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版本更新 · v5arXiv:2604.13112

A Lightweight Global-Target Framework for Multi-Domain No-Reference Image Quality Assessment in UAV Imagery

Koffi Titus Sergio Aglin, Anthony K. Muchiri, Celestin Nkundineza

原文摘要

Reliable image quality assessment is essential in applications where large volumes of images are acquired automatically and must be filtered before further analysis. In many practical scenarios, a pristine reference image is unavailable, making no reference image quality assessment (NR-IQA) particularly important. This paper introduces Multi-Metric Image Quality Assessment (MM-IQA), a lightweight multi-metric framework for NR-IQA. It combines interpretable cues related to blur, edge structure, low resolution artifacts, exposure imbalance, noise, haze, and frequency content to produce a single quality score in the range [0,100].MM-IQA was evaluated on five benchmark datasets (KonIQ-10k, LIVE Challenge, KADID-10k, TID2013, and BIQ2021) and achieved SRCC values ranging from 0.647 to 0.830. Additional experiments on a synthetic agricultural dataset showed consistent behavior of the designed cues. The Python/OpenCV implementation required about 1.97 s per image. This method also has modest memory requirements because it stores only a limited number of intermediate grayscale, filtered, and frequency-domain representations, resulting in memory usage that scales linearly with image size. The results show that MM-IQA can be used for fast image quality screening with explicit distortion aware cues and modest computational cost.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.06583

Trajectory Inference of Human Aging from Cross-Sectional DNA Methylation Data

Chandan Gupta, Syed Haider, Pietro Liò

原文摘要

DNA methylation (DNAm) serves as one of the most robust molecular biomarkers of biological aging. While conventional epigenetic clocks accurately predict chronological age from high-dimensional CpG profiles, they treat aging as a static regression task, meaning they can only output a single score rather than simulating how an entire profile continuously changes over time. To reconstruct these continuous dynamics, we frame lifelong human epigenetic aging as a trajectory inference problem across discrete age snapshots derived from widely available cross-sectional data. We introduce a two-stage computational pipeline: first, an age-regularized Variational Autoencoder (VAE) maps high-dimensional CpG profiles onto a chronologically ordered latent manifold while preserving a generative decoder bridge back to the original methylation space. Second, we model the continuous movement across this latent space via Regularized Unbalanced Optimal Transport (RUOT) that unifies deterministic drift, random diffusion, and non-conservative mass changes. By resolving this RUOT formulation using the DeepRUOT framework, our model fluidly accommodates population-level density shifts like survivorship bias and cellular attrition without requiring rigid biological priors. Evaluated on a large-scale, 80-year pan-tissue dataset, our model demonstrates robust distribution interpolation and uncovers a prominent late-life surge in the learned growth field that mathematically captures the variance expansion driven by stochastic epigenetic drift. Finally, by decoding continuous latent paths back to individual CpG sites, we reconstruct and empirically verify distinct biological aging archetypes, offering a rigorous, generative paradigm for simulating human molecular aging.

q-bio.QM · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.04663

BF16 Component-Product Emulation of FP32 and FP64 GEMM on Intel AMX

Bing Cui, Yu Liu

原文摘要

Modern CPUs increasingly integrate high-throughput matrix engines optimized for low-precision AI workloads, while many scientific computing applications still rely on FP32 and FP64 GEMM to meet their numerical accuracy requirements. This paper presents a method that combines BF16 matrix products on Intel Advanced Matrix Extensions (AMX) to emulate real square FP32 and FP64 GEMM. For FP32 GEMM, each element in both input matrices is decomposed into three BF16 components, and six selected component products are evaluated to target FP32-level accuracy comparable to oneMKL SGEMM. For FP64 inputs within the supported BF16 exponent range, the method uses a simplified fixed six-slice Ozaki decomposition. Each retained BF16 product is first produced in FP32, then widened and accumulated in FP64. Four product-count settings retain 6, 10, 15, or 21 component products, exposing the accuracy-performance tradeoff relative to oneMKL DGEMM. The FP32 schedule reuses component data and retains partial sums in AMX tiles. AMX-FP32 outperforms oneMKL SGEMM at tested orders $N\ge4096$ on 64 threads and also achieves gains for smaller matrices at lower thread counts. AMX-FP64 variants retaining fewer component products can outperform oneMKL DGEMM at sufficiently large orders; more products improve accuracy at additional cost.

cs.MS · cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.13651

DrugCoding: A Scientific Programming Agent for Drug Discovery

Junwei Hu, Zhuohao Lin, Kun Li, Hengnan Zhang

原文摘要

Drug discovery involves multiple interconnected research stages, including molecular design, property prediction, and virtual screening. These processes are characterized by complex workflows, interdependent computational tasks, and diverse specialized tools, placing substantial demands on the coordination of scientific models and computational resources. However, existing large language model (LLM)-based scientific agents still face challenges in autonomous planning of complex tasks, adaptive selection of specialized models, and reliable execution of multi-stage scientific computations, limiting their effectiveness in automating complex drug discovery workflows. To address these challenges, we propose DrugCoding, a scientific programming agent system that integrates scientific task planning, adaptive model routing, multi-agent collaboration, and programmatic tool execution. DrugCoding enables autonomous task planning and dynamic model adaptation for complex research objectives. It can flexibly invoke specialized scientific models, autonomously generate and execute code according to task requirements, and leverage multi-agent collaboration to support efficient execution and reliable validation of complex scientific tasks. To evaluate DrugCoding's performance in scientific tool invocation and multi-stage task execution, we conduct multi-turn tool-calling experiments across diverse language models and scientific tool interfaces. Experimental results demonstrate that DrugCoding achieves an average session-level Pass@1 of 97.35% on the full benchmark, outperforming the strongest baseline, Codex, by 9.24%. On the challenging subset, DrugCoding achieves 97.92%, exceeding the strongest baseline.

cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.19766

Analyzing and Mitigating Poisoning Attacks on Flow-Based Intrusion Detection

Florian Spelter, Johannes F. Loevenich, Tobias Hürten, Erik Adler, Maxime Schwarzer, Laurin Holz, Roberto Rigolin F. Lopes

原文摘要

Although Artificial Intelligence (AI)-based Intrusion Detection Systems (IDSs) are increasingly deployed in operational networks, their dependence on large-scale training data introduces a critical vulnerability to stealthy poisoning attacks. We demonstrate that latent backdoors can be successfully implanted in flow-based IDS models by poisoning as little as 0.001% of the training data, maintaining near-nominal performance on clean inputs while ensuring high attack success rates. To address this threat, we propose a diagnostic-first methodology that localizes trigger-relevant features through constrained perturbation analysis and detects poisoning-induced behavioral drift by comparison with a distilled baseline model. The proposed methodology further enables targeted mitigation via automated data sanitization or neuron pruning. Our findings expose the operational risks of near-undetectable backdoors and provide a scalable approach to analyzing, quantifying, and mitigating backdoor exposure in AI-based IDS.

cs.CR · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.01520

The risk of KV cache compression

Lukas Haverbeck, Carmen Amo Alonso, Andres Felipe Posada-Moreno, Sebastian Trimpe, Marco Pavone

原文摘要

Transformer inference on long sequences is expensive because softmax attention repeatedly reads from a large KV cache. The prevalent approach to this bottleneck is KV cache compression, which replaces the full cache with a compact summary. Despite its practical importance, the design of such summaries is largely driven by empirical experimentation. On the theoretical side, existing results show that KV cache compression can be impossible in the worst case, but offer little systematic guidance for designing algorithms in regimes where accurate compression is possible. We bridge this gap by characterizing the minimax risk of KV cache compression in terms of the intrinsic compressibility of a cache, revealing when and how accurate compression is possible. In particular, we show that, once attention can sharply retrieve individual tokens, compression can only uniformly outperform random sampling by exploiting information about future queries, regardless how redundant the cache is inherently. Additionally, our results yield novel design principles for KV cache compression under causal masking that map efficiently to prefill and autoregressive decoding while achieving minimax-optimal risk. We instantiate these principles in a practical algorithm and illustrate how our theoretical findings can be put to practical use. Overall, our results opens a principled avenue for KV cache compression with theoretical guarantees.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.18594

Autonomous Cyber Operations Gym for Training and Evaluating AI Models for Defence

Tobias Hürten, Johannes F. Loevenich, Florian Spelter, Erik Adler, Laurin Holz, Maxime Schwarzer, Thies Moehlenhof, Roberto Rigolin F. Lopes

原文摘要

This paper proposes an Autonomous Cyber Operations (ACO) gym, a unified framework integrating high-fidelity network emulation, realistic user behaviour modelling for benign network traffic and adversarial profiles, and the generation of cybersecurity scenarios through open toolkits. By supporting live and deferred feature extraction at the packet and flow levels, the gym facilitates the creation of labelled synthetic datasets for training Deep Neural Network (DNN)-based Intrusion Detection Systems (IDSs), as well as developing and evaluating modular components for autonomous defence agents through emulated interactions. While evaluation of a DNN classifier within the gym shows high in-domain performance, achieving near-perfect recall on adversarial network flows, our experiments reveal a significant sensitivity to domain shift, showing that even minor changes in network composition, such as variations in traffic protocol distribution, can lead to a catastrophic collapse in model transferability and detection performance.

cs.CR · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2410.11433

Hessian-Informed Flow Matching

Christopher Iliffe Sprague, Mert Can Kurucu, Riccardo Tedoldi, Alessandro Tibo, Jon Paul Janet, Hossein Azizpour

原文摘要

Data from physical systems often concentrate near stable states corresponding to minima of an energy function. Around these states, the curvature of the energy landscape shapes the local covariance structure of the data, which is often highly anisotropic: small perturbations around a data point raise its energy more sharply in some directions than in others. However, standard flow matching commonly connects noise to target data through isotropic conditional paths that do not explicitly follow this physical structure. We introduce Hessian-Informed Flow Matching (HI-FM), which uses the energy Hessian at each data sample to encode this local curvature in the conditional probability paths used for training. By locally linearizing energy-driven dynamics, we derive anisotropic paths that move samples toward their targets at different rates in different directions, without changing the standard flow matching objective. We evaluate HI-FM through molecular generation on Hessian-QM9 and GEOM-Drugs, using SemlaFlow as a shared backbone. With Hessians calculated offline only for the training data, across both datasets, HI-FM improves the physical and geometric quality of generated molecules while maintaining comparable chemical validity. Under a matched optimizer-update budget, HI-FM trained with only $10\%$ of Hessian-QM9 achieves lower mean energy, strain, and bond-length $W_1$ than the full-data SemlaFlow baseline. The results support local energy curvature as a useful inductive bias for flow matching.

cs.LG · eess.SY

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.20336

WithEveryone: Unified Planning and Identity Grounding for Group Image Generation

Hengyuan Xu, Qixun Wang, Yiji Cheng, Miles Yang, Zhao Zhong, Wei Cheng, Xingjun Ma

原文摘要

Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, the model must bind every reference to a distinct person and location, while training-time identity losses must establish correspondence among several noisy predicted faces. We introduce WithEveryone, a unified framework for generating group images of 5-10 reference identities. WithEveryone injects each selected identity as an addressed token, predicts a structured identity-layout plan, and renders the plan as a visual condition. Its key objective, Layout-Grounded ID Loss, uses annotated face regions to supervise the intended identities directly, avoiding unstable embedding-based face matching; ID Representation Forcing additionally trains a prediction for each identity before image synthesis. WithEveryone leads in identity similarity (0.538 versus GPT-Image-2.5's 0.472), with lower Copy-Paste (0.149 versus 0.197), 94.5% identity coverage, and 9.6% identity collisions. These results show that explicit identity-layout grounding enables identity-preserving generation to scale to larger groups without relying on direct reference-face copying.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.26514

vSV-ViT: Variable-size SuperVertex Vision Transformer for Cortical Surface Learning in Alzheimer's Disease

Geonwoo Baek, Ikbeom Jang

原文摘要

Learning from 3D meshes is challenging because the data reside on non-Euclidean surfaces embedded in 3D space. This is particularly evident in domains such as brain cortical surface analysis, where existing models typically rely on ROI-agnostic, face-based, or fixed-size patches. Such patches can duplicate boundary vertices, conflate anatomically distinct regions, or incorporate non-cortical vertices such as those of the medial wall. We develop variable-size supervertex (vSV) partitioning, which groups cortical surface vertices into vSV patches similar to superpixels in images. Building on these vSVs, we design Variable-size SuperVertex Vision Transformer (vSV-ViT), a Vision Transformer that processes vSVs through padding, mask-aware patch embedding, and vertex position embedding. We demonstrate the benefits of the proposed framework through a quantitative analysis of vertex assignment in surface partitioning and downstream tasks using Alzheimer's disease brain image datasets. For downstream tasks, we use meshes of cortical thickness and cortical curvature as input to various encoder models and evaluate classification performance. The superior predictive accuracy of vSV-ViT over recent surface-based models supports its effectiveness for cortical surface learning, with AD-related and sex prediction serving as downstream validation. Extending the framework to other cortical surface applications and broader 3D mesh domains remains future work. Code is available at https://github.com/labhai/vSV-ViT.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.10982

Catch-Only-One: Non-Transferable Examples for Model-Specific Authorization

Zihan Wang, Ethan Ma, Zhongkui Ma, Shuofeng Liu, Akide Liu, Derui Wang, Minhui Xue, Guangdong Bai

原文摘要

Recent AI regulations increasingly emphasize the need for mechanisms that preserve the utility of data for AI innovation while preventing misuse, particularly by enforcing purpose limitation in downstream AI applications. In practice, enforcing this principle remains challenging, as released data can be trivially fed into arbitrary models beyond its declared intent. Existing approaches attempt to mitigate this risk by either perturbing data or retraining models to limit unintended use. These strategies, however, offer no protection against inference by unknown or externally trained models, or fundamentally rely on control over the training or deployment. In this work, we introduce non-transferable examples (NTEs), recoded data that act as a task-level "ciphertext" decodable only by a designated model. Whereas adversarial examples exploit directions of high model sensitivity, NTEs leverage the complementary insensitive subspace. We propose a training-free, data-agnostic method that recodes data within a model-specific low-sensitivity subspace, preserving outputs for the authorized model while degrading unauthorized ones through subspace misalignment. We establish formal bounds certifying authorized-model fidelity and showing that unauthorized degradation scales with measurable spectral misalignment between models. Empirically, NTEs preserve performance across diverse vision backbones and state-of-the-art vision-language models under common preprocessing, while unauthorized models collapse even under adaptive reconstruction attacks. These results establish NTEs as a practical means to preserve intended data utility while preventing unauthorized exploitation. Our project is available at https://trusted-system-lab.github.io/model-specificity.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.13675

OpticalRAG: Pixel-Space Compression for Token-Efficient Retrieval-Augmented Generation

Senhao Liu, Yuheng Zhang, Chunyu Wei, Yueguo Chen, Xinran Zhang

原文摘要

Retrieval-augmented generation (RAG) is bottlenecked by the token budget of large language models: feeding long documents into the context window is expensive, while aggressive textual compression discards fine-grained information that is irreversibly lost in a one-dimensional token sequence. We argue that this bottleneck is not fundamental to information density but to the choice of compression \emph{modality}. Modern visual encoders can pack the contents of a rendered text page into roughly a hundred visual tokens with little semantic loss, opening a new compression-fidelity tradeoff that purely textual methods cannot reach. We present \textbf{OpticalRAG}, a framework that renders documents as page images, encodes them with a frozen vision encoder, and distills the retrieved visual tokens into as few as $16$ injected tokens before passing them to the LLM. Two ingredients make this work without expensive cross-modal training: (i) \emph{Encoder-Bridged Retrieval}, which projects text-query states into the visual feature space of the frozen encoder, through a projection shared with the compressor, to retrieve visual tokens from a text query, with no contrastive training; and (ii) \emph{Query-Driven Distillation}, a lightweight cross-attention compressor that condenses the retrieved visual tokens into a small set of query-conditioned prefix tokens. On LongBench-v2, BAMBOO, and LooGLE-v2, OpticalRAG achieves the best compression-aware accuracy (CAP) on all three benchmarks while injecting only $16$ tokens, and improves the closed-book Qwen2.5-7B-Instruct backbone by $5.17$ points on LongBench-v2. Code is available at https://github.com/brighterr/OpticalRAG.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.22294

SLIDERS: Evidence Synthesis with Autonomous Database Reconciliation for Systematic Reviews

Harshit Joshi, Priyank Shethia, Jadelynn Dao, Monica S. Lam

原文摘要

Systematic reviews requires comprehensive evidence synthesis from large document corpora to answer targeted research questions and are foundational in finance, social sciences, and other technical fields. Manual construction of evidence tables is labor-intensive, and recent LLM-based assistants relying on embedding or keyword search often fail to meet the coverage standards of systematic reviews. We introduce SLIDERS, a novel LLM-based methodology for automatically assembling evidence tables tailored to research questions for systematic reviews. SLIDERS extracts full-text excerpts that serve as direct evidence or as provenance for structured data. Core to SLIDERS is an autonomous evidence reconciliation coding agent that iteratively writes SQL to analyze and reconcile extracted evidence, bringing together information fragmented across documents, resolving inconsistencies and removing duplicates across records. We evaluate SLIDERS on BrowseComp+, achieving 90% accuracy, and show that it outperforms all baselines on additional benchmarks within the LLM's context window. To assess scalability, we introduce two new evidence-synthesis benchmarks, WikiCeleb100 (3.9M tokens) and FinQ100 (36M tokens), which include follow-up questions beyond the original research query. Although SLIDERS synthesizes evidence knowing only the original question, it answers 78.9% and 55.2% of all questions correctly on WikiCeleb100 and FinQ100, respectively

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.09364

SafeOrchestrate: Runtime Checked Intent-Driven Orchestration for Cyber-Physical Systems

Muhammad Ashfaq, Ahmed R. Sadik, Ewan Warburton, Pyry Kotilainen, Niko Mäkitalo, Tommi Mikkonen

原文摘要

Modern cyber physical systems (CPS) integrate heterogeneous sensors, actuators, controllers, and services, composed at runtime rather than at design time. Orchestrating such systems through natural language (NL) is attractive, but unconstrained large language model (LLM) tool use is risky because actions must respect device capabilities, runtime state, and safety constraints. We present Safe Orchestrate, a reference architecture for runtime checked, intent driven CPS orchestration. Given an NL intent, it derives atomic goals, generates a runtime checkable specification for each, grounds the specifications to device affordances, and verifies every candidate action against runtime state and safety constraints before execution. In a Web of Things (WoT) instantiation, the architecture produces Linear Temporal Logic (LTL) style specifications from the NL intent, grounds them to Thing Description (TD) affordances exposed through the Model Context Protocol (MCP), and checks candidate actions against satisfiability modulo theories (SMT) backed guardrails. We evaluate this instantiation on simulated smart home, greenhouse, and industrial automation tasks against a direct LLM to MCP baseline. Safe Orchestrate maintains 100% required action accuracy on every task and 100% MCP Execution Similarity (MCP ES) on all but one, where MCP ES is 92%. By contrast, the baseline degrades on multi device, event triggered, and ordered tasks, with MCP ES falling to 13%. These results suggest that combining LLM based specification generation with role aware grounding and runtime constraint checking improves reliability for open CPS orchestration without enriched TD annotations or planning domain expertise.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.21573

CHRep: Cross-modal Histology Representation and Post-hoc Calibration for Spatial Gene Expression Prediction

Changfan Wang, Xinran Wang, Donghai Liu, Fei Su, Lulu Sun, Zhicheng Zhao, Zhu Meng

原文摘要

Spatial transcriptomics (ST) enables spatially resolved gene profiling but remains expensive and low-throughput, limiting large-cohort studies and routine clinical use. Predicting spatial gene expression from routine hematoxylin and eosin (H&E) slides is a promising alternative, yet under realistic leave-one-slide-out evaluation, existing models often suffer from slide-level appearance shifts and regression-driven over-smoothing that suppress biologically meaningful variation. CHRep is a two-phase framework for robust histology-to-expression prediction. In the training phase, CHRep learns a structure-aware representation by jointly optimizing correlation-aware regression, symmetric image-expression alignment, and coordinate-induced spatial topology regularization. In the inference phase, cross-slide robustness is improved without backbone fine-tuning through a lightweight calibration module trained on the training slides, which combines a non-parametric estimate from a training gallery with a magnitude-regularized correction module. Unlike prior embedding-alignment or retrieval-based transfer methods that rely on a single prediction route, CHRep couples topology-preserving representation learning with post-hoc calibration, enabling stable neighborhood retrieval and controlled bias correction under slide-level shifts. Across the three cohorts, CHRep consistently improves gene-wise correlation under leave-one-slide-out evaluation, with the largest gains observed on Alex+10x. Relative to HAGE, the Pearson correlation coefficient on all considered genes [PCC(ACG)] increases by 4.0% on cSCC and 9.8% on HER2+. Relative to mclSTExp, PCC(ACG) further improves by 39.5% on Alex+10x, together with 9.7% and 9.0% reductions in mean squared error (MSE) and mean absolute error (MAE), respectively.

cs.CV · q-bio.QM

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.09542

NS-VLA: Towards Neuro-Symbolic Vision-Language-Action Models

Ziyue Zhu, Shangyang Wu, Shuai Zhao, Zhiqiu Zhao, Jian Zhang, Shengjie Li, Yi Wang, Anh Tuan Luu 等 11 位作者

原文摘要

Vision-Language-Action (VLA) models ground natural-language instructions in visual observations to generate actions for robotic manipulation. On long-horizon tasks, existing VLA models face structure-blind backbones, backbone-bound generalization, and flat single-objective optimization. To address these challenges, we propose a novel Neuro-Symbolic Vision-Language-Action (NS-VLA) framework. It introduces a Neuro-Symbolic Encoder that parses the instruction into a primitive plan tracked by a gated monotone stage pointer, a Neuro-Symbolic Solver that conditions a chunked VLA policy on the active sub-instruction, and Hierarchical Joint Policy Optimization, which fits the pointer on demonstration segments and refines the action policy with success-filtered online learning. Even when trained on few demonstrations, NS-VLA clearly leads on LIBERO and the held-out LIBERO-Plus (76.5% and 51.8% against 71.2% and 45.4% for its backbone), exceeds the strongest baseline by 12.7 points on long-horizon tasks, and raises real-robot success from 40.0% to 60.0%. Our code is publicly available at https://github.com/Zuzuzzy/NS-VLA.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.35885

Local Agreement Does Not Imply Global Consensus in LLM Panels

Machiko Hirota, Akshara Nadayanur Sathis Kanna, Ujwal Kumar, Phan Xuan Tan

原文摘要

Local coordination in an LLM panel need not produce a common answer. We study 50-agent panels that update clock-time estimates after observing their neighbours. This circular response space separates agreement across the whole panel from agreement within neighbourhoods. Increasing the reasoning effort of gpt-5-mini reduces variation in local agreement, but all 20 higher-effort trials retain a one-twist configuration: neighbouring estimates remain close while the panel spans the clock. Degree-preserving rewiring instead produces global synchronisation in all 25 primary clock controls. These classifications are unchanged across 2,187 combinations of diagnostic thresholds and reporting windows. A radius-, effort-, and passage-matched scoring task also shows more uniform local agreement after rewiring, without establishing greater scoring accuracy. A simple averaging model helps interpret the clock results but predicts their configurations poorly. The experiments support measuring global agreement and local structure separately, rather than inferring consensus from uniform local agreement.

cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00176

Coinduction in Grounded Arithmetic

Bryan Ford

原文摘要

A grounded arithmetic counts a sentence true only when a terminating process establishes it; a sentence no such process settles has no value. Such a logic can state that an event happens, but not that it never happens under unbounded choice. This paper extends such a logic so that it can. The base is Reflective Grounded Arithmetic (RGA) with Inductive Grounded Arithmetic (IGA)'s least-fixed-point reading and ground induction. It adds a separate greatest-fixed-point former $ν$ with Park's coinduction rule, proves that one former cannot carry both ground induction and coinduction, and adds a dichotomy rule: the classical disjunction between a least-fixed-point atom and the greatest-fixed-point atom of its dual, under a derivable premise that the body's leaves are decided. The systems are sound for an explicit model reading every $ν$-body at its greatest fixed point, hence consistent; they agree with the least model where it has a value and prove sentences it leaves without one. The dichotomy trades the underivable hypothesis that an order is well-founded for the derivable one that its steps are decided, at the price of a change of model. With ground induction the dichotomy derives the omniscience rule of the sibling system OGA, so the system has no semantics whose certificates for universals are its own proofs; without the dichotomy it has one, though unsound for the least model. The systems prove, as single sentences, that every decided tree is well-founded or has an infinite branch given by a term, total in the new model; that random assignment terminates while its divergent twin runs forever; that a fair loop terminates and the alternating-bit protocol delivers every message; that open games are determined; that the termination gap in Nakata and Uustalu's trace semantics closes; and that Cinderella wins the bucket game at capacity three. Isabelle/HOL checks every result.

math.LO · cs.LO · cs.PL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08827

ActPrune: Action-Grounded Visual Token Pruning for Efficient Vision-Language-Action Models

Sungwon Moon, Minji Kim, Hyemin Boo, Daeun Song, Jiyoung Lee

原文摘要

Vision-language-action (VLA) models perform versatile manipulation tasks from language instructions, but their inference cost limits how fast a robot can respond in closed-loop control. Visual token pruning reduces this cost; however, prior methods estimate token redundancy from encoded visual embeddings or attention, only after the visual encoder has processed the entire observation. We propose ActPrune, a training-free token pruning method that moves the pruning decision ahead of the vision encoder by grounding it in a geometric prior derived from the robot's own action. The action chunk predicted at the previous control step is already available before the current observation is encoded, and it specifies the end-effector and camera motion between consecutive observations. The same action affects the two standard camera views asymmetrically: in the fixed third-person view, ActPrune retains patches along the projected end-effector trajectory together with semantic anchors, while in the wrist view, it prunes patches that remain unchanged after compensating for the action-induced camera motion. Extensive experiments on two simulation environments (LIBERO and VLABench) and four real-robot tasks demonstrate that ActPrune achieves up to 1.61$\times$ speedup and 59.5% FLOPs reduction with negligible degradation in success rates. The project site is http://mmai.ewha.ac.kr/actprune/.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.11098

Efficient VLA Inference via Retrieval-Based Speculation and Hardware Acceleration

Zhengyang Cheng, He Zhu, Zeyu Guo, Zihan Liu, Anbang Wu, Yiming Gan, Jingwen Leng, Yu Feng 等 9 位作者

原文摘要

Vision-language-action (VLA) models enable robots to understand and execute complex instructions, but their autoregressive (AR) action generation introduces significant latency on resource-constrained robotic platforms. Speculative decoding can accelerate AR-based inference by parallelizing action generation and verification; however, existing approaches rely on costly draft models, which are inefficient on edge platforms. In this paper, we propose Zora, a hardware-software co-design framework for accelerating AR-based VLA inference. Specifically, Zora introduces a lightweight retrieval-augmented generation (RAG)-based draft model that exploits robotic dynamics without additional neural draft models. We further reduce batch verification overhead through a kinematics-guided action-acceptance strategy and a KV-cache-sharing attention mechanism. To address the retrieval overhead introduced by RAG-based drafting, we co-design dedicated hardware support, Zora core, to address the memory irregularity in RAG-based drafting. We evaluate Zora on four VLA models with the LIBERO benchmark. Zora retains comparable accuracy while achieving up to 3.4$\times$ speedup with software-only optimization and up to 7.6$\times$ speedup with hardware support over GPUs.

cs.AR · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.41405

Conditional Neural SDEs for Financial Time-Series Generation

Sina Molavipour, Alireza M. Javid, Björn Löfdahl, Mikhail Nechaev

原文摘要

Conditional generation of time-series paths is a central problem in probabilistic modeling, where the goal is to simulate future trajectories that are both realistic and responsive to external context. It has direct applications in finance and economics. Neural stochastic differential equations provide a natural framework for this task by combining continuous-time stochastic dynamics with learnable drift and diffusion functions. However, directly modeling the target state can be unstable in conditional settings, especially when the target and conditioning variables have strong level dependence, limited training data, or different dimensions. We propose a residual-anchor Neural SDE architecture that decomposes the target into a learned conditional anchor and a stochastic residual. The anchor captures the context-dependent baseline, while the Neural SDE (Stochastic Differential Equation) models the residual dynamics around this baseline. This separation reduces the burden on the stochastic dynamics and generalizes spread-based representations to more general conditioning settings. We evaluate the approach on real-world financial time series, including conditional interest-rate scenarios and a comparison with a time-series foundation model. The results show that the proposed model can generate plausible conditional paths, maintain competitive point accuracy, and provide calibrated uncertainty estimates.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.07579

Per-Query Recall Guarantees for Approximate Nearest Neighbor Search in Dynamic Vector Databases

Woong-Kee Loh, Suan Lee

原文摘要

Vector search now runs as an operator inside database systems, where a missed neighbor makes a query answer wrong, yet approximate nearest neighbor (ANN) indexes are tuned only for a target average recall. Tuned to an average recall of 0.99, indexes left 5-19% of in-distribution queries and, without a query log, up to 99.6% of out-of-distribution queries below it (more under updates) and changed a top-5-tag aggregate over the results for up to 27% of queries. In the spirit of approximate query processing, we present PQR-Search, a $k$-ANN operator that guarantees $\Pr[\mathrm{Recall}(q) < τ] \le δ$ for every query and, for $τ< 1$, $\mathbb E[\mathrm{Recall}(q)] \ge τ$. The probability is over a random rotation inside the index alone, so no distributional assumption is needed, only that queries and updates do not depend on it, and the guarantee holds after insertions and deletions without retuning. PQR-Search prunes 1-bit codes of rotated IVF residuals with exact error quantiles and predicts its cost without data-fitted constants. On 20 datasets of up to 100M vectors, failure rates stayed at most $δ$ in every audit, including the NeurIPS'23 streaming runbook, and the aggregate changed in at most 0.04% of trials. The guarantee costs 2.5$\times$ the single-query latency of HNSW tuned as above and 1.3$\times$ lower batch throughput (geometric means). PQR-Search was 26$\times$ faster than FAISS exhaustive search in batch, faster than IVF with $n_{\rm probe}$ chosen afterwards to meet the target on every test query, and 27-76$\times$ faster than PUFFINN, which also guarantees per-query recall.

cs.DB

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.13597

Compute the Interactions, Not the Attributes: What a Fact Layer Must Supply in the Chinese Sexagenary System

Huixue Han, Jiaying Liu

原文摘要

Language-model systems in rule-governed domains often pair the model with a deterministic fact layer, but what that layer should compute is rarely measured. We measure it in the Chinese sexagenary system underlying BaZi chart analysis, where every fact has one table-checkable value: 682 items over twelve fact categories and 160 transit--natal collision items, each posed on a ladder of rungs that differ only in how much of the computation is done for the model. Answering directly, none of eleven open-weight models gets more than 4\% of sexagenary days or 1\% of collisions right, and supplying the attributes of every pillar does not change the collisions. A complete list of computed relations is not enough either: it leaves Qwen3-32B at 1\%, with relations listed for other pairs misattributed to the queried pair, whereas a query-filtered layer from follow-up runs, stating only the queried relations pillar by pillar, gives 92\%. On chess positions, two predictions locked in advance hold: attributes do not help, and unqueried relations are again misattributed. Three proprietary reasoning models compute 68--95\% of collisions unaided, while reasoning in smaller open models overrides supplied facts. A fact layer should compute the interactions and return the ones the question asks about.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2512.05551

Automated Code Review Assignments: An Alternative Perspective of Code Ownership on GitHub

Jai Lal Lulla, Raula Gaikovina Kula, Christoph Treude

原文摘要

Code ownership is central to ensuring accountability and maintaining quality in large-scale software development. Yet, as software supply chain attacks threaten project health and quality assurance, mechanisms for assigning and enforcing responsibility have become critical. In 2017, GitHub introduced the CODEOWNERS feature, a file in the repository that names the developers responsible for specific files and automatically requests them as reviewers, to strengthen accountability and protect critical parts of the codebase. Little is known about how CODEOWNERS is adopted and practiced. We present the first large-scale empirical study of CODEOWNERS usage across over 844,000 pull requests with 1.9 million comments and over 2 million reviews. We identify 10,287 designated owners, the developers named in CODEOWNERS files, track their code review activities, and compare them with contribution-based owners, the developers who contributed at least 5% of a file's lines. Results indicate that designated owners take part in a moderate share of the pull requests that touch their files, that their collaborative behavior resembles that of contribution-based owners, and that after adoption merge time and review effort grow more slowly, or fall, relative to their pre-adoption trend. Using regression discontinuity design analysis, we further find that designated owners' review, decision (approval and merge), and authoring activity jumps upward at adoption, and that over the following year they do more review and decision work per pull request than before, whereas contribution-based owners do less review work per pull request. Our results position CODEOWNERS as a promising yet sparsely adopted mechanism for improving software governance and resilience. We discuss how projects can leverage this alternative ownership method to enhance security and accountability in open-source development.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.24237

Computational Complexity of the Interval Ordering Problem

Simeon Pawlowski, Vincent Froese

原文摘要

We study an interval ordering problem introduced by Dürr et al. [Discrete Appl. Math. 2012] which is motivated by applications in bioinformatics. The task is to order a given set of n intervals with the goal of minimizing a certain objective which is defined via a given cost function $f$ which assigns a cost to the exposed part of each interval (that is, the pieces not covered by previous intervals). We develop a dynamic programming approach which solves the problem with $O(2^n\text{poly}(n))$ oracle calls to $f$ and arithmetic operations. Moreover, our approach yields polynomial-time algorithms for all cost functions $f$ such that $f-f(0)$ is subadditive or superadditive. This answers an open question for the function $f(x)=2^x$. We contrast these results by proving a running time lower bound of $2^{n-1}$ for any algorithm that solves the problem for every function $f$ (with oracle access only) and further proving NP-hardness for some classes of simple functions. Thus, we significantly narrow the gap regarding the computational complexity of the problem.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.16140

Internal Reasoning and Consistency in Reflective Grounded Arithmetic

Bryan Ford

原文摘要

Reflective Grounded Arithmetic (RGA) is a paracomplete arithmetic allowing direct expression of any general-recursive computation. Every RGA term is a program and truth is grounded evaluation, with a proof certificate at each universal quantifier. This paper shows, in a machine-checked Isabelle/HOL development, that such a system is powerful enough to carry out its own metamathematics. A fuel-indexed decider of RGA's grounded semantics and checkers for RGA's proofs are compiled primitive-recursive RGA terms. RGA proves internally that its decider never affirms 0 = 1, and that every well-formed term it affirms has an RGA proof accepted by a transparent test. RGA proves that, for each valid RGA proof, its conclusion is true. RGA proves that each grounded-true well-formed term is provable. RGA proves that no proof in either of two decided classes of RGA proofs derives 0 = 1. RGA's truth predicate also serves as a medium for proof by reflection, of the kind that adds nothing to the system. RGA derives disquotation for quantifier-free formulas as rules in every context, at a free variable, and proves, as one sentence, that whenever its normalizer for polynomials in one variable gives two terms the same normal form, the equation between them is true at every value of the variable. Each such polynomial identity then follows from that sentence by closed evaluation and disquotation, with no reflection rule. A companion paper proves Gödel's incompleteness theorems for RGA and its extensions.

math.LO · cs.LO · cs.PL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.12063

From Token Bills to Benchmark Weeks: Making Auto-Research for Data Systems Verifiable and Affordable

Kaiwen Zheng, Srijan Srivastava, Zheng Yan, Xinpeng Zhao, Shaohua Li, Chieh-Jan Mike Liang, Tianyu Chen, Haoxian Chen 等 9 位作者

原文摘要

AI auto-research is poised to forge bespoke data systems, from key-value stores and indexes to query engines, each engineered for a specific workload and hardware platform. Yet this promise rests on fragile ground. An agent optimizes what it can measure, not necessarily what users need. Natural-language requirements leave ample room for specification gaming, producing systems that blaze through benchmarks while silently losing updates or violating consistency. Worse, each research iteration consumes tokens, while validating a candidate under real concurrency, memory pressure, and failure conditions can stretch the entire research process into weeks. In this paper, we envision a more cost-efficient and verifiable approach to auto-research. Precise and composable specifications anchor the search, lightweight performance critics prune costly dead ends, and falsification and deductive verification make correctness an integral part of the process rather than an afterthought. By treating correctness, performance, research time, and token cost as equally important objectives, we aim to transform AI-generated data systems from plausible artifacts into trustworthy, production-grade systems whose behavior can be understood, audited, and ultimately relied upon.

cs.DB

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.12477

MEME: Multi-entity & Evolving Memory Evaluation

Seokwon Jung, Alexander Rubinstein, Arnas Uselis, Sangdoo Yun, Seong Joon Oh

原文摘要

LLM-based agents increasingly operate in persistent environments where they must store, update, and reason over information across many sessions. While prior benchmarks evaluate only single-entity updates, MEME defines six tasks spanning the full space defined by the multi-entity and evolving axes, including three not scored by prior work: Cascade and Absence (dependency reasoning) and Deletion (post-removal state). Evaluating six memory systems spanning three memory paradigms on 100 controlled episodes, we find that all systems collapse on dependency reasoning under the default configuration (Cascade: 3%, Absence: 1% in average accuracy) despite adequate static retrieval performance. Prompt optimization, deeper retrieval, reduced filler noise, and most stronger LLMs fail to close this gap. The largest gain comes from a file-based agent paired with Claude Opus 4.7 as its internal LLM, which partially closes the gap at roughly 70x the baseline cost. Code and data are available on the project page: https://seokwonjung-jay.github.io/meme-eval/

cs.LG · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.34480

When Does an Image Determine the Answer? Benchmarking Visual Answerability across Charts and Scenes

Sungguk Cha, Mintae Kim, Youngsub Han, Byoung-Ki Jeon, Sangyeob Lee

原文摘要

Reliable visual question answering requires correct answers when evidence is sufficient and abstention when it is not. We introduce a benchmark of 3,000 question groups: 1,000 each from PlotQA charts, CLEVR rendered scenes, and GQA photographs. Each group keeps the question fixed across original, answer-preserving, and evidence-removing images; charts and CLEVR also include answer-changing edits. Models receive each image independently. A group succeeds only if every supported answer and every required abstention is correct. For charts and CLEVR, executable witnesses establish ambiguity under the declared task semantics: two admissible complete charts or scenes give different answers but yield the same masked observation. GQA scoring compares source labels with a permissive rule that also credits masked-image answers compatible with the full-image reference. Across 72,000 responses from six model configurations, the best complete-question success is 57.0% on charts, 43.5% on CLEVR, and 33.7% on GQA under source labels. On charts, the strongest configuration makes correct answer-or-abstain decisions on 96.2% of individual images and on all five images of 83.5% of questions. Requiring correct answers lowers success to 57.0%: in 26.5% of all chart questions, every decision is correct but at least one answer is wrong. Adding answer correctness also reverses a chart ranking based on decision accuracy. Evaluating all response obligations together reveals failures that per-image decision accuracy conceals.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2505.08755

Computing Projective Implicit Representations from Poset Towers

Tamal K. Dey, Florian Russold

原文摘要

A family of simplicial complexes connected by simplicial maps and indexed by a finite poset $P$ is called a poset tower. Poset towers subsume multi-parameter filtrations, zigzag filtrations, and one-parameter simplicial towers, while allowing arbitrary finite posets and simplicial maps. The homology of a poset tower is a $P$-persistence module. To compute it globally over $P$, we consider the chain complex segment of $P$-persistence modules $C_{\ell-1}\xleftarrow{\partial_{\ell}}C_\ell \xleftarrow{\partial_{\ell+1}}C_{\ell+1}$ induced by the simplices of the tower. Unlike in one-critical multi-filtrations, the chain modules $C_\ell$ need not be projective and may have a complicated structure. We address the problem of replacing this segment by projective modules and $P$-graded matrices while preserving homology. The resulting projective implicit representation (PiRep) plays the role of the graded boundary-matrix representation in the classical persistence algorithm: it converts simplicial data into algebraic input on which persistent homology can be computed globally over $P$. In particular, a PiRep can be used as input to algorithms for computing minimal presentations of persistent homology. We give an efficient algorithm to compute a PiRep from a poset tower. It constructs degreewise minimal presentations and asymptotically minimal second terms of projective resolutions of the chain modules $C_\ell$, lifts the boundary maps $\partial_\ell$ to these resolutions, and assembles the resulting data into a PiRep using an additional correction term. The method is tailored to chain complexes induced by poset towers and computes the required algebraic data combinatorially, exploiting their special structure and avoiding general-purpose algebraic reduction. In the context of poset towers, it is fully general and can serve as a foundation for efficient algorithms on specific posets.

math.AT · cs.CG

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arXiv ↗PDF ↗