% EOS), eliminating truncation confounds. While our evaluation uses 200 curated prompts—sufficient for demonstrating the phenomenon but requiring broader validation—cross-validation confirms generalization beyond training data. ALPS enables practical applications including budget-constrained inference, request scheduling, and resource allocation. The probe adds negligible overhead (~16KB direction vector, single dot product), making ALPS practical for production deployment.
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