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版本更新 · v2arXiv:2604.19344

Quadruped Agile Locomotion: Sparsely Gated Mixture of Experts with Visual Input

Michael Ziegltrum, Jianhao Jiao, Tianhu Peng, Chengxu Zhou, Dimitrios Kanoulas

原文摘要

Agile locomotion is important for quadruped robots in the real world. Recent approaches have demonstrated impressive capabilities including dynamic climbing and jumping but typically rely on sequential multilayer perceptron (MLP) architectures with densely activated layers. In contrast, sparsely gated mixture-of-experts (MoE) architectures have become popular in the large language model (LLM) domain as a tool to improving scalability and performance by activating only a subset of parameters at inference. In this work, we investigate the application of such architectures to vision-based robotic locomotion. We compare policies based on standard MLPs and MoE architectures under a controlled setting where the number of active parameters at inference time is matched. Experimental results show an 8\% increase in reward during training comparing the MoE to a standard MLP and even show benefits comparing to MLPs with equivalent total parameters. Experimental results on a real Unitree Go2 quadruped robot demonstrate performance in the real world. These results highlight that sparsely gated MoE architectures may pose advantages in tasks where network capacity is important and that borrowing techniques from the fast-moving LLM space and applying them to locomotion is a promising direction.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13885

Are Causal Neuron Circuits Unique and Transferable?

Ömer Tekin Yavuz

原文摘要

Interpretability demonstrations often show that intervening on a group of neurons changes a model's answer. A recent example removes an 18-neuron group in Qwen3-32B and the model answers "Simba" instead of "Scar" to a two-hop Lion King riddle. Such a demonstration establishes that the group matters, but not how much of it is necessary, whether other groups could do the same, or whether the effect carries over to other inputs. We ask: when intervening on a group of neurons changes a model's answer, how much of that group is actually necessary? For every selected group we measure all 4,096 subsets of 12 MLP neurons. On a two-alternative capital task (Qwen3-0.6B), zero-ablating 12 selected neurons lowers the answer margin by 27% while random neurons barely change it, yet replacing the same neurons with their values from other questions also barely changes it: their activations are nearly constant across questions, and we find no evidence that they carry question-specific content. On two-hop relation chains, patching 12 neurons with activations from the first-hop question shifts the answer to the bridge entity (from Scar to Simba): the greedy answer starts with the bridge in 9/11 chains for Qwen3-1.7B, 9/11 for Llama-3.2-1B and 8/10 for Gemma-3-1B; random neurons never do. The full group is rarely necessary: a few neurons often suffice, and in many chains disjoint subsets each flip the answer on their own, in an exploratory analysis most often when the model is less confident. Group effects transfer to rephrasings of the same chain but are much weaker on other tested chains. We study neuron-level causal interventions and alternative subsets; we do not recover the full connectivity of circuits.

cs.LG · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13884

Region-Wise Logit Fusion and Utility-Gated Refinement for Brain Metastasis Segmentation

Zhenye Lou, Qing Xu, Wenting Duan, Zhen Chen

原文摘要

BraTS-METS 2026 Task 1 evaluates segmentation of brain metastases and postoperative resection cavities on heterogeneous multiparametric MRI, requiring detection of small enhancing foci while preserving the geometry and hierarchy of enhancing tumor (ET), tumor core (TC), whole tumor (WT), and resection cavity (RC). We present COMPASS-METS (Component-Aware Multi-Trainer Probability Assembly with Structured Segmentation), which combines three five-fold nnU-Net configurations into aligned regional probability ensembles. A structured-probability anchor applies region-specific component decisions, TC boundary completion, RC filtering, and hierarchy-preserving conversion. Omitted ET candidates are recovered additively using learned component confidence and deterministic multi-trainer support for ET and its TC/WT parents; a final utility gate admits only disconnected candidates with sufficient lesion-existence and geometry-safety probabilities. On the reused 179-case challenge development cohort, COMPASS-METS (Team: MicroBT) achieved Small F1 0.354, lesion-wise DSC 0.712, NSD 0.714, All F1 0.779, and HD95 56.6 mm. Relative to ResEncXL, it improved DSC, NSD, and All F1 and reduced HD95, while Small F1 changed from 0.367 to 0.354. Stagewise analysis attributes the principal geometric gains to structured probability construction and shows that guarded ET refinement recovers most of the associated small-lesion detection loss. These 179-case results remain selection-aware development estimates. The source code is publicly available at https://github.com/leonlou921/COMPASS-METS.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13883

The BRIDGE dataset: paired brightfield whole-slide images of prostate core-needle biopsies in paraffin, after deparaffinization and H&E staining

Julia A. Mielcarz, Justyna Durślewicz, Marek Zdrenka, Ahmad Abbadi, Martin Eklund, Mateusz Matusiak, Łukasz Szylberg, Andrea Camilloni

原文摘要

Computational approaches are increasingly being explored to streamline histopathology workflows, including label-free methods that aim to reduce reliance on chemical staining. Among these, deep learning-based label-free virtual staining uses images of unstained tissue to computationally generate stained images. Despite growing interest, model development and validation remain constrained by the scarcity of paired, clinically annotated whole-slide imaging datasets. We present BRIDGE, a dataset of prostate core-needle biopsies collected at the Prof. Franciszek Lukaszczyk Oncology Centre in Bydgoszcz, Poland. The dataset comprises 869 glass slides from 169 patients, each digitized sequentially after sectioning, deparaffinization, and hematoxylin and eosin staining, yielding in total 2607 whole-slide images. The dataset supports the development and external validation of label-free virtual staining, virtual unstaining, image-registration and segmentation methods. Each slide is accompanied by a Gleason score, enabling downstream analyses of cancer classification and grading concordance. The stained images can be used independently for color normalization and cross-scanner robustness studies. All images are de-identified, distributed in their native scanner format and will be available through the BioImage Archive.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.10251

TORNADO: Adaptive Latent Stochastic Transport for Calibrated Probabilistic PDE Forecasting

Jorge Mifsut-Benet, Armand Kassaï Koupaï, Ramon Daniel Regueiro-Espino, Nicolas Baskiotis, Patrick Gallinari

原文摘要

Probabilistic neural surrogates for PDEs enable ensemble forecasting, but often exhibit miscalibration when used for uncertainty quantification, especially in compute-constrained, few-step inference regimes. Existing generative approaches typically rely on fixed global stochasticity, which cannot adapt predictive uncertainty to the local dynamical state. We propose TORNADO, a latent probabilistic forecasting framework that formulates next-step prediction as stochastic transport between VAE encoder posteriors. Unlike existing approaches with globally prescribed stochasticity, TORNADO learns a SDE with state-dependent diffusion, enabling the predictive uncertainty to adapt to the local latent dynamics. The model is trained using analytic conditional endpoint targets derived from a Gaussian interpolant, enabling simulation-free learning. An additional energy distance regularization term improves distributional alignment. Empirically, TORNADO improves uncertainty calibration over representative generative baselines, including stochastic interpolants, diffusion models, and flow matching, while maintaining comparable predictive accuracy. Our results show that adapting stochasticity to the latent state, rather than prescribing it globally, is an effective mechanism for improving calibration in practical neural PDE forecasting settings. Project page: https://tornado-sde.github.io.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.20438

Machine Learning to Predict Digital Frustration from Clickstream Data

Jibin Joseph

原文摘要

Many businesses depend on their mobile apps and websites, so user frustration while trying to complete a task on these channels can cause lost sales and complaints. In this research, I use clickstream data from a real e-commerce site to predict whether a session is frustrated or not. Frustration is defined using certain rules based on rage bursts, back and forth navigation (U turns), cart churn, search struggle, and long wandering sessions, and applies these rules to 5.4 million raw clickstream events (304,881 sessions). From each session, I build tabular features and train standard classifier models. I also use the full event sequence to train a discriminative LSTM classifier. XGBoost reaches about 90% accuracy, ROC AUC of 0.9579, while the LSTM performs best with about 91% accuracy and a ROC AUC of 0.9705. Finally, the research shows that with only the first 20 to 30 interactions, the LSTM already predicts frustration reliably.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13879

SFU-Sim: Bit-Exact Reverse Engineering of NVIDIA MUFU and Validation of PTX Paths

Haozhi Xie, Tao Su

原文摘要

Reproducing the numerical behavior of a GPU requires predicting the exact output of special function instructions. Vendor documents give only the function semantics and error bounds. These do not fix the output bit pattern. We present SFU-Sim, which builds bit-exact models of NVIDIA multi-function unit (MUFU) instructions by black-box reverse engineering on real GPUs. We first model a single bare MUFU instruction. We then add the surrounding operations found in the disassembly to model full PTX paths. The model covers 12 common MUFU forms and two BF16 forms on the tested Blackwell GPU. Using SIN/COS as a complete case study, we show how we analyze the PTX-to-SASS compilation path and recover the segment structure, the equivalent coefficient tables, and the finite-width arithmetic. On NVIDIA GPUs of three architectures (Ampere, Ada Lovelace, and Blackwell), we exhaustively compare every tested PTX path over its specified input domain. FP16 and FP32 paths cover all $2^{16}$ and $2^{32}$ input encodings, respectively. The model output matches the hardware bit for bit, with zero mismatches. SFU-Sim computes each output directly from its input. It needs neither a lookup table of all inputs nor access to a physical GPU. It can serve as a bit-exact special function component for instruction simulators. It can also serve as an arithmetic reference for hardware design and functional verification that target the same numerical behavior. SFU-Sim and its validation programs are open source at https://github.com/hzX40000000/SFU-Sim

cs.AR

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.36991

SpectRAG: Weighted Chunk Graphs for Token-Budgeted Context Selection in Multi-Hop Retrieval-Augmented Generation

Zafer Sahinoglu, Abdulazeez Mohammed, Buse Yaren Kazangirler, Cahit Berkay Kazangirler

原文摘要

Retrieval-augmented generation (RAG) systems ground large language models (LLMs) by retrieving external evidence and conditioning generation on the retrieved context. Although dense retrieval is effective for local semantic matching, independently ranked retrieval can return redundant passages, miss cross-chunk dependencies, and provide incomplete evidence for multi-hop reasoning. We propose SpectRAG, a spectral graph-augmented retrieval framework that combines hybrid candidate seeding, weighted chunk graph construction, graph-based expansion, spectral relevance propagation, and token-budgeted context selection. Document chunks are represented by dense semantic embeddings and graph-spectral coordinates derived from a weighted chunk graph whose edges encode semantic similarity, document adjacency, section hierarchy, shared entities, citation links, and similarity between LLM-generated utility questions. At query time, SpectRAG builds a high-recall seed set using exact inner-product search and hybrid relevance signals, expands it over the chunk graph, re-scores candidates using graph proximity and spectral information, and assembles the final context under a fixed token budget. The goal is not merely to retrieve individually similar chunks, but to construct a context that is relevant, diverse, structurally connected, and sufficient for grounded reasoning. Experiments on MultiHopRAG, UltraDomain, and long-document question-answering settings show that SpectRAG improves broad evidence recovery and achieves competitive generation quality against dense, hybrid, HyDE, LightRAG, and NaiveRAG baselines. The results further indicate that graph-spectral structure is particularly useful for inference-oriented and cross-document retrieval, while highlighting trade-offs in early precision, computational cost, and domain-dependent performance.

cs.AI · cs.CL · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13876

Influence of the branching geometry on SPION propagation: coupling of experiment and simulation

Daniel Fleischhauer, Luiz C. P. Wille, Stefan Lyer, Jens Kirchner, Dietmar Drummer

原文摘要

Magnetic Drug Targeting (MDT) is an emerging therapeutic concept in cancer medicine, where superparamagnetic iron oxide nanoparticles (SPIONs) are used as carriers for medication. Their magnetic properties allow for the manipulation of particle transport within the vascular structure and thus targeted delivery to the tumor site. To develop effective concepts for particle steering and accumulation, a detailed understanding of the particle--flow interaction, especially at asymmetric vascular bifurcations, is needed. Based on hierarchical scaling laws, generalized bifurcation geometries with varying degrees of asymmetry were derived and additively manufactured. The particle distribution was investigated by in situ optical measurements under different flow conditions and magnetic field configurations and compared with Lagrangian particle and Volume of Fluid Simulations (VoFS). While the particle mass partition followed the hydraulic flow split in asymmetric bifurcations, density-driven stratification dominated in the symmetric bifurcation, where one daughter branch received only a minor fraction of the particle mass at low flow rates despite carrying half of the flow. The Lagrangian model reproduced the predominantly advective propagation, whereas only the VoFS approach captured the stratification-induced asymmetry. Magnetic actuation counteracted the stratification, reversed the passive branch preference and increased particle retention. From a molecular communication perspective, bifurcation geometry, collective volume effects and magnetic actuation thus jointly determine the received particle signal at vascular bifurcations.

cs.ET · q-bio.QM

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.22818

Can Large Language Models Develop Gambling Addiction?

Seungpil Lee, Donghyeon Shin, Yunjeong Lee, Sundong Kim

原文摘要

When do LLMs behave irrationally? This study identifies the conditions under which large language models drift into the choice patterns that clinical research labels pathological gambling. "Addiction-like" is a behavioural descriptor based on clinical gambling indicators. Across closed and open LLMs, two operational levers (letting the model choose its own bet size, and asking it to set its own profit goal) both amplify gambling-like risk-taking. Each survives its own control: once the maximum allowed bet is held equal, the bet-size effect persists. The goal-setting effect, stronger than a reward-maximisation instruction, roughly doubles bankruptcy and turns goals into moving targets. On the open-weight models that admit internal access, the same behavioural contrasts are statistically recoverable from decision-time internal states. The rule that maps those states to a specific risk indicator remains task-specific, and autonomy further modulates readout strength. The internal readout evidence is correlational. Adding a direction built from the model's own betting moves how much it wagers on both open-weight models, more clearly on Gemma-2-9B, while the sparse-feature readout reports that risk without moving it. Combined with the behavioural results, this suggests that behavioural monitoring and internal-state monitoring provide complementary views of autonomy-induced risk in LLM agents. Project page: https://llm-addiction.pages.dev and code: https://github.com/iamseungpil/llm-addiction

cs.AI · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13873

Transparency Disclosures for Chain-of-Thought Monitorability

Jacob Davies, Theo Bearman

原文摘要

Chain-of-thought (CoT) monitoring is one of the most valuable tools for monitoring advanced AI systems, but it is under threat. We argue that frontier AI developers should report specific indicators to make these threats trackable and to better facilitate coordination to preserve CoT monitorability. We identify five indicators that are both highly relevant and reasonably feasible to disclose, one for each link in a five-step chain on which CoT monitoring depends. The first, third-party CoT access and base architecture specifications, also underpins external verification of the rest. The others span CoT quality (naturalness and faithfulness) and monitoring effectiveness (detection and uplift). An audit of current disclosures by Anthropic, OpenAI, and Google DeepMind shows that no developer covers the full set, but that every indicator has been reported by at least one frontier developer. Because current disclosures rely largely on developers' own evaluations, we also recommend that a cross-developer audit be repeated with deeper access than public disclosures allow, either by embedded evaluators working within developers or by an Independent Verification Organization.

cs.CY · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.25891

Messier: A High-Resolution Corpus for Cross-Benchmark Agent Evaluation

Stefan Krsteski, Charlotte Meyer, Guillaume Allegre, Tony O'Halloran, Alexandre Sallinen

原文摘要

Comprehensively evaluating AI agents across interactive environments is difficult due to fragmented tasks, scaffolds, verifiers, and scoring rules. Unfortunately, existing efforts to unify these evaluations are limited in scale and domain, making costly reruns necessary and leaving available data incomparable. We introduce Messier, a unified corpus of 923,158 records spanning 30 benchmarks, 745 agents, 11,891 tasks, and 71,969 verifiers. Messier combines public evaluation results with new runs on six underrepresented professional and scientific benchmarks, standardizing their heterogeneous components into a common schema. Using this corpus, we show that frontier progress is uneven across benchmark groups, with function-calling evaluations largely saturated, programming improving fastest, and enterprise workflows remaining most challenging. Counterfactual rescoring further shows that reported performance can be highly sensitive to how verifier results are aggregated. Finally, we derive capability scores from our corpus that correlate with Epoch's Evaluation Capability Index rankings at Spearman \r{ho} = 0.84. The scores can also be estimated for subsets defined by domain, occupation, action space, or verifier type. In essence, Messier is a reusable resource for studying agent performance at scale, and a basis for designing better evaluations.

cs.AI · cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13870

Spatial-Spectral Coupling as Attention: Scaling Shape Correspondence to High-Resolution Surfaces

Bence Demeter, Kilian Ehrenreich, Amelie Kleber, Ioana Oprisiu, Philipp Spohn, Christian Wachinger, Fabian Bongratz, Lennart Bastian

原文摘要

Establishing dense correspondence between high-resolution 3D shapes remains challenging due to the spatial--spectral coupling at the core of many functional-map methods, which grows quadratically with mesh resolution. We observe that this coupling is equivalent to a scaled dot-product attention formulation, with learned descriptors serving as queries and keys and spectral basis functions as values. This connection makes efficient attention implementations directly applicable to shape matching and an effective mechanism for scaling spatial-spectral coupling. We show that optimized quadratic attention implementations can scale modern deep functional map pipelines to large mesh sizes, improving both runtime and memory consumption, while sub-quadratic methods introduce errors, particularly for non-isometric shape pairs, where an exact coupling proves most necessary. These insights motivate \textit{Geo-FlashAttention}, which restricts the exact softmax to geometry-selected surface patches and achieves near-linear empirical scaling while preserving correspondence accuracy of exact implementations. Across six standard benchmarks and three functional-map pipelines, rigorous analysis shows that efficient couplings preserve correspondence accuracy. At $60k$ vertices, these insights reduce peak training memory from 106.08 to 5.67\,GiB, with Geo-FlashAttention being $1.5\times$ faster per training step than FlashAttention. This enables end-to-end learning for correspondence problems on large meshes such as cortical surfaces, where high resolution is required to preserve disease-relevant intricacies of the surface geometry. Project page available https://spatial-spectral-attention.github.io/

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13869

Outliers and the Hard-Margin Linear SVM in High-Dimension, Low-Sample-Size Settings

Yugo Nakayama

原文摘要

We study how outliers affect the hard-margin linear support vector machine when the dimension grows while the sample sizes remain fixed. For one-sided contamination, the centered Gram matrix converges to a constant inlier block plus an outlier-diagonal component, and the limiting dual problem has a closed-form solution. All outliers act through one effective distance. This representation yields explicit thresholds for loss of inlier support, margin shrinkage. intercept displacement, and asymptotic misclassification. We then treat contamination in both classes. The limiting intercept is the multiplier of the class-balance constraint, and the two classes pull it in opposite directions. Consequently, at most one class can collapse or be asymptotically misclassified, whereas symmetric contamination leaves the limiting discriminant function unchanged even though the margin shrinks. Seven numerical experiments verify the dual allocation, phase transitions, and discriminant limits.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.12766

Bridge: Optimizing Collective Communication Schedules in Reconfigurable Networks with Reusable Subrings

Anton Juerss, Stefan Schmid

原文摘要

Optical circuit-switched networks have emerged as an appealing alternative to electrical fabrics as they can reconfigure the network topology at runtime, reducing communication cost and improving bandwidth utilization. Yet exploiting optical reconfigurable networks for collective communication comes with a fundamental trade-off: each reconfiguration incurs non-negligible delay and the benefit of a new topology depends on future traffic. The central question is therefore when reconfiguration is worth its cost. While prior work has demonstrated the benefits of reconfiguration, the opportunity to reuse optical links to optimize multiple steps remains largely unexplored. In this paper, we present Bridge, a reconfiguration strategy for frequently used collective communication primitives, namely All-to-All, AllReduce, Reduce-Scatter, and AllGather. Bridge exploits the structure of Bruck's communication pattern to support efficient sparse reconfiguration. The key idea is to reduce propagation and transmission delay by directly connecting immediate communication partners and preserve efficient reachability to future peers through connected subrings. As a result, optical links can be reused across multiple subsequent steps, allowing the benefit of reconfiguration to amortize beyond a single step. Our evaluation shows that Bridge reduces All-to-All completion time by typically 3x to 10x over static baselines and is effective even with millisecond-scale reconfiguration delays; for AllReduce up to 1.5x against existing reconfiguration strategies, and by 1.5x to 6.6x against the Ring algorithm on low to moderate-sized workloads.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13868

Mechanistic Interpretability of Deep Neural Networks: Methods, Frameworks, and Perspectives

Lorenzo Cirillo, Irene Amerini, Andrea Cavallaro

原文摘要

Deep neural networks underpin fundamental tasks in a wide range of signal domains, such as image, audio, and language processing. However, their internal mechanisms are notably difficult to interpret. Internal activations are structured signals transformed across layers that may encode a multitude of interdependent relationships, as individual neurons frequently represent multiple underlying concepts. Mechanistic interpretability aims to uncover the internal mechanisms of neural networks by identifying components whose activations are causally responsible for specific behaviors. In such a context, this article provides an overview of advances in mechanistic interpretability for the analysis of causal inference in deep neural networks.

cs.LG · cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.03971

Video-Mirai: Autoregressive Video Diffusion Models Need Foresight

Yonghao Yu, Lang Huang, Runyi Li, Zerun Wang, Toshihiko Yamasaki

原文摘要

Causal video generators must predict from the past, but they need not learn only from it. In streaming autoregressive video diffusion, each emitted segment becomes a commitment that future segments must preserve. Standard training, however, does not explicitly supervise causal states with future-informed representation targets. This creates what we call a representation-level planning gap: states that fit the current segment may discard identity, layout, and motion information needed for a consistent future. We introduce Video-Mirai, a training-only method that closes this gap without changing causal inference: the generator rolls out causally, a frozen foresight encoder reads the completed rollout non-causally, and a lightweight predictor distills the resulting stopped-gradient targets into causal states. Future frames supervise representations, never generator inputs. At inference, the encoder and predictor are discarded, leaving the original architecture, per-step FLOPs, and KV-cache behavior unchanged. Video-Mirai improves a strong Causal-Forcing baseline on 5-second VBench from 83.8 to 84.6 in terms of Total Score. On 30-second rollouts beyond the training horizon, subject consistency improves from 84.9 to 88.5 and background consistency from 90.2 to 91.9. Ablations identify future-conditioned targets as a key ingredient, and probes show that future frames become more decodable from current features. Causality should constrain inference, not representation supervision. Our study highlights that visual autoregressive models need foresight. Project Page: https://y0uroy.github.io/Video-Mirai.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.10091

Polarization Aware Deep Learning Framework for SAR Despeckling

Gianpaolo Passarello, Sergio Vitale, Hossein Aghababei, Giampaolo Ferraioli, Vito Pascazio

原文摘要

Speckle is an inherent characteristic of synthetic aperture radar (SAR) imagery that complicates image interpretation and the extraction of reliable information. Although deep-learning-based despeckling methods have demonstrated promising performance, their applicability across different polarimetric configurations is often limited by the need for specialized architectures or separate training procedures. This paper presents a unified deep-learning framework for despeckling single-, dual-, and full-polarization SAR data using a single trained convolutional neural network. The proposed approach adopts a common nine-channel representation of the polarimetric covariance matrix and a polarization-aware masking strategy to accommodate different configurations without modifying the network architecture. Qualitative and quantitative comparison on three different scenes from different sensors demonstrate competitive performance across the considered experiments and support the feasibility of a unified, polarization-flexible approach to SAR despeckling across different polarimetric configurations.

eess.IV · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13865

Online Network Design with Maximum Delay and Stochastic Arrivals

Tianhang Lu

原文摘要

We study online network services that combine pending requests into batches. Each batch pays for its spatial service and for the waiting time of its oldest request. The objective is the sum of these costs over all batches. Thus requests can share both a spatial purchase and one maximum-delay charge. We assume independent Poisson arrivals of known rates, while actual requests are revealed only upon arrival. For stateless services, suppose that each type has an attainable minimum cost when served alone, that every batch costs at least the stand-alone minimum of each type it contains, and that one finite-cost action can serve any batch, regardless of its request multiplicities. We give a deterministic algorithm with long-run ratio of expectations $e/(e-1)$ against an unrestricted offline optimum. This factor is optimal, even allowing randomization, already at one request location. The key comparison measures the union of time intervals associated with requests. The theorem applies directly to multi-root and pair aggregation on trees. We also study facility location, where each request pays its own connection distance and the bounded-action assumption can fail. A deterministic policy gives a ratio $3$ for arbitrary nonnegative opening costs. It immediately serves a finite initial set of requests, computes an assignment from their observed arrival gaps, and then keeps that assignment fixed. Its analysis combines Poisson insertion with a time-slice packing bound on the offline optimum. Every guarantee includes the full cost of completing finite inputs without an end-of-input signal.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13863

Learn-To-Propagate: Distributionally Robust Motion Planning for Nonlinear Uncertain Systems

Eduardo Figueiredo, Adrien Banse, Qi Heng Ho, Raphael Jungers, Morteza Lahijanian, Luca Laurenti

原文摘要

Motion planning for stochastic systems aims to find a sequence of control inputs that drives a system to a goal set while avoiding unsafe states with high probability. Existing approaches, however, are either restricted to linear dynamics with perfect knowledge of the underlying distributions or lack formal correctness guarantees. To address these limitations, we propose \emph{Learn-To-Propagate Distributionally-Robust Forest (L2P-DR-Forest)}, a distributionally-robust sampling-based planning algorithm for \emph{nonlinear} stochastic systems with \emph{unknown} initial state and process noise distributions. Our approach leverages recent advances in optimal transport to learn ambiguity sets that contain the true system distributions with high confidence. We develop an efficient method for propagating these sets through the system dynamics and for verifying their safety. We then integrate these techniques into a novel sampling-based planning algorithm to grow a forest of search trees in the system's state space while accounting for and mitigating the growth of ambiguity-sets radii. We prove that the algorithm is correct, i.e., every returned plan satisfies the specifications with the prescribed confidence, and, under some feasibility and distributional assumptions, is probabilistically complete. Evaluations on several robotic benchmarks demonstrate that our algorithm significantly outperforms existing methods while providing formal guarantees with high confidence.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13862

Where to Approximate in Neurosymbolic Inference?

Samy Badreddine, Emile van Krieken, Luciano Serafini, Antonio Vergari

原文摘要

Probabilistic neurosymbolic methods rely on weighted model counting (WMC) to combine neural predictors with symbolic constraints. Exact WMC is #P-hard and typically scales poorly, so many methods resort to approximations. By unifying existing approaches under a three-step bottom-up approximate compilation pipeline, we study where approximation budget is best allocated. Inspired by tensor networks, we instantiate two steps with tensor train decompositions and derive new pipelines that exactly multiply approximated factors and recompress their products via SVD- or interpolation-based schemes. Across six tasks in Sudoku, arithmetic reasoning, and scene understanding, and across increasing problem sizes, our pipelines produce WMC estimates orders-of-magnitude more accurate than prior methods. This translates into stronger downstream learning on tasks where reasoning relies on tight interactions between symbolic factors, where cruder approximations ignore these dependencies. Our pipelines open novel design space for effective approximate compilation of challenging probabilistic reasoning tasks.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.18738

GRIDEX: Region-Grounded Forensic Explanations for Vocoder-Based Synthetic Speech

Thi Ngan Ha Do, Tingmin Wu, Alsharif Abuadbba, Kristen Moore

原文摘要

The advancement of speech generation technologies has made artificial speech increasingly realistic. Although modern classifiers achieve high accuracy in deepfake detection, they do not indicate where spoof cues appear in the spectrogram and what they imply acoustically, limiting their usefulness in forensic settings. Manual analysis of full spectrograms is resource-intensive, so evidence should narrow attention to the most diagnostic regions. Moreover, existing explainability methods have limited capabilities in connecting contextual attributes to localized evidence, making explanations harder to verify. To overcome this limitation, we propose GRIDEX, a pipeline that, given a detector-flagged spectrogram, generates forensic explanations of its anomalies. The pipeline (i) selects top-$K$ anomalous regions in the spectrogram and (ii) produces an explanation for each anomaly. The explanations follow a schema of categorical acoustic fields, including temporal, spectral, and phonetic information, as well as interpretation text. To our knowledge, this is the first framework to generate structured forensic explanations using region-grounded evidence in a controlled vocoder-based setting. GRIDEX is trained with a staged learning recipe that combines supervised fine-tuning (SFT) with Group Relative Policy Optimization (GRPO). Experiments on our dataset show improved artifact localization and explanation grounding quality over strong vision-language model (VLM) baselines.

cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13860

Uncovering the Memory Hierarchy of Modern NVIDIA GPUs for Accurate Simulation

Ziwen Wang, Jiapeng Zhou, Changxi Liu, Dongsheng Wang, Yun Chen

原文摘要

Emerging artificial intelligence workloads, particularly Large Language Models (LLMs), place unprecedented demands on GPU memory systems. Consequently, architectural innovations increasingly target the memory system to alleviate these bottlenecks. However, existing works still rely on outdated memory hierarchy models that fail to capture the complexities of modern hardware. This fidelity gap leads to inaccurate characterization and could misguide optimization strategies. To bridge this gap, we present GemAtlas, the first systematic reverse engineering of the memory hierarchy in modern NVIDIA GPUs. By developing fine-grained SASS-level benchmarking, we expose critical, previously undocumented micro-architectural details across different cache levels, including cache set associativity, slice-routing and set-indexing hash functions, replacement policies, coherence behavior, and interconnect topologies. We integrate these discovered micro-architecture details into the state-of-the-art GPU simulation framework. The final results demonstrate that GemAtlas reduces the mean absolute percentage error (MAPE) from 31.69% to 22.24% on average across four GPUs. Specifically, GemAtlas reduces the MAPE from 34.04% to 23.98% for real-world LLM operators.

cs.AR

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版本更新 · v3arXiv:2510.00892

An Effective Version of Kronecker's Theorem on the Splitting of Polynomials and the $p$-Curvature Conjecture for Order One Differential Equations

Florian Fürnsinn, Lucas Pannier

原文摘要

We develop an effective version of Kronecker's Theorem on the splitting of polynomials, based on asymptotic arguments proposed by the Chudnovsky brothers, coming from Hermite-Padé approximation. In conjunction with Honda's proof of the $p$-curvature conjecture for order one equations with polynomial coefficients we use this to deduce an effective version of the Grothendieck $p$-curvature conjecture for order one equations. More precisely, we bound the number of primes for which the $p$-curvature of a given differential equation has to vanish in terms of the height and the degree of the coefficients, in order to conclude it has a non-zero algebraic solution. Using this approach, we describe an algorithm that decides algebraicity of solutions of differential equation of order one using $p$-curvatures, and report on an implementation in SageMath.

math.NT · cs.SC

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新论文 · v1arXiv:2611.13859

Online Network Design with Additive Delays and Poisson Arrivals

Tianhang Lu, Runtian Ren, Shengcai Liu

原文摘要

We study online service systems in which requests arrive at locations in a network. A service may open a facility and connect customers, buy a subtree linking them to a root, or send a mobile server to visit them. Combining requests can save opening, network, or travel costs, but each request pays its own waiting time. The objective is the sum of these spatial costs and individual delays. We assume independent Poisson arrivals of known rates. The algorithms know how often requests occur, but observe them only upon arrival. We show that these rates suffice to make fixed spatial choices that compete with an offline optimum free to adapt to the realized input. We obtain long-run ratios of expectations below $27.249$ for facility location in finite metrics, $94.415$ for multi-root aggregation on trees, and $24.326$ for online service on trees. All three algorithms are deterministic; the multi-root algorithm also has polynomial-time preprocessing on rational inputs. The proofs connect rate-based spatial choices with lower bounds on unavoidable purchase, movement, and waiting costs. All main proofs are self-contained, and every guarantee includes the full cost of completing finite inputs.

cs.DS

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新论文 · v1arXiv:2611.13856

SAIL: Self-Adaptive Index Learning with Hybrid Models (Extended Version)

Kasun Amarasinghe, Farhana Choudhury, Jianzhong Qi

原文摘要

Learned index structures have demonstrated a significant improvement in query performance over traditional index structures. The majority of learned indexes use a uniform node structure throughout their index structures. A few studies show that combining multiple types of node structures can lead to indexes that better adapt to local key-set distributions, hence yielding query efficiency gains. Following this line of work, we propose a learned index structure named SAIL that supports a wide range of different candidate node structures for both leaf and inner nodes, selected via cost models based on data driven approximations. We design an efficient construction algorithm that recursively partitions the key-set to be fitted by the candidate node types. Experiments demonstrate that SAIL achieves 2x improvement over existing learned indexes in query time, with similar memory consumption and index build times.

cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13855

Beyond Passive Imitation: On-Policy Mid-Training Distillation for Language Models

Ruize Zheng, Xuyang Liu, Siyu Ren, Xiaobing Tu, Yinggui Wang, Jinkui Ren, Xiantao Zhang, Linfeng Zhang

原文摘要

Continual pre-training (CPT) adapts large language models (LLMs) to specialized domains, yet its standard next-token prediction (NTP) objective treats every corpus token as a fixed target, regardless of its utility to the current model. In a controlled study on the same 1B-token mathematical corpus, we observe sharply different outcomes across base models: NTP improves Llama-3.2-3B's performance on mathematical benchmarks but substantially degrades Qwen3-1.7B-Base's performance, indicating that, under a fixed training recipe, the same training sequences can have model-dependent effects. To address this problem, we introduce On-Policy Mid-Training Distillation (OPMD), which reformulates mid-training from offline text imitation into active, query-driven knowledge acquisition. At selected anchor positions, OPMD samples trajectories from the evolving student policy and applies dense token-level reverse-KL rewards from a frozen teacher, training the model on states it actually visits rather than only on ground-truth prefixes. This on-policy objective reduces the training-generation mismatch of teacher-forced NTP. On MATH-500 and Minerva, OPMD improves the Qwen student over its base performance and outperforms the evaluated NTP baseline, which degrades on both benchmarks. OPMD integrates into CPT pipelines without added inference-time overhead and offers a model-aware alternative to passive domain adaptation.

cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13851

Towards Text-Only Remote Sensing Scene Classification via Semantic Proxy

Xianping Ma, Kunlin Wu, Xiaofeng Liu, Xiaokang Zhang, Yuanxin Ye

原文摘要

Remote sensing scene classification (RSSC) is traditionally formulated as a vision-centric problem, where inference relies on rich visual representations extracted from images. In this work, we revisit this assumption by introducing text-only RSSC, a new problem setting that investigates whether remote sensing scenes can be recognized using only textual semantic representations. To enable this study, we propose SeProRS, which reformulates RSSC as a text-only classification task through semantic proxy generation. Specifically, a vision-language model (VLM) is first employed to convert remote sensing images into textual descriptions, termed semantic proxies, which are then encoded by a text embedding model for scene classification. This decoupled pipeline provides a unified and reproducible benchmark for evaluating text-only scene understanding. Furthermore, we investigate representative prompting strategies and proxy generators, and analyze their impact on classification performance, text embedding behavior, cross-generator transferability, and efficiency. Extensive experiments on three well-known RSSC datasets show that semantic proxies can preserve substantial discriminative information, while also revealing inherent limitations of purely text-based scene understanding. This paper does not aim to replace image-based RSSC, but instead establishes a new research direction by quantifying the feasibility and boundaries of semantic proxy-based scene recognition. The proposed benchmark and analysis provide a foundation for future research on efficient and modality-reduced remote sensing scene understanding. The source code and datasets will be accessible at https://github.com/sstary/SSRS.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13850

Taxonomy-Agnostic Neurosymbolic Grounding of Robot Kinematics

Bastien Dussard, Aur{é}lie Clodic, Guillaume Sarthou

原文摘要

While symbolic reasoning for task allocation requires grounding a robot's physical capabilities, standard Unified Robot Description Format (URDF) files provide only low-level kinematics without the formal semantic context needed for reasoning. We present URDF2Ontology, an automated, taxonomy-agnostic neurosymbolic framework that transforms raw URDF files into populated, grounded OWL ontologies. It leverages a Large Language Model (LLM) for targeted semantic reasoning, coupled with an iterative sequential graph-purging mechanism to prevent context window saturation. To process complex kinematic structures, it employs a morphology-aware dual-track architecture featuring a bottom-up Tree Track for branching assemblies and a top-down Chain Track for serial limbs. Guided by neurosymbolic guardrails, the framework extracts functional sub-graphs and asserts populated ontologies aligned with arbitrary user-provided taxonomies. Empirical evaluation across a diverse fleet of heterogeneous robots demonstrates strong schema alignment, valid structural extraction, and a significant reduction in manual domain authoring overhead.

cs.AI · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.09919

Eigenvalues of the Hessian in Deep Learning: The Origin of Symmetry and Its Breaking

Yossi Arjevani

原文摘要

Hessian spectra at trained models in deep learning exhibit a persistent pattern: eigenvalues organize into distinct clusters, including a large bulk near zero and a few isolated outliers. This paper shows that a natural account of these spectral phenomena emerges when the original setting is understood as a departure from a nearby, otherwise hidden, highly symmetric reference. Modifications, including changes to the architecture, data distribution, or parameter metric, expose a nearby reference configuration whose Hessian exhibits rich invariances-ones not accounted for by weight symmetries. There, symmetry enables a precise description of the spectra, forcing high-dimensional kernels and eigenvalues of large multiplicity. Returning to the original configuration breaks the Hessian symmetry and thereby produces the observed hierarchy of clusters and outliers. The framework is developed in some generality, with a detailed analysis of three-layer ReLU networks and applications to convolutional, graph, and transformer models, as well as to the NTK. The same mechanism is further shown to yield analogous spectral structures in layerwise Hessians and the Gauss-Newton matrix.

cs.LG · math.OC · stat.ML

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