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新论文 · v1arXiv:2611.11402

A Per-Step Load-Balancing Engine for Expert-Parallel MoE Serving

Jingyuan Li, Maolin Wang, Hayden So

原文摘要

In expert-parallel Mixture-of-Experts serving, a layer finishes only when the most loaded GPU of its group does. Token routing shifts at every decode step and concentrates on a few experts, so most of the group's reserved GPU time waits on stragglers. Recovering that time means re-deciding, at every layer of every decode step, how each replicated expert's tokens divide between its copies. The exact linear-program solvers that make this decision optimally take longer than many of the layers they serve, and their working set outgrows GPU shared memory at deployment scale. We introduce Relief, a bidirectional pairwise relaxation that makes the linear program unnecessary, coming within a fraction of a point of the per-step optimum in a few passes of integer halving over the token counts. We build and measure three deployments, CUDA kernels on the serving GPU, the same verified engine on an FPGA over PCIe, and a fixed-function block beside the accelerator. On decode routing from three open MoE families, Relief holds the balance near the per-step optimum at every group size and layer time measured. Scored as effective utilization relative to that optimum, the kernel alone matches or exceeds every deployable scheme at every group size, from the hundred-microsecond layer on. The on-die engine widens that lead at every point, recovering up to 37.3 percentage points over the deployed static placement, while adding 0.02% of a current accelerator's transistors.

cs.AR · cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.34436

Hitting sets From Derivations: Waring Rank and Noncommutative ABPs

Shubham Bhardwaj, Foram Lakhani, Partha Mukhopadhyay, Ramprasad Saptharishi, Pratik Shastri, Amit Kumar Sinhababu

原文摘要

We give an explicit hitting set of size $\operatorname{poly}(n,d,r)$ for the class of $n$-variate degree-$d$ polynomials whose partial derivative space is bounded by $r$, over any field $\mathbb{F}$ of characteristic zero (or large enough). In particular, this yields a polynomial sized hitting set for the class of depth-$3$ powering circuits. The proof extends to the setting of noncommutative ABPs and provides a polynomial sized hitting set (of matrices) for the class of noncommututive polynomials that can be computed by polynomial sized noncommutative ABPs (this was also one of the results reported by Open AI~\cite{OpenAI26} in their release on 6 Oct 2026). The main technical insight is the construction of a ``formal derivation'' and properties of the associated Wronskian with respect to this derivation, which was previously studied by Moura \cite{Moura_2004} in a very different context. The proofs in this paper are elementary and completely self-contained. AI disclosure: A subset of the stewards obtained a weaker statement (hitting sets for polynomials with bounded partial derivative space) during conversations \cite{astra_proof} with OpenAI GPT-6 Astra. Extensions of this idea to the broader class of noncommutative ABPs were obtained by the other stewards (via further conversations with Astra), prior to OpenAI's dump on 6 Oct 2026. The proofs presented in this writeup is a rewriting (in the authors' words) of the proofs for both settings obtained by the models in a form that we believe is understandable to researchers. Although the proof is technically identical to OpenAI's release, we believe this exposition is self-contained, simpler, and more valuable to the community. The absurdity of the situation is not lost on us.

cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12998

Linear Probes to Bayesian Manifolds: Geometry of Arithmetic in Large Language Models

Anshul Kumar, Neha Prasad, Manoj Kumar, Barnabás Póczos, Deeksha Varshney

原文摘要

Linear probes are a standard method to study geometric structures, such as helices and circles, from the activations of large language models (LLMs), and such structure is often taken as evidence that the model computes with it. Probe accuracy alone cannot establish this, because it conflates four separate claims. These are (1) that the concept is decodable from the activations, (2) that they form a specific shape, (3) that the shape belongs to the probed concept rather than to a related concept the model also represents, and (4) that the output of the model depends on the shape. We call these claims decodability, shape, ownership, and causal use, and we test them separately with a four-stage Bayesian pipeline. Stage 2 compares candidate shapes and selects the best one only when its evidence passes a predefined threshold. Stage 3 removes a set of related concepts, specified in advance, and tests whether the shape is owned or inherited. Stage 4 intervenes on the shape to test whether the output of the model depends on it. We apply the pipeline to two-digit addition and multiplication in GPT-J 6B, Llama 3.1 8B, and Pythia 6.9B. Nearly every concept is decodable, but the decoded directions overlap substantially across concepts. Every strongly supported comparison selects a ribbon, which is a helix whose radius changes with the value, or a torus. Of 1,330 cells with an ownership verdict, 19% of shapes are owned and 81% are inherited. Owned and inherited shapes move the output about equally often. Intervening on the shape retains approximately 61% of the effect of intervening on the full probe while using at most five dimensions, against a median of nine for the probe. Our results indicate that a probe verdict alone overstates what a model computes with, and we propose that probe results be reported as verdicts on all four claims rather than as a single accuracy.

cs.LG · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11394

Self-Healing by design: Immune-system inspired autonomous cyber resilience

Shari Finner, Swarna Kumarswamy-Das, Bart Gijsen

原文摘要

Digital innovations have strongly improved ship capabilities, including command, control & communication, navigation and combat management. In the wake of digitisation and geopolitical stress, resilience against cyber-attacks has become a key challenge. Autonomous Cyber Resilience (ACR) methods, inspired by features of the human immune system, provide a promising perspective. Proofs of concept of autonomous self-healing and self-protecting software have been realised in cloud environments using containerisation, monitoring and deployment technology. However, developing a suitable "cyber immune system" for more complex digital systems is still an intricate challenge. In this paper, we share insights on how ACR methods can be combined with resilience mechanisms present in critical naval and aeronautical manoeuvring systems. Using TNO's "OpPic" application, we show how TNO's Self-Healing for Cyber Security software can provide autonomous mission- and threat-adaptive resilience. On this basis, six guidelines for the design of autonomous cyber resilient systems are derived. The ultimate goal is to enable operators to oversee and control their system's "cyber immune response", to be able to continuously adapt to evolving threats and mission priorities.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.25097

PhysElite: How Far Are LLMs from Solving Olympiad-Level Physics Problems?

Ruoran Xu, Wending Gao, Liyunfeng Chen, Aixin Shi, Haoyu Cheng, Zixiang Fang, Yiqiang Zou, Qiufeng Wang

原文摘要

Understanding how (multimodal) large language models perform on physics problems requires benchmarks that reflect the difficulty and breadth of expert-level physical reasoning. Existing physics benchmarks remain limited in the following two important ways: (1) short of high-difficulty datasets, and (2) lack of comprehensive coverage of visual forms, knowledge points, and step-by-step solution processes. As a result, model performance on current datasets may not be fully representative of their ability to solve complex physics problems. To address these issues, we present PhysElite, a large-scale bilingual multimodal benchmark for Olympiad-level physics reasoning. PhysElite contains 11,586 Olympiad-tier problems. For each problem, we provide corresponding visual diagrams, step-by-step bilingual Chinese-English solution derivations, and the final answer. We benchmark 18 open-source and closed-source MLLMs, and find that even the strongest model reaches only 33.7% answer accuracy. We additionally conduct step-level process evaluation to diagnose where models fail in the reasoning chain. Our datasets are released at https://github.com/PremiLab-Math/PhysElite.

cs.AI · cs.MM · math-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11386

What Travels Across Languages? Reading, Transfer and Erasure of Cross-Lingual Concept Subspaces in Bangla Idioms

Syed Rifat Raiyan, Yousuf Ibne Kamal Niloy

原文摘要

Cross-lingual concept analysis requires distinguishing the information that a subspace retains from the classifiers and erasers that transfer through it. We study these operations in six frozen encoders using 10,359 Bangla (Bengali) idiom senses, eight input conditions, and figurative-class and sentiment labels. Class-mean directions provide the most consistent evidence of shared coarse meaning: in post hoc analyses, they outperform random-subspace, permuted-label and covariance-matched nulls in all 12 model-label tests under balanced accuracy. A prospective internal re-split supports idiom-alone sharing in 10 of 12 tests, spanning five encoders. The original confirmatory logistic-regression family rejects both planned nulls in 4 of 12 tests, where English directions gain only a few accuracy points above the majority-class floor (3.0-7.3). Classifier transfer depends on the input map and regularization: English-trained class-mean readouts beat both zero-shot nulls in 8 of 12 discovery tests, and stronger source-selected penalties improve transfer through logistic and LEACE directions. These readouts use Bangla-validation-selected layers and unlabeled target preprocessing. Input controls show greater coarse-class sharing for explicit glosses than for idiom counterparts. Reading, however, does not imply effective erasure: English-fitted LEACE removes less than one percentage point of Bangla figurative-class accuracy, compared with 4-11 points for Bangla-fitted LEACE; INLP shows a similar asymmetry. Pattern-filter analysis, covariance-weighted input maps and a binary Gaussian overlap bound explain how these operations can diverge. The analogous multiclass ordering is an empirical diagnostic, observed in 11 of 12 cells. The results support an estimator-sensitive account of cross-lingual concept sharing and motivate evaluating reading, classifier transfer, erasure and overlap separately.

cs.CL · cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11382

The comparison of density estimation for transfer learning in Banach, Hilbert, Euclidean spaces

Maryam Alsadat Kazemian, Azam Bastanfard

原文摘要

Domain adaptation or DA methods are indispensable when target labels or computational resources are scarce, yet the domain kernel density and subspace alignment approaches completely assume a Hilbertian geometry. They measure distributional discrepancy with symmetric L2 divergences over Euclidean feature spaces or reproducing kernel Hilbert spaces (RKHS). This preprint summarizes the manuscript Non-Hilbertian Domain Adaptation. Total Variation Alignment via L1 Density Regularization on Feature Manifolds, which proposes Banach Manifold Transfer Learning or BMTL. BMTL makes two coupled design decisions. The first one is that features are embedded on a geodesic manifold rather than treated as points of a flat Euclidean space, and the second one is that the density matching regularizer is formulated in an L1 Banach norm, whose minimization is equivalent, by Scheffe's theorem, to contracting the Total Variation or TV distance between domain densities, a bounded metric that weights all regions of the sample space uniformly. The L2 formulation, by contrast, squares deviations and therefore amplifies errors in the tails, over emphasizing density outliers that do not reflect meaningful distributional shift. Experiments on MNIST-USPS, COIL20, and Office+Caltech10 show consistent gains; the L1 variant beats an otherwise identical L2 ablation on every task, for example 92.44% vs. 89.80% on USPS->MNIST and surpasses the strongest baseline DMDA by +3.68% on average over twelve Office+Caltech transfers. This document situates the contribution with an explicit comparison of Euclidean, Hilbert, and Banach geometries.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11374

Small Data Generative AI for Culturally Specific Artistic Creation: A Case Study on the Sonic Space of sarir

Arshia Sobhan, Paul Triana, Philippe Pasquier

原文摘要

Large-scale generative AI (GenAI) models have become increasingly integrated into contemporary creative practice, yet they often reproduce cultural biases and fail to meaningfully engage with non-Western artistic traditions. In response, this paper explores a small-data approach that foregrounds artist-curated and culturally informed dataset creation and model training. Through a case study on sarir (the distinctive sound produced by the reed pen in Persian calligraphy), we demonstrate how small-data GenAI can operate within a highly situated cultural context that lies beyond the representational reach of large-scale GenAI models. We construct a bespoke sarir audio dataset and train two models, RAVE and StyleGAN2, adapting each to the sonic domain through audio synthesis and spectrogram generation. The resulting models learn the fine-grained timbral and gestural nuances of sarir, enabling both real-time interaction and extended generative output. By comparing these results with state-of-the-art text-to-audio models, we clearly demonstrate the disparity in their performance and highlight the expressive possibilities unlocked through the small data approach. This focused case study demonstrates the feasibility of small-data GenAI for culturally specific artistic creation and underscores the value of further investigations that extend this approach to other cultural materials and artistic domains.

cs.SD · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11365

MG-Cache: Multi-Granularity Caching for Accelerating Diffusion Transformer Inference

Zesen Chen, Youxuan Xu, Shigang Li

原文摘要

Diffusion Transformer (DiT) models, while having become a powerful backbone for generative synthesis, suffer from high inference cost on generating high-resolution images. Diffusion caching has emerged as a promising acceleration technique to reduce inference cost. However, existing caching strategies fail to accurately identify the varying importance of different computational modules and rarely coordinate cache reusing across the timestep and layer granularities, making it difficult to control error accumulation. To address these limitations, we propose multi-granularity caching (MG-Cache) for DiTs, a novel and fully training-free approach to exploit computational redundancy both on timesteps and Transformer layers. Our core innovation contains a two-stage caching strategy: an offline timestep profiling analysis that identifies redundant timesteps for cache reuse, followed by an adaptive layer-wise caching strategy that combines input stability with importance of each Transformer layer to selectively cache stable Attention and MLP submodules. Extensive experiments on mainstream open-source DiT models validate the effectiveness of our approach, achieving significant acceleration while maintaining high fidelity to the uncached output, with up to a 4.18x acceleration on HunyuanVideo.

cs.LG · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11364

OOM: Offload-Optimized Full-Parameter MoE Training via Memory-Execution Co-Design

Ruijia Yang, Shiyuan Lin, Chengxi Liao, Zeyi Wen

原文摘要

Offloading model state to host memory and streaming layer weights let a single multi-GPU node train large mixture-of-experts (MoE) models. GPU memory then becomes a performance budget, shared by resident expert state, retained activations, and execution worksets. The best allocation depends on the expert execution path, batch size, and sequence length. We present OOM (Offload-Optimized MoE), a full-parameter MoE training system that co-designs expert execution and memory allocation. For expert-parallel execution, OOM updates resident experts on their owning GPUs and jointly plans state residency and MoE activation retention according to pipeline dependencies. For local execution, OOM bounds GPU worksets through dependency-aware partitioning; in hybrid models, it also retains attention results on the host to avoid costly recomputation and selectively regenerates layer boundaries through cheaper layers to make room. Calibrated cost models guide joint configuration within GPU and host budgets to minimize step time; OOM selects among the configured paths through short executions. On four H100s, OOM trains with 1M-token contexts, whereas FSDP2, ZeRO-Offload, and MegaTrain exhaust GPU memory at 512K. It also trains a 122B-parameter MoE model, on which all three exhaust host memory. On four 24 GB RTX 4090s, it trains 512K-token contexts and 1,024 1K-token sequences per step without gradient accumulation. Across matched-global-batch comparisons, OOM delivers 1.58x and 4.08x geometric-mean (up to 2.14x and 7.77x) throughput over CPU-offloaded FSDP2 on the two platforms while using the least host memory among the evaluated systems -- as little as 1/3.0 of FSDP2's.

cs.DC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00627

Autonomous Analysis of Thermoelectric Materials Big Data by Multi-Agentic AI and Construction of a Knowledge Base and Specialized AI

Yusuke Hashimoto, Takaaki Tomai

原文摘要

Large databases of paper-derived experimental materials data hide essential information on materials. Their analysis is demanding, large-scale and complex, making manual knowledge extraction very difficult. To address this challenge, we developed Multi-Agentic AI (MAA) for automated, autonomous analysis of materials experimental big data and a specialized AI built on the obtained knowledge. MAA's nine role-specific AI agents, under predefined instructions and quality checks, autonomously perform data extraction, compositional feature generation, machine-learning modeling, feature-importance analysis and knowledge-base recording, iterating while inheriting previous knowledge. Delegating numerical processing to code makes large-scale data tractable; recording every choice and result enables later tracing to sources. In four thermoelectric-data experiments varying theme and cycle counts, the knowledge graph grew: knowledge clustered by theme and vocabulary was shared across themes. The specialized AI, searching and reading these records, answered materials questions citing sources. Retrospective verification also revealed errors, traced through the records to their sources.

cs.AI · cond-mat.mtrl-sci · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11349

AOP-Smart-Finder: A High-Throughput Human-AI Hybrid Framework for AOP Discovery and Evidence Mapping from Large-Scale Literature

Qinjiang Niu, Lu Yan

原文摘要

The rapid growth of scientific literature and the high heterogeneity of biological terminology and mechanistic descriptions make it difficult for researchers to integrate scattered research evidence, identify connections among biological events, and construct mechanistic networks. Here, we developed a high-throughput human-AI hybrid framework, AOP-Smart-Finder, for biological event discovery, relationship mining, and Adverse Outcome Pathway (AOP) construction and evidence mapping from large-scale literature. This framework integrates Large Language Models (LLMs), AOP-Smart retrieval-augmented generation (RAG) technology, and Python-based automation, supporting two analysis modes: one based on a single compound and the other based on a predefined set of biological events. In the di(2-ethylhexyl) phthalate (DEHP) case, the system processed 12,983 articles within 30 min and included 1,093 of them, from which it extracted 1,589 biological events (including 458 official key events [KEs] and 1,131 candidate KEs) and 2,519 key event relationships (KERs). In the amyotrophic lateral sclerosis (ALS) case, the system identified 55 stressors from 437 articles and extracted 278 biological events and 548 event relationships. Following manual curation and Weight of Evidence (WoE) evaluation, a mechanistic network comprising 16 KEs and 23 KERs was ultimately constructed. This framework can either use existing AOPs as knowledge guidance for mechanistic expansion and evidence supplementation, or discover novel event connections and construct mechanistic networks around user-specified research topics. It provides a high-throughput, traceable, human-AI hybrid methodology for complex biological mechanism elucidation and AOP discovery driven by large-scale literature.

cs.CL · q-bio.QM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11348

Separation of Skilling Context for Standard Operating Procedure-based Agents

Mitsuhiro Nakayama, Toshiaki Sota, Takehiko Yamaguchi, Michiaki Tatsubori

原文摘要

Standard Operating Procedures (SOPs) are kept concise for human readability, so LLM agents that generate executable skills from them lack the tacit knowledge and implementation details needed for automation. Human intervention fills these gaps, but must be repeated as SOPs evolve---a problem we call \textit{SOP-skill drift}. We propose managing SOPs and Contextual Knowledge (CK) separately, together with a \textit{contract-aware} evaluation that distinguishes genuine violations from CK-resolved differences. On eight Indeed-derived workflows (single LLM), it raises the functional-equivalence rate from 12\% to 58\%. Under three SOP-revision perturbations, previously extracted CK already covers 79.5\% of newly introduced automation gaps.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11346

IntentLens: Grounding Underspecified Multimodal Queries for Recommendation via Tool-Augmented Reasoning

Xiao Chen, Dong Fang, Haitao Li, Qing Li

原文摘要

As intelligent personal assistants are increasingly deployed in real-world scenarios, recommendation paradigms are shifting from passive preference matching to inferring user intent from underspecified multimodal inputs. In practical interactions, users typically issue a reference image paired with a vague natural language query, keeping key visual attributes, personalized preferences, and item-side constraints implicit. Existing recommendation methods either leverage historical user interactions augmented with multimodal item features or assume fully articulated textual queries; both paradigms struggle to resolve underspecified multimodal queries for accurate item matching. To address this limitation, we propose IntentLens, a tool-augmented recommendation framework that grounds ambiguous multimodal queries into query-relevant evidence prior to user-item matching. IntentLens follows a ground-then-rank pipeline: a shared multimodal LLM leverages visual tools, user memory modules, and item attribute retrieval tools to (i) infer latent user intent from image-text inputs and (ii) augment each candidate item with fine-grained, query-conditioned evidence. Subsequently, a lightweight ranker scores candidates based on the grounded representations. To facilitate research in this novel setting, we construct two benchmarks with underspecified multimodal queries, namely \textsc{GoogleReview-MMTool} and \textsc{Yelp-MMTool}. Extensive experiments on these benchmarks demonstrate that IntentLens outperforms competitive MLLM and retrieval baselines, and exhibits strong generalization to unseen categories and datasets.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2310.01830

AI-Generated Images as Data Source: The Dawn of Synthetic Era

Zuhao Yang, Yu Wei, Fangneng Zhan, Kunhao Liu, Muyu Xu, Xiaoqin Zhang, Ling Shao, Shijian Lu

原文摘要

The advancement of visual intelligence is intrinsically tethered to the availability of large-scale data. In parallel, generative Artificial Intelligence (AI) has unlocked the potential to create synthetic images that closely resemble real-world photographs. This prompts a compelling inquiry: how much visual intelligence could benefit from the advance of generative AI? This paper explores the innovative concept of harnessing these AI-generated images as new data sources, reshaping traditional modeling paradigms in visual intelligence. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability, the rapid generation of vast datasets, and the effortless simulation of edge cases. Built on the success of generative AI models, we examine the potential of their generated data in a range of applications, from training machine learning models to simulating scenarios for computational modeling, testing, and validation. We probe the technological foundations that support this groundbreaking use of generative AI, engaging in an in-depth discussion on the ethical, legal, and practical considerations that accompany this transformative paradigm shift. Through an exhaustive survey of current technologies and applications, this paper presents a comprehensive view of the synthetic era in visual intelligence. A project associated with this paper can be found at https://github.com/mwxely/AIGS .

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11340

Agentic Attribution of Cloud Resource Ownership

Tiltan Gilat, Gal Engelberg, Ariel Zadok, Leon Goldberg

原文摘要

Security tools raise findings across cloud environments, and AI agents now investigate and prioritize them. A finding is still resolved by people: it has to reach the people responsible for fixing it and accountable for the resource it concerns. Ownership is the organizational context that decides who that is. Where recorded, it is declared by hand in configuration management databases or owner tags and goes stale unless kept current; accounts are shared, and automation creates much of the infrastructure. The knowledge sits in work records, code history, audit logs and identity directories, encoded in each organization's own conventions. Recovering it poses four challenges: the signal is scattered across sources, none of them authoritative; the conventions that carry it differ between organizations; reaching an owner takes multi-hop links; and those related to a resource differ in the strength of their relation. We define ownership as a set of entities, people or teams, each related to the resource by one of five patterns ordered by strength. We present a reference architecture for agentic ownership attribution that derives ownership from these records instead of declaring it: it profiles an organization offline into tested signal procedures and an ownership playbook, then attributes each resource with a coordinator agent and parallel investigators, with confidence computed by rules. On 100 resources of 50 Amazon Web Services (AWS) resource types in one organization, we pool the architecture's candidates with those of an unprimed general-purpose agent and verify every evidence piece against the records. The architecture reaches 92.1% weighted recall and 86.6% weighted precision, against 54.8% and 77.4%, at 0.55 times the agent's cost per unit of coverage. The difference lies in people who build and change resources without an explicit owner, found through work records and code history.

cs.CR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11334

MoT3DVG: A Benchmark for Outdoor 3D Visual Grounding with Motion-Aware Descriptions and Temporal Cues

Shijia Zhao, Xusheng Guo, Qiming Xia, Xun Huang, Kezheng Xiong, Zhihong Liu, Chenglu Wen

原文摘要

3D visual grounding (3DVG) localizes language-referred objects in 3D scenes, with broad applicability in both indoor and outdoor environments. However, existing 3DVG research focuses on static indoor objects, limiting its use in dynamic outdoor autonomous driving. Motion descriptions, which capture the temporal dynamics of moving objects, offer a promising way to address this challenge. We therefore introduce MoT3DVG, a large-scale dataset for dynamic-aware outdoor 3DVG with temporally evolving motion descriptions. It contains 850 scenarios with 31,128 frames from nuScenes dataset, and provides 144,568 language prompts with motion-aware descriptions for dynamic objects across time. We further propose DynaVG, a novel framework that effectively leverages temporal cues for outdoor 3DVG. In addition to the standard modality-specific encoders, DynaVG introduces a Short-Long Integrated Dynamic Encoding (SLIDE) module before the language-point cloud alignment. SLIDE models both local motion cues and global temporal context via short-term window encoding and long-term window shifting. Extensive experiments demonstrate its state-of-the-art performance on MoT3DVG, validating the effectiveness of motion-aware encoding. Our work reveals open challenges and promising directions for future research in outdoor 3DVG.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.18394

Self-Aware Object Detection via Degradation Manifolds

Stefan Becker, Simon Weiss, Wolfgang Hübner, Michael Arens

原文摘要

Object detectors achieve strong performance under nominal imaging conditions but can fail silently when exposed to blur, noise, compression, adverse weather, or resolution changes. In safety-critical settings, it is therefore insufficient to produce predictions without assessing whether the input remains within the detector's nominal operating regime, which we refer to as self-aware object detection. We introduce a degradation-aware framework based on degradation manifolds, which structure a detector's feature space according to image degradation rather than semantic content. Our method augments a standard detection backbone with a lightweight embedding head trained via multi-layer contrastive learning, where images sharing the same degradation composition are pulled together while differing configurations are separated. Unlike prior contrastive IQA approaches, our method performs detector-aligned input-fidelity monitoring: it uses task-backbone features and a nominal prototype to detect degradation-induced shifts, rather than estimating perceptual image quality in a standalone representation. To anchor the representation, we estimate a pristine prototype from clean training embeddings, defining a nominal operating point in feature space. Self-awareness is then derived as geometric deviation from this reference, providing an intrinsic image-level signal of degradation-induced shift that is independent of detection confidence. Experiments on synthetic corruptions, cross-dataset zero-shot transfer, and natural weather-induced shifts show strong pristine-degraded separability, consistent behavior across detector architectures, and robust generalization across datasets with substantial content variation. These results suggest that degradation-aware detector representations provide a reliable signal for monitoring input-fidelity shifts.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11330

When Credit Turns Against Tools: Transition-Aware Credit Assignment in Agentic Reinforcement Learning

Xiangyu Lu, Zhanke Zhou, Jiazhe Ning, Chentao Cao, Jiangchao Yao, Bo Han

原文摘要

Agentic reinforcement learning with verifiable rewards trains language models through multi-turn tool interactions, yet their reasoning performance and tool-calling ability can decline during training. We trace this degradation to three coupled mechanisms: (1) tool-token imbalance produces negative overall tool credit when incorrect trajectories contain more tool tokens per turn than correct ones; (2) aggregation amplification can further strengthen this negative signal as token-mean aggregation gives longer incorrect trajectories greater weight in policy updates; and (3) failure-driven retries prolong trajectories as models tend to call tools again after execution errors, extending potential negative credit to recovery attempts. To counter this suppression, we propose Transition-Aware Credit Assignment (TACA), which targets the transition from reasoning to tool use through supervision of both call initiation and generation. Specifically, TACA uses Advantage Projection to enforce a nonnegative mean advantage on tool-call tokens within each prompt group through a minimal uniform shift. Additionally, TACA guides tool-use decisions with Decision Contrast, comparing rewards for tool-assisted and direct answers to the same prompt. Through Supervised Replay, it reinforces tool-call generation using calls from previously sampled correct trajectories. Across models and tasks, TACA improves performance and sustains more reliable tool interaction during training, raising mean accuracy over MT-GRPO on six reasoning benchmarks by 4.61 percentage points for Qwen2.5-1.5B and 3.61 for Qwen2.5-3B. Our code is available at https://github.com/tmlr-group/TACA.

cs.LG

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新论文 · v1arXiv:2611.11317

Understanding Spike Encoding across IoT Sensing Modalities: An Empirical Study

Yuxuan Hou, Di Yu, Linshan Jiang, Jin Zhang

原文摘要

Spiking neural networks (SNNs) offer a path to energy-efficient sensing on Internet of Things (IoT) devices. The input encoder shapes that opportunity by changing the cues available for recognition and the work required to process them. We study these choices across wireless, inertial, surface electromyographic, and tactile sensing. For wireless activity recognition, interleaving reduces estimated energy for arithmetic in the network layers, latency from the prepared input window to returned scores, and energy on the graphics processing unit and system-on-chip (GPU/SoC) rail, compared with repeating the full window on Jetson AGX Orin. With the same input size at each SNN time step, repeating a compact input can yield higher mean development accuracy than interleaving. The inertial experiments show that higher accuracy can come at higher device cost. Direct input can also be faster and more accurate than an event encoder while using less energy on the measured rail, even when its estimated energy for arithmetic in the network layers is higher. Level crossings improve grasp recognition on a later day for previously observed users. In tactile slip detection, level crossings shift the balance between missed slips and false alarms, and checkpoint selection can reverse which input offers higher slip recall. We use these findings to outline how edge sensing systems can profile encoders against the signal structure, the errors that matter to the task, and the cost of the full inference pipeline.

cs.NE

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新论文 · v1arXiv:2611.11308

SkillCODER: Towards Auditing and Attribution of Copyright Infringement in LLM Agent Skills

Enhao Huang, Chunshu Xia, Yiming Li, Yuchen Yang, Bingrun Yang, Zhan Qin, Dacheng Tao, Kui Ren

原文摘要

Agent skills package reusable workflows and task-specific procedures into deployable natural-language artifacts for LLM agents. Their scale and readability make them valuable but vulnerable to unauthorized redistribution and repackaging. Existing prompt copyright auditing methods struggle with long skills and introduce token-level perturbations that compromise readability. We propose SkillCODER for copyright auditing and buyer attribution in a black-box, content-only setting. During embedding, SkillCODER parses a skill into a section-aware intermediate representation, SkillIR, to identify structural anchors. It distributes conditional instructions across these anchors to form an Artifact-Gated Capsule, designed to activate under verification queries and remain dormant otherwise. Controlled-Vocabulary Error-Correcting Codes encode buyer-specific fingerprints through controlled lexical choices within the capsule. During verification, the defender issues paired positive and negative probes, extracts capsule evidence, and recovers ownership and buyer identity through error-correcting decoding. Across three domains, six models, and three agent frameworks, SkillCODER achieves 99.2% owner verification and 99.3% buyer Top-1 attribution accuracy while preserving task fidelity, substantially outperforming prompt-level baselines. Embedded signals remain robust under paraphrase, compression, clause deletion, and reorganization. Under five adaptive attacks, including a method-aware LLM sanitizer and multi-buyer collusion, ownership evidence remains reliably detectable while buyer attribution degrades gracefully.

cs.CR

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新论文 · v1arXiv:2611.11303

NCCL-BOT: Closing the Loop on NCCL's Cost Model with Online Bandits

Hanan Shteingart, Amedeo Sapio, Chen Avin, Devendar Bureddy, Manjunath Gorentla Venkata, Srivatsan Krishnan, Shie Mannor, Sreeram Potluri 等 10 位作者

原文摘要

NCCL picks an algorithm-protocol pair for every collective from an analytic cost model (AUTO) that never sees a measured latency. The model is a strong prior but a brittle final decision: priced on forced-arm runs, its choice is exact on most contexts and badly wrong in a few. We built NCCL-BOT, a tuner plugin that closes the loop: a per-context bandit, warm-started from the model, learns from measured latencies. We replay a forced-arm H100 sweep, tuned at 4-8 nodes and scored at 16-32, and price the model's choice on the same forced arms; that sweep has no stock-NCCL run. The bandit cuts the contexts where the model's choice loses more than 10% from 19 to 1 (p99 103% to 9.9%, mean 6.1% to 1.6%; p=0.047 over nine scenarios after Holm correction). The prior and continued learning both matter: cold-started bandits, and committing after one probe of each arm, lose more on the mean in every scenario. Supervised selectors fit once on the same data and tuned with the same budget match the mean, and their heavier tail is not significant (7 against 1 contexts above 10%, p=0.06); at seen scales they match or beat online selection. With hyperparameters frozen from H100, UCB1 cuts AUTO's p99 from 60% to 23% and from 56% to 15% on two GB200 fabrics, but its contexts above 10% go from 16 to 8 on one and from 10 to 13 on the other, and scenario-level tests do not separate the two. Live, on two latency-emulated node sets of one H100 system with one run per policy, the learners repair most of a gross AUTO error on one (selection loss 31% to 6-8% for deterministic Greedy and UCB1; average all-reduce bandwidth 1.18-1.24x stock) but average 0.94-0.99x stock on the other, and on both lose to stock below 1 MiB (on the first through plugin overhead); on H100 the cost model is exact there offline, so small sizes can be left to it. We do not show a net end-to-end win.

cs.DC · cs.LG

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版本更新 · v2arXiv:2611.00887

Multi-quadrotor systems interconnected via structurally unknown forces and moments

Kaiyue Xing, Shivansh Pratap Singh, Sarthak Mishra, Di Liu, Spandan Roy, Simone Baldi, Leonid Fridman

原文摘要

This work addresses the challenge of controlling multi-quadrotor systems subject to tight uncertain couplings. We consider interconnection forces and moments among the quadrotors to be state-dependent functions of the coordination errors, without assuming knowledge of their functional/parametric structures, e.g., no linear-in-parameters assumptions are made. We only rely on general state-dependent quadratic bounds valid for several Euler-Lagrange mechanical systems. We propose a coordination strategy embedding adaptive laws dedicated to compensating for the lack of structural knowledge. Unlike conventional decoupled position-attitude designs, we present a coupled stability analysis accounting for the position-attitude couplings induced by the interconnection forces and moments. The analysis, along with real experiments involving cooperative aerial transportation of rope-interconnected payloads, demonstrates the effectiveness of the proposed solution in compensating for these complex interconnections. Comparative experiments show that, when a payload drop induces a disturbance propagating through the ropes across the whole formation, the proposed strategy reduces significantly post-disturbance transients.

cs.RO

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新论文 · v1arXiv:2611.11278

An Adaptive, Context-Aware, and Stacked Attention Network-Based Recommendation System to Capture Users' Temporal Preference

Jung-Hsien Chiang, Chung-Yao Ma, Chi-Shiang Wang, Pei-Yi Hao

原文摘要

Recommendation systems have become more important since the widespread use of the Internet. The large amount of information means that it is difficult for users to discover they really need. However, users' preferences can change over time because of the age and the impact of social networks. Recommendation systems must capture users' preferences and recommend suitable items, which is a difficult challenge. This study proposes a novel recommendation system that adapts to the changing preferences of users. By learning and adapting to users' changing preferences better recommendations are provided. This study uses context factors as additional information to model users' preferences more precisely. For the proposed recommendation system, the context is based on users' interaction items within each defined time interval. This study proposes two novel attention mechanisms for the recommendation system. The contextual item attention module captures contextual information, changing pattern in users' preference and the importance of items. The multi-head attention module extends the diversity of users' preferences and adapts to changing preferences. The recommendation performance is improved using additional item's temporal information to model the contextual item's representation. Experiments compare the proposed algorithm with several state-of-the-art recommendation methods using three real-world datasets. The experimental results demonstrate that the proposed context-aware recommendation model outperforms traditional methods and demonstrate the effectiveness with which contextual information is captured by the attention mechanism.

cs.IR · cs.LG

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新论文 · v1arXiv:2611.11263

Beyond Node Attributes: A Controlled Evaluation and Ablation of Local Interaction Statistics for Ethereum Phishing Detection

Thanh Dat Tran, Duy Loi Duong, Duy Luu Hoang, Minh Huy Tran, Vo Minh Thu Le, Nhat Tung Le

原文摘要

Methods for detecting phishing accounts on Ethereum are usually evaluated as complete pipelines that differ in features, architecture, and training protocol, which makes the contribution of interaction information hard to isolate. This paper evaluates the Unlabeled Feature Extension (UFE), a block of 12 local interaction statistics computed from each account's incoming and outgoing call and transfer edges without reading class labels. UFE is appended to the 14 source features of HEIG-Phish and tested with six model families: Logistic Regression, Random Forest, LightGBM, Graph Attention Network, GraphSAGE, and Meta-IFD. Within each pair of Baseline and Plus variants, the data partition, model configuration, random seed, decision threshold, and sampled neighborhoods are identical; only the input feature block changes. Across 20 predetermined stratified partitions of the same 2,763 labeled externally owned accounts, UFE increased test Macro-F1 by 1.0 to 9.1 percentage points across the six models. Every pair improved on all 20 partitions, all Nadeau-Bengio corrected 95% confidence intervals lie above zero, and the false-positive rate decreased for all six models. An exact-edge Random Forest ablation over 10 predetermined partitions associates most of the gain with the incoming transfer-edge sum attribute; once this feature is present, an incremental contribution of the remaining 11 UFE columns was not established. The results support reporting feature-augmented baselines alongside graph models, but they show stability under re-partitioning of one static, transductive benchmark rather than temporal or cross-dataset generalization.

cs.LG · cs.AI

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新论文 · v1arXiv:2611.11262

Beating the Probabilistic Bound for Hashing and Improving Lower Bounds for Trifferent Codes

Chen Yuan, Ruiqi Zhu

原文摘要

We improve the probabilistic lower bounds on the rates of hash codes and list-recoverable codes. For perfect $q$-hashing, we prove $R_q>B_q$ for every $q\geq 3$. Our main result concerns non-perfect hashing. For every $q\geq 4$, we prove $R_{q,3}>B_{q,3}+\frac{1}{250000}.$ In particular, the case $q=9$, combined with the Tetra code, strictly improves the Körner-Marton lower bound $R_3\geq \frac14\log(9/5)$ for trifferent codes. We also extend the method to $(p,2,3)$-list recovery. For every $q\geq 4$ and $0\leq p<\frac{q-2}{12(q-1)}$, we prove $R_q^{\mathrm{LR}}(p)>B_q^{\mathrm{LR}}(p)$. For every fixed $0\leq p<1/18$, the rate improvement is bounded below by a positive constant independent of $q\geq 4$. The same holds for every fixed $0\le p<1/12$ when $q$ is sufficiently large.

cs.IT

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新论文 · v1arXiv:2611.11249

Understanding When and Where Self-Supervised Speech Models Learn Phonemes and Prosodic Cues through Layer-Wise and Time-Resolved Probing

Masaru Tanibata, Shun Takahashi, Hiroki Ouchi, Sakriani Sakti

原文摘要

Self-supervised speech models (S3Ms) encode a rich hierarchy of acoustic, prosodic, and phonetic information, yet when and where it emerges during pretraining remains unclear. We train frozen-encoder probes on every Transformer layer across pretraining checkpoints of two matched models: HuBERT-Base, whose stage 2 is a separately initialized model trained on new pseudo-label targets, and single-objective wav2vec 2.0-Base. Our five tasks span an acoustic-to-abstract gradient: absolute pitch (F0), speaker-relative pitch, lexical stress, phoneme boundaries, and 38-way phoneme identity. Acoustic-prosodic cues and phone boundaries are decodable from the earliest checkpoints and barely shift across layers, whereas phoneme identity is constructed during pretraining, re-localizing to upper-middle layers under HuBERT's stage-2 targets at a matched update count. Acquisition order therefore tracks abstraction from the acoustic surface, not the segmental/suprasegmental distinction, extending to prosody an account established for phonetic, lexical and syntactic structure; the no-switch wav2vec 2.0 shows no comparable re-localization.

cs.CL

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版本更新 · v2arXiv:2603.10801

PolGS++: Physically-Guided Polarimetric Gaussian Splatting for Fast Reflective Surface Reconstruction

Yufei Han, Chu Zhou, Youwei Lyu, Qi Chen, Si Li, Boxin Shi, Yunpeng Jia, Heng Guo 等 9 位作者

原文摘要

Accurate reconstruction of reflective surfaces remains a fundamental challenge in computer vision, with broad applications in real-time virtual reality and digital content creation. Although 3D Gaussian Splatting (3DGS) enables efficient novel-view rendering with explicit representations, its performance on reflective surfaces still lags behind implicit neural methods, especially in recovering fine geometry and surface normals. To address this gap, we propose PolGS++, a physically-guided polarimetric Gaussian Splatting framework for fast reflective surface reconstruction. Specifically, we integrate a polarized BRDF (pBRDF) model into 3DGS to explicitly decouple diffuse and specular components, providing physically grounded reflectance modeling and stronger geometric cues for reflective surface recovery. Furthermore, we introduce a depth-guided visibility mask acquisition mechanism that enables angle-of-polarization (AoP)-based tangent-space consistency constraints in Gaussian Splatting without costly ray-tracing intersections. This physically guided design improves reconstruction quality and efficiency, requiring only about 10 minutes of training. Extensive experiments on both synthetic and real-world datasets validate the effectiveness of our method.

cs.CV

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新论文 · v1arXiv:2611.11248

FESOM2.8 in single precision: climate fidelity over six decades and performance on CPUs and GPUs

Suvarchal Kumar Cheedela, Nikolay V. Koldunov, Sergey Danilov, Jan Streffing, Dmitry Sidorenko, Thomas Jung

原文摘要

Halving the floating-point precision of an ocean model can make it substantially faster, but whether the rounding errors stay negligible over climate time scales has rarely been tested. We implement single precision in the global ocean-sea-ice model FESOM2 and compare 60-year hindcasts with double-precision controls. A first implementation lost salt steadily, accumulated a heat-content deficit of 129 ZJ, and shifted major ocean fronts. Controlled experiments trace the drift to the tracer update: small increments added to a large salinity value are rounded away, and the increments themselves are biased. Accumulating tracer tendencies and layer thicknesses in 64 bits, and carrying the rounding error of each tracer update into the next, removes the drift for at most 8% additional runtime. Single- and double-precision simulations then differ in heat content and mean state about as much as two double-precision runs with slightly perturbed initial conditions, sea ice differs by at most 0.2%, and he salinity difference is four orders of magnitude smaller than before. In the coupled model AWI-ESM3, single precision adds no detectable heat source or sink over 60 years. Single precision speeds up the 60-year hindcasts by a factor of 1.35, and the model by up to 1.66 on meshes of up to 5 km resolution; on GPUs, a C++/Kokkos version runs up to 1.27 times faster and needs 43% less memory. Whether reduced precision is safe for climate integrations is decided by long-term budgets, not by short tests.

physics.ao-ph · cs.MS · cs.PF

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版本更新 · v2arXiv:2607.12380

SinAE: A Single-Architecture Flow-Matching Autoencoder for Cross-Domain Atomic Systems

Yuxuan Ren, Fan Yang, Jianhua Yao, Yatao Bian

原文摘要

Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its Small molecules, crystals, and proteins all reduce to atoms in 3D space, yet their generative pipelines remain fragmented across domains, each with its own graph, equivariant, or frame-based architecture. Cross-domain training would mitigate per-domain data scarcity, but direct generation in 3D coordinate space cannot easily handle the heterogeneous structural priors of all three domains, and no prior latent autoencoder is simultaneously lossless and architecturally general across all three. We introduce SinAE, a single-architecture flow-matching autoencoder for molecules, crystals, and proteins, with vanilla Transformer encoder and decoder and no equivariant, graph, or domain-specific operators. Rather than requiring the encoder to capture fine-grained geometry, SinAE shifts the reconstruction burden into an iterative flow-matching decoder, achieving near-lossless reconstruction across domains and reducing reconstruction errors by orders of magnitude relative to prior latent baselines. The same per-token latent supports a standard Diffusion Transformer prior that reaches strong performance on molecular, crystal, and protein generation benchmarks. Joint molecule--crystal training strictly improves both domains, providing direct evidence of cross-domain transfer through a shared atomic latent. Code is available at https://github.com/BlueWhaleLab/SinAE .

cs.LG

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