Computer Vision
EndoLive: Real-Time Style Transfer for Endoscopic Endonasal Skull Base Surgical Video
Griffin Hurt and Calvin Brinkman
Complex
surgical procedures around critical anatomy, such as the endoscopic endonasal skull base surgery, requires significant practice and training on the part of the surgeon before they are allowed to perform the operation on a live patient. This training in typically done in cadaveric specimens, due to them containing the same critical structures as a living human. However, cadavers are not a perfect 1-to-1 substitute for a living patient. The dead and preserved tissues of a cadaver are colored completely differently than a living human, and – without complex and expensive pumping systems – do not bleed in the same way. As a result, identifying the critical pieces of anatomy that make this procedure so complex can be quite different in a live case than in a surgeon's cadaveric practice. This paper presents EndoLive, a framework for real-time style transfer between cadaveric endoscopic
video and living human endoscopic
video. Our method combines the ConStructS GAN model for realistic style transfer for
surgical applications, with the HyPER-GAN model that can learn complex translations and perform them in real-time. We train EndoLive on unpaired cadaveric and live images taken from an endoscope, and test the trained model with cadaveric
video, on a variety of devices. Experimental results demonstrate that EndoLive can perform cadaveric-to-live translation at speeds well above the minimum necessary for real-time, while maintaining semantic consistency of critical anatomical structures. Our source code is available at https://github.com/griffhurt/endolive.
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Machine Learning
Do Your Own Research: Learning to Forecast by Learning to Search
Yusuf Afifi, Artur Kiulian, Anton Polishko et al. (+3 more)
Outcome-based reinforcement learning can train
language models to forecast real-world events, but prior
forecasting work either freezes research context before training or deploys agentic research only at test time, so the skill of gathering evidence is never shaped by the reward. We introduce an agentic
forecasting environment,
dataset, and harness built from 2,100+ resolved Polymarket questions; the
agent acquires its own context at rollout time (web search, page reading, and financial time series, all restricted by layered leak filtering to information published before each question's cutoff), and we train Qwen3.5-35B-A3B (3B active parameters) on it with single-epoch GRPO under a Brier-score reward. Training changes how the
agent interacts with information: calibration improves 30-40%, and search attempts fall from 3.8 to 2.25 per rollout as evidence discipline is learned. Evaluated in an identical harness against four frontier models, the trained policy also finishes ahead of every frontier model tested at evidence-based
forecasting, including Claude Opus 4.5 (soft-Brier 0.254 vs. 0.256, n=265), at about 5% of the inference cost, and its margin is widest on the hardest questions, the ones the crowd itself had not decided. We release the environment,
dataset, and per-rollout records as a reusable harness for temporal
forecasting agents.
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All authors Yusuf Afifi, Artur Kiulian, Anton Polishko, Mykola Khandoga, Hamudi Naanaa, Alina Krasnobrizha
Machine Learning
Sharp Non-Asymptotic Analysis of the Penalized Challenger in $\beta$-EB-TCI for Bernoulli Bandits
Nam Nguyen and Tuan Quang Dam
Top-two
algorithms are simple and effective for fixed-confidence best-arm identification, but their sharp non-asymptotic behavior is still not well understood. We study this problem for Bernoulli bandits through $\beta$-EB-TCI, the empirical-best top-two rule of Jourdan et al., whose challenger is chosen using a Bernoulli transportation cost with a logarithmic count penalty. We prove that, after the empirical leader has become the true best arm and its sampling fraction stays close to $\beta$, the stopping time is $T_{\beta}^{\star}(\mu)\log(1/\delta)$ up to lower-order concentration terms. We also show that, in this regime, every challenger is sampled linearly often. Thus, for the original
algorithm without forced exploration, the main remaining difficulty is to control when the empirical leader becomes permanently correct. These results imply a non-asymptotic high-probability bound for all Bernoulli instances with a unique best arm. If the
algorithm satisfies a finite-mean sufficient-exploration condition, the bound further yields the sharp expected sample complexity. In particular, this gives the sharp expectation result for the unguarded Bernoulli rule when all arm means are pairwise distinct, using the sufficient-exploration result of Jourdan et al. Finally, if we add a mild forced-exploration rule that contributes only $O(\sqrt{Kt})$ pulls up to time $t$, we obtain a self-contained expected sample-complexity
theorem for any number of arms under the unique-best-arm assumption. We also identify a limitation of proof strategies that try to handle equal suboptimal means through a single index-comparison argument.
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Distributed Computing
MoE-CORE: Coordinated Expert Offloading and Residency for Memory-Constrained MoE Inference
Ke Yang, Yongji Gao, Xushi Li et al. (+16 more)
Sparse expert activation reduces MoE models' computation, yet expert weights can exceed limited device
memory. Offloading makes inference feasible on a compact AI appliance but exposes host-to-device transfers to the inference path. We present MoE-CORE, a system that coordinates expert offloading and residency for
memory-constrained MoE inference. It stages complete expert layers in alternating buffers during prefill. During decode, it combines nonuniform layer-wise cache capacity, domain-informed initialization, routing-history-aware replacement, and cross-layer prefetching. The main configuration executes router-selected experts exactly; an optional score-based substitution path handles eligible low-score misses. The main comparison uses 1K- and 128-
token output caps for MoE-CORE and vLLM Prefetch, respectively. Across five workloads per model, MoE-CORE records a mean time per output
token (TPOT) of 38.0-44.8 ms versus 1268.9-1269.1 ms for the evaluated vLLM Prefetch configuration on DeepSeek-V4-Flash-W4A8; the corresponding values on GLM-5.2-W4A8C8 are 206.6-220.5 and 5941.5-5941.8 ms. Under an 84-GB NPU-
memory cap, the best measured DeepSeek GSM8K configuration achieves a TPOT of 21.5 ms with approximate expert substitution and multi-
token prediction (MTP) at depth 2. These results support coordinated expert residency and transfer scheduling under a device-
memory constraint. The code is here.
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All authors Ke Yang, Yongji Gao, Xushi Li, Kui Luo, Sicheng Zhang, Tianming Zhou, Keyi Liu, Shufang Lu, Aoxuan Chen, Jie Meng, Jingchun Gao, Dan Li, Xinkai You, Dan Li, Zhixiang Xia, Yan Shi, Yang Liu, Yanjia Zeng, Liangjun Feng
Cryptography and Security
A Hybrid Approach to Malware Detection: Integrating Few-Shot Model-Agnostic Meta-Learning with Autoencoders
Emmanuela Andam, Yasir Abbas Zaidi, Abdelali Hadir et al. (+2 more)
Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors. These attacks are typically launched through phishing emails, malicious downloads, or exploitation of software vulnerabilities to gain system access. Once inside, the malware encrypts files and demands a ransom, often in cryptocurrency, for the decryption key. Conventional detection methods often struggle with novel or scarce samples, leaving systems vulnerable. To address these challenges, this paper proposes a hybrid deep learning framework that combines an Autoencoder Feature Extractor (AFE) with a Model Agnostic Meta Learning (MAML) classifier for few shot malware detection. The AFE generates compact latent features that reduce noise and dimensionality, while the MAML classifier rapidly adapts to new threats using limited labeled data. Experiments conducted on the Ransomware
Dataset 2024 demonstrate the effectiveness of the framework in binary classification tasks. Across one to fifty shot settings, the proposed model consistently achieves high accuracy, F1 score, and Matthews Correlation Coefficient values, maintaining reliable classification even under extreme scarcity. These results highlight the model's robustness and effectiveness in adapting to limited data scenarios, demonstrating the potential of combining feature extraction with meta learning to enhance resilience against malware, particularly in sectors such as healthcare, manufacturing, and public infrastructure, where cyberattacks can cause significant operational and financial disruption.
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All authors Emmanuela Andam, Yasir Abbas Zaidi, Abdelali Hadir, Emmanuel Grant, Naima Kaabouch
Machine Learning
Latent JEPA: Abstract Future Prediction for Latent Reasoning in Chemistry
Xinjian Zhao, Yaoyao Xu, Xuemin Chen et al. (+2 more)
Large
language models offer a promising foundation for chemical
reasoning, bringing together chemical knowledge and multistep problem solving. Chemical intuition can provide an initial sense of plausible outcomes before the details of a solution are fully worked out. Inspired by how such expectations complement explicit analysis, we study how continuous latent thoughts can be trained to anticipate informative aspects of future solutions without verbalizing every intermediate step. We introduce Latent JEPA, a framework that combines autoregressive learning with joint-
embedding prediction of one or more future views. For chemical
reasoning, we develop textual and molecular prediction objectives that connect latent thoughts to both subsequent
reasoning and molecular outcomes. Experiments on ChemCoTBench show gains in molecular optimization and on several editing and reaction metrics. Representation analyses show that future prediction makes latent thoughts more informative about molecular outcomes and strengthens their correspondence with chemical structure. These findings support abstract future prediction as a learning principle for connecting continuous latent
reasoning with scientific outcomes.
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All authors Xinjian Zhao, Yaoyao Xu, Xuemin Chen, Xiaozhuang Song, Tianshu Yu
Computer Vision
Anti-Persona: Disrupting Unauthorized Identity Binding and Recognition in Personalized Vision–Language Models
Abhishek Basu, Fahad Shamshad and Karthik Nandakumar
Few-shot personalization enables large
vision–
language models (LVLMs) to learn user-specific visual concepts for applications such as personalized
retrieval and subject-aware querying. However, it also creates a
privacy risk: an adversary can bind a target identity from a few reference images and subsequently detect that identity in new images through natural-
language queries. We introduce Anti-Persona, an image-level defense against unauthorized identity binding and recognition in personalized LVLMs. Our key insight is that identity personalization relies on visual features shared across multiple reference images. We aggregate these features into an identity prototype and optimize visually subtle perturbations that disrupt prototype
alignment in the
vision-encoder space. Spatial smoothing and low-frequency preservation further promote visual fidelity and practical resilience to image compression. The resulting protection does not depend on a specific
prompt and supports both proactive anti-personalization and reactive image protection. Experiments on two representative personalized LVLMs demonstrate protection rates of up to $95.0\%$ while preserving visual fidelity. The method remains stable across
prompt variations and evaluated identity-query tasks, and improves black-box transfer under encoder mismatch.
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Robotics
TouchTherm: Building Multimodal Digital Twins of Objects for Tactile and Thermal Rendering
Yitao Zhang, Hong Ying, Haoran Guo et al. (+3 more)
Robotic simulation and virtual reality increasingly require object assets that capture not only visual geometry but also the physical cues underlying tactile and thermal interaction. Existing 3D
datasets and reconstruction methods primarily represent object-scale geometry and visual appearance, overlooking microscale surface structure for high-fidelity haptic rendering and transient temperature dynamics for temperature-aware interaction. We present TouchTherm, a framework for constructing simulation-ready visuo-tactile-thermal object assets from real-world objects. For visual and tactile reconstruction, we combine structured-light scanning with multiview normal maps obtained from photometric stereo. The normal maps are registered to the scanned geometry and transformed into tangent space to recover local micro-height fields for optical tactile rendering, while the coarse mesh handles collision detection. For thermal reconstruction, we capture synchronized multiview infrared
videos of natural cooling following controlled heating and reconstruct a physics-regularized dynamic thermal field. Experiments on 20 objects show that the reconstructed micro-height fields preserve dominant surface structures and recover higher-frequency details beyond the coarse geometry, while the thermal fields achieve held-out surface-temperature MAEs of 0.465 degrees C and 0.592 degrees C at 30 s and 45 s, respectively. The resulting tactile assets support synthetic-to-real object recognition from tactile observations, while a glove-based VR system demonstrates spatially and temporally varying thermal feedback. These results highlight the potential of TouchTherm for
multimodal sensory simulation and temperature-aware virtual interaction.
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All authors Yitao Zhang, Hong Ying, Haoran Guo, Xiaoying Zhou, Guanyu Chen, Chenxi Xiao
Computer Vision
Latent-Foresight: End-to-End Learning Predictable Representations for Latent World Models
Efstathios Karypidis, Spyros Gidaris and Nikos Komodakis
Predicting the future
evolution of a scene is a fundamental capability for world modeling. Recent work has shown that operating in the feature space of
Vision Foundation Models (VFMs) yields semantically rich representations that support diverse future scene understanding tasks. However, existing approaches rely on two-stage pipelines, where VFM features are first compressed using fixed dimensionality reduction (e.g., PCA) or independently trained autoencoders, and a separate predictor is trained on top of the resulting frozen latent space. This decoupling between representation learning and temporal prediction, as well as approaches that apply predictors directly on raw VFM features, provides no guarantee that the latent space is structured for predictable dynamics. In this work, we propose Latent-Foresight, an end-to-end framework that jointly learns a latent tokenizer and a flow-based generative dynamics model, explicitly shaping the representation to support temporal predictability. To enable stable joint optimization, we introduce several key design choices that prevent latent collapse and align reconstruction with generative objectives. Extensive experiments show that our approach learns more temporally coherent latent representations and consistently outperforms two-stage baselines across multiple future scene understanding tasks and prediction horizons, while eliminating separate training stages, including during high-resolution adaptation. We provide the implementation code and model weights at https://github.com/Sta8is/Latent-Foresight
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Information Theory · Cross-listed from quant-ph
Universality Sacrifices Reliability in Classical-Quantum Channel Coding
Kaito Watanabe, Masahito Hayashi, Takaya Matsuura et al. (+1 more)
Universal channel coding enables communication without a complete description of the channel. For classical channels, universal codes can attain both capacity and the optimal high-rate reliability. We show that this compatibility fails for classical-
quantum channels in general; that is, the optimal reliability in the channel-aware scenario is not always achievable with universal coding due to the ignorance of the
unitary rotation of the output system. We exhibit a family of classical-
quantum channels for which one cannot achieve the channel-aware optimal reliability by a fixed coding scheme. We further derive a converse bound on the reliability for
unitary-invariant decoders, a natural assumption for the universal coding scheme, that can be strictly smaller than the optimal channel-aware error exponent. Conversely, we construct a channel-independent encoder-decoder pair and establish a universally achievable bound on the reliability that matches this converse bound in the high-rate regime, thereby characterizing the optimal universal reliability. Specifically, the channel-aware and universal exponents are governed by the Petz and sandwiched Rényi divergences, respectively. These divergences coincide for commuting outputs but differ for noncommuting ones, explaining why universality preserves optimal reliability classically but can reduce it quantumly. Our results showcase the fundamental reliability cost of performing the classical-
quantum channel coding task universally.
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All authors Kaito Watanabe, Masahito Hayashi, Takaya Matsuura, Hao-Chung Cheng
Computer Vision
Fewer Tokens, Better Action: GPT-6 Astra Robot Agents with 14% Higher Success Rate but 65% Fewer Tokens
Ruiyang Si, Jianxin Bi, Shunyu Yang et al. (+12 more)
Vision-
language model (VLM)
agents can control
robots through visual feedback and action primitives, but repeated LLM calls and redundant observations incur substantial
token overhead. We introduce PyRUA-Lean, an interactive code-execution framework in which the
agent composes
robot primitives, including
vision-
language-action (VLA) policies, into Python
cells that check and retry locally and return only the images and state they request. Across 700 simulated task instances from LIBERO-PRO, RoboTwin 2.0, and RoboCasa365, it raises success from 63.1% to 71.7% over tool calling with the same GPT-6 Astra planner and primitives, using 49% fewer LLM calls and 65% fewer input
tokens on jointly solved instances. On RoboDojo, without any VLA policy or privileged information, it reaches 51.6% average success rate (Score 58.2), 19.4 points above the top entry of the public leaderboard. Based on the PyRUA-Lean framework, we train PyRUA-Lean Coder v0.1-4B, an open 4B model, with GRPO on 45 LIBERO-PRO instances; it improves from 18.8% to 44.1% on unseen task instances, surpassing the Qwen3.5-27B base model.
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All authors Ruiyang Si, Jianxin Bi, Shunyu Yang, Rui Ni, Wenbo Huang, Qiang Wang, Shulong Jiang, Yintianrun Zhang, Feixiang Ruan, Kewei Zhang, Duomin Wang, Xiuyu Li, Haiwen Feng, Zhen Dong, Daquan Zhou
Computation and Language
A rubric landscape for evaluating clinical reasoning in large language models: what exists, what is missing, and what needs to be combined
Zhangshu Joshua Jiang, Zina Ibrahim and James T. Teo
Exam-style accuracy does not establish whether large
language models (LLMs) reason well over clinical records. We define clinical
reasoning as integrating and updating evidence across time and sources to form, revise and justify a patient's problem representation and a defensible plan. This structured narrative review maps three literatures: medical education assessment instruments, clinical LLM
benchmarks published from 2023 onwards, and general-domain methods for evaluating long-form generation. We examine six dimensions: problem representation, temporal synthesis, differential and management
reasoning, counterfactual
reasoning, calibrated uncertainty, and
reasoning faithfulness. Preprints are included and flagged. No single instrument covers all six dimensions. Problem representation and differential or management
reasoning are reasonably covered, although reliability varies by instrument and setting. TIMER-Eval targets temporal synthesis, and ER-Reason assesses sequential diagnostic belief updating. Dedicated uncertainty and counterfactual evaluations are emerging, but their applicability to longitudinal free-text
reasoning remains limited. Factual completeness is well theorised in general-domain evaluation, with early clinical evidence of important omissions. Faithfulness remains the weakest dimension, with one identified clinical causal-ablation study on multiple-choice questions. Existing tools should be combined through binary rubric items, separate completeness and correctness scores, case-specific importance weighting with non-compensable safety caps, temporal order-consistency checks, and chance-corrected reliability reporting. Further design work is needed for calibrated uncertainty, counterfactual
reasoning and faithfulness over longitudinal free-text records. This review provides a design rationale, not a validated instrument.
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Machine Learning
Graph Representation via Elements of Discrete Morse and Cobordism Theories
Jennifer Rozenblit, Chenguang Yang, Yuxin Liu et al. (+2 more)
Topology is, by its nature and design, suited to structure that is nonlinear, multiscale, and nonstationary - however, within machine learning, its use remains largely confined to topological data analysis. We advocate that tools from low-dimensional
topology which have remained almost exclusively contained within the domain of pure mathematics (such as Morse theory) offer a strong, complementary, and yet virtually unexplored perspective on the hidden structure of data-generating processes and learning tasks built upon them. Here we introduce concepts from cobordism theory and harness tools from discrete Morse theory to improve the performance of graph
diffusion models through our pipeline MG-Diff. Further, we derive theoretical guarantees and sufficient conditions so that under a positive decision-gap, the Morse-theoretic tools and their application for induced
diffusion guidance are stable under small perturbations. Finally, we illustrate the utility of discrete Morse theory in application to graph
diffusion models for spatio-temporal graph
forecasting and graph regeneration, and argue that these applications are only a small window into the part of what low-dimensional
topology can offer to the field of machine learning.
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All authors Jennifer Rozenblit, Chenguang Yang, Yuxin Liu, Yuzhou Chen, Yulia Gel
Artificial Intelligence
Rethinking Knowledge Retrieval for Generation: A Survey on RAG Architectures and Applications
Meghana Sunil, Shravya V, Shravan Venkatraman et al. (+1 more)
Large
Language Models (LLMs) have demonstrated remarkable fluency and versatility across natural
language tasks but remain fundamentally limited by their static knowledge and susceptibility to
hallucinations, especially in domains requiring up to date or attribute grounded information.
Retrieval Augmented Generation (RAG) addresses these challenges by integrating external
retrieval mechanisms with generative models, enabling dynamic, context aware generation grounded in verifiable data sources. This survey presents a comprehensive examination of RAG as a modular and evolving paradigm that enhances factual reliability, adaptability, and task
alignment in LLM based systems. We formalize the RAG framework through its three foundational components
retrieval, generation, and augmentation and survey state of the art methods spanning dense and sparse retrievers, fusion strategies,
embedding optimizations, and reinforcement learning based
retrieval policies. Anchored around four emerging axes efficiency, security, user centric interactivity, and complex
reasoning we categorize recent innovations and highlight their implications for scalability, robustness, and personalization. The paper also reviews advances in evaluation protocols, domain specific applications, and architectural variants such as Naïve RAG, Advanced RAG, and Modular RAG. Finally, we identify persistent challenges and outline future directions aimed at advancing the integration of
retrieval with LLMs for more grounded, interpretable, and controllable generation.
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All authors Meghana Sunil, Shravya V, Shravan Venkatraman, Joe Dhanith PR
Machine Learning
Learning to Predict Distributions over Weight Updates for Test-Time Adaptation
Azal Ahmad Khan, Keshav Ramji, Tahira Naseem et al. (+2 more)
Hypernetworks have recently shown success in dynamically adapting the parameters of Large
Language Models (LLMs) at runtime based on signals such as task descriptions or additional demostrations. Here we ask: how much adaptation signal can be obtained using only the input query to an LLM?. To answer this, we study query-conditioned Hypernetworks for LoRA estimation. Further, we introduce distributional Hypernetworks, able to produce not only point estimates of parameter adaptors, but also a distribution over possible LoRAs. For this we propose a simple end-to-end loss using a differentiable Monte Carlo approximation and explore multiple distribution parametrizations including regression and convex combination variants. Results show that even using the mean of the learned distribution can outperform deterministic hypernetworks. Crucially, the learned distribution enables a different form of test-time scaling: instead of spending additional compute only by sampling more
token sequences from a fixed model, we sample weight updates, yielding multiple adapted models for the same query. Performance improves as more weight samples are considered and remains stronger than corresponding
token-sampling adaptation baselines. Finally, we find that generated updates can transfer across queries, suggesting that the hypernetwork learns reusable structure in how the model should adapt. Together, these results show that query-conditioned distributions over weight updates can support both adaptation and test-time scaling.
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All authors Azal Ahmad Khan, Keshav Ramji, Tahira Naseem, Ali Anwar, Ramón Fernandez Astudillo
Machine Learning · Cross-listed from stat.ML
Error-Corrected Inference-Time Scaling for Imperfect Diffusion Models
Zuokai Wen, Louis Grenioux, Weinan E et al. (+1 more)
Inference-time scaling adapts pretrained
diffusion models to new sampling tasks without additional training. Existing methods rely primarily on Monte Carlo sampling with more particles, yet are premised on the pretrained model being exact. In practice, data and training limitations make the model imperfect, and these methods inherit its error. More particles reduce Monte Carlo error but cannot remove the mismatch between the endpoint and the desired target or the error in tracking the prescribed probability path. We introduce the Energy-based Feynman-Kac Corrector (EBFKC), a framework for energy-based
diffusion models that corrects these errors on the fly given a reference energy. We first derive Feynman-Kac dynamics that track a prescribed path exactly in the continuous-time population limit even when the model is imperfect, and approximate these dynamics using sequential Monte Carlo with variance-controlling guidance. To remove the endpoint mismatch, we use the pretrained energy as a surrogate along the
diffusion path and progressively incorporate the discrepancy between the learned and target terminal energies. Experiments on Gaussian mixture models, particle systems, alanine dipeptide, and alanine tetrapeptide show that our method closely matches target distributions and molecular free-energy profiles under annealing and reward tilting, whereas standard inference-time scaling baselines retain substantial sampling errors.
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All authors Zuokai Wen, Louis Grenioux, Weinan E, Jiequn Han
Computer Vision
CLoSeR: Closing the Loop for Long-Context Streaming Reconstruction
Moyang Li, Zihan Zhu, Wei Zhang et al. (+2 more)
Feedforward foundation models have recently shown remarkable 3D reconstruction capabilities. However, existing models exhibit large tracking drift in long-context streaming reconstruction due to error accumulation. In this paper, we revisit loop closure with streaming reconstruction foundation models to enable accurate, drift-free, kilometer-scale reconstruction. Specifically, our method detects loop candidates through global descriptor
retrieval, and constructs loop-conditioned windows to estimate the relative poses between looped frames. Given the observation that our adopted streaming reconstruction backbone produces a globally consistent scale, we optimize all frame poses on the SE(3)
manifold with sequential and loop closure constraints, avoiding the pose graph optimization on the Sim(3) or higher-dimensional SL(4)
manifolds employed in prior works. Extensive experiments show that our method reduces drift and produces consistent geometry on kilometer-scale sequences, significantly outperforming the state of the art. Code is available at https://github.com/MoyangLi00/CLoSeR.git.
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All authors Moyang Li, Zihan Zhu, Wei Zhang, Marc Pollefeys, Daniel Barath
Sound
LAST: Looped Audio Spectrogram Transformer
Haider Al-Tahan, Sean O'Brien, Anastasia Razdaibiedina et al. (+1 more)
Increasing depth of
transformer models improves recognition, but it comes at a substantial cost. Each additional layer requires more parameters, which makes the process computationally inefficient. We ask whether additional processing can focus on integrating features already computed. Looped Audio Spectrogram
Transformer (LAST) first processes all
tokens, then reuses the same blocks to refine only the class
token over fixed audio features, thereby making later passes inexpensive. On AudioSet, ten-pass LAST achieves 0.345 mean average precision, exceeding a twelve-layer sequential
transformer by 2.1% relative with 49.4% fewer parameters, 42% fewer multiply-accumulate operations, and 9.8% higher measured throughput. Across separately trained models, increasing the pass count from two to ten improves accuracy while adding only 1.2% computation. Further evaluations show improved robustness to temporal masking and various other auditory augmentations, with better generalization on classification tasks with music, environmental, and event sounds.
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All authors Haider Al-Tahan, Sean O'Brien, Anastasia Razdaibiedina, N. Apurva Ratan Murty
Cryptography and Security · Cross-listed from math.NT
Supersingularity and Superspeciality Verification of Abelian Surfaces
Maria Corte-Real Santos, Gioella Lorenzon and Krijn Reijnders
Supersingular abelian surfaces are essential in isogeny-based cryptography. Despite this, we have no efficient
algorithm to verify if a given abelian surface is supersingular. In this work, we initiate this research topic by giving an efficient Monte Carlo
algorithm to verify if an abelian surface over $\mathbb{F}_p$ is supersingular in $O(\log p)$ with negligible failure probability, and an efficient conclusive
algorithm if the order is smooth. We derive this
algorithm by a careful analysis on the structure of supersingular Jacobians over $\mathbb{F}_p$. Furthermore, we derive efficient
algorithms to verify if an abelian variety of any dimension is minimal or maximal, and to verify if a Jacobian of any dimension is superspecial.
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Systems and Control
Interactive Power Flow in the Browser
Samuel Talkington, Frederik Geth, Qian Zhang et al. (+2 more)
This paper introduces tellegen, an open source framework for interactive power flow (PF) and optimal power flow (OPF) studies that run in the browser. This provides intuitive and democratized access to power system analysis tools compiled to WebAssembly. A user can drag and drop a case file, click and drag to change a nodal demand or line rating, preview the impacts via sensitivity analysis, and obtain an exact re-solve on release. User case files and results stay entirely on the device: tellegen transmits zero Critical Energy/Electric Infrastructure Information (CEII). The framework comprises a core numerical engine for PF and OPF, reusable browser components, saved studies, and structured WebMCP tools for agentic interaction. We evaluate the transmission OPF solver by comparing objectives with PGLib baselines; the distribution PF solver by comparing voltages and currents with OpenDSS; and the WebAssembly execution times by comparing with PowerModels.jl. On realistic synthetic grids, WebAssembly OPF solves take only 25-43% longer than native binary solves. The implementation shows how an engineer can distribute an executable numerical study as a URL, reducing installation and hosting requirements while keeping case data local.
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All authors Samuel Talkington, Frederik Geth, Qian Zhang, Le Xie, Skyler Liu
Computation and Language
Cross-Lingual Alignment for Decoder-Only Models using MoE Routers
Lucas Bandarkar, Clark Peng, Ahmed Haj Ahmed et al. (+2 more)
Cross-lingual contrastive learning has been a core component of
multilingual encoder training, but the ability to explicitly align representations is not possible in decoder-only LLMs because of varying
multilingual tokenization. However, a growing amount of research suggests that even in LLMs, higher cross-lingual representational
alignment leads to improved cross-lingual transfer. In this paper, we propose a novel approach to reimagine cross-lingual contrastive learning given the architectural constraints of modern LLMs. Rather than applying an auxiliary
alignment loss on hidden states, we propose using the outputs of the mixture-of-experts (MoE) routers as the target for
alignment. Router outputs lend themselves better to pooling over many
tokens, enabling more reliable cross-lingual comparisons at the sequence-level. Controlled continual pre-training experiments on four open-source MoEs show that incorporating this routing loss also aligns the underlying hidden representations across
languages. Most importantly, this loss improves
multilingual performance on our diverse evaluation suite, demonstrating the potential of cross-lingual MoE router
alignment.
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All authors Lucas Bandarkar, Clark Peng, Ahmed Haj Ahmed, Aditi Khandelwal, Nanyun Peng
Hardware Architecture
Timing-Driven Logic Remapping with Local Physical Context
Zijian Jiang, Hongyang Pan, Cunqing Lan et al. (+1 more)
The timing behavior of a mapped
circuit depends on both its logic implementation and the physical environment in which that implementation is realized. Revisiting mapping decisions after placement therefore requires a search procedure that accounts for surrounding timing constraints, fanout loads, and interconnect effects. We study local remapping in this setting and develop a framework that couples discrete mapping search with physical implementation feedback. Timing-critical regions are isolated through bounded windows whose interfaces retain the context of the surrounding
circuit. Within each window, a mixed-integer formulation jointly selects logic cuts, signal polarities, and library
cells under a delay model informed by estimated locations and interconnect parasitics. A continuous relaxation filters the search space before discrete optimization produces alternative implementations with similar modeled timing and different structural choices. These implementations are reconstructed and assessed through legalization, routing-based parasitic estimation, and timing analysis. Physically validated improvements are incorporated into the design, and the updated context guides subsequent searches. The framework provides a systematic way to revisit local logic implementations while accounting for their interaction with an existing placement.
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All authors Zijian Jiang, Hongyang Pan, Cunqing Lan, Keren Zhu
Computer Vision
MoLE: Mixture of Latent Experts for Complementary Visual Reasoning
Yingcheng Liu, Tianyi Jiang, Yujuan Ding et al. (+5 more)
Latent visual
reasoning equips
vision–
language models with continuous intermediate states that can process visual evidence without explicit textual
reasoning traces or repeated image operations. However, existing methods often allow multiple latent
tokens to access the same visual evidence through shared value projections, providing no mechanism for them to extract complementary visual information; simply increasing the latent budget can therefore yield redundant latent representations. We argue that effective latent
reasoning should encourage different latent
tokens to extract complementary visual information, and thereby act as specialized visual experts. Based on this insight, we propose MoLE, a Mixture of Latent Experts framework that controls both what visual evidence each latent visual expert observes and how it transforms that evidence. MoLE isolates latent visual experts during evidence extraction and uses dedicated latent summary experts to aggregate the complementary representations of latent visual experts. A two-stage training pipeline first forces visual evidence through this latent pathway and then restores direct visual access, requiring neither predefined expert roles nor intermediate visual targets. Across five visual
reasoning benchmarks, MoLE achieves an average score of 78.6, outperforming data-matched supervised fine-tuning by 4.9 and the strongest evaluated latent visual
reasoning baseline at the same latent budget by 3.6. Representation analyses show lower latent-state similarity and more diverse visual
attention, while masking the latent pathway reduces average performance by 9.2. These results demonstrate that specializing latent computation is more effective than merely increasing the number of latent
tokens.
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All authors Yingcheng Liu, Tianyi Jiang, Yujuan Ding, jiangbo Ai, Xun Jiang, Guoqing Wang, Wei Ye, Yi Bin
Computer Vision
DecomVoxel: Harnessing 3D-Native Priors with Guided In-situ Denoising Optimization for Decompositional Scene Reconstruction
Junfeng Ni, Zirui Zhou, Yixin Chen et al. (+5 more)
Decompositional scene reconstruction aims to reconstruct high-quality objects and background, yet existing methods still struggle with the level of quality under heavy occlusions. While generative priors offer a potential solution, 2D image-based priors often suffer from multi-view inconsistency due to a lack of 3D awareness. Conversely, 3D-native priors provide stronger structural inductive biases but frequently lead to spatial drift and misalignment within complex scenes. To address these issues, we propose DecomVoxel, formulating object completion as a guided in-situ denoising optimization that bridges 3D-native priors with neural scene reconstruction. Our framework introduces a reformulated epsilon-based
distillation loss to ensure stable latent refinement, alongside adaptive spatial guidance that utilizes occupied and vacant anchors with temporal annealing to suppress generative
hallucinations and mitigate spatial drift. Experiments on Replica and ScanNet++ show that DecomVoxel significantly outperforms state-of-the-art methods while faithfully preserving the original spatial layout, structural fidelity, and style-consistent texture. Our method pushes the boundary of decompositional reconstruction by delivering high-quality textured meshes with clean
topology, geometry, and appearance, providing a robust solution for the decompositional reconstruction of complex real-world scenes. Code is available at https://github.com/DecomVoxel/DecomVoxel.
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All authors Junfeng Ni, Zirui Zhou, Yixin Chen, Yu Liu, Nan Jiang, Zhifei Yang, Song-Chun Zhu, Siyuan Huang
Emerging Technologies
Standard Quadratic Formulations of Many NP Problems: A Simplex-Based Compilation Framework for Combinatorial Optimization
Mohammad-Ali Miri, Babak Emami and PoJen Wang
The standard quadratic program (StQP) minimizes a quadratic form over nonnegative variables that sum to one. We compose classical graph reductions with regularized Motzkin–Straus clique formulations to express discrete optimization problems in this continuous domain. The graph
matrix has diagonal entries $\tau$, zeros on edges, and ones on nonedges. For $0<\tau<1$, its minimum is $\tau/\omega(G)$, where $\omega(G)$ is the clique number. Its strict local minimizers are precisely the uniform distributions on maximal cliques, and its global minimizers encode maximum cliques. At $\tau=1/2$, integer scaling gives coefficients in $\{0,1,2\}$ and minimum $1/\omega(G)$, yielding an NP-complete StQP threshold problem with a restricted coefficient alphabet. We give explicit formulations for satisfiability, coloring,
Hamiltonian cycles, independent set, vertex cover, set packing, three-dimensional matching, and graph isomorphism. A regularized weighted clique formulation combined with local-state compatibility graphs gives an exact compiler for finite-domain factor models specified by complete local tables, including QUBO, with at most four simplex coordinates per binary pair factor. The catalog covers Karp's 21 problems: twelve use direct graph formulations, and nine use factor-state formulations, including six obtained through binary-linear feasibility. For each route we record dimensions, coefficient structure, and recovery rules. We analyze interaction count, coefficient range, objective separation, perturbation tolerance, support recovery, and decoding overhead. The separation bounds quantify the effects of clique size, factor weights, and offsets. In the complete factor-state construction, every assignment, including each suboptimal assignment, is a strict local minimum.
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Robotics
Robot Learning on Discrete Surfaces: Theory and Applications
Matteo Dalle Vedove, Fares J. Abu-Dakka, Luigi Palopoli et al. (+2 more)
All the objects composing our world are enclosed within surfaces. Yet, most
robot learning and motion generation frameworks treat surfaces as constraints ignoring their intrinsic geometry. This gap is acute for polyhedral meshes–the standard output of CAD and 3D reconstruction–whose discrete
geometric structure remains unexploited. In this paper, we propose a unified discrete Riemannian framework that enables
robot learning directly on polyhedral surface meshes. Using discrete differential geometry, we define logarithmic and exponential maps, parallel transport, and ambient-space projections that remain well-defined across faces, edges, and vertices. We instantiate the framework in three learning paradigms: (i) Dynamic Movement Primitives (DMPs), an improved exponential-map computation and a fixed-tangent-cone forcing-term encoding with parallel transport yield better cross-surface generalisation and stability over prior mesh-based approaches. (ii) Gaussian Process (GP), a geodesic-based kernel with practical admissibility control, enables regression at arbitrary mesh locations without smoothness assumptions. (iii) Riemannian Flow Matching (RFM), mesh-native operators improve generative quality over spectral baselines while reducing training time. The framework is validated in simulation against state-of-the-art methods and demonstrated on two real-
robot scenarios: generalising user-drawn trajectories across different surfaces and planning polishing motions on RGB-D-reconstructed surfaces.
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All authors Matteo Dalle Vedove, Fares J. Abu-Dakka, Luigi Palopoli, Daniele Fontanelli, Matteo Saveriano
Machine Learning
Same Reward, Different Skills: When Multimodal RL Learns to Look
Haocun Ye, Xinlong Jiang, Qile Chen et al. (+6 more)
Reinforcement learning with verifiable rewards (RLVR) improves
vision-
language benchmark scores even without visual information during training. With images at test, blind-trained models recover roughly half of the real-image gain at 3B and nearly four fifths at 7B. Prolonged real-image training can erode grounding while
benchmark gains persist. Both findings expose the same gap: an image in the
prompt is not an image in the learning signal. Our design rule, visual resolvability, asks that visual evidence be necessary for a correct answer and that the task remain learnable. We test it on counterfactual coordinate scenes in which the question stays fixed and the target is never named, so a correct answer requires finding the target in the image. With standard GRPO and correctness-and-format rewards, a 7B model raises its accuracy at finding the target (discovery) from 0.425 to 0.875 on held-out scenes denser than any it trained on, and it improves on question types it never trained on. Two controls locate the source of the gain. Replacing test images with gray canvases drops discovery to zero; training on gray canvases instead, at matched step 30 and in each of four seeds, yields essentially none of the gain even when the model is then tested with real images. The learned skill carries over to grounding tasks built independently of the training corpus. A caption that answers the training question, added to the same images, reward and budget, cuts the gain by nearly two thirds. Changing what reward requires changes what RL learns.
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All authors Haocun Ye, Xinlong Jiang, Qile Chen, Bingyu Wang, Teng Zhang, Shubai Chen, Tingyu Wu, Zhenkun Zheng, Yiqiang Chen
Cryptography and Security
Detection and Resolution of Periodic Artifacts in OpenDP's Discrete Laplace Sampler
Cesare Gerolimetto Fabrello, Valeria Rossi, Alberto Trombetta et al. (+1 more)
Differential
privacy implementations rely on precise sampling from noise distributions to provide formal
privacy guarantees. We report the discovery of systematic artifacts in OpenDP's discrete Laplace sampler that manifest as periodic distortions in the output distribution. Through systematic testing, we trace these artifacts to a faulty implementation in the rational arithmetic library used by the bernoulli_exp1 function, a low-level primitive that implements sampling from Bernoulli(e^(-x)) distributions. We present a diagnostic methodology that isolates the faulty component in the nested sampling hierarchy and propose an alternative implementation based on exact rational arithmetic that eliminates the artifacts. Statistical validation with 10^6 samples confirms that the corrected sampler produces outputs indistinguishable from the theoretical distribution at the tested precision level.
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All authors Cesare Gerolimetto Fabrello, Valeria Rossi, Alberto Trombetta, Massimo Caccia
Robotics
Towards Physical Underwater Robotic Assistance for Scuba Diver Movement in Confined Spaces
Demetrious T. Kutzke and Junaed Sattar
Scuba divers are taught to control their depth to avoid rapid ascents and descents, which could result in serious injuries such as gas embolisms and barotrauma. However, many
underwater tasks necessitate lateral control, maintaining distance between subsea structures such as coral reefs, submerged drilling instrumentation, or unexploded ordnance. In this work, we discuss a first-of-its-kind wearable robotic solution providing thruster-actuated directional guidance to a diver, as distinct from prior propulsive-assistance exoskeletons. We introduce “Robotic Assisted Diver Movement in Confined Spaces” (RADMCS), a wearable
robot that assists divers in maintaining a fixed distance from subsea structures by leveraging perception techniques in
monocular depth estimation and force-feedback from submersible thrusters to provide haptic feedback. Its small and compact form factor creates a foundational platform that could be expanded to include more sophisticated control and
navigation behaviors. We present results from Institutional Review Board (IRB) in-water studies with eight human scuba diver participants on threshold sensitivity tests in both a closed-water swimming facility and ocean environments; distance-maintaining experiments in a closed-water facility; and form, fit, and function testing in the ocean. We demonstrate that relatively low thrust values (10 percent of maximum) allow robotic direction of a human's movement using the physical sensation of the
robot's guidance.
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Computer Vision
MapLightning: Online Vectorized HD Map Construction with 1D Map Tokens
Shen Zheng, Anurag Ghosh, Mani Ramanagopal et al. (+1 more)
Online vectorized HD map construction is essential for scaling safe autonomous driving and requires accurate, real-time inference. Prior methods typically rely on dense bird's-eye-view (BEV) grids as the intermediate representation. We propose MapLightning, which replaces the dense BEV grid with a compact set of 1D learnable map
tokens. To construct map
tokens from image features, we choose self-
attention over vanilla cross-
attention because it enables joint interactions and contextual aggregation among image and map
tokens. Our
transformer-based mapper concatenates map and image
tokens, applies full self-
attention, discards the image
tokens, and retains the updated map
tokens for decoding. This design offers three advantages. First, our representation is efficient, using fewer
tokens, consuming less
memory, and running faster. Second, the lightweight design allows the map decoder to use full rather than deformable cross-
attention for better global context. Third, unlike BEV-based methods, our network does not use camera projection parameters, making it robust to camera-extrinsic perturbations. MapLightning uses up to 16.7$\times$ fewer intermediate
tokens than dense BEV-based methods and achieves state-of-the-art accuracy and efficiency on nuScenes and Argoverse~2. Its lightweight variant surpasses MapTRv2 by +10.1 mAP on nuScenes and +16.2 mAP on Argoverse~2, while delivering 1.73$\times$ faster inference (40+ FPS) with 53% less
memory. We further show improvements on uncertainty-aware map construction and downstream trajectory prediction. Code and models will be released.
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All authors Shen Zheng, Anurag Ghosh, Mani Ramanagopal, Srinivasa Narasimhan