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版本更新 · v8arXiv:2503.20550

Structural Origins of Cubic Complexity in Pebble Motion

Tomoki Nakamigawa, Tadashi Sakuma

原文摘要

The pebble motion problem (PMP) asks whether one configuration of labeled pebbles on a graph can be transformed into another by moving pebbles to adjacent unoccupied vertices. Kornhauser, Miller, and Spirakis proved that the worst-case shortest solution length on an $N$-vertex graph is $Θ(N^3)$, but the graph structures responsible for this cubic behavior remained unclear. We first show that cubic complexity cannot occur on trees: every feasible instance on an $N$-vertex tree admits a solution sequence of length $\mathrm{O}(N^2\log N)$, constructible in time linear in its length. This is the first $\mathrm{o}(N^3)$ upper bound for PMP on trees. We then extend the method to general graphs. When $2\le N-n=\mathrm{O}(1)$, every instance on a feasible puzzle admits a solution of length $\mathrm{O}(n\,\mathrm{CL}(G)\,\mathrm{D}(G))$, where $\mathrm{D}(G)$ is the diameter and $\mathrm{CL}(G)$ is the maximum length of a shortest cycle through a vertex; this recovers the classical $\mathrm{O}(N^{3/2})$ bound for square grids. We further show that any graph with worst-case shortest solution length $Θ(N^3)$ has a unique biconnected component $X$ of order $Θ(N)$, while all structures attached through any one cut vertex of $X$ have bounded total size. Moreover, $X$ contains no cycle of unbounded sublinear order, and any two intersecting cycles of order $Θ(N)$ share at least as many vertices as there are blanks. Finally, cubic complexity occurs when a cycle of order $Θ(N)$ contains all but $\mathrm{O}(1)$ vertices, whereas two $Θ(N)$-cycles meeting at one vertex have complexity exactly $Θ(N^2\log N)$. We conjecture that the former construction essentially characterizes the cubic regime.

math.CO · cs.CC · cs.DM

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.26261

REFLEX: RNA Ensemble Generation via Flexibility-Calibrated Stochastic Bridge

Wanli Ma, Tianmeng Hu, Ke Li

原文摘要

RNA molecules often function through multiple experimentally observed conformational states. A central challenge is to capture ensemble diversity without sacrificing structural fidelity: samples may collapse toward averaged representative structures or drift into unsupported conformational regions. We introduce REFLEX (RNA Ensemble generation calibrated by FLEXibility), a sequence-conditioned generator for RNA backbone-frame ensembles based on a heteroscedastic stochastic bridge on SE(3). During training, experimental B-factors calibrate residue-wise bridge widths, exposing each observed conformer through local perturbation neighborhoods with residue-specific scales while keeping constrained regions sharper. A learned flexibility condition guides the bridge-field network to recover target conformer-specific frames from these calibrated noisy states. At inference, REFLEX uses only the input sequence, requiring no MSA-subsampling heuristics. On a multi-conformer RNA benchmark, REFLEX achieves the best precision-recall trade-off for ensemble coverage across evaluation thresholds, while recovery-stress ablations indicate gains in target-frame recovery under stronger perturbations.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03339

ArabGeoText: Tool-Augmented Vision-Language Models for Arabic Geospatial Text Spotting

Yakoub Bazi, Sarah Alsaleh, Mohamad M. Al Rahhal, Mohamed A. Mekhtiche, Mansour Zuair

原文摘要

Geospatial text spotting involves locating and transcribing geographic labels, such as road names, district names, and landmarks, in raster maps. These labels are often small, densely arranged, and arbitrarily oriented. Although Arabic text recognition has been studied extensively in documents and natural scenes, Arabic geospatial text spotting remains underexplored. We introduce ARABGEOTEXT, an Arabic-centric benchmark covering 970 geographic locations in Riyadh and Jeddah, with 17,875 manually annotated text instances per view type. For each location, we pair an OpenStreetMap (OSM) rendering with a composite created by overlaying that rendering on the corresponding remote sensing (RS) imagery. The paired views preserve label content and pixel geometry, allowing evaluation across different backgrounds. Geographic partitions support in-city and city-disjoint evaluation of detection, recognition, and end-to-end spotting. Experiments with direct Qwen3.5-4B spotting show difficulties in recovering all labels within a tile. We therefore propose a tool-augmented vision-language framework that combines YOLO26 localization and crop rectification with Qwen3.5-4B recognition adapted through low-rank adaptation (LoRA). The framework outperforms direct LoRA-adapted Qwen3.5-4B spotting across both image views and test cities. With localization held fixed, reader adaptation further improves recognition over the pretrained zero-shot reader. The dataset and code will be made publicly available at https://github.com/yakoubbazi/ArabGeoText.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.02386

Monadic Second-Order Logic of Permutations

Vít Jelínek, Michal Opler

原文摘要

Permutations can be viewed as pairs of linear orders, or more formally as models over a signature consisting of two binary relation symbols. This approach was adopted by Albert, Bouvel and Féray, who studied the expressibility of first-order logic in this setting. We focus our attention on monadic second-order logic. Our results go in two directions. First, we investigate the expressive power of monadic second-order logic. We exhibit natural properties of permutations that can be expressed in monadic second-order logic but not in first-order logic. Additionally, we show that the property of having a fixed point is inexpressible even in monadic second-order logic. Secondly, we focus on the complexity of monadic second-order model checking. We show that there is an algorithm deciding if a permutation $π$ satisfies a given monadic second-order sentence $\varphi$ in time $f(|\varphi|, \operatorname{tw}(π)) \cdot n$ for some computable function $f$ where $n = |π|$ and $\operatorname{tw}(π)$ is the tree-width of $π$. On the other hand, we prove that the problem remains hard even when we restrict the permutation $π$ to a fixed hereditary class $\mathcal{C}$ with mild assumptions on $\mathcal{C}$.

math.CO · cs.LO · math.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33941

TurboWAM: Learning What Matters for Fast World Action Models

Guile Wu, Shuangzhi Li, Xingxin Chen, Chengjie Huang, Sam Hosseini, Tongtong Cao, Yingxue Zhang, Dongfeng Bai

原文摘要

World action models have demonstrated promising performance in robot policy learning, yet their high inference latency remains a critical bottleneck for real-world deployment. Recent approaches have sought to improve inference efficiency by eliminating test-time future-frame imagination or accelerating action prediction. However, aggregating representations from all video world model layers can introduce redundant computation, while standard backpropagation may distribute action-supervision gradients unevenly across layers, limiting the effectiveness of world representation learning. In this work, we propose TurboWAM, a fast world action model that learns to efficiently aggregate and optimize world representations for action prediction. Specifically, TurboWAM introduces an efficient world representation aggregation mechanism that restricts aggregation to a bounded range of relevant world model layers identified through layer relevance analysis. Within this bounded range, TurboWAM estimates aggregation weights for different world model layers based on the current action denoising state and combines them with learnable layer-wise weights to fuse world representations. Moreover, TurboWAM employs a world model gradient modulation mechanism that decomposes action-supervision gradients into a layer-shared component and layer-specific residual components, enabling more balanced gradient propagation across world model layers. This mechanism operates exclusively during training and introduces no additional inference computation. Together, these designs enable more efficient world representation learning and low-latency action prediction. Extensive experiments on multiple robot manipulation benchmarks and real-world manipulation tasks show that TurboWAM achieves low inference latency while maintaining competitive task success rates compared with state-of-the-art methods.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2501.19214

A SPIDER-type stochastic subgradient method for expectation-constrained nonconvex nonsmooth optimization

Wei Liu, Yangyang Xu

原文摘要

Many real-world problems, such as those with fairness constraints, involve complex expectation constraints and large datasets, necessitating the design of efficient stochastic methods to solve them. Most existing research focuses on cases with no {constraint} or easy-to-project constraints or deterministic constraints. In this paper, we consider nonconvex nonsmooth stochastic optimization problems with expectation constraints, for which we build a novel exact penalty model. We first show the relationship between the penalty model and the original problem. Then on solving the penalty problem, we present a single-loop SPIDER-type stochastic subgradient method, which utilizes the subgradients of both the objective and constraint functions, as well as the constraint function value at each iteration. Under certain regularity conditions (weaker than Slater-type constraint qualification or strong feasibility assumed in existing works), we establish an iteration complexity result of $O(ε^{-4})$ to reach a near-$ε$ stationary point of the penalized problem in expectation, matching the lower bound for such tasks. Building on the exact penalization, an $(ε,ε)$-KKT point of the original problem is obtained. For a few scenarios, our complexity of either the {objective} sample subgradient or the constraint sample function values can be lower than the state-of-the-art results by a factor of $ε^{-2}$. Moreover, on solving two fairness-constrained problems and a multi-class Neyman-Pearson classification problem, our method is significantly (up to 466 times) faster than the state-of-the-art algorithms, including switching subgradient method and inexact proximal point methods.

math.OC · cs.CC · cs.LG · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03769

Metadata-Conditioned Audio Transformers for Adaptive Respiratory Sound Classification

George Kontogiannis, Pantelis Tzamalis, Sotiris Nikoletseas

原文摘要

Respiratory sound classification from auscultation recordings is confounded by heterogeneous acquisition conditions, including recording device, auscultation site, and patient characteristics, that introduce systematic variability unrelated to the underlying pathology. Although such metadata is often available, existing approaches either ignore it or rely on naïve fusion strategies that do not model how distinct metadata sources influence different components of the acoustic representation. We propose a family of metadata-conditioning mechanisms for the Audio Spectrogram Transformer (AST), progressing from gated residual fusion to Feature-wise Linear Modulation (FiLM) adapted to the Transformer architecture. We further introduce two novel variants: Token-Aware FiLM (TAFiLM), which applies distinct modulation to classification and patch tokens, and Soft-Factorized FiLM (SoftFiLM), which learns soft masks to align metadata factors with disentangled feature subspaces. Experiments on the ICBHI benchmark demonstrate consistent gains across all conditioning strategies. SoftFiLM achieves 64.11% on the official 4-class task (+3.94% over the baseline) and 72.40% on the 2-class task, establishing a new state-of-the-art on the 2-class task and competitive performance on the 4-class benchmark.

cs.SD · cs.AI · eess.AS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03330

The SIMPLE Law for Energy Cost of LLM Inference

Hiari Pizzini Cavagna, Andrea Proia, Daniele Cesarini, Zeynep Kiziltan, Andrea Bartolini

原文摘要

Generative AI is billed per token, yet tokens are not equally expensive. Each token enters the KV-cache and raises the cost of those that follow, so energy grows non-linearly along a sequence whose tokens look identical. No analytical model states what a token costs, and the most accurate SOTA model is a fit, not a law: it absorbs architecture into fitted coefficients, requiring per-LLM fitting. We derive closed-form expressions of the computational and memory-access complexity of LLM inference in terms of architectural hyperparameters, covering MHA and GQA, and compare a family of analytical models of increasing granularity. From these, we obtain the SIMPLE Inference Model to Predict LLMs Energy (SIMPLE), a four-parameter law that separates prefill from decode, and decode into KV-cache cost and next-token computation. Over traces of 13 dense LLMs from six families on NVIDIA H100 GPUs, it attains R2 = 0.969 and 11.97% MAPE, 3.1-3.5x below the SOTA, without per-LLM calibration. The SIMPLE law explains as well as predicts: prefill energy grows quadratically with the hidden dimension, so larger LLMs suffer most from long inputs; and more aggressive GQA KV grouping reduces the per-token energy by up to 3x relative to standard MHA. Increasing the reasoning token budget pushes output length beyond the analytically derived energy minimum, driving super-linear energy growth; although accuracy grows with the budget, energy grows faster, so Intelligence per Joule peaks at an intermediate budget and falls by up to 3.14x across a 4.0x reasoning-budget range. Agentic serving raises energy per request by 13.0x relative to non-agentic ones, driven by the growth of both the prefill and the KV-cache terms with input length; while prefix caching removes most of the prefill cost (-93%), the KV-cache term is unaffected, so even a cached agentic request costs 4.8x more.

cs.AI · cs.LG · cs.PF

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.14746

Selective Safety Steering via Value-Filtered Decoding

Bat-Sheva Einbinder, Hen Davidov, Yee Whye Teh, Yarin Gal, Yaniv Romano

原文摘要

While large language models (LLMs) are trained to align with human values, their generations may still violate safety constraints. A growing line of work addresses this problem by modifying the model's sampling policy at decoding time using a safety score. However, existing decoding-time steering methods often intervene unnecessarily, modifying generations that would have been safe under the base model. Such unnecessary interventions are undesirable, as they can distort key properties of the base model such as helpfulness, fluency, style, and coherence. We propose a new test-time steering method designed to reduce such unnecessary interventions while improving the safety of responses. Our approach filters tokens using a value-based safety criterion and provides an explicit bound on the probability of false interventions. A single threshold hyperparameter controls this bound, allowing practitioners to trade off higher rates of unnecessary intervention for better output safety. Across multiple datasets and experiments, we show that our value-filtered decoding method outperforms existing baselines, achieving better trade-offs between safety, helpfulness, and similarity to the base model.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03327

From FAIR to AI-FAIR Metadata-First Infrastructure for AI-Enabled Science

Elizabeth G. Campolongo, Eric R. Sokol, Marty Downs, Gina Glenne, Gregory E. Maurer, Sydne Record, Dustin R. Rubenstein, Matthew J. Thompson 等 12 位作者

原文摘要

Artificial intelligence (AI) technologies and their capabilities have advanced rapidly over the past few years, leading to widespread interest and adoption in the biological and ecological sciences. However, the data infrastructure and computational resources that provide the foundation for AI workflows in ecology and biodiversity science remain fragmented. Moreover, the flow of value remains largely unidirectional: data move from collectors, through repositories and aggregators into models, and finally to research products or management actions. The derived value, such as corrected taxonomies, annotations, quality flags, and insights, rarely returns to source communities. We propose an extension of the original FAIR principles to address the needs of AI-ready infrastructure: the AI-FAIR metadata criteria, which place AI-enabled science in the context of a bidirectional Question-Data-Model triangle. This framework is designed to facilitate large-scale content-based analysis across diverse collections and ensure that the scientists generating the data benefit from the AI-enabled advances built on their data. Alongside these criteria, we introduce an agentic evaluation workflow that both rates existing datasets on the AI-FAIR and traditional FAIR criteria and provides actionable feedback where critical metadata is missing or incomplete. This modular dataset evaluation tool, paired with a domain-appropriate checklist, translates these principles into a reusable assessment of machine-learning readiness, AI-readiness for specified tasks, traceability, and ethical and CARE-aligned stewardship. Through this approach we aim to create a community-informed system to close the model data loop, so that AI also enriches data to further enable scientific discovery.

cs.AI · cs.DB · cs.HC · cs.IR · q-bio.QM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03325

End-to-End NTT on FPGA for FHE Acceleration

Mohamed Allam, Thomas Spendlhofer, Hugo Sanz-González, Antonio J. Peña

原文摘要

Fully homomorphic encryption (FHE) enables privacy-preserving computation on encrypted data without decryption. Despite its strong security guarantees, FHE incurs substantial computational and memory overhead. In FHE-based deep learning inference, on average 63% of the CPU time is spent on the number theoretic transform (NTT) and its inverse (INTT), which reduce the complexity of polynomial multiplication from $O(N^2)$ to $O(N \log N)$. Prior FPGA works follow two approaches: accelerating entire FHE schemes, which yields significant speedups but ties them to specific parameters and algorithms, or accelerating only the NTT, which applies across schemes, algorithms, and libraries. Most FPGA NTT designs require interconnects that grow with parallelism and utilize significant resources, which complicates routing on multi-die FPGAs. Additionally, to the best of our knowledge, end-to-end NTT acceleration within an FHE context has so far been left unexplored. We design a fully unrolled, delay-commutator streaming NTT/INTT kernel, and floorplan it across the three dies of an AMD Alveo V80. At 400 MHz, the kernel transforms a polynomial of $2^{16}$ coefficients every 5.12 $μ$s, the highest throughput among FPGA designs at this size. Offloaded end to end, it is up to 22x faster than the CPU per transform. We integrate the kernel into the Lattigo FHE library and evaluate it end to end. With 128-bit secure parameters, it accelerates core FHE operations, such as key switching and bootstrapping, by 1.8-2.2x, and neural network inference by 1.8-2.1x.

cs.CR · cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03323

A Symbolic-Neural Framework for Context-Aware Evidence Extraction from Mobile SQLite Databases

Jeel Piyushkumar Khatiwala, Divyangkumar Patel, Dianxiang Xu, Weifeng Xu

原文摘要

Reliable forensic evidence extraction from mobile SQLite databases is challenging because application schemas are undocumented, evolve across releases, and differ across operating systems, making schema-specific methods brittle and difficult to maintain. We present SNEEF, a symbolic-neural framework for context-aware evidence extraction that encodes each SQLite row as context-value pairs with standardized provenance metadata and column labels, uses a language model to recognize nine forensic entity types, and binds each accepted entity to its source table and row through the PA-EEP provenance protocol. We evaluate SNEEF against examiner-verified ground truth from five corpora built from public Android and iOS device images, spanning multiple application versions. SNEEF achieves an F1 score of 0.967 for evidence entities extracted from SQLite row content, excluding application names, compared with 0.728 for a context-free configuration, with consistent gains across all corpora. We also introduce an Evidence Failure Mode taxonomy to characterize why extraction errors occur.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03321

uTECH Turbo-MOVES: The Intersection of Software Architectural Innovation and Sustainable Mobility Analysis

Cafer Avcı, Alireza Yazdiani, Roopak Srinivasan, Megh Khaire, Mohammad Tayarani, H. Oliver Gao

原文摘要

Transportation is a major source of energy consumption, greenhouse gas emissions, and criteria air pollutants, making efficient emissions estimation essential for environmental planning and policy analyses. The U.S. Environmental Protection Agency's Motor Vehicle Emission Simulator (MOVES) supports detailed emissions modeling, but its computational demands constrain large-scale scenario analyses. This study introduces uTECH Turbo-MOVES, a software architecture that expedites MOVES computation without modifying its Calculator modules or input/output structure. By combining RunSpec partitioning, container isolation, in-memory intermediate storage, and concurrent workload orchestration, it improves execution efficiency. We evaluate six benchmark cases spanning County, Default, and Project scales and both Emission Inventory and Emission Rates modes. Results demonstrate a measured 5.5x and an estimated 15.3x per-run speedup for Default-scale workloads, and an 84.8x campaign speedup for an eight-county Emission Rates analysis. Although gains vary by workload and hardware configuration, uTECH Turbo-MOVES enables more extensive environmental assessments and transportation policy evaluation.

cs.SE · cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.07688

TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation

Ziheng Chen, Jiali Cheng, Zezhong Fan, Hadi Amiri, Diyuan Wu, Gabriele Tolomei, Yang Zhang

原文摘要

Generative recommendation formulates next-item prediction as autoregressive generation over semantic ID (SID) sequences derived from users' historical interactions, making modern recommender systems structurally similar to large language models (LLMs). As privacy and safety concerns grow, these systems increasingly require concept unlearning to remove sensitive or harmful concepts associated with items. However, existing LLM unlearning methods cannot be directly applied to generative recommendation. Unlike word tokens with explicit semantics, SIDs are abstract identifiers that are often shared by both forget and retain items, leading to severe conflicts between concept removal and recommendation utility preservation. To address this challenge, we propose TRACER, an end-to-end concept unlearning framework based on token reassignment. Rather than directly suppressing shared SIDs, TRACER reassigns concept-related items to alternative tokens that better facilitate forgetting while minimizing side effects on retained items. We further introduce a coherence regularizer to preserve semantic consistency among retain items during unlearning. Experiments on real-world recommendation datasets demonstrate that TRACER effectively removes target concepts while substantially better preserving recommendation utility than existing unlearning baselines.

cs.IR · cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03768

Multi-layer Indexer: Overlapping Selection and Attention in DeepSeek Sparse Attention

Serdarcan Dilbaz, Arjun Krishna, Kiran Kamble, Daniel M. Bikel

原文摘要

Sparse attention cuts the cost of long-context inference by making the attention matrix sparse: each query attends only a subset of the sequence, chosen by a fixed pattern or, in recent production LLMs, learned per query. In DeepSeek Sparse Attention (DSA), the attention of DeepSeek-V3.2 and the GLM-5 family, every layer is preceded by a lightning indexer that scores the whole cache and keeps the k=2048 highest-scoring tokens for the layer to attend. At long context this indexer scan, not the sparse attention it feeds, is where the decode time goes. But adjacent layers select nearly the same tokens, so a layer can attend the selection of the previous indexer layer instead of its own. This removes the dependency: each indexer then runs concurrently with the attention of its layer. The resulting Multi-layer Indexer (MLI) needs no retraining and no new weights. Its default realization changes no kernel, running inside SGLang's production decode graph; an optional fused decode kernel accelerates it further on DeepSeek-V3.2. MLI composes with IndexCache (the cross-layer reuse GLM-5.2 ships as IndexShare): IndexCache drops the indexer from some layers, and MLI hides the scan on the rest. We serve DeepSeek-V3.2 and GLM-5 under IndexCache's protocol at retentions 1/2 to 1/32 (the fraction of layers that keep an indexer) and contexts 10K to 195K. At equal retention the composition decodes faster than IndexCache: up to 14.5% faster on DeepSeek-V3.2 and 9.8% on GLM-5 at 1/2, and 7.9% and 5.1% at 1/4, largest where IndexCache keeps the most indexers and growing with context. At retention 1/r it is faster still than IndexCache at 1/(2r) while keeping twice the fresh selections: at 128K it reaches 1.32x the unmodified model's decode throughput at 1/4, above IndexCache's 1.28x at 1/8. Its LongBench, RULER and LongBench v2 accuracy stays within a few percentage points of IndexCache's.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2405.01708

A deep causal inference model for interpretable travel behaviour analysis

Kimia Kamal, Bilal Farooq

原文摘要

Transportation policy assessment often involves causal questions, yet traditional travel behaviour lacks the ability of distinguishing causal association from non-causal correlations. We present the deep CAusal infeRence mOdel for traveL behavIour aNAlysis (CAROLINA), a novel framework that addresses both observed and unobserved confounders in discrete choice modelling through causal discovery and the strengths of deep neural networks. Within this interpretable framework, we also introduce a Generative Counterfactual model for forecasting individuals' behaviour by adapting the Normalizing Flow method. Through the case studies of virtual reality-based pedestrian crossing behaviour, revealed preference travel behaviour from London, and synthetic data, we demonstrate the effectiveness of our proposed models in isolating causal relationships from confounding associations. Our proposed causal framework enhances predictive accuracy by employing data-driven algorithms, while maintaining interpretability. In addition, the counterfactual results show that intervention mechanisms that can reduce pedestrian stress levels lead to a 35.35% increase in individuals experiencing shorter waiting times. External interventions that reduce travel distances of non-mandatory trips in London lead to a 47% increase in the possible use of active and public transit modes.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03319

TrafficInstruct: A Multi-Task Network Traffic Dataset and Benchmark for Instruction Tuning

Zhengdao Li, Hui Wen, Zixuan Wan, Weifeng Zhang, Yan Wu, Benlong Wu, Nenghai Yu, Weiming Zhang 等 9 位作者

原文摘要

Instruction tuning for multi-task network traffic analysis requires data with clearly defined sources, supervision targets, and evaluation conditions. We introduce TrafficInstruct, a newly collected dataset comprising raw packet captures and 55,000 structured supervised fine-tuning records across ten tasks: malware traffic detection, botnet detection, malicious DNS-over-HTTPS detection, Web attack detection, advanced persistent threat detection, encrypted VPN detection, Tor behavior detection, encrypted application/service classification, website fingerprinting, and application classification under concept drift. An automated workflow captures traffic from controlled software executions, attack experiments, and application activities. Labels are supported by task-specific collection evidence, including process or socket attribution, activity records, and execution outcomes. Filtering, sanitization, and task-specific transformations produce instructions, traffic observations, and structured answers. We verify sample sources and partition the dataset by complete collection sessions, keeping all samples from the same session within one partition. The benchmark defines allowed inputs, deterministic label mappings, fixed training/validation/test partitions, and task-wise scoring rules. We evaluate majority-class, random-forest, and ET-BERT baselines, an unadapted 27B language model, and independently trained task-specific LoRA adapters on a shared frozen base. Adapters are selected using validation data and evaluated on the corresponding test sets. Task-specific adaptation improves classification performance, supporting instruction tuning across diverse traffic-analysis tasks.

cs.CR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.12352

Spatial Mixing and Deterministic Approximate Counting of Multi-spin Systems beyond Bounded Degree Graphs

Zhidan Li, Kuan Yang

原文摘要

We develop a framework for deterministic approximate counting of multi-spin systems beyond bounded-degree graphs. The algorithm recursively constructs rational polytopes containing the true marginal vectors and uses linear-fractional programming to obtain certified bounds on marginal ratios. For positive interactions on graphs of polynomial connective constant $D$, we establish strong spatial mixing whenever $Dc<1$, where $c$ bounds the Birkhoff contraction coefficients of the interactions. Under the same condition, we also give a deterministic fully polynomial-time approximation scheme (\textbf{FPTAS}) for the partition function. We further extend the framework to proper colorings of sparse Erdős-Rényi random graphs using recursion on permissive blocks. For every fixed $η\in(0,1)$, sufficiently large fixed $d$, and fixed integer $q\ge(2+η)d$, we obtain an \textbf{FPTAS} for counting proper $q$-colorings of $G\sim\mathcal G(n,d/n)$ with high probability over $G$. This improves the leading constant $3$ in the earlier counting guarantee of Yin and Zhang (APPROX/RANDOM, 2016) to $2$, and asymptotically matches the spatial mixing regime established by Yin (ICALP, 2014).

cs.DS · math.PR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.40278

Computational Bounds for $f$-Routing

Oren Renard, Nicholas Spooner

原文摘要

The $f$-routing protocol is a leading candidate for quantum position verification (Kent, Munro, and Spiller, Physical Review A, 2011), but security guarantees for explicit functions remain limited. We prove unconditional resource lower bounds for uniform attackers. Our techniques bypass communication-complexity bounds central to previous works, which are inherently at most linear in the input length. We show that, for input length $n$ and a sufficiently small constant $ε>0$, a uniform strategy running in time $T$ and succeeding with probability at least $1-ε$ on every input implies the following computational bounds on $f$. 1. If the strategies are arbitrary quantum channels, then $f\in \mathrm{QSZK}(\mathrm{poly}(T))$, where $\mathrm{QSZK}(T)$ is the class of languages having quantum statistical zero-knowledge proofs in which the verifier runs in time $T$ and the simulator in time $\mathrm{poly}(T)$. 2. If the strategies are Clifford+T circuits using at most $t$ magic gates, then $f\in \mathrm{DTIME}(\mathrm{poly}(T2^t))$. 3. If the strategies are explicit Pauli-sparse unitaries acting on at most $T$ qubits and having at most $s$ nonzero Pauli coefficients, then $f\in \mathrm{DTIME}(\mathrm{poly}(Ts))$. Time and space hierarchies then yield explicit functions secure against superlinear and even all polynomial-time adversaries under our computational restrictions. These bounds exceed the bounded-storage qubit bound $q\leq\log n$ of Bluhm, Christandl, and Speelman (Nature Physics, 2022) for the inner product function $f=\mathrm{IP}$, at the cost of restricting adversarial computation and increasing honest evaluation complexity.

quant-ph · cs.CC

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新论文 · v1arXiv:2611.03317

Steering with Multilingual Lexicons for Sentiment Control in Low-Resource Languages

Lucinda Laurent, Muhammad Dehan Al Kautsar, Alfan Farizki Wicaksono, Fajri Koto

原文摘要

Activation steering offers a lightweight mechanism for controlling model behavior, but existing methods depend on sentence-level contrastive data that is unavailable for most of the world's languages. We show that multilingual sentiment lexicons are a viable substitute across three model families: aggregated multilingual lexicons outperform English-derived steering vectors for mid- and low-resource languages, while monolingual lexicons remain stronger for languages a model has specialized in during pre-training. The steering efficacy is mechanistically dictated by both representational density and pre-training priors. Large-magnitude English vectors overwhelm the compact representations of low-resource languages, causing "linguistic collapse." This fragility is mitigated by the norm-dampening of multilingual aggregation. Architectural biases, including bilingual specialization penalty and latent numerical rigidity, also heavily constrain cross-lingual steering responsiveness. Ultimately, our work establishes lexicons as a practical steering signal and offers a comprehensive mechanistic account of cross-lingual steering dynamics.

cs.CL

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版本更新 · v3arXiv:2610.20840

A Phenomenology of Neural Geometry in Vision-Language-Action Models

Mozes Jacobs, Aiden Swann, Mathilde Papillon, Siddharth Boppana, Fenil R. Doshi, Usha Bhalla, Thomas Icard, Matt Feiszli 等 19 位作者

原文摘要

Robotic manipulation is inherently geometric, but what shape does the world take inside the policy that performs it? Prior work on vision-language-action (VLA) policies establishes that task-relevant variables are linearly readable from activations, yet readability certifies only that a variable is present and says nothing about the shape its values trace, which may be curved and multi-dimensional. Using controlled sweeps, natural rollouts, linear probes, and activation interventions, we build a taxonomy of seven task-relevant concepts in VLA models. We find that realized geometry is predominantly multi-dimensional and is shaped by the constraints under which the policy acts. Physical joint limits, for example, leave wrist orientation an open arc rather than a circle, and distance to the target, a single scalar, is realized in two orthogonal subspaces, a coarse ruler for the approach and a fine ruler for the grasp. The same geometries reappear in two further VLA models on real-world robot data, indicating that they are neither model-specific nor artifacts of simulation. We demonstrate how the findings are actionable by steering along the geometry a concept realizes. Overall, this work lays out an initial phenomenology of realized geometry in VLA policies and demonstrates that a concept's shape carries as much importance as its readability.

cs.RO · cs.LG

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新论文 · v1arXiv:2611.03313

Regular Separability for Bidirected VASS

Łukasz Orlikowski, Georg Zetzsche

原文摘要

The regular separability problem asks, given two languages, whether there exists a regular language that contains one of them and is disjoint from the other. We study regular separability for bidirected VASS. We first show that the problem remains Ackermann-complete when the input consists of two languages recognised by bidrected VASS. To find a subclass of VASS in which the problem has lower complexity we define alphabetically bidirected VASS as ones where bidirectedness is also visible in the input letters. Our main result establishes that regular separability for alphabetically bidirected VASS is EXPSPACE-complete. The key to this result is that, in this setting, regular separability coincides with disjointness. As a first ingredient in this equivalence, we also show that for alphabetically bidirected Z-VASS, regular separability and disjointness are equivalent. Consequently, regular separability for these systems is in polynomial-time. In fact, it is log-space inter-reducible with non-solvability of integer linear Diophantine equation systems. Finally, we observe that the picture changes dramatically in the presence of a zero test, namely, we show that regular separability for alphabetically bidirected one-counter automata is undecidable.

cs.FL

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新论文 · v1arXiv:2611.03309

MultiInpaintCOCO: Fine-Grained Visual Understanding in Multiple Languages

Andrei-Alexandru Manea, Jindřich Libovický, Philipp J. Rösch

原文摘要

Previous research shows that vision-language (VL) models struggle to understand fine-grained image details and tend to focus on the overall layout. Moreover, any weakness in English tends to be further amplified in the multilingual setting. To study that, we extend InpaintCOCO, an English dataset focused on fine-grained visual details, to 13 typologically diverse languages across five language families, creating MultiInpaintCOCO - a new multilingual benchmark of 953 minimal-pair examples. We further test several state-of-the-art models in multilingual multimodal retrieval, considering both light VL CLIP-like encoders and large language models. Our results show that performance degrades substantially for lower-resource languages, with the strongest predictor being the data resources available for the languages rather than typological similarity to English. In cross-lingual retrieval, most models exhibit a systematic bias toward English: when given a correct caption in a non-English language and an incorrect one in English, models preferentially select the English caption. This bias varies considerably across architectures. Among the tested models, Gemma 3 shows the strongest English preference while Qwen 3 is the least biased, despite both reaching similar monolingual accuracy across languages. The code is available at https://github.com/ufal/multi-inpaint-coco.

cs.CL

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新论文 · v1arXiv:2611.03308

LLMs Depend on Specialist Tools for Out-of-Distribution Generalization in MS/MS Structural Elucidation

Gabriel Asher, Cailum M. K. Stienstra, Tomo Oga, Devesh Shah Antonio H. de O. Fonseca, Niall O'Connor, Michael Widrich

原文摘要

A frontier language model was recently reported to solve 1D NMR structural elucidation without tools. We ask whether this capability transfers to tandem mass spectrometry (MS/MS) in the de novo setting, where the model must propose a structure directly from the spectrum without a candidate list or reference library. To separate reasoning from recall, we build a 100-analyte benchmark from MassSpecGym, split by each analyte's textual footprint into in-distribution (well documented in literature) and out-of-distribution (essentially undocumented) arms. We hold the model, harness, and output format fixed and vary only the LLM's access to domain-specific tools. Without tools, a frontier LLM reaches 52% Top-1 accuracy in-distribution but only 2% out-of-distribution. Only access to de novo structure generators improves out-of-distribution accuracy, raising it to as much as 38% Top-1, and the resulting agent outperforms the standalone de novo models both in and out of distribution. These results suggest that LLMs are not effective at de novo MS/MS structural elucidation on their own, and that further gains depend on the specialist tools available to the agent rather than on the LLM itself.

cs.LG · cs.AI · cs.CL · stat.ML

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新论文 · v1arXiv:2611.03307

Safe-Ballbot: Tilt-Consistent MPC with Control Barrier Functions for Ballbot Navigation

Duc Cuong Vu, Tung Lam Nguyen

原文摘要

Ball-balancing robots (ballbots) must lean to accelerate, and the lean is limited by the wheel torques and by safety. Planners that use the full robot model are expensive, while planners that ignore the balance dynamics produce references that cannot be followed. This paper presents a two-layer navigation scheme for a three-omni-wheel ballbot. The upper layer is a model predictive controller on a \emph{jerk model} of the ball position, with discrete-time control barrier functions for static, moving, rotating and concave obstacles. The lower layer is a hierarchical sliding mode controller with an extended state observer and a robust exponential control barrier function on the tilt (HSMC-ESO-ECBF). The layers are linked by an acceleration bound derived in closed form from the largest steady tilt that the barrier function admits, so that the plan never asks for a lean the tracker refuses. For obstacles whose motion is only predicted, the obstacles are inflated by conformal prediction radii that bound the prediction error with a prescribed probability. In MuJoCo, with omni-wheel roller contacts and torque saturation, the scheme keeps at least $10$\,cm of clearance, tracks the plan within $2$\,cm RMS, keeps the tilt below the limit and plans in $9$--$12$\,ms, $10$--$15$ times faster than a full-model nonlinear MPC that enters the inflated obstacles; with predicted obstacles, the conformal inflation removes the collisions of a constant-velocity predictor in $30$ random crossing scenarios and reduces them in an endless run on a closed path.The source code and video illustrations are available at https://safe-ballbot.github.io

cs.RO · eess.SY

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版本更新 · v2arXiv:2601.07342

From Structured Graphs to Unstructured Evidence: Agentic Root Cause Analysis on Digital Twins

Nicolas Tacheny

原文摘要

Root cause analysis over telecom and datacenter infrastructures is commonly addressed by methods that consume structured data, from graph traversal and rule-based correlation to probabilistic and learned models. Yet a digital twin of such an infrastructure cannot model everything at a reasonable cost. Operators therefore record much of what changes as unstructured data attached to the graph, where the discriminating evidence often sits. Methods restricted to structured attributes cannot use it. A diagnosis needs a layer that interprets this data. An agentic layer is a natural candidate, since which fragment matters depends on hypotheses that emerge during the investigation. We demonstrate its feasibility with ReAct, the most widely used agentic architecture. A single LLM interprets the descriptions and explores the graph through a typed tool interface exposed via the Model Context Protocol (MCP). On a benchmark of nine synthetic scenarios covering single-node, multi-node and distractor-heavy evidence (360 runs across four LLMs), a frontier-tier model names the root cause and its type on every run. It reaches 96.7% end-to-end diagnostic accuracy, which also requires the exact supporting evidence. A mid-tier model reaches 86.7%. Accuracy drops for every model when the evidence must be combined across several resources.

cs.AI

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新论文 · v1arXiv:2611.03305

Calibrated Multiaccuracy: Deterministic and Randomized Sample Complexity

Jiuyao Lu, Georgy Noarov, Aaron Roth

原文摘要

Calibrated multiaccuracy asks that a predictor be globally calibrated and multiaccurate with respect to a bounded test family $G$; that is, its residuals should be uncorrelated with every test $g(x)\in G$. This weakening of multicalibration suffices for many applications. Its sample complexity was not known to improve on that of full multicalibration, which is $\widetildeΘ\left(\frac{\log|G|}{\varepsilon^2} + \frac{1}{\varepsilon^{3}}\right)$ when $|G|$ is at least a sufficiently large power of $\log(1/\varepsilon)$. We resolve the sample complexity of $\varepsilon$-calibrated multiaccuracy, showing that it scales as $$ \widetildeΘ\left(\frac{\log|G|}{\varepsilon^2} + \frac{1}{\varepsilon^{5/2}}\right), $$ separating it from the complexity of multicalibration. The upper bound is obtained by an algorithm that outputs a randomized predictor. For deterministic predictors, we establish the tight rate $$ \widetildeΘ\left( \frac{\log|G|}{\varepsilon^2} + \frac{(\log|G|)^{1/3}}{\varepsilon^{8/3}} \right) $$ whenever $\log|G|=Ω(\log(1/\varepsilon))$. When $\log|G|\le\varepsilon^{-1+κ}$ for any fixed $κ\in(0,1)$, this rate is polynomially larger than the rate for randomized predictors. Thus, unlike for multicalibration, calibrated multiaccuracy exhibits a genuine sample-complexity separation between deterministic and randomized predictors. The matching upper and lower bounds show that the unusual cube-root dependence on $\log|G|$ is intrinsic. At $\log|G|\asymp\varepsilon^{-1}$, the minimax rates for randomized and deterministic calibrated multiaccuracy both coincide with the minimax rate for multicalibration.

cs.LG · cs.DS · math.ST

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新论文 · v1arXiv:2611.03302

EventGPT: Next-Event Pretraining on Banking Customer Histories

Denis Burakov

原文摘要

A model pretrained to predict a customer's next event can estimate the probability of future events without training on their labels and provide a reusable representation for supervised downstream banking tasks. We pretrain EventGPT, a hierarchical causal transformer, by next-token maximum likelihood estimation on the public Multimodal Banking Dataset (MBD-mini: 100,224 clients, 38.8 million transactions and 66.3 million geolocation pings). Keeping one state per event, EventGPT trains in about two hours on one L4 GPU at windows up to 8,192 tokens. From its next-event distribution, with no labels, EventGPT scores five targets of the form "the client makes a transaction of a given kind for the first time next month" at ROC-AUC 0.79 to 0.92. Gradient boosting on 114 engineered features needs 10,000 to 300,000 labeled client-months to match it on four targets and stays below it on the fifth. For product prediction, frozen EventGPT embeddings score below the engineered features (mean ROC-AUC 0.787 against 0.808 over five test folds), and a rank-averaged ensemble of EventGPT, profile fields and token counts with the engineered features adds 0.014 to 0.017, of which 0.007 to 0.016 is EventGPT's own. The code and pretrained weights are released.

stat.ML · cs.LG · q-fin.ST

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版本更新 · v2arXiv:2606.14348

Detecting Historical Turning Points in Italian Media: A Complex Systems Approach to a Diachronic News Corpus

Dario Zarcone, Salvatore Miccichè, David Sanchez

原文摘要

Introduction: The increasing availability of large-scale textual corpora has opened new possibilities for data-driven, quantitative approaches to historical analysis using Natural Language Processing (NLP). However, diachronic corpora with historical relevance from the pre-digital era remain scarce and often incomplete. Methods: We present a quantitative approach to historical analysis based on the reconstruction and exploration of a diachronic corpus of around 600,000 articles from the Italian newspaper "La Repubblica", covering all the articles published from the first of January 1985 to the 31st of December 2000 - a period of major political, social, and geopolitical change in Italy and globally. Using NLP techniques, we analyze the text at both lexical and semantic levels; we then apply tools from complex systems and statistical physics to trace shifts in media discourse over time. Results: The word usage in the corpus is bursty, with a heavy-tailed distribution of Fano factors that is robust across timescales. Changepoints in word salience and the block structure of semantic similarity allow us to detect key transition periods, such as the transition from the First Republic to the Second Republic in Italy, or major international conflicts like the Gulf War or the Kosovo War, without relying on prior labeling. Periods of war correspond to lower entropy in the semantic space, indicating a more focused discourse. Discussion: The results show how combining computational linguistics with ideas from complex systems can offer new quantitative insight into historical changes, opening up new paths for studying the dynamics of media and society through large-scale textual data.

physics.soc-ph · cond-mat.stat-mech · cs.CL

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新论文 · v1arXiv:2611.03299

Quadratic Reconfiguration of Plane Spanning Paths on Two-Layer Point Sets

Samuel Avril, Yan Gerard, Guilherme D. da Fonseca

原文摘要

Consider a crossing-free polygonal path with $n$ vertices and a flip operation that removes an edge and adds another one, without changing the vertex set and keeping the path crossing free. It is conjectured that any two such paths can be transformed into each other by a sequence of flips. Recently, Kleist et al. (2024) proved the conjecture for the special cases of vertices arranged in two convex layers. However, the number of flips in their proof may be exponential. In this paper, we show that $O(n^2)$ flips suffice. More precisely, for a two-layer point set $S=L_0\cup L_1$, where $L_0$ and $L_1$ denote the outer and inner layers, respectively, our method uses $O(|L_0|+|L_1|^2)$ flips.

cs.CG

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