Robotics
MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving
Thomas Steinecker, Denis Trescher, Alexander Bienemann et al. (+2 more)
Reinforcement learning constitutes a promising approach owing to its potential for superhuman performance and self-learned policies. However, its application to real-world autonomous driving remains scarce, particularly in unstructured environments, because of the challenges associated with sim-to-real transfer for unstructured environments. In this work, we present MILER, an end-to-end policy framework with zero-shot sim-to-real transfer. During offline training, we employ a custom semantic mid-level representation (MLR) simulator and train the policy network using reinforcement learning, with its control outputs applied directly to a bicycle model. During deployment on the real vehicle, camera and
LiDAR data are processed by BEVFusion to generate a semantic bird's-eye-view representation consistent with that of the MLR simulator. The actions generated by the policy network are not applied directly to the real vehicle. Instead, we employ a trajectory-
alignment strategy that enables zero-shot sim-to-real transfer of both perception and control. We extensively evaluate the proposed framework on a diverse test track comprising numerous challenges, including various obstacles, hairpin curves, velocities of up to 33.6 km/h, and off-road sections. In total, we drove 17.3 km with two different vehicles on a 3.0 km test track without human intervention, thereby demonstrating the effectiveness of our approach. Furthermore, the entire software stack runs on a Jetson AGX Orin.
Read the full abstract ▾
All authors Thomas Steinecker, Denis Trescher, Alexander Bienemann, Thorsten Luettel, Mirko Maehlisch
Machine Learning
Video DeltaNet: A Video-Native Hybrid Attention for Livestream Video Generation
Haocheng Xi, Yiming Xie, Hexu Zhao et al. (+8 more)
Video diffusion models repeatedly process long spatiotemporal
token sequences during denoising, making
attention a major computational bottleneck. Linear
attention offers an appealing alternative and has been widely adopted in recent large
language models, but directly applying it to
video models often fails to preserve the fine-grained interactions required for high-quality generation. We present
Video DeltaNet (VDN), which combines local Softmax
attention with bidirectional linear
memory for long-range
video context. Its linear branch introduces
Video Delta
Attention (VDA), which updates
memory once per frame by jointly incorporating its spatial
tokens. Separate output projections and learnable gates calibrate the two branches, while a staged teacher-
alignment recipe progressively introduces the new pathway into pretrained models. We instantiate VDN on MiniMax H3, applying the hybrid to
video-to-
video interactions while retaining Softmax for interactions involving text or audio. With eight-step
distillation and an optimized SGLang serving stack, VDN-H3 completes DiT denoising for a 14.3-second, 768p
video in 6.70 seconds on eight NVIDIA B200 GPUs, corresponding to a 14.5x speedup over the 50-step dense H3 baseline on the same GPU count. GitHub code available at: https://github.com/OpenVDN/vdn-minimax-h3. Weights available at: https://huggingface.co/OpenVDN/vdn-minimax-h3
Read the full abstract ▾
All authors Haocheng Xi, Yiming Xie, Hexu Zhao, Yiwen Zhang, Michael Liu, Thomas Creavin, Kurt Keutzer, Xiuyu Li, Zhaoyang Lv, Chenfeng Xu, Haiwen Feng
Distributed Computing · Cross-listed from quant-ph
Efficient Non-Uniform Quantum Hermite Transform through Adaptive Sampling
Nitay Mayo and Aryeh Lev Zabokritskiy
On the span of the first $N$ oscillator modes, Gauss–Hermite quadrature gives an exact change of basis between mode coefficients and $N$ weighted position space samples. We implement this transform with $O(N\operatorname{polylog}(N,1/\varepsilon))$ logical gates and polylogarithmic
quantum width. The operator-error bound $\varepsilon$ holds on arbitrary superpositions and includes all auxiliary registers. The construction uses signed averages on adaptive windows to convert uniform-grid samples into weighted Hermite-root samples. Their varying widths control the amplification cost, giving the near-linear bound.
Read the full abstract ▾
Computers and Society
Harnessing Generative UI for Education: Tailored Learning Interactives
Alisa Kovshov, Anisha Choudhury, Anna Iurchenko et al. (+26 more)
It is well established that students learn best through active engagement, particularly via interactive experiences tied to real-world concepts. Creating such experiences, however, typically requires labor-intensive design, costly development, and continuous teacher guidance to achieve measurably effective outcomes. Recent advances in generative UI open the door for a future of practice where the creation of tailored interactive experiences is automated. However, off-the-shelf models lack optimization for pedagogical precision and learning principles, often yielding unsatisfactory results for complex pedagogical constructs. To address these limitations, we present learning interactives, a structured framework leveraging the generative UI capabilities of Gemini to construct effective customized interactive simulations. We evaluate our approach through both an initial study with educators as well as a pedagogical rating of a varied set of simulations by expert teachers.
Read the full abstract ▾
All authors Alisa Kovshov, Anisha Choudhury, Anna Iurchenko, Amy Keeling, Avinatan Hassidim, Ayelet Shasha Evron, Ayça Çakmakli, Diana Akrong, Femi Olanubi, Gal Elidan, Ian Li, Ido Lerer, Katherine Chou, Lidan Hackmon, Michal Gordon, Nir Kerem, Niv Efron, Preeti Singh, Rena Levitt, Rotem Yulzary, Shlomi Ben Shimon, Sophie Allweis, Tracey Lee-Joe, Tzvika Stein, Yaniv Carmel, Yael Haramaty, Yishay Mor, Yossi Matias, Yuri Lev
Social and Information Networks
An Interpretable Approach to Money Laundering Detection in Transaction Graphs using Pass-Through Templates
Paolo Climaco
Layering is a key stage of the money laundering process in which assets are moved through intermediate entities to obscure their origin. We model this movement as a transaction graph, a labeled directed multigraph where nodes represent entities, such as individuals or organizations, and edges represent transactions between them. This work addresses the detection of layering patterns in transaction graphs. We define pass-through templates, a class of transaction graphs whose structure is indicative of a specific layering pattern in which entities receive funds and rapidly forward them. We formulate detecting instances of these templates within a larger transaction graph as a maximum-cardinality, maximum-weight matching problem. The resulting method uses Edmonds' blossom
algorithm, which provably finds an optimal solution to this problem. Here we present a case study on publicly available Ethereum transaction data. We identify 283 pass-through template instances, chained through shared address nodes into a densely interconnected network that moves hundreds of millions of dollars in stablecoin value. We find that six addresses in the identified network are sanctioned by the U.S. Department of the Treasury for associations with criminal organizations, providing evidence of ties between the network and sanctioned criminal entities.
Read the full abstract ▾
Computation and Language
On-Demand Attention: Language Models Know When to Recall
Haibo Feng, Ruiqi Liang, Dongyang Jin et al. (+2 more)
Reasoning and agentic workloads increasingly demand efficient long-context inference. Yet full-
attention decoding reads the growing history at every step, although the benefit of global access varies across prediction positions. We find that, before global
attention is computed for the current step, the decoding states available after local computation in frozen pretrained models already contain information predictive of its benefit over local
attention. Building on this finding, we introduce On-Demand
Attention (ODA), a local-first decoding method: after local computation, a lightweight recall head decides whether to recompute the current step with global
attention. ODA trains only the recall head with modest data and compute budgets, leaving pretrained weights unchanged and retaining the complete historical KV cache so that information skipped at one step remains available for later access. Experiments across model scales and families, including hybrid
attention backbones, show that ODA recovers most of the performance lost under local
attention while substantially reducing the frequency of global
attention. Controlled long-context measurements in vLLM further show that GPU-side conditional execution translates fewer global reads into practical decoding speedups over full
attention. These findings show that pretrained decoding states can support both
token prediction and decisions about accessing distant information, allowing models to allocate global computation as needed during decoding.
Read the full abstract ▾
All authors Haibo Feng, Ruiqi Liang, Dongyang Jin, Hanyang Peng, Shiqi Yu
Artificial Intelligence
Q&A on Any Spreadsheet Requires Interpreting Its Grid Structure
Zofia Smoleń
Semantic
cell annotation improves chunking interpretability for spreadsheets in LLM-driven RAG systems, aiding answer generation through enriched context rather than improved
retrieval accuracy. We propose a novel framework of splitting any spreadsheet into interpretable chunks using
cell role annotation. Our framework beats the state of the art, yet it faces a hard ceiling. Spreadsheets are fundamentally two-dimensional unstructured data with continuous relationships and infinite potential
cell roles. Because classification models are restricted to finite, pre-defined classes, they cannot perfectly capture this structural nuance, even with human-level annotation. We show that addressing the spreadsheet-to-LLM bottleneck requires moving beyond discrete
cell classification. Instead, the field must develop dimensionality-reduction techniques to directly flatten 2D unstructured spreadsheets into 1D unstructured text. Text chunks would be easier for downstream RAG to interpret and generate from.
Read the full abstract ▾
Robotics
Underwater Visual Target Tracking with Target-Specific Depth Estimation and Adaptive Model-Fusion Predictive Control
Yuheng Zhou, Haiyang Cheng, Yanqi Feng et al. (+5 more)
Vision-based
underwater target tracking is challenged by unreliable depth measurements and unknown target motion. This paper proposes a stereo visual-servoing framework for an autonomous
underwater vehicle (AUV). For perception, the framework derives a stable 3D relative state from stereo images through target-specific depth extraction and Kalman filtering. It constructs a target-depth mask from color, disparity, and temporal cues to select reliable target pixels, and then filters the resulting depth measurement and detected image center separately. For control, the framework decouples yaw regulation from translational control, avoiding computationally expensive coupled multi-DOF optimization and enabling real-time translational MPC. The translational controller employs adaptive model-fusion predictive control, combining constant-velocity and zero-velocity target models to accommodate different target-motion patterns. It updates the model weights using historical prediction errors and computes translational commands subject to actuation, following-distance, and field-of-view constraints. Through simulations and real-world experiments, we validate the effectiveness of the proposed framework and show it has better performance than existing frameworks.
Read the full abstract ▾
All authors Yuheng Zhou, Haiyang Cheng, Yanqi Feng, Pangkit Fong, Mei Xuan Lee, Marcus Gee, Chongrong Fang, Jianping He
Distributed Computing
PixelFlow: Token-Level Workload Management for Efficient Distributed DiT Serving
Zhexiang Zhang, Minchen Yu, Yifan Sun et al. (+3 more)
Online image generation with
Diffusion Transformers (DiTs) must meet latency service-level objectives (SLOs) while using GPU resources efficiently. Existing systems improve GPU utilization by batching multiple requests for joint execution. However, request-level batching offers limited control over batch size: batches may be too small to saturate GPU compute, while larger ones may violate latency SLOs. Globally coordinated scheduling introduces further delays by requiring independently progressing GPUs to synchronize before admitting new work. We present PixelFlow, a distributed DiT serving system that addresses these limitations through
token-level workload management. Its key idea is to use image
tokens (the units a DiT processes to generate an image) to divide and batch request workloads at a finer granularity. This allows each GPU to take on a portion of additional work under latency constraints. By distributing these portions across GPUs, PixelFlow accommodates more concurrent requests, improving GPU utilization while reducing queueing delays. To realize this flexibility, PixelFlow provides a runtime that splits requests into variable-sized partitions and batches them efficiently on each GPU. To reduce the resulting communication overhead, it optimizes
token placement to limit cross-GPU data exchange while balancing GPU workloads. It further exploits similarity across denoising steps to overlap the remaining transfers with computation. An SLO-aware scheduler groups GPUs to share compute resources among requests with compatible latency requirements. Each group progresses independently, synchronizing with others only when their combined resources are needed to admit a new request. Evaluation with Stable
Diffusion 3 and FLUX.1-dev on H100 GPUs shows that PixelFlow improves SLO attainment by up to 43% and achieves up to 2.8 times the goodput of state-of-the-art DiT serving systems.
Read the full abstract ▾
All authors Zhexiang Zhang, Minchen Yu, Yifan Sun, Xu Bai, Xingliang Yuan, Adel N. Toosi
Artificial Intelligence
Deep Noir: Autonomous Steering Discovery via Architectural Chronometry in Transformer Models
Frank E. Bobe III, Gregory D. Vetaw, Darshan W. Bryner et al. (+2 more)
Activation steering modifies LLM behavior at inference time, but identifying where and how strongly to steer remains manual. We introduce Deep Noir, a framework that uses Logit Lens convergence and causal head-level attribution to autonomously discover optimal steering parameters. Across three scales (1B x 3, 2-3B x 2, and 7-9B x 4), our engine achieves 16.7 percentage-point improvement on spam at 1B (standard deviation 4.7; 39 runs), with gains increasing to 21 to 42 percentage points at 7-9B across four architectures. On SST-2 sentiment, it achieves a 13.1 percentage-point improvement with zero code changes. Mechanistic grounding enables automated discovery of intervention points that generalize across tasks and architectures. On sentiment, RepE without head masking fails to improve over baseline, while Deep Noir improves all models (p less than 0.01). We further show that steering creates a predictable
prompt-injection attack surface whose vulnerability increases monotonically with steering magnitude. This finding is relevant to
agent systems deploying steered classifiers.
Read the full abstract ▾
All authors Frank E. Bobe III, Gregory D. Vetaw, Darshan W. Bryner, Matthew G. Cook, Jose L. Salas-Vernis
Human-Computer Interaction
What Parents Can See: Divergent Accounts of Youth AI Companion Use in Parenting and Teenager Subreddits
Thomas Berkane, Anne Bischops, Anika Mellacheruvu et al. (+1 more)
Youth increasingly use AI companions, and parents are the primary mediators of that use. How effective that mediation can be depends on whether parents are aware of how adolescents actually use these systems and what risks and benefits such use carries; nevertheless, prior work has only studied these demographic groups in isolation, and existing taxonomies attend almost entirely to risk. We analyze 1,628 Reddit posts about youth AI companion use from parenting and teenager communities (2023–2026); develop a codebook covering modes of use, risks, benefits, and parental mediation; and apply it at corpus scale with an LLM. The two communities yield divergent accounts. Teenagers most often discuss receipt of emotional support from AI companions (31% of teenager posts vs. 19% of parenting posts), whereas parents most often discuss teenage use of AI companions for romantic and sexual interaction (36% vs. 25%). Teenagers are not unaware of other risks, however; indeed, attachment and dependence is the risk they raise most (19%), close to the parental rate (16%). Teenagers also describe benefits that risk-centered taxonomies do not capture and parents rarely mention, most notably emotional support (27% vs. 5%). We argue these differences track what a given kind of use makes visible to someone outside the conversation. Chatting with a companion for hours every night leaves a trace beyond the chat itself; sexting with a character stands out when a parent reads the log; venting about a fight with a friend does neither, since it looks like any other conversation. The first surfaces as dependence, the second as sexual content, and the third as emotional support, which is the one parents most often miss. Parental guidance and system design should attend to use cases that reach parents by neither route, emotional support foremost among them.
Read the full abstract ▾
All authors Thomas Berkane, Anne Bischops, Anika Mellacheruvu, Maimuna Majumder
Human-Computer Interaction
Ageing, Digital Literacy, and Interaction Modality in Immer-sive Virtual Reality: Psychomotor Performance, Cognitive Flexibility, and Their Processing-Speed Association
Panagiotis Kourtesis, Katerina Denaxa, Lydia Asimakopoulou et al. (+2 more)
Extended reality (XR) increasingly supports training and cognitive assessment, yet the age sensitivity of its interaction techniques is unclear. This study examined age, digital literacy, and interaction modality as correlates of psychomotor and cognitive-flexibility performance in immersive virtual reality (VR). Two hundred and two adults (19-90 years) completed a five-mode Fitts' law task (eye-gaze, head-gaze, controller ray-casting, virtual finger, and controller direct touch), the Trail Making Test in VR (TMT-VR), and a digital-literacy questionnaire. Age was associated with slower task times across modes, but controller direct touch carried the steepest relative age gradient yet remained among the fastest in absolute terms; technique altered relative age sensitivity without determining absolute efficiency. Higher digital literacy was associated with faster TMT-VR completion but not Fitts task times. A Fitts-derived speed score predicted TMT-VR performance beyond age and partly accounted for its age association, consistent with shared processing-speed variance; an exploratory full-battery extension confined the Fitts-score association to completion-time and error-adjusted-time indices and the digital-literacy association to error-adjusted-time indices, not wrong-target errors, mean selection distance, or relative Part B indices. Age-inclusive XR assessment should standardise interaction modality, evaluate rather than exclude mid-air direct selection, and interpret cognitive scores alongside digital literacy and psychomotor speed.
Read the full abstract ▾
All authors Panagiotis Kourtesis, Katerina Denaxa, Lydia Asimakopoulou, Petros Roussos, Maria Roussou
Data Structures and Algorithms
An $\widetilde{O}(n^3)$-time FPRAS for the Permanent
Xiaoyu Chen, Heng Guo, Eric Vigoda et al. (+1 more)
We give an FPRAS for the permanent of an $n\times n$ $0/1$
matrix with running time $\widetilde O(n^3)$. Our
algorithm extends to a strongly
polynomial FPRAS for arbitrary nonnegative
matrices with the same running-time bound (but in the unit-cost arithmetic model). Jerrum, Sinclair, and Vigoda (2004) gave the first FPRAS for the permanent of a nonnegative
matrix. The running time was subsequently improved to $\widetilde{O}(n^7)$ by Bezáková, Štefankovič, Vazirani, and Vigoda (2008), and recently to $\widetilde{O}(n^6)$ by Chen, Vigoda, and Yang (2026). We introduce a multicommodity-flow bound inspired by electrical flows, replacing the usual path-length factor by routing energy. For the classical JSV chain with perfect-matching weights multiplied by $n^2$, we prove a relaxation-time bound of $O(n^3\log n)$ and obtain comparable bounds for estimating the stationary probabilities of having a specified pair of unmatched vertices. We use a separate chain on assignments to generate a warm start at any given temperature, avoiding the long initialization of the JSV chain. Finally, we replace the JSV chain with a new chain that uses only $n$ hole weights and speeds up hole-weight learning by a factor of $n$.
Read the full abstract ▾
All authors Xiaoyu Chen, Heng Guo, Eric Vigoda, Xiongxin Yang
Machine Learning
Don't Mask the Environment: Observation Supervision Changes How Agents Explore Under RL
Juzheng Zhang, Disha Makhija, Manoj Ghuhan Arivazhagan et al. (+2 more)
Agent trajectories record what an
agent does and what happens next. Yet standard supervised fine-tuning (SFT) applies loss only to
agent-authored action
tokens, using environment observations as context but not as prediction targets. We ask whether this convention provides the best initialization for subsequent reinforcement learning. We introduce ActObs, which also supervises the observation
tokens already present in each trajectory. Although deployed
agents never generate observations, learning to predict them encourages the policy to model action consequences without adding data, parameters, sequence
tokens, or forward passes. The methods perform similarly after SFT but diverge after GRPO. On Qwen3-4B, GRPO from ActObs achieves higher pass@k at every evaluated sampling budget than its action-only counterpart on Terminal-Bench 2.0. On Qwen3-8B, it trades some pass@1 reliability for higher pass@k (+3.4 pp at pass@16) and solves more distinct tasks. The advantage extends to cross-domain code editing on aider-polyglot (+4.2 pp at pass@1 at 4B), whose tasks are unseen during SFT and RL. ActObs retains more
entropy during RL while requiring less policy movement, leaving the final policy closer to its SFT initialization. Our analysis traces this difference to SFT: action and observation gradients rapidly become orthogonal, while action-only training leaves a large residual observation gradient and degrades environment prediction below the base model. Joint supervision prevents this one-sided specialization, preserving consequence prediction and preparing the policy for downstream exploration.
Read the full abstract ▾
All authors Juzheng Zhang, Disha Makhija, Manoj Ghuhan Arivazhagan, Vinayshekhar Bannihatti Kumar, Rashmi Gangadharaiah
Numerical Analysis
A Unified Framework for Wasserstein Convergence of ULMC Methods beyond Log-Concavity: Old and New
Wanjie Lyu, Xiaojie Wang and Bin Yang
As a fundamental task across computational statistics, scientific computing and machine learning, sampling from high-dimensional probability distributions has received increasing
attention in recent years. Numerous sampling
algorithms have been proposed, among which underdamped Langevin Monte Carlo (ULMC) methods based on underdamped Langevin dynamics (ULD) have emerged as a class of efficient ones. In this work, we introduce a “universal" predictor-corrector formulation that bridges Euler-type, UBU-type and randomized schemes through different choices of method parameters. Notably, the “universal" integrator induces two novel classes of low-cost integrators, termed low-cost randomized integrators (LC-RIs) and low-cost UBU integrators (LC-UBUIs), as well as their exponential-free variants based on
polynomial and rational approximations. The resulting new UBU-type and randomized schemes require only one gradient evaluation and two Gaussians per iteration, considerably reducing the number of gradient evaluations or Gaussians per iteration required by existing counterparts. Further, a general framework of long-time error analysis is developed for general discretization schemes in a probability metric. Under certain smoothness and non-log-concavity conditions, we rely on the unified framework to establish non-asymptotic $\mathcal{W}_1$-error bounds of both old and new schemes, revealing convergence rates of order $\mathcal{O}(d^{\frac{1}{2}}h)$ for Euler-type schemes, order $\mathcal{O}(d h^2)$ for UBU-type ones and order $\mathcal{O}(d^{\frac{1}{2}}h^{\frac{3}{2}})$ for randomized ones. In the strongly convex setting, the same non-asymptotic error bounds can be recovered in $\mathcal{W}_2$-distance. Numerical experiments corroborate the theoretical findings.
Read the full abstract ▾
Computation and Language
Summarization Bias: The Directional Collapse of Objective Projection into Told-Mode Labels in Large Language Models — A Conceptual Framework and Registered Test Protocol
Levent Bulut
This paper introduces and operationalizes summarization bias: a proposed systematic tendency of large
language models (LLMs) to represent narrative meaning as an abstract summary label rather than as the reconstructable inferential structure that produces it. Within the Bulut Doctrine, narrative effect is theorized along a told-shown axis: in told mode, emotional and informational content is declared explicitly and requires little reader reconstruction; in shown mode, that content is suppressed at the surface and must be reconstructed from physical cues and indirection (Objective Projection). Shown mode is the higher-load condition the doctrine is designed to measure. The claim is that LLMs fail along this axis in a specific direction. Summarization bias is hypothesized to operate in two regimes: (i) a generative regime, in which a model asked to render an
emotion through Objective Projection defaults to declaring it instead; and (ii) an evaluative regime, in which a model judging narrative quality rewards told-mode explicitness and under-detects shown-mode suppression. The evaluative regime is the more consequential, since LLMs increasingly serve as judges and reward models, and a directional bias toward told mode would impose a selection pressure degrading prose toward flat declaration. This report does not claim the bias is validated. It defines the construct, situates it against LLM-as-judge biases, rereads a completed independent reliability study as directional evidence consistent with it, and pre-registers a two-regime test with decision rules under which the construct would be abandoned.
Read the full abstract ▾
Robotics
MoWAM: Explicit Future Motion Prediction for Efficient World Action Models
Jiayu Wang, Bin Zhu, Yue Yu et al. (+1 more)
World Action Models (WAMs) improve
robot policy learning by incorporating future dynamics, yet explicitly generating future
videos at inference introduces substantial computational overhead. Removing future generation improves efficiency, but leaves future dynamics only implicitly encoded in observation features, which can limit robustness under distribution shifts. We propose MoWAM, an efficient WAM that replaces future
video generation with explicit future motion prediction. Instead of reconstructing the complete future scene, MoWAM models structured
robot motion as a compact abstraction of the future, capturing how the
robot is expected to evolve under the current scene and interaction constraints. A Mixture-of-
Transformer architecture learns future visual dynamics during training while jointly predicting motion and action, allowing
video generation to be removed entirely at inference while retaining an explicit representation of the future. The compact motion representation further enables efficient inference-time scaling by sampling multiple candidates of motion and action pairs and selecting among them with a motion-aware task-progress verifier. Experiments on LIBERO, LIBERO-Plus, and real-world manipulation tasks demonstrate that MoWAM achieves strong in-distribution performance, improved out-of-distribution robustness, and higher average real-world success than representative WAM baselines. In addition, performance improves as more candidates are explored, demonstrating that explicit future motion provides an effective and efficient basis for inference-time scaling.
Read the full abstract ▾
All authors Jiayu Wang, Bin Zhu, Yue Yu, Jingjing Chen
Machine Learning · Cross-listed from math.OC
Stable Movement for Nondual Lipschitz Convex Optimization: Efficiency and Nearly Optimal Oracle Rates
David Martínez-Rubio and Cristóbal Guzmán
We study efficient
algorithms for realizing the first-order oracle complexity of optimization of $G$-Lipschitz convex functions with respect to the $\ell_q$-norm over an $\ell_p$-ball of radius $R$, where $1\leq p,q\leq\infty$. For $p<q$, we obtain error $\widetilde{O}_{p,q}(GR/T^{1/p-(1/q-1/2)_{+}})$ after $T$ oracle queries, attaining the lower bounds in a COLT 2015 open problem (Guz15b) with efficient
algorithms. In particular, the rate is $\widetilde{O}(GR/T)$ for Euclidean Lipschitzness with an $\ell_1$-ball domain of radius $R$ ($p=1,q=2$). Our solution consists of reducing convex Lipschitz optimization to the chasing nested convex sets problem (BBE+20) in sublevel sets of an evolving maximum of affine lower bounds, that is, the bundle (LNN95): at each query we either find a point with low function value or we produce a deep cut in the current sublevel of the bundle that we chase. The dichotomy between stability of selectors and forced movement by deep cuts bounds the number of iterations of the
algorithm nearly optimally. For nested convex subsets of the $\ell_p$ ball $R B_p^d$, we introduce novel stable selectors whose movement is nearly optimal in high dimensions. A Monte Carlo average of the proposed selector achieves near-optimal rates with high probability and can be implemented for our optimization
algorithm in
polynomial time in the real-arithmetic model.
Read the full abstract ▾
Computer Vision
Should This Case Be Adapted? Prediction Fragmentation Controls Test-Time Adaptation
Lili Wang, Jing Li, Xiaowen Sun et al. (+5 more)
Episodic test-time adaptation resets a frozen segmenter to source weights $M_0$ on each case and adapts for a fixed step count. A fixed horizon conflates a cohort-level question, how far to adapt, with an irreducibly per-case one, whether this case should be adapted at all. Cohort means hide that decision: on cross-vendor cardiac MRI the mean $\Delta$Dice from adaptation is statistically indistinguishable from zero while 58.7% of cases are individually made worse. We quantify this harm as harmful accepted area (HA), the harmful fraction of the edited area a controller deploys. Held-out tuning gives a stronger baseline than a fixed horizon, but the budget it selects transfers on neither of the two main medical
benchmarks, and no global budget can condition on the case. We show that prediction fragmentation—the disagreement geometry between $M_0$ and the adapted mask $M_k$—predicts HA with no labels or extra backward passes at decision time, comparably on three
benchmarks (Spearman $\rho$ 0.50–0.60), at a quarter of gradient-norm's latency. A case-level router built on it cuts HA from 0.228 to 0.139 on a
benchmark that took no part in its design, with the design frozen and only cut-points recalibrated there. On the cardiac
benchmark the design was selected on, the router cuts HA from 0.129 to 0.013 at matched Dice and 1.10 deployed updates, against the retrospective-best budget found post hoc on evaluation labels, and reduces that 58.7% to 20.0%, an upper bound we quantify. Where the retained cases are not net-helped (as on prostate), the router still cuts HA but concedes accuracy, a boundary we report. Thresholds are fit once on a labeled split disjoint from evaluation; decisions use no labels or gradients. The template ports across architecture and domain (nnU-Net$\to$SegFormer, Cityscapes$\to$ACDC) with coordinate, thresholds and per-bucket actions instantiated per domain.
Read the full abstract ▾
All authors Lili Wang, Jing Li, Xiaowen Sun, Xiangyu Hu, Zhuangzhuang Gu, Jian Liu, Srihari Nelakuditi, Yan Tong
Data Structures and Algorithms
Large-Scale Trade-Off Curve Computation for Incentive Allocation with Cardinality and Matroid Constraints
Yu Cong, Chao Xu and Yi Zhou
We consider a large-scale incentive allocation problem where the entire trade-off curve between budget and profit has to be maintained approximately at all times. The application originally comes from assigning coupons to users of ride-sharing apps, where each user can have a limit on the number of coupons assigned to them. We consider a more general form, where the coupons for each user form a matroid, and the set of coupons assigned to each user must be an independent set. We show the entire trade-off curve can be maintained approximately in near real time.
Read the full abstract ▾
Game Theory
A Nearly Tight Lower Bound for Matroid Intersection Prophet Inequalities
Dimitris Fotakis, Charalampos Platanos and Thanos Tolias
We study prophet inequalities under intersections of $q$ partition matroids, where an online
algorithm irrevocably selects elements with independent nonnegative values drawn from known distributions and revealed in an
adversarial order. We prove an $\Omega(q/\log q)$ lower bound on the competitive ratio. Together with the known $O(q)$ upper bounds, this resolves, up to a logarithmic factor, the optimal dependence on $q$, an open question posed by Correa, Cristi, Fielbaum, Pollner, and Weinberg (IPCO 2022) and Saxena, Velusamy, and Weinberg (ITCS 2023). Our construction also yields an $\Omega(d/\log d)$ lower bound for $d$-single-minded auctions, where buyers request fixed bundles of at most $d$ unit-capacity items. Our construction and analysis build on the big-decisions-first framework of Rubinstein and Singla (STOC 2026). Previous lower bounds for this problem hold even for i.i.d. Bernoulli values, whereas our instance uses nonidentical distributions. For i.i.d. Bernoulli values, we prove a $q^{1-o(1)}$ lower bound, improving the previous $q^{1/2+\Omega(1/\log\log q)}$ of Saxena, Velusamy, and Weinber (ITCS 2023). To this end, we construct $p^p$ pairwise qualitatively independent partitions into $p$ blocks on a ground set of only $p^{2+o(1)}$ points.
Read the full abstract ▾
Robotics
HOPHY: A Hierarchical Hypergraph Representation for Off-Road Path and Mission Planning
Pranay Meshram, Charuvahan Adhivarahan, Prithvi Poddar et al. (+4 more)
Mission-level autonomy for disaster response, search and rescue, and tactical UGV operations requires repeated path and mission planning as terrain conditions,
agent types, and objectives change. Pixel-grid search is costly for repeated kilometer-scale queries, while semantic abstractions must maintain valid costs and connectivity as conditions change. We present HOPHY (Hierarchical Off-Road Planning using Hypergraphs), a reusable hierarchical terrain representation that organizes map-scale terrain into geometrically connected semantic regions (GSNodes), connectivity-preserving critical regions (Coarse Regions), and typed hyperedges for terrain,
agent, and weather context. Hyperedge intersections select affected regions and incident edges for state updates without rebuilding the hierarchy. Across real off-road maps spanning kilometer-scale areas, HOPHY achieves 100% planning success and less than 0.01% median cost deviation from the oracle (pixel A*), with substantially lower query and replanning latency than the evaluated pixel and abstraction baselines. Applied to a multi-
robot task-allocation (MRTA) problem, these gains reduce total computation by 79x over pixel A* and 7.2x over the fastest abstraction baseline, with mission makespan comparable to pixel A*. Finally, we demonstrate HOPHY on a physical Clearpath Jackal that successfully executes a 1.5-km, eight-task mission across mixed-surface outdoor terrain and a blockage-triggered replanned route.
Read the full abstract ▾
All authors Pranay Meshram, Charuvahan Adhivarahan, Prithvi Poddar, Ehsan Tarkesh Esfahani, Chen Wang, Souma Chowdhury, Karthik Dantu
Machine Learning · Cross-listed from quant-ph
TetrisCNN for interpretable detection of phases of matter from experimental quantum simulator data
Kacper Cybiński, Björn van Zwol, James Enouen et al. (+5 more)
Detecting phases of matter in general relies on identifying the correct order parameter - a task that remains notoriously difficult for unknown transitions and traditionally is guided by physical intuition and educated guess. Neural networks have recently offered an alternative route by locating phase transitions in known models without any a priori physical knowledge. Yet these approaches remain black boxes and only identify phases without elucidating their properties. Moreover, they often struggle when confronted with realistic, noisy experimental data, which constitute the ultimate testbed for automated methods in physics. Here, we bridge these perspectives by introducing TetrisCNN, a convolutional architecture with parallel branches of differently shaped filters, reminiscent of Tetris blocks, that learns sparse, interpretable latent representations directly in terms of
spin correlators. Applied to experimental snapshots of two-dimensional Ising and XY
quantum simulators measured in multiple bases, the network not only detects phase transitions and crossovers but also expresses its latent representation and decision boundaries as symbolic formulas built from experimentally measurable
spin correlators. This framework opens the way to integrating interpretable neural networks with
quantum simulators to uncover and understand new phases of matter.
Read the full abstract ▾
All authors Kacper Cybiński, Björn van Zwol, James Enouen, Guillaume Bornet, Thierry Lahaye, Antoine Browaeys, Antoine Georges, Anna Dawid
Robotics
Custom PX4 firmware for autonomous hybrid aerial-marine missions
Andrea Capuozzo, Fabio Ruggiero and Vincenzo Lippiello
Mapping and monitoring aquatic environments can benefit from hybrid aerial-amphibious drones able to combine flight and water-surface
navigation within the same mission. This paper presents a PX4 firmware extension for such platforms, introducing manual and autonomous marine
navigation modes integrated with the standard PX4 mission pipeline and QGroundControl interface. The proposed framework preserves existing flight functionalities and safety mechanisms while enabling unified planning and execution of hybrid aerial-marine missions with differentiated aerial and marine waypoints. Simulated case studies validate the implementation and demonstrate stable surface
navigation under calm and wavy conditions.
Read the full abstract ▾
Machine Learning · Cross-listed from math.OC
The First-Order Oracle Complexity of Lipschitz Convex Optimization in Nondual Settings
Brian Bullins, Cristóbal Guzmán, David Martínez-Rubio et al. (+1 more)
We study first-order black-box convex optimization over an $\ell_p$-ball for objectives Lipschitz in the $\ell_q$-norm, solving in the affirmative the nonsmooth version of the COLT open question (Guz15b) on whether the geometry of a smaller feasible set ($p < q$) can improve convergence rates in convex optimization, and matching prior lower bounds up to logarithmic factors. Our rates include $\widetilde{O}(1/T)$ for convex Euclidean-Lipschitz optimization over the $\ell_1$-ball, improving on the $O(1/\sqrt{T})$ classical rate under general assumptions. The key technical device is a new online learning game, where the comparator is evaluated using the maximum of affine losses observed so far. We bound the value of this game above and below in terms of a combinatorial online learning quantity: the sequential fat-shattering dimension, which we characterize for the $\ell_p / \ell_q$ case. Our results generally apply when the feasible set $\mathcal{X}$ and the set of possible subgradients $\mathcal{H}$ admit a type of minimax
theorem, advancing on a fundamental question by Sridharan (Sri12, Section 10.1.2, Q3). As a
geometric consequence of our analysis, of independent interest, we obtain estimates for the expected distance of a convex hull of samples to their mean in several
Banach geometries, a version of the celebrated Wendel's
theorem (Wen62), but quantitative and for bounded general distributions as opposed to centrally symmetric ones.
Read the full abstract ▾
All authors Brian Bullins, Cristóbal Guzmán, David Martínez-Rubio, Mathieu Molina
Computation and Language
HerHealthEval: Evaluating Multilingual and Register-Sensitive Understanding of Women's Health Communication
Hassan Saeed Hassan Albattra, Mazen Mohammed Bahgat, Rahatara Ferdousi et al. (+2 more)
Large
language models are increasingly used in healthcare communication, yet most evaluations emphasize response quality while assuming that the user's concern has been interpreted correctly. We introduce HerHealthEval, a controlled evaluation framework for
multilingual understanding of women's-health communication. For each clinical case, HerHealthEval provides matched versions in English, French, and Modern Standard Arabic using six communicative forms: canonical, clinical, layperson, indirect or hedged, emotionally concerned, and deliberately under-specified. The first five express the same underlying concern and retain the same clinical information, whereas the under-specified form intentionally omits relevant details to test whether the model recognizes that clarification is needed. We evaluate a
multilingual instruction model and QLoRA-adapted variants on concern classification, risk calibration, clarification behavior, parse compliance, and cross-form consistency. Results reveal that aggregate accuracy and consistency can conceal safety-relevant failures. A
multilingual adaptation model reaches 0.994 under-triage in French and Arabic under
language-asymmetric risk supervision. A controlled re-adaptation using source-derived,
language-invariant risk labels reduces under-triage to 0.572 and 0.558, respectively. These findings show that robust
multilingual healthcare evaluation requires explicit testing of register variation, uncertainty handling, and the provenance and invariance of adaptation labels.
Read the full abstract ▾
All authors Hassan Saeed Hassan Albattra, Mazen Mohammed Bahgat, Rahatara Ferdousi, Hana Essam Sayed Ahmed Amrya, Mariam Mousa
Robotics
Towards Scaling Marine Perception with Synthetic Data
Haoyu Ma, Onur Bagoren, Anja Sheppard et al. (+6 more)
Scalable machine learning in challenging
underwater environments is strongly limited by the lack of labeled real-world training data. This data is often expensive and laborious to gather, making large-scale real-world data challenging to gather and curate. However, simulated data can help close the gap, enabling many learning-based tasks for
underwater perception. In this work, we extend OceanSim, an IsaacSim-based
underwater perception simulator, with a Synthetic Data Generation (SDG) pipeline for training models to be used in
underwater scenarios. The proposed pipeline enables users to generate large, automatically labeled, photorealistic
datasets with configurable scene appearance, structure, and sensor settings. We evaluate the pipeline on a real-world sea urchin detection task and study how different forms of synthetic scene variation affect sim-to-real performance. Based on these experiments, we discuss findings on our results, main limitations of the current pipeline and identify future directions for improving
underwater rendering fidelity, scene diversity, and the evaluation of sim-to-real generalization. The open-source code can be found at https://github.com/umfieldrobotics/OceanSim.
Read the full abstract ▾
All authors Haoyu Ma, Onur Bagoren, Anja Sheppard, Elias Fandi, Ashrith Edukulla, Tanner Aslan, Natasha Sieh, Jingyu Song, Katherine A. Skinner
Computational Complexity · Cross-listed from math.CO
Schrijver-Delsarte rigidity in association schemes and undecidability of quantum graph homomorphism
Lorenzo Ciardo, Iris Hebbeker, Gideo Joubert et al. (+2 more)
We prove RE-completeness of the
quantum homomorphism problem parameterised by families of graphs derived from the classic metric association schemes. These include Kneser graphs, $q$-Kneser graphs, and the complements of Johnson, Grassmann, and Hamming graphs. Our proof develops a spectral method for establishing non-contextuality of
quantum polymorphisms. It combines an equality analysis of Roberson's bound on the projective packing number in terms of Schrijver's theta with a structural argument inspired by Erdős-Ko-Rado theory.
Read the full abstract ▾
All authors Lorenzo Ciardo, Iris Hebbeker, Gideo Joubert, Jana Kreiß, Antoine Mottet
Machine Learning
RISC-V and machine learning: a survey
Shriman Keshri, Apparna Singh, Chinmaya Kumar Palo et al. (+2 more)
The intersection of open-source processor architectures and machine learning is driving the demand for customizable, efficient, and accessible hardware. This survey examines the state of the RISC-V ISA in machine learning applications, analyzing current capabilities, challenges, and future directions based on recent research. The analysis covers academic and commercial implementations, software frameworks, and real-world applications. The RISC-V machine learning ecosystem is evaluated, from instruction set extensions and core implementations to compiler optimizations and deployment strategies. Key contributions include a unified taxonomy of RISC-V ML implementations, a comparative analysis of performance and design trade-offs, an evaluation of software toolchain maturity, and the identification of emerging trends in instruction set extensions and specialized accelerators. Findings reveal progress in energy efficiency, specialized instruction development, and framework integration, while highlighting challenges in standardization, verification complexity, and ecosystem fragmentation. The analysis proposes four research directions to address current limitations: specialized neural processing extensions, adaptive and modular processor architectures, security frameworks, and energy-efficient multi-domain architectures. These directions provide a roadmap for advancing RISC-V as a foundational platform for next-generation machine learning systems.
Read the full abstract ▾
All authors Shriman Keshri, Apparna Singh, Chinmaya Kumar Palo, Shreya Adya, Subhankar Mishra
Machine Learning
Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Sambit Mishra, Yingying Wang, Christine K. Johnson et al. (+1 more)
Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptions. Existing identifiability research primarily focuses on continuous variables under additive noise models, often neglecting mixed
datasets containing ordinal scales, counts, and continuous measurements. This paper investigates causal discovery in Directed Acyclic Graphs (DAGs) where nodes follow either an ordinal distribution (via an ordered logit model) or a regular one-parameter exponential family distribution. We prove that the edge direction between an ordinal and an exponential family node is distributionally identifiable for generic parameter values. Our findings generalize previous Ordinal-Poisson results to the broader exponential family. Computationally, we introduce a score-based exhaustive search and a masked continuous optimization framework using DAGMA for larger graphs. Numerical results validate the theory, recovering edge orientations within a Markov equivalence class that are unidentifiable under classical structural equation models.
Read the full abstract ▾
All authors Sambit Mishra, Yingying Wang, Christine K. Johnson, Urbashi Mitra