Computer Vision
Physical AI Smart Spaces: A Large-Scale Benchmark for Multi-Camera 3D Perception in Smart Spaces
Yuxing Wang, Yizhou Wang, Anqi Li et al. (+13 more)
Physical AI Smart Spaces is, to the best of our knowledge, the first
benchmark to simultaneously provide large-scale, multi-class, and multi-camera 3D perception data for indoor smart spaces. It contains over 280 hours of synchronized 1080p footage captured by nearly 1,800 cameras in warehouses, hospitals, retail venues, and similar settings, together with automatic annotations for multi-camera identities, 2D bounding boxes, 3D bounding boxes, camera calibration, and depth where available. The
benchmark spans Isaac Sim synthetic generation, Cosmos Transfer appearance augmentation, and real-world Sim2Real evaluation. For the real-world target, we include two warehouse deployments with time-synchronized streams, automatic VGGT-based calibration, and a 3D labeling interface that projects world-frame 3D boxes into each view for cross-camera verification. We describe the
dataset scope, annotation and calibration schema, generation workflow,
benchmark protocols, and official evaluation system, which standardizes submission format, and leaderboard reporting. A central contribution is a 3D instantiation of Higher Order Tracking Accuracy (HOTA), extending the usual 2D box-based tracking evaluation to 3D locations and 3D boxes. We further report empirical baselines from the AI City Challenge leaderboards, showing how methods evolve from person-only 3D location tracking to multi-class 3D box tracking under realistic smart-space constraints. The release is available at https://huggingface.co/
datasets/nvidia/PhysicalAI-SmartSpaces.
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All authors Yuxing Wang, Yizhou Wang, Anqi Li, Shuo Wang, Sameer Satish Pusegaonkar, Haoquan Liang, Jiajun Li, Shenxin Jiang, Jianhe Yuan, Shangru Li, Tongwei Dai, Zihao Chen, David C. Anastasiu, Sujit Biswas, Xunlei Wu, Zheng Tang
Machine Learning · Cross-listed from stat.ML
High-Dimensional Asymptotics and Dataset Selection for Private Transfer Learning
Filip Kovačević, Edwige Cyffers, Stefano Sarao Mannelli et al. (+1 more)
To commit to buying external data or participate in collaborative learning, one must decide whether the additional data will improve prediction enough to justify the cost. This comes with several challenges: (i) the decision often relies only on aggregated statistics available publicly, rather than individual-level data; (ii)
covariate and model shifts can induce negative transfer, so the additional data deteriorates rather than improves performance; (iii) if the data is sensitive, its privatization requires the injection of noise, which can also offset the benefit of a larger sample size. In this paper, we model the problem of
dataset selection through high-dimensional regression with multiple heterogeneous sources and a weighted ridge
estimator. Our approach uses only summary statistics and it gives
privacy guarantees either on labels only or jointly on features and labels, in terms of $\rho$-zero-concentrated differential
privacy. The main technical contribution is a deterministic equivalent of the test error, which captures the interactions between sample size, covariance structure, model shift, regularization and
privacy noise. Our theory allows to optimize hyperparameters (weights and ridge regularizers) and, more broadly, to decide when private external
datasets are useful without accessing the data itself but only relying on population-level quantities. This provides a theoretically tractable foundation for private transfer learning, which we support via experiments on both synthetic and real-world
datasets.
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All authors Filip Kovačević, Edwige Cyffers, Stefano Sarao Mannelli, Marco Mondelli
Artificial Intelligence
Answering clinicians' questions over trial evidence tables with verifiable, feedback-driven language models
Manan Roy Choudhury, Suparno Roy Chowdhury, Swastik Sahoo et al. (+5 more)
Systematic reviews condense clinical trials into evidence tables, yet clinicians can interrogate these tables only through database queries, and many questions concern attributes that the table does not record, such as a drug's target class or a harmonised endpoint. Here we introduce FD-SCoPE, a
language-model framework that answers both kinds of question, exposes the query, the selected trials and the derivation rule behind every answer, and learns from expert corrections. On an oncology evidence table of 159 immune checkpoint inhibitor trial records, FD-SCoPE completed all 140 clinician-style tasks (alternatives, 90.7-97.9%). For questions needing derived attributes it retrieved 99.3% of relevant trial records at a positive predictive value of 89.8% and outperformed four alternative approaches (derived-value F1 77.7% versus 64.8-73.4%). Corrections on 299 questions, simulated from reference answers, raised F1 on 1,201 unseen questions from 77.9% to 84.9%.
Language models coupled with executable queries, verified programs and expert feedback can give clinicians auditable access to trial evidence.
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All authors Manan Roy Choudhury, Suparno Roy Chowdhury, Swastik Sahoo, Muhammad Ali Khan, Kaneez Zahra Rubab Khakwani, Mohamad Bassam Sonbol, Irbaz Bin Riaz, Vivek Gupta
Machine Learning
DISSOLVR: An Interpretable and Fast Framework for Aqueous and Organic Solubility Prediction
Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal et al. (+2 more)
High-fidelity solubility prediction is fundamental to pharmaceutical development and environmental partitioning, where accurate modeling must couple molecular structure with thermodynamic behavior across diverse chemical environments. However, recent advancements have been dominated by deep learning architectures that often sacrifice physical interpretability for predictive power. We challenge this trend by showing that state-of-the-art performance does not require such non-transparent architectures. To address this, we introduce DISSOLVR, a transparent framework for molecular solubility prediction. In addition, we perform a comprehensive literature review and a benchmarking study against various methods. We show that DISSOLVR approaches the aleatoric limit of experimental uncertainty and achieves OOD generalization through structural invariance, derived by mapping molecules to physically-grounded descriptors. Then, we present an LLM-assisted post-hoc explanation pipeline that bridges the gap between symbolic model artifacts and chemically grounded narratives. Finally, a comparative
benchmark of a survey involving 22 expert chemists reveals that expert evaluators provide deep insights.
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All authors Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal, Sayan Ranu, Tarak Karmakar
Systems and Control · Cross-listed from math.OC
Control of Markov Jump Linear Systems with Uncertain Lumpable Cluster Observations
Carlos A. F. Persiani, Ram Padmanabhan, Melkior Ornik et al. (+1 more)
In this paper, we consider the control of Markov jump linear systems when the active system mode is not exactly known. We rather assume that this true mode is only known to belong to an observed cluster of modes, making both the dynamics and the associated transition probabilities uncertain. We construct a min-max optimal control problem in this setting, with the objective of robust regulation. Using results in regularized least mean-square optimization, we describe how the solution of this problem can be reduced to a Riccati recursion, such that the controller depends only on knowledge of which cluster the mode lies in. We subsequently establish convergence of the recursion and stability of the resulting closed-loop system. A numerical example illustrates the effectiveness of the proposed methodology.
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All authors Carlos A. F. Persiani, Ram Padmanabhan, Melkior Ornik, Marco H. Terra
Information Retrieval
Adaptive Sparsity Optimization with Learnable Soft Top-K and Per-Term Thresholding for Efficient Retrieval
Wentai Xie, Parker Carlson, Shanxiu He et al. (+1 more)
Recent work on neural sparse
retrieval has demonstrated strong relevance by leveraging Large
Language Models (LLMs) for semantic term expansion. However, learned models paired with previous sparsification techniques still yield overly long document and query vectors partly due to a large LLM vocabulary, imposing a serious challenge to
retrieval time and space efficiency. This paper proposes a scheme for optimizing model sparsity through a synergy of adaptive strategies, including learnable soft top-K, per-term thresholding, and FLOPs regularization to increase the sparsity of query and document vectors. Experimental results with Lion-SP model on the MS MARCO and BEIR
datasets demonstrate that the proposed scheme can outperform the baselines by significantly reducing the average query and document lengths. Our scheme can achieve much shorter
retrieval latency and lower storage cost while maintaining highly competitive relevance.
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All authors Wentai Xie, Parker Carlson, Shanxiu He, Tao Yang
Software Engineering
Improving the Energy-Efficiency of the Code Generated by LLMs through Effective Prompting
Ritika Rekhi, Bing Zhang, Md Arman Islam et al. (+7 more)
As AI-assisted programming becomes increasingly mainstream, the environmental impact of AI-generated software has emerged as an important consideration. This motivates evaluating LLM-generated code beyond functional correctness by considering execution efficiency and energy consumption. However, despite substantial advances in code generation, frontier LLMs are rarely evaluated based on the energy efficiency of the code they produce. In this work, we conduct a comprehensive evaluation of 21 prompting strategies for energy-efficient code generation and identify 8 strategies for evaluation across 10 widely used open-weight and proprietary LLMs. We evaluate their effectiveness for both Python and C++ code generation relative to a baseline
prompt. Across the evaluated models, the selected prompting strategies achieved energy reductions of up to 25% for Python and 17% for C++ code generation. At the model level, Python energy reductions reached up to 50% for Granite-4.0-H-Small, 39% for Claude 4.5 Haiku, and 28% for MiniMax M3, while C++ reductions reached up to 57% for Granite-4.0-H-Small and 6.4% for Qwen3-Coder-480B-A35B-Instruct. These results demonstrate that prompting strategies can substantially influence the energy consumption of LLM-generated code, although their effectiveness varies across models and programming
languages. Our findings highlight the importance of incorporating energy efficiency into the evaluation and optimization of LLM-based code generation and provide practical insights into designing
prompts for more sustainable AI-assisted programming.
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All authors Ritika Rekhi, Bing Zhang, Md Arman Islam, Jaya Krishna Pasham, Yeswanth Chitturi, Akshay Paramesha, Isha Valiveti, Asif Imran, Bekir Turkkan, Tevfik Kosar
Cryptography and Security
Pincer: Resource Authorization for Agents using a Digital Twin
Mayank Rathee, Alexander Stepanov, Shalin Madabhavi et al. (+3 more)
Coding
agents have become increasingly long-horizon, autonomous, reliant on general-purpose shell and maintain their own persistent
memory for self-improvement. While these capabilities have made the
agents powerful, they have also made them harder to defend against external adversaries. Defenses that restrict this architecture — typed tools, information-flow control, or policy prediction engines — give up too much functionality to be adopted.
Agents deployed today (e.g. Claude, Codex) rely on a combination of user-mediated and automode sandboxing as their primary defense. In user-mediated sandboxing, user-maintained policies decay over time and repeated permission requests cause user fatigue, while auto mode's tool-call classifiers learn no user-specific policy and are not meant to defend against
adversarial setups. Pincer is a new defense that operates at the resource layer and works alongside existing defenses at the tool-call layer like the auto mode. At the core of Pincer lies a digital twin, an isolated-context model that automatically learns and enforces dynamic user-specific least-privilege policies. The digital twin keeps continually learning the user's preferences allowing it to act as the user's proxy for the
agent's permission requests. To emulate the learning phase, we propose a new usercentric
dataset with examples following a multi-day transcript of user-
agent interaction. Our evaluation shows that Pincer performs strongly on both security and utility in comparison to several baselines which includes variants of LLM judges and adaptations of Conseca (HotOS '25). We highlight attack types where Pincer's design leads to a significant security improvement compared to all other baselines, while outperforming the baselines even for other types of attacks.
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All authors Mayank Rathee, Alexander Stepanov, Shalin Madabhavi, Jinhao Zhu, Raluca Ada Popa, Ion Stoica
Artificial Intelligence
Mitigating Social Sycophancy via Pluralistic Preference Optimization
Stephane Hatgis-Kessell, Myra Cheng, Xiaoxuan Hou et al. (+4 more)
Personal advice, including relationship advice, now ranks among the most common uses of generative AI. But
language models (LMs) exhibit sycophancy: they affirm users much more often than humans do, which can make people overconfident and less willing to repair their relationships after a conflict. Prior work on mitigating sycophancy has focused on factual settings where a response can be checked against a ground truth answer, while mitigations for social sycophancy (e.g., personal advice, where there is no ground truth) have relied on simple prompting and post-training methods with limited effectiveness. Our insight is that social sycophancy occurs in part because LMs overly center on the user and fail to consider the perspectives of other stakeholders impacted by the user's behavior. To address this problem we propose Pluralistic Preference Optimization (PlurPO): given inputs describing interpersonal conflicts, the LM identifies and simulates the relevant stakeholders, and is then trained to prefer and generate responses acceptable to all stakeholders. PlurPO uses only signals the model produces about its own outputs, without ground-truth labels. PlurPO substantially reduces social sycophancy across four
datasets and four model families compared to prior methods. For example, on statements of intent to cause harm, where the users' actions should not be endorsed, PlurPO reduces the endorsement rate by 89% on average across four models. On general advice questions, where the target is to match the endorsement rate of human responses, it closes the gap by more than half, from 17.8% to 8.0% on average. The preference
dataset constructed by PlurPO for an 8B model also effectively transfers to mitigating sycophancy in a larger (32B) model. Our results indicate that social sycophancy can be reduced by leveraging a model's own capabilities to simulate a plurality of relevant perspectives.
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All authors Stephane Hatgis-Kessell, Myra Cheng, Xiaoxuan Hou, Qian Hu, Rahul Gupta, Natasha Jaques, Emma Brunskill
Computer Vision
DAGS: Disentangled Appearance-and-Geometry Steering of a Frozen Image DiT for Temporally Stabilized Generative Rendering
Karthik Mohan Kumar, Damian Andrysiak, Pedro Antonio Pena et al. (+2 more)
Diffusion transformers (DiTs) generate high-fidelity images from text and image conditions, but their outputs carry large variance and their faithfulness to a desired target depends heavily on how the condition is supplied. We present DAGS, a lightweight,
attention-free, disentangled appearance and geometry conditioning scheme that steers a frozen image DiT to produce high-fidelity, highly faithful, and independently controllable renders. Two small convolutional encoders compute conditioning features once per frame and inject them as a learned, per-layer, element-wise residual into the image
tokens, avoiding the quadratic cost of stacking conditions through
attention. Because control and temporal handling live outside the frozen backbone, we retain its vast pretrained prior and eliminate backbone-overfitting risk. We further add a small recurrent lighting stabilizer and a training-free temporal guidance term that, coupled with our conditioning, elevate a per-frame image model into a streaming renderer. DAGS produces controllable, high-quality renders at a fraction of the compute of path tracing; it is not real-time, trading compute for controllability and quality. On a matched 1-spp + G-buffer input, per-frame DAGS reconstructs +8.6 dB / +10.1 dB PSNR over the real-time denoiser Intel OIDN and the
diffusion renderer RGB<->X while being 2.5-8x more temporally stable perceptually (temporal-LPIPS flicker).
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All authors Karthik Mohan Kumar, Damian Andrysiak, Pedro Antonio Pena, Kunal Tyagi, Rama Harihara
Computational Complexity · Cross-listed from quant-ph
The complexity of entangled graph colouring via polymorphisms
Eric Culf
Constraint satisfaction problems (CSPs) with operator assignments to the variables provide a well-structured setting to study the decision complexity of the entangled value of classes of nonlocal games. Due to the CSP dichotomy
theorem, the complexity of constraint satisfaction problems with classical assignments can be fully understood by studying the
symmetries of the CSP, in terms of the polymorphisms of the underlying relational structure. In this work, we show that the polymorphism-based reductions between CSPs can be generalised to gap-preserving reductions between entangled CSPs based on an entangled analogue of the polymorphisms. This reduction allows us to show undecidability of entangled graph colouring with more than three colours, a problem that has proved resistant to prior hardness reductions based on commutativity gadgets.
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Numerical Analysis
Learning Closure of Dynamical Systems with Kernel Ridge Regression
Evan Habbershaw, John Harlim and Senwei Liang
We develop a closure modeling framework for identifying missing components of dynamical systems using Kernel Ridge Regression (KRR). The framework addresses two classes of closure problems: difference-equation closures arising in ODE and PDE settings, and algebraic closures arising from moment closure in kinetic equations. For the first class, we derive an error bound in an ODE setting that quantifies contributions from time integration, approximation of unresolved scales, and interpolation required to couple unresolved-scale effects to the resolved solver. Numerical experiments on the Lorenz-63 system and the Kuramoto-Sivashinsky equation demonstrate accurate long-horizon predictions and substantial improvements over an LSTM-based closure model. For the second class, we consider moment closure for a one-dimensional kinetic equation by modeling discrepancies between kinetic and macroscopic fluxes as a function of the resolved macroscopic variables. We compare global KRR models based on PCA coordinates with spatially local models. While the global model performs well for unimodal initial conditions, its accuracy deteriorates for bimodal initial conditions. Spatially local models with appropriate modeling inputs improve robustness and achieve higher predictive accuracy.
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Machine Learning
OpenGameEval: Benchmarking Agentic Programming and Exploration in a Stateful Game Engine
Eray Turkel, Mengsha Sun, Kartik Ayyar et al. (+6 more)
We present OpenGameEval, a
benchmark and evaluation framework for agentic game development inside Roblox Studio. It runs
language models as
agents in reproducible, stateful game-engine sessions and scores each run with executable checks, both on the edited scene and in a simulated play session. Most agentic coding
benchmarks require exploration but score only final task success. OpenGameEval separates observation tools from editing tools in its eight-tool action space, so exploration can be measured directly. We measure the pass rates and exploration behavior of 13 frontier models on 84 human-curated core tasks, with 16 attempts per task. The tasks are hard for current models. The best model solves 51.7% of tasks on a single attempt and 39.4% five times out of five, and no tested model solves six of the tasks. Models at the frontier reach similar pass rates by solving different tasks: splitting tasks by the kind of work they require spreads the top five by 5.0pp on script-authoring tasks and 12.5pp on scene-change tasks. Exploration behavior predicts whether a run succeeds. Holding task and model fixed, a run that inspects every object a reference solution touches before acting on it passes 13.4pp more often than a run that inspects none of them on scene-only tasks, and 9.8pp more often on script-only tasks. We release the task suite, its place files, the per-task annotations, a plugin that runs the tasks inside Roblox Studio, and an updated leaderboard under the MIT license at https://github.com/Roblox/open-game-eval.
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All authors Eray Turkel, Mengsha Sun, Kartik Ayyar, Sean Dunigan, Jack Lu, Vlad Shcherban, Hsiang-Shun Shih, Xin Wang, Tiantian Zhang
Human-Computer Interaction
Santiago's A.T. Field: Visualizing Urban Accessibility through an Evangelion-Inspired Interface
Eduardo Graells-Garrido, Ignacio Pérez-Messina and Claudio Gaete
Science-fiction interfaces are often reproduced for their appearance, and their diagnostic logic is rarely rebuilt around real data. Here we describe Santiago's A.T. Field, an interactive visualization of urban accessibility built from a diagnostic interface in Episode 13 of Neon Genesis Evangelion. We translated that interface in five stages: phenomenon, field, progression, interface, and response. A microscopic Angel became a relational urban problem, and a fictional scanning
lattice became an accessibility field computed with the E2SFCA method. Every element of the fictional display is tied to a quantity of the model. Three tensions appeared when we made the fictional
language operational: the saturated palette of the genre against color-
vision deficiency, the motion of the genre against the reduced-motion preference, and the per-frame cost of the animation. The urgency of the fictional command center has its own cost, because emergency aesthetics can cast urban populations as threats. These three tensions are what the translation cost, and they are what transfers to other attempts of this kind.
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Computer Vision
Oracle headroom without signal: null-calibrated evaluation of candidate selection for thermal heart rate estimation
Mohammad Rakibur Rahman, Nhi Nguyen, Sasan Sharifipour et al. (+4 more)
Camera-based physiological monitoring can produce multiple estimates from several facial regions, extraction methods, and processing settings. Signal quality indices aim to select reliable estimates without a physiological reference, and their potential is often assessed with an oracle that selects the estimate closest to the reference in each window. This retrospective selection can reward chance agreement. We model the effect with order statistics. For K independent candidates unrelated to the reference, the expected oracle error decreases approximately as 1/K. We analyze thermal heart rate estimation on 96 iBVP recordings with 168 candidates per 10 s window. The oracle achieves a mean absolute error of 0.91 bpm, compared with 10.74 bpm for the best fixed configuration, 18.03 bpm for the best quality index, and 8.61 bpm for a constant predictor. With K = 24, a forehead signal from another recording matches the correct one, with 4.62 against 4.61 bpm. Oracle evaluations should report candidate count, valid coverage, and matched null controls.
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All authors Mohammad Rakibur Rahman, Nhi Nguyen, Sasan Sharifipour, Le Nguyen, Manuel Lage Cañellas, Miguel Bordallo López, Constantino Álvarez Casado
Software Engineering
An Extensive Empirical Study on Evaluation Metrics for Combinatorial Interaction Testing
Lisha Qin, Chenhui Cui, Tao Li et al. (+3 more)
Combinatorial interaction testing (CIT) is a black-box testing method that has received extensive
attention in both research and practice over recent years. Its primary objective is to construct an effective combinatorial test suite that detects software failures caused by parameter interactions. As a fundamental component of the CIT testing process, the evaluation metric plays a critical role in assessing and comparing combinatorial test suites, as well as in evaluating various test generation techniques. For CIT practitioners, selecting an appropriate evaluation metric is both important and challenging, given the wide variety of available options. Nevertheless, no prior work has systematically addressed this problem. To fill this gap, this paper first provides a comprehensive survey of black-box evaluation metrics for combinatorial test suites, offering rigorous definitions, clear classifications, illustrative examples, and complexity analyses. We then conduct an extensive empirical study involving eight open-source projects, encompassing 32 test scenarios and 295,624 combinatorial test suites. In this study, we examine the correlation between each static evaluation metric and fault-detection effectiveness using two correlation measures. Experimental results show that the Value Combination Coverage (VCC) metric serves as a valid predictor for test-suite evaluation. However, distribution-based metrics generally incur lower computational costs than interaction coverage-based ones. The choice of an appropriate metric should also account for the test suite's inherent properties, as these characteristics can substantially influence the effectiveness of the evaluation. Finally, we provide practical guidelines to assist CIT practitioners in selecting suitable evaluation metrics for assessing or comparing combinatorial test suites.
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All authors Lisha Qin, Chenhui Cui, Tao Li, Rubing Huang, Shikai Guo, Lei Ma
Machine Learning
Neuron merging via inverse-activation regression for post-training compression of sigmoid neural networks
Ao Kuniya and Jun Ohkubo
As neural networks continue to grow in scale, model compression is becoming increasingly important for efficient inference under limited computational resources. Structured
pruning methods remove
neurons or channels that are estimated to be less important, but the removed units may still contain useful information. From the viewpoint of coarse-graining a trained network, it is valuable to ask which information should be retained when multiple neuronal degrees of freedom are consolidated. In this paper, we discuss cluster-based merging methods for compression of trained neural networks. In addition to a data-free contribution-weighted averaging method, we propose
neuron-merging methods in which
neuron responses are mapped back to the pre-activation space via the inverse activation function, and the weights and biases of each representative
neuron are estimated using the least-squares method. We also examine both a data-assisted strategy with actual training inputs and a data-free strategy using randomly generated inputs. The comparisons provide empirical evidence, in the tested sigmoid networks, that weight information is particularly useful for clustering whereas activation information is useful for representative-
neuron reconstruction in the merging process.
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Artificial Intelligence
How to Have a Sensitive Debate: An Instance-Optimal Protocol for AI Debate
Jiawei Li, Zhiyang Xun, Lijie Chen et al. (+1 more)
As powerful AI systems reach and sometimes surpass the abilities of human experts across a range of cognitively demanding tasks, the problem of accurate oversight and supervision of these systems has become increasingly urgent. One promising approach is AI debate, which seeks to leverage a debate between two powerful AIs to break complex questions down into simpler claims that can be easily judged directly. Theoretical work on debate has formalized this intuition in the
language of computational complexity theory, where the goal is to design protocols (i.e., rules of the debate game) that provide rigorous guarantees on correctness for judging solutions to complex problems with limited supervision. Specifically, the current best protocol has been shown to work for all problems that have sufficiently stable decompositions into subproblems. In this paper, we design a new protocol for this same class of problems that improves on the prior work in several ways. First, correctness holds in a worst-case rather than an average-case sense. Second, being honest and correct is a dominant-strategy
equilibrium for both debaters, rather than a Stackelberg
equilibrium. Finally, we prove black-box lower bounds, showing that our new protocol is instance-wise optimal. That is, no protocol for this class of problems can outperform ours while making only black-box queries to human judgments. We obtain these results by relating the notion of stable problem decompositions to the concept of fractional block sensitivity from query complexity.
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All authors Jiawei Li, Zhiyang Xun, Lijie Chen, Jonah Brown-Cohen
Machine Learning
Test-time Multi-agent Coordination by Decomposed Value Gradient Flow
Dongsu Lee, Haoran Xu and Amy Zhang
Offline multi-
agent reinforcement learning (MARL) faces a persistent trade-off. Expressive generative policies can represent multi-modal coordination in the data, but cannot distinguish high-value regions, while value-optimized policies exploit the learned Q-function but collapse the multi-modal into a single dominant mode. A single
agent's mode collapse can break joint coordination, and simultaneous drift across
agents can push the joint policy into unseen regions of the action space. We propose scalable coordination via optimal unified transport (SCOUT), the first offline MARL framework to combine a generative foundation model with a learned value function through test-time action refinement. SCOUT trains two decoupled components: a flow-matching behavioral prior and a decomposed value function. At test-time, it transports behavioral samples toward high-value regions via Stein variational gradient descent. The number of transport steps controls adaptive test-time scaling, replacing a fixed regularization coefficient. Under the individual-global-max (IGM) principle, we prove a single-term KL bound on the joint soft-value gap that vanishes as transport converges, with an irreducible additive residual proportional to the IGM violation. Empirically, SCOUT achieves the best average performance across discrete and continuous offline MARL
benchmarks and yields performance improvements in all offline-to-online configurations.
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Cryptography and Security
Out of Sync, Out of Sight: Phantom State Attacks against IIoT Intrusion Detection
Sabrine Ennaji, Elhadj Benkhelifa and Nadia Kabachi
Machine learning-based intrusion detection systems (IDS) are critical for securing Industrial Internet of Things (IIoT) environments. Most
adversarial research against them perturbs the feature vector or the traffic that produces it, and depends on gradient access, repeated model queries, or a learned model of benign traffic. A smaller line of work reshapes packet timing without querying the detector, but makes malicious traffic mimic a learned model of benign timing. Across these approaches, one assumption of industrial monitoring pipelines has received little
attention: temporal synchronization. An IDS reconstructs operational state by aggregating telemetry into sliding or tumbling windows, so its view depends not only on what is observed but on when each observation falls relative to a window boundary. We introduce the Phantom State Attack (PSA), which exploits that dependence under a passive, zero-query threat model. Rather than modifying packets, perturbing features, querying the classifier, or fitting any model of benign traffic, PSA injects bounded timing drift calibrated to the attack flow's own inter-arrival variability, moving observations across the nearest window boundary by the minimal shift needed. The IDS then reconstructs a phantom state that diverges from the true process state. We evaluate PSA on ToN-IoT and CIC IIoT 2025 (DataSense), against Random Forest, MLP and XGBoost, measuring detection degradation, synchronization distortion, stealth, and attacker cost. PSA degrades detection on flows carrying enough packets for window-boundary redistribution, and leaves others almost unchanged, so its effect is conditional. A query-based baseline reaches higher raw success but needs many queries per window, while PSA needs none. The results identify temporal aggregation as an attack surface reachable under weaker assumptions than prior evasion techniques.
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Computation and Language
Evaluating Multi-Dimensional Generalization of Large Language Models in Temporal Extraction Tasks
Fahmid Shahriar Iqbal, Ritam Dutt, Soumitra Das et al. (+2 more)
Time and event expression extraction are fundamental temporal
reasoning tasks, but the problem remains difficult due to annotation ambiguity, domain sensitivity, and unstable model behavior. Existing evaluations focus on in-domain performance, offering limited insight into reliability under distribution shifts. We evaluate multiple model configurations across families, architectures, and
reasoning strategies over four dimensions of generalization, examining transfer from base performance, cross-dimensional correlations, and the effects of scale, architecture, and prompting. This provides a systematic study of how prompted LLMs generalize in time and event expression extraction tasks. We find that strong base-task performance generally predicts better generalization. However, this relationship weakens under substantial distribution shifts. Inductive prompting performs most consistently across domain shift,
adversarial perturbations, compositionality, and length increase, while gains from scale, architecture, and deductive and abductive prompting strategies are uneven and dimension-specific. We conclude that LLM generalization in temporal extraction tasks cannot be predicted from any single dimension alone and cannot be reliably inferred from in-domain or single-dimension evaluations, highlighting the need for
reasoning strategies that generalize across dimensions.
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All authors Fahmid Shahriar Iqbal, Ritam Dutt, Soumitra Das, Arnav Verma, Sagnik Ray Choudhury
Numerical Analysis · Cross-listed from math.LO
Uniform Computability of Isotypic Block Diagonalization for Finite-Group Representations
Konrad Burnik
We study the universal isotypic block-diagonalization problem in the Type-2 theory of effectivity (TTE). We obtain a uniform computability result for the universal block diagonalizer for commutants of given finite group representations. As an application, when the given representation is orthogonal, we compute ordinary $QR$ factorizations blockwise in the isotypic basis. After transforming back to the original coordinates this yields a factorization $A=QX$, where $Q$ is orthogonal and $X$ is generally not upper triangular. Moreover, given a Wedderburn-adapted orthogonal basis, the transformed-back factors can be computed within the commutant.
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Machine Learning
Reward Inflation: A Healthy Stimulus for Reinforcement Learning
Ganghun Lee, Minji Kim, Minsu Lee et al. (+1 more)
Reward serves as the primary learning signal in reinforcement learning (RL). However, while reward magnitudes are typically held fixed throughout training, their temporal modulation remains underexplored. In this paper, we propose reward inflation, a gradual scaling of rewards over the course of training, and show that it can act as a healthy stimulus for RL. Theoretically, reward inflation induces an implicit recency weighting that upweights recent transitions during policy updates, enabling faster adaptation. We further show that, by sustaining gradient signals as the policy saturates, reward inflation suppresses the emergence of dormant
neurons and helps preserve plasticity. Empirical results on ALE games and MuJoCo tasks corroborate these findings, showing that an appropriate level of reward inflation benefits a broad range of tasks. Finally, we introduce Fed, an adaptive variant that adjusts the inflation level on the fly, and find that it often improves upon fixed inflation.
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All authors Ganghun Lee, Minji Kim, Minsu Lee, Byoung-Tak Zhang
Cryptography and Security
CITADEL: CWE-Guided Insertion of Hardware Trojans via Analysis of DFG-Enabled LLMs
Jayanth Thangellamudi, Raghul Saravanan, Sudipta Paria et al. (+2 more)
The increasing sophistication of Hardware Trojans (HTs) and system-level vulnerabilities poses significant risks to modern integrated
circuits. However, constructing realistic HT scenarios, remains a substantial burden: researchers must manually analyze complex RTL structures, identify plausible weaknesses, and craft stealthy, synthesizable insertions that preserve functional correctness. This paper introduces CITADEL CWE-Guided Insertion of Trojans via Analysis of DFG-Enabled LLMs, a framework that leverages Large
Language Models (LLMs) and Data Flow Graphs (DFGs) to automate CWE-grounded HT synthesis. CITADEL uses structured CWE semantics together with DFG-derived structural context to assist the user in identifying relevant vulnerabilities, localize the module surrounding the chosen insertion point, and perform intent-conditioned RTL modification. The framework produces minimal, synthesizable, and interface-preserving HTs with ultra-rare triggers. Experimental evaluation across diverse RTL designs demonstrates that all generated HTs are 100% syntactically correct, remain undetectable under large-scale random simulation, and are functionally triggerable under their intended activation conditions. These results highlight CITADEL as a scalable and principled method for generating realistic HT
benchmarks.
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All authors Jayanth Thangellamudi, Raghul Saravanan, Sudipta Paria, Swarup Bhunia, Sai Manoj P D
Artificial Intelligence
How To Train Your World Model: Fine-tuning vs RAG for LM-based World Modeling
Dhananjay Ashok, Shantanu Agarwal, Vivek Datla et al. (+2 more)
World models (WMs) simulate the transition dynamics of environments, enabling
agents to plan over the consequences of their actions. In text-based environments, fine-tuning a
Language Model (LM) to serve as a WM has emerged as a dominant paradigm. However, despite the widespread success of non-parametric approaches such as
Retrieval Augmented Generation (RAG),
retrieval for LM-based world modelling remains underexplored. We conduct a systematic evaluation across five diverse environments spanning
embodied, web
navigation and social settings, comparing fine-tuning and RAG-based approaches for LM-based world modelling. Our study reveals that fine-tuning often outperforms RAG, with fine-tuned WMs enabling
agents to obtain higher rewards on 15/20 settings. While both construction paradigms benefit from additional and more diverse exploration, RAG-based approaches prove more data-efficient, and fine-tuning approaches disproportionately benefit from scaling the amount of experience collected. With a focus on RAG-based WMs, we devise a procedure that uses counterfactual intervention to estimate the error rate of the
retrieval stage, and show that retrievers consistently surface suboptimal transitions from the experience buffer. Hoping to address this failing, we study a variety of query reformulation strategies, demonstrating that a hierarchical approach outperforms the traditional
retrieval pipeline. Finally, we compose our findings into a hybrid world modelling system that parametrically captures core environment dynamics, while learning to rely on
retrieval from an actively maintained
memory store. Our hybrid system consistently outperforms other methods across multiple environments and models, showcasing the robustness of the approach and the applicability of our findings.
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All authors Dhananjay Ashok, Shantanu Agarwal, Vivek Datla, Jonathan May, Alfy Samuel
Machine Learning · Cross-listed from stat.ML
ENCORE: Exact Non-equilibrium COntrol with Replica Exchange for Diffusion Generation
Jiahao Yu, Saifuddin Syed, José Miguel Hernández-Lobato et al. (+1 more)
Inference-time control steers a pretrained generative model towards a target distribution without retraining. We study tilted targets $\pi_0\propto G_0\,p_0$, where $p_0$ is the sampler output distribution and $G_0$ is an evaluable reweighting function. Existing approaches rely on sequential annealing with sequential Monte Carlo (SMC) or parallel annealing with replica exchange (RE). Sequential control is exact but needs large particle populations, whereas no exact parallel control method exists: existing RE corrections approximate an intractable time reversal and are biased. We propose Exact Non-
equilibrium COntrol with Replica Exchange (ENCORE), the first exact parallel control method. Each replica stores its generation trajectory, so the upward move is a truncation and the intractable time reversal is never simulated. We prove target invariance and show that the resulting dynamics are those of non-
equilibrium replica exchange with the exact time reversal as forward proposal. Under regularity conditions, our
diffusion analysis shows that both sequential and parallel control become unstable under refinement of the time discretisation without guidance, whereas guided proposals remain stable and yield diagnostics for tuning the schedule and the computational budget. Across synthetic targets, Boltzmann sampling of biomolecules, and image generation, ENCORE achieves competitive accuracy and diversity, remains robust to sampler perturbations, and applies to distilled samplers where existing RE corrections are unavailable.
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All authors Jiahao Yu, Saifuddin Syed, José Miguel Hernández-Lobato, Jiajun He
Databases
TREMOR: Template Matching for Large Seismic Data Collections
Manos Chatzakis, Rodrigo Flores-Allende, Mikael Freire et al. (+3 more)
Seismic station networks continuously record the ground velocity at several locations on earth in the form of waveform data series, which seismologists analyze to detect various kinds of geophysical events, including earthquakes. Some of these events are of particular interest: they are called templates and are used to search the seismic data collections for matching, similar events. This is known as template matching, and is a fundamental task in seismology, serving as the backbone for various seismic analyses. However, template matching requires extensive processing times, especially for seismic collections that exceed the
memory capacity of a single machine. This poses a significant challenge to seismologists and is becoming worse as the seismological
datasets continue to grow in size. In this paper, we introduce TREMOR, a distributed data series processing framework for template matching, designed to efficiently handle large waveform collections. We apply TREMOR to two representative real-world seismic use cases for template matching and, through an extensive experimental evaluation, we demonstrate its efficiency, with TREMOR being up to 12x faster than the best competing method, while returning the same, exact results.
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All authors Manos Chatzakis, Rodrigo Flores-Allende, Mikael Freire, Yoann Cano, Léonard Seydoux, Themis Palpanas
Computation and Language
A generative-informed neuro-symbolic framework for syntactic ambiguity resolution: Evidence from Arabic DPs
Mohammed Damom, Mohammed Q. Shormani, Muneef Y. Alshawsh et al. (+5 more)
Syntactic ambiguity poses a persistent challenge for Arabic NLP, particularly in morphologically rich nominal constructions where multiple structu6ral interpretations may be compatible with the same surface sequence. This study proposes a generatively informed neuro-symbolic framework for resolving structural ambiguity in Modern Standard Arabic (MSA) DPs. The framework integrates generative syntactic notions with AraBERT by representing ambiguity as a candidate-based decision task in which linguistically motivated alternatives are explicitly constructed and evaluated through candidate-conditioned input representations. Findings indicate that the model achieved 96.88% accuracy, 95.92% macro-F1, 96.83% weighted F1, and 93.94% binary F1 on the unseen evaluation set. Class-level analysis revealed asymmetric performance, with recall of 99.71% for High/VP Attachment (N1) and 89.26% for Low/NP/Embedded Attachment (N2), indicating greater difficulty in recovering the embedded interpretation. The study concludes that formal syntactic representations can be operationalized within
Transformer-based NLP as an explicit interface between linguistic structure and contextual neural modeling, providing a controlled and interpretable approach to Arabic syntactic ambiguity resolution and beyond.
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All authors Mohammed Damom, Mohammed Q. Shormani, Muneef Y. Alshawsh, Ashraf A. Naji, Mustafa Ali Alhamzi, Fawwaz An-Nashef, Jameel Ahmed Elayah, Noman AL-Sayadi
Machine Learning
Autoregressive Differentiable Method for Integer Programming
Ouns El Harzli and Yudong Cao
We introduce an autoregressive differentiable method to solve 0-1 integer programs. We fix an arbitrary order of the binary variables and we train a
transformer to predict the next bit while remaining in the feasible set. Our method is first trained on feasible incumbents provided by any solver, thus allowing us to initialize the
transformer in the feasible set. Our procedure then implements a Lagrangian penalty to penalize infeasible solutions, and the
transformer is further trained to explore the feasible set using Gumbel-softmax activations on the relaxed objective. We have tested our method on non-convex instances of quadratic knapsack problem and demonstrated consistent improvement upon state-of-the-art open-source solvers for dense problems up to 10,000 binary variables. In particular, we empirically demonstrate a phenomenon akin to a tunneling effect where the effective change of variables from binary variable to the continuous weights of the
transformer that the method implements enables crossing barriers in the relaxed objective landscape.
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Robotics
CriticHack: Evaluating Visual Rewards Under Robot Policy Optimization
Jiaxuan Luo, Xingguo Xu, Shanshan Wang et al. (+2 more)
Learned visual reward models are increasingly used to optimize
robot policies, yet a reward model can score an execution that acts on the wrong object as highly as one that completes the task. We show that optimizing such a reward can amplify these wrong-object failures while reward and task success both rise, so the signals a practitioner would normally monitor look healthy. We fine-tune every denoiser parameter of a
diffusion policy against Robometer on a drawer task. Starting from a supervised policy with no prior reward exposure, five training runs raise task success by 10.2 percentage points and wrong-object failures by 10.9 points on 512 evaluation seeds, whereas five runs trained on the simulator's task-completion signal raise success without amplifying wrong-object failures (difference 9.2 points, 95% CI 5.6 to 13.0). The amplification recurs from a policy previously optimized against learned rewards, under the policy's native
diffusion sampler, at matched distance from the initial policy, and across constrained-policy experiments with two critics and two optimizers. A tilt model explains when it occurs: under KL-regularized optimization, an outcome becomes more frequent whenever its expected reward under the initial policy exceeds the population average. Robometer separates successes from failures well overall (AUROC .81) but scores wrong-object failures slightly above successes (AUROC .37), so optimization raises both. The same model predicts the outcome shifts across 26 constrained settings (Spearman .89), including those in which task success falls, and Robometer's own published success-termination recipe inherits the error. A frozen outcome verifier redirects the same optimization toward the requested task.
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All authors Jiaxuan Luo, Xingguo Xu, Shanshan Wang, Yuhan Zhou, Zhen Zhang