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新论文 · v1arXiv:2611.04624

Polyp Matching Using Deep Learning

Kristian Dalsbø Hindberg, Puneet Sharma, Benedicte Schelde-Olesen, Ulrik Deding, Jakob Frederik Frøkjær Justsen, Jan-Matthias Braun, Esmaeil S. Nadimi

原文摘要

A key challenge in colon capsule endoscopy (CCE) analysis is accurately identifying whether multiple images depict the same polyp, which is critical to avoid over-counting and unnecessary clinical interventions. Additionally, this prevents prolonged follow-up colonoscopies where endoscopists search for duplicate polyps without precise localization. This study presents a novel contrastive learning-based approach to polyp matching using a Siamese neural network architecture. The model was trained on a dataset from the Danish CareForColon2015 trial. The dataset comprises 5,103 image pairs of confirmed same polyp and 5,103 pairs of different polyps, annotated by experienced clinicians. The proposed model employs ResNet variants (ResNet18, ResNet34, ResNet50), DenseNet121 and EfficientNet B0/B3 as backbones and utilizes contrastive learning with cosine vector distance metric for classification. Experimental results demonstrate high classification accuracies of up to 92% and AUC values close to 0.98. These results suggest that automated polyp matching has the potential to reduce polyp over-counting, improve patient risk stratification, and reduce unnecessary follow-up colonoscopies.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2507.18229

From Individual Learning to Market Equilibrium: Correcting Structural and Parametric Biases in RL Simulations of Economic Models

Ruxin Chen, Zeqiang Zhang

原文摘要

Applying Reinforcement Learning (RL) to economic modeling reveals a fundamental tension: standard RL agents do not behave as competitive price-takers. While macroeconomic models typically assume agents take aggregate conditions as given, a naive single-agent RL algorithm learns to internalize its impact on the environment, effectively exerting market power. We illustrate this divergence in a search-and-matching model with concave production, showing that a standard RL agent converges to a monopsonistic allocation rather than the competitive equilibrium. Furthermore, we show that RL's approach to time discounting distorts standard intertemporal economic trade-offs. To resolve these issues, we propose a Calibrated Mean-Field Reinforcement Learning framework that restricts the agent to optimize against an exogenous aggregate state to restore price-taking behavior, and recalibrates the RL reward structure to reflect true economic opportunity costs. By iteratively updating the aggregate state, our algorithm converges to a stationary fixed point that recovers the competitive equilibrium. This approach provides a method for embedding learning algorithms into dynamic general equilibrium models.

econ.GN · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.35107

DoAtlas-2: A Foundation for Self-Evolving Causal Biomedical Discovery

Yulong Li, Rong Xia, Yuxuan Zhang, Jianxu Chen, Xiwei Liu, Haochen Xue, Maosheng Li, Yuhang Liu 等 15 位作者

原文摘要

We introduce DoAtlas-2, a foundation for self-evolving causal biomedical discovery that organizes knowledge around causal mechanisms and advances through external evidence from human populations. DoAtlas-2 integrates 771 research resources covering more than 720,000 participants in 48 countries, from longitudinal clinical phenotypes, medical imaging, and continuous physiological signals to eight molecular layers, together with an evidence network of approximately 4.7 million literature-derived records over 93,566 concepts and 149,383 candidate causal relations. DoAtlas-2 autonomously formulates research questions from evidence gaps and unresolved mechanisms, prespecifies their causal designs, and generates validated analyses. Supporting, challenging, and unresolved results continuously revise mechanistic interpretations, the causal evidence state, and the discovery frontier, so that DoAtlas-2 self-evolves within a closed loop of hypothesis generation, empirical testing, and renewed discovery. DoAtlas-2 has systematically evaluated 2,031 research questions. In the Human Phenotype Project (HPP), it formulated 4,014 candidate pathway questions across vascular, early-glycemic, and hepatic-metabolic systems, and screening of the first 1,079 yielded statistical support for 756. Representative studies identify blood pressure as a convergence node linking adiposity, hepatic, and lipid phenotypes to vascular outcomes, and show that an adiposity-inflammation-blood-pressure pathway is largely attenuated by joint adjustment for body mass index (BMI) and smoking. The discovered vascular network constitutes a completely interpretable predictive foundation, admitting exact attribution of every prediction and closed-form mediation effects. DoAtlas-2 thereby unifies causal mechanism discovery, population-evidence testing, and interpretable prediction within one continuously evolving foundation.

cs.AI · q-bio.QM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00006

Cultural Evolution Drives the Emergence of Conventions in Neural Emergent Communication

Maxime Toquebiau, Jérôme Botoko Ekila, Paul Van Eecke, Katrien Beuls

原文摘要

Recent studies of language evolution with neural agents often use models where agents can either speak or listen, where there are only two agents in the population, or where agents can share internal representations. These choices prevent the study of how language emerges through cultural evolution, i.e. through the cumulative adaptation of individual behaviours to fit the communicative needs of the community. We tackle this issue with a new model that trains populations of bi-directional neural agents in a fully distributed fashion to solve a referential game. We demonstrate the effectiveness of this approach on multiple image datasets and show that it is able to scale to large populations. We also show that more conventional languages are easier to learn, providing a direct advantage for cultural transmission. Overall, this work provides a novel approach to neural emergent communication that opens up new directions for evolutionary linguistics.

cs.NE · cs.CL · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04620

Refined CMA-ES for CEC 2026 Bound-Constrained Single-Objective Optimization

Adam Stelmaszczyk, Rafał Biedrzycki, Jarosław Arabas

原文摘要

We present a simplified and parameter-adjusted variant of CMA-ES designed for the CEC 2026 Bound-Constrained Single-Objective Optimization competition. Unlike typical algorithmic developments that increase complexity by adding mechanisms, Refined CMA-ES systematically removes components that do not improve performance, such as the flat-land escape, while retaining standard features that provide measurable benefits. Adjusting key parameters (population size and initial step-size) yields substantial gains, outperforming the baseline CMA-ES and several state-of-the-art competitors. The paper highlights the practical relevance of individual CMA-ES components under the CEC 2026 ranking, which equally weights solution accuracy and convergence speed, demonstrating that simplification combined with improved parameter settings can lead to superior performance.

cs.NE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2410.13508

Formalizing Hyperspaces and Operations on Subsets of Polish Spaces over Abstract Exact Real Numbers

Michal Konečný, Sewon Park, Holger Thies

原文摘要

Building on our prior work on axiomatization of exact real computation by formalizing nondeterministic first-order partial computations over real and complex numbers in a constructive dependent type theory, we present a framework for certified computation on hyperspaces of subsets by formalizing various higher-order data types and operations. We first define open, closed, compact and overt subsets for generic spaces in an abstract topological way that allows short and elegant proofs with computational content coinciding with standard definitions in computable analysis and constructive mathematics. From these proofs we can extract programs for testing inclusion, overlapping of sets, et cetera. To enhance the efficiency of the extracted programs, we then focus on Polish spaces, where we give more efficient encodings based on metric properties of the space. As various computational properties depend on the continuity of the encoding functions, we introduce a nondeterministic version of a continuity principle which is natural in our formalization and valid under the standard type-2 realizability interpretation. Using this principle we further derive the computational equivalence between the generic and the metric encodings. Our theory is fully implemented in the Coq proof assistant. From proofs in this Coq formalization, we can extract certified programs for error-free operations on subsets. As an application, we provide a function that constructs fractals in Euclidean space, such as the Sierpinski triangle, from iterated function systems using the limit operation. The resulting programs can be used to draw such fractals up to any desired resolution.

cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04619

Collaborative Search Meets Large Language Models: An Empirical Study

Peiyuan Gong, Jiamian Li, Erhan Zhang, Fengming Liang, Kexin Liu, Yue Chen, Jiaxin Mao

原文摘要

Collaborative search enables users to address complex information needs jointly. Advances in large language models (LLMs) enable autonomous agents to support collaborative information seeking within shared communication spaces. However, little is known about how these agents shape search experiences, behaviors, and outcomes compared with conventional search tools. We conduct a user study with 80 students working in 40 pairs. Each pair completes four collaborative search tasks under four scenarios: QAAgent, which answers queries using an LLM's parametric knowledge; CoSearchAgent, which uses multi-user conversational context to reformulate queries, retrieve web information, and generate source-grounded answers, while asking clarifying questions when needed; an in-chat search plugin that returns result lists; and manual Google Search. After each task, participants complete several questionnaires assessing their search experience and independently write a report; after all tasks, they participate in individual interviews. Results show that LLM-based agents reduce perceived workload, support collaboration, and reshape communication and evidence-use practices. They also encourage more independent paraphrasing, albeit at the cost of report richness. To the best of our knowledge, this is the first systematic empirical comparison of LLM-based agents and conventional tools for collaborative search across search experience, user behavior, and collaborative outcomes.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04616

Gradient-Guided Orientation Learning for Weakly Supervised Oriented Object Detection

Junyong Jang, Juheon Park, Minyoung Jeon, Jaehyup Lee

原文摘要

Weakly supervised oriented object detection (WS-OOD) aims to reduce the annotation cost of rotated bounding boxes by leveraging cheaper annotations such as horizontal boxes or points. However, these weak annotations provide no explicit orientation information, forcing existing methods to infer object angles through indirect geometric consistency or equivariant constraints. Such indirect supervision often becomes ambiguous and unstable under real-world appearance variations, including illumination changes, color shifts, shadows, and background clutter. In this paper, we present a simple yet effective gradient-guided orientation learning strategy for WS-OOD. Motivated by the observation that the input image itself contains useful orientation priors, our method extracts dominant gradient-based orientation cues from object regions and converts them into pseudo-angle labels for auxiliary supervision, enabling the detector to learn orientation-aware representations without requiring rotated bounding box annotations or extra learnable parameters. Furthermore, since AP can mask substantial angular errors when spatial IoU remains high, we introduce an orientation-aware evaluation metric that incorporates angular discrepancies into the standard AP protocol. Extensive experiments on weakly supervised oriented detection benchmarks demonstrate that the proposed pseudo-angle supervision improves both detection performance and orientation estimation quality, while the proposed metric provides a more faithful assessment of angle prediction than conventional AP alone.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.20701

Stable Movement for Nondual Lipschitz Convex Optimization: Efficiency and Nearly Optimal Oracle Rates

David Martínez-Rubio, Cristóbal Guzmán

原文摘要

We study efficient algorithms for realizing the first-order oracle complexity of optimization of $G$-Lipschitz convex functions with respect to the $\ell_q$-norm over an $\ell_p$-ball of radius $R$, where $1\leq p,q\leq\infty$. For $p<q$, we obtain error $\widetilde{O}_{p,q}(GR/T^{1/p-(1/q-1/2)_{+}})$ after $T$ oracle queries, attaining the lower bounds in a COLT 2015 open problem (Guz15b) with efficient algorithms. In particular, the rate is $\widetilde{O}(GR/T)$ for Euclidean Lipschitzness with an $\ell_1$-ball domain of radius $R$ ($p=1,q=2$). Our solution consists of reducing convex Lipschitz optimization to the chasing nested convex sets problem (BBE+20) in sublevel sets of an evolving maximum of affine lower bounds, that is, the bundle (LNN95): at each query we either find a point with low function value or we produce a deep cut in the current sublevel of the bundle that we chase. The dichotomy between stability of selectors and forced movement by deep cuts bounds the number of iterations of the algorithm nearly optimally. For nested convex subsets of the $\ell_p$ ball $R B_p^d$, we introduce novel stable selectors whose movement is nearly optimal in high dimensions. A Monte Carlo average of the proposed selector achieves near-optimal rates with high probability and can be implemented for our optimization algorithm in polynomial time in the real-arithmetic model.

math.OC · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04614

Policy Network Diffusion

Jiate Liu, Deheng Ye

原文摘要

Diffusion models have shown remarkable progress in AI-generated content, including images and videos. In this paper, we explore whether diffusion models can generate the parameters of policy networks in reinforcement learning (RL), and whether the generated policies exhibit generalization capabilities. To achieve this, we propose Diffusion-based Conditional Policy Network Parameter Generation (DPPG), which consists of a Transformer-based autoencoder and a task-conditioned diffusion model. The autoencoder achieves a high compression rate by patchifying network parameters, while the task-conditioned diffusion model generates corresponding parameters conditioned on different environments. Our experiments demonstrate that, with a single unified DPPG model, we can synthesize high-quality policies across a diverse set of tasks from different environment domains, including Classic Control, Atari games, and MuJoCo. Additionally, our method can generate networks with a larger number of parameters compared to previous approaches. Furthermore, we find that in previously unseen environments, DPPG can generate parameters that achieve faster convergence and reach competitive performance, showcasing the potential future applications of parameter generation via diffusion.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04611

CAPSkip: Cancellation-Aware Prefix Skipping for Selective Backpropagation

Heejin Choi

原文摘要

Per-step selective back-propagation forwards every example of a mini-batch but back-propagates only some of them. Most rules rank examples by loss or gradient magnitude, but such scalar scores ignore cancellation between per-example gradients: the gradient removed by a set can be much smaller than its individual magnitudes suggest. We introduce CAPSkip (Cancellation-Aware Prefix Skipping). Among the examples the model already classifies correctly, CAPSkip skips the longest prefix, in increasing order of last-layer gradient norm, whose vector sum stays within a relative budget $b$ of the batch's last-layer gradient. In exact arithmetic this bounds the change of the raw final-layer gradient, using rank-one contributions already available from the forward pass; the bound does not extend to deeper layers, optimizer updates, or the training trajectory. Because the cumulative distortion is not monotone in the prefix length, CAPSkip scans all prefixes; a factorised Gram matrix makes the scan cheap and keeps it on the device. With activation reuse and selected-row backward, CAPSkip fine-tunes DeiT-S on CIFAR-100 for 103 epochs in 0.576x the time of 103-epoch dense training, and is +0.25 pp [+0.08, +0.42] above an approximately time-matched 59-epoch dense run (n=3).

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04609

Making Smell Actionable in Design: Lexicon, Movement Patterns, and Gesture Cards

Anran Feng, Shengyu Gao, Xingyu Zhao, Zhenyu Qian, Jingrui An

原文摘要

Smell is powerful in design, yet it often remains vague and difficult to work with in studios and classrooms. While olfactory HCI has produced devices, toolkits, and scenario-based applications, designers still lack teachable, shareable resources that link smell to bodily action. This paper presents a three-phase research program that makes smell actionable by moving from lived smell experiences to movement patterns and, finally, to gesture resources. We study olfactory-motor mappings and package the results as teachable design representations through a progressively narrowing method: (1) extracting smell-verb-intent relations from user narratives; (2) capturing and clustering embodied enactments into standardized upper-limb trajectory families; and (3) translating these trajectories into gesture card representations for design teaching and ideation through design workshops. We present a three-phase translational framework and empirically derived smell-gesture correspondences that structure the smell-intent-trajectory-gesture pathway, together with reusable assets (lexicon, trajectory atlas, gesture tables, and cards) that support teaching, reuse, and adaptation across contexts.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.05191

Cutting Down the Tower: Single-Exponential Envy-Free Cake Cutting

Qilin Ye, Yannan Bai

原文摘要

Envy-free cake cutting is a central problem in fair division with a striking divide between existence and computation. Classical topology guarantees that envy-free allocations exist, yet finding one efficiently (in the Robertson-Webb query model) has resisted decades of work. The seminal work by Aziz and Mackenzie established the existence of a bounded protocol for every $n$, but its query bound is $n^{n^{n^{n^{n^n}}}}$. Subsequently, Sokolov modified their protocol and sharpened its analysis, obtaining a bound of $n^{8n^2(1+o(1))}$ queries, the best deterministic upper bound known before this work. In contrast, the general lower bound, due to Procaccia, is merely $Ω(n^2)$. We close much of this gap with a deterministic protocol using $n^{O(1)}2^n$ queries, the first single-exponential bound for general valuations. Like Aziz and Mackenzie, we repeatedly allocate cake without envy until some agents can leave with their pieces, guaranteed not to envy anyone who stays, even if a staying agent receives all the remaining cake. To reach this state, Aziz and Mackenzie generate $n^{n^{n^n}}$ partial allocations so that enough of them can be redistributed without conflict. Instead, we show that only polynomially many partial allocations suffice. Our key idea is to ``prune'' scarce allocations upfront: if only a few allocations can help a certain pair of agents, we discard them and handle that pair another way, leaving plenty of allocations for every remaining pair so each gets a distinct one. As a result, we make only polynomially many calls to Aziz and Mackenzie's subroutines, and this gives us our $n^{O(1)}2^n$ bound.

cs.GT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.39550

Beyond Preference Following: Benchmarking Profile-Query Arbitration in Personalized Text Transformation

Tenghao Ji, Qichen Sun, Zhuoran Lu

原文摘要

Writing assistants use persistent preferences to personalize responses, but these can conflict with the current request. Responses can meet every requirement yet serve the task poorly, while task failures need not involve following a conflicting preference. We introduce Profile Conflict Arbitration (PCA-Bench), a benchmark of 64 rewriting tasks and 256 task-preference pairs. We construct these pairs by varying the profile preference for each fixed text and request, covering different relations between the preference and the request. We assess whether responses meet requirements, how well they achieve the request's goals, and whether they follow preferences. Across three LLMs, we find that when preferences compete with task goals, a higher success rate in meeting both requirements and preferences does not mean better responses: it can come with a greater quality loss compared with responses without a profile. Similar rates of preference following do not imply the same ability to meet the current task's requirements when preferences conflict with them.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.08259

Stable Voting Rules on the Edge of Optimal Metric Distortion

Ziyi Cai, Fabian Frank, Jabari Hastings, Prasanna Ramakrishnan, Kangning Wang, Qilin Ye

原文摘要

We prove the existence of a randomized voting rule with metric distortion at most $2.1248$, within $0.013$ of the lower bound of $2.11264$. Our rule comes from a generalization of \emph{stable $k$-lotteries} developed in the context of committee selection. In contrast to prior work, our rule samples from a single distribution derived from a zero-sum game, without mixing between voting rules. Our result also gives sharp distortion bounds for stable $k$-lotteries. In particular we show that stable $2$-lotteries have distortion $7/3$, despite only relying on aggregate preferences over triples of candidates, and that stable $2$-lotteries are optimal in this class of voting rules.

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04604

Sparse-View 3D Coronary Reconstruction from Vessel Segmentations Using a Diffusion Prior

Tianyuan Wang, Kenan Niu, Stefano Stramigioli

原文摘要

Reconstructing three-dimensional (3D) coronary anatomy from sparse angiographic views could support percutaneous coronary intervention, but limited angular coverage and imperfect vessel segmentations introduce substantial geometric ambiguity. We propose a vessel-specific reconstruction framework combining a learned 3D diffusion prior, visual-hull initialization, and projection-based data consistency. The unconditional diffusion model is trained on coronary vessel masks without paired 2D--3D data and reused across different view counts without retraining. During inference, a scaled visual hull initializes reconstruction, while projection-based updates constrain diffusion refinement using patient-specific vessel observations. We evaluated the framework on simulated angiograms derived from ImageCAS computed tomography angiography volumes using two, four, and eight views at two contrast levels. Across 50 evaluation coronary-tree cases, the proposed method achieved higher mean Dice and clDice than visual hull, deep image prior (DIP), and hash-grid implicit neural representation (INR) at all view counts under reduced contrast. With eight views, improvements over DIP, the best-performing baseline at lower contrast, were approximately 0.13 in Dice and 0.22 in clDice. At higher contrast, performance remained competitive with hash-grid INR, the best-performing eight-view baseline. The preferred diffusion starting timestep depended on observation quality and view count, with excessive refinement reducing accuracy at higher contrast. Projection guidance improved both metrics across all conditions. Averaging stochastic reconstructions further improved accuracy, while sample disagreement localized errors with average-precision gains of 0.14--0.21 over error prevalence. These findings support diffusion-prior reconstruction from sparse vessel observations, although clinical validation remains necessary.

eess.IV · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.29898

A Sharp Dynamic Haussler Lemma

Shihao Shao, Dacheng Tao

原文摘要

Haussler's covering lemma (JCTA 1995) gives the sharp $(C/δ)^d$ metric-entropy bound for binary VC classes under normalized $\ell_1$ distance. We prove a sharp global dynamic analogue for bounded real-valued classes in normalized $\ell_p$, for every $1\le p<\infty$. Writing $d=\operatorname{sfat}_{\varepsilon/32}(\mathcal F)$, we obtain a global dynamic cover of size at most $(C/\varepsilon^p)^d$ for every horizon and $0<\varepsilon<1/2$. Here a single family of deterministic causal experts, fixed independently of the instance sequence, covers every target on every sequence. For $0<\varepsilon<1/4$, a matching worst-case lower bound $(c/\varepsilon)^{pd}$ holds already for binary classes on one fixed sequence, even with real-valued experts. The constants are universal, independent of $p$. For $p=2$, this resolves the horizon-independent sequential $\ell_2$ covering problem of Rakhlin, Sridharan, and Tewari (PTRF 2015) in a stronger global form. For $p=1$, it gives the sharp $(C/δ)^{\operatorname{Ldim}(\mathcal H)}$ global dynamic covering bound for binary classes, resolving questions of Alon, Ben-Eliezer, Dagan, Moran, Naor, and Yogev (STOC 2021) and Malliaris and Moran (Trans. AMS 2024). Our proof combines rank-decreasing correction schedules and accurate sampled prefixes with exact continuations after target identification. Shared-parent routing distributes these continuations among eligible prefixes, eliminating the logarithmic overhead.

cs.LG · cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.08524

Stablecoin Integration and Stress Transmission Through Crypto-Asset Service Providers: Evidence from Austria

Pietro Saggese, Michael Sigmund, Burkhard Raunig, Esther Segalla, Bernhard Haslhofer, Christos Makridis

原文摘要

Stablecoins are increasingly entangled with traditional finance, making their response to stress a financial stability concern. However, blockchain pseudonymity and the lack of geographic identifiers force existing work to rely on indirect proxies to infer and locate market participants. In this paper, we exploit a regulatory registry that directly identifies the on-chain addresses of every crypto-asset service provider (CASP) registered in Austria at the end of 2024, reconstructing their Bitcoin, Ether, USDT, and USDC activity through May 2025. Austrian CASPs intermediate roughly $30 billion in cumulative external transfers, while transfers among them are negligible. Combining two transaction-level signals, interaction with CASP cold wallets and on-chain transaction frequency, we classify CASP counterparty activity as retail-like or institutionally mediated. Around stress events, the two groups exhibit distinct flow responses that are obscured in aggregate data. After the FTX bankruptcy, we observe retail-like withdrawals and institutional deposits across assets. After Silicon Valley Bank (SVB) failure, retail-like USDC deposits and institutionally mediated withdrawals are consistent with USDC's asymmetric redemption access. Registry-based, transaction-level measurements thus offer a reproducible basis for cross-jurisdictional monitoring of how cryptoasset markets transmit risk.

q-fin.GN · cs.CR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.05335

Soundness of the GKR Protocol for Sparse-Merkle Updates, with a Machine-Checked Core

Jinwook Kim

原文摘要

The GKR soundness theorem bounds the acceptance of a false output claim for a fixed circuit and input. It does not say what the outputs of a compiled circuit mean for that input, and that has to be proved for each family of circuits. For a sparse Merkle tree over a compressing hash, heuristically almost every root admits a valid path for almost any leaf, so the needed statement concerns the witness as well as the claim. We prove per-input soundness for membership, non-membership and update claims, for trees of every depth, in the delegation setting of GKR: the verifier reads the circuit input in the clear and leaves the node hashes to the circuit. Assuming correct hash sub-circuits, every accepted input, honestly encoded or not, determines a valid witness, and the chain constraint cannot be dropped. If the witness read from an input fixed before the challenges is not valid, this verifier accepts with probability at most $\bigl(s_{\mathrm{out}}+\sum_i(8s_i+1)\bigr)/|\mathbb{F}|$ for a circuit with $2^{s_{\mathrm{out}}}$ outputs and $2^{s_i}$ wires read at layer $i$. This covers cube gates for the Poseidon2 S-box and, with $|\mathbb{F}| = p^4$, circuits over the 31-bit KoalaBear field with challenges from its quartic extension. The implemented node hash, a truncated permutation, is not collision resistant, so the statement that accepted runs for one key and root assert the same leaf assumes position binding of the tree. All our results are proved in the paper. Isabelle/HOL also checks a layer-chain bound, the exactness of the verifier's checks on encoded witnesses in a constraint-level model with abstract hash sub-circuits and, over one field, their composition for claims with no valid witness. All bounds concern the interactive public-coin protocol.

cs.CR · cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04603

SCANO: Restoring Boundary-Condition Completeness in Sampled Meshes with a Sliced-Cross-Attention Neural Operator for 3D Thermal Fields

Tianyu Li, Zhiwei Cao, Qingang Zhang, Ruihang Wang, Binyang Song, Yonggang Wen

原文摘要

Training neural operators on million-cell meshes can exceed GPU memory limits. Models are therefore trained on subsamples and deployed on whole meshes or selected regions. However, subsampling can distort boundary information, degrading accuracy. We ask when boundary conditioning under sampling stays consistent with whole-mesh training, and how to aggregate and inject boundary data. Representing the solution as an integral of boundary data against type-dependent measures, we show that subsampling distorts this conditioning in three ways: incomplete boundary coverage or a sample-dependent global state, face weighting by count rather than area, and compression of boundary inputs into a low-dimensional vector. This analysis yields four measurable consistency conditions checkable before training. We satisfy them with two backbone-agnostic modules. Boundary Quadrature Attention (BQA) aggregates all boundary faces, typed and area-weighted, into slice tokens that every query point reads at every layer. Anchored Global State (AGS) estimates the global state from anchors independent of the sampled points. Adding both to a scalable message-passing and slice-attention backbone yields SCANO. We evaluate the approach on controlled and public benchmarks and a data-centre thermal simulation dataset (DCTS) containing million-cell meshes. On both benchmarks, BQA improves every backbone, reducing whole-mesh error up to 15-fold. On all three datasets, AGS aligns regional with whole-mesh predictions, cutting regional error at least tenfold for most backbones on the public benchmark. Together, the modules nearly halve the best original backbone's whole-mesh error on both benchmarks. SCANO predicts a room-scale field in under one second, over 100 times faster than whole-room anchor self-attention.

cs.LG · physics.comp-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02416

Fault-Free Profiles and Decision-Level Training for Root Cause Analysis via Small Language Models

Kyoohyun Lee, Woohyeok Park, Taeyoon Kim, Sungyong Baik, Kyungyong Lee

原文摘要

Root cause analysis (RCA) in microservice systems requires distinguishing failure sources from routine fluctuations and propagated symptoms. Small language models (SLMs) offer a practical solution under cost and privacy constraints, and recent approaches improve their capabilities through structured diagnostic procedures. However, performing diagnostic analysis does not ensure correct root-cause identification. Our empirical study shows that final decisions often favor large anomaly scores despite the model's own propagation analysis suggesting otherwise. Moreover, the input does not reveal whether these apparently anomalous signals are common during normal operation. These findings motivate our system, which improves how SLMs assess evidence and select root causes through Historical Frequency Profiling and Biased-Decision Correction. Historical Frequency Profiling quantifies how often metric and log signals are flagged as anomalous in the fault-free period, providing context for distinguishing routine deviations from potential failure evidence. Biased-Decision Correction identifies points where the model reaches different conclusions from the same initial analysis and uses preference optimization to favor more accurate diagnoses. Against baselines including the 14B OpsAgent and RCA-Agent with commercial models, our system with a 4B model achieves the highest or tied-highest accuracy on all four datasets, reaching 48.7% on Bank compared with 18.5% for OpsAgent.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04598

Estimating "Realized" Skewness using Convolutional Neural Network

Haozhe Jiang, Ostap Okhrin, Michael Rockinger

原文摘要

We propose a new estimator of low-frequency skewness (monthly) that exploits high-frequency data (5 min) through a direct functional mapping consisting of layers of Convolutional Neural Networks followed by Multi Layer Perceptrons. Monte Carlo experiments involving stochastic volatility models, with and without jumps, show that the estimator reduces finite-sample bias relative to existing realized-skewness estimators and remains accurate under model misspecification. Empirically, our estimator exhibits temporal stability and delivers cross-sectional performance in actual skewness-sorted portfolios. We characterize the finite-sample bias of conventional plug-in skewness estimators and establish risk consistency properties of the proposed NN estimator under standard regularity conditions.

math.ST · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02566

CR-Bench-v2: Evaluating the Real-World Utility of AI Code Review Agents

Kristen Pereira, Neelabh Sinha, Rajat Ghosh, Debojyoti Dutta

原文摘要

The rapid growth of automated code generation has created a pressing need for automated code review. Yet code review resists full automation: it depends on context beyond the \textit{diff}, on the author's intent, and on judgments about design and maintainability on which even expert reviewers disagree. Existing code review benchmarks lack standardization and rely largely on noisy human review comments scored with text-similarity metrics, which reward surface overlap and fail to measure precision. We introduce \texttt{CR-Bench}, a defect-centric evaluation framework that recasts SWE-Bench issues as pull-request review tasks with full repository context. After filtering for review-detectable defects and grounding each to a single originating pull request, \texttt{CR-Bench} contains 584 instances, with a 174-instance \texttt{CR-Bench-Verified} subset derived from SWE-Bench Verified. Each instance pairs a pull request with a known defect, its ground-truth fix, and a taxonomy label for bug category, impact, and severity. We propose two complementary metrics: \emph{Known-Defect Detection Rate} (KDDR), which measures whether an agent identifies the labeled defect, and \emph{Usefulness Rate}, which measures the fraction of review comments that are actionable. We evaluate eight code review agents over four backbones, plus commercially available Gemini Code Assist, using three independent LLM judges. Even the strongest systems detect only about one-third of known defects (KDDR $\approx$ 33\%), and Gemini Code Assist detects 8.1\%.

cs.SE · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.40000

NORmal: Architecting 3D Ferroelectric NOR Flash as Main Memory -- From Co-Fabricated BEOL Arrays to 200B-Class Multi-Agent LLMs in a Smartphone

Zhuoran Li, Zhuohang Bian, Yibo Zhao, Xuxiao Yang, Youwei Zhuo

原文摘要

Smartphone agents increasingly work as teams, in which a large planner directs parallel workers across long sessions. Running such a team on the device requires 200B-class models and contexts that exceed phone DRAM many times over, yet existing tiers treat flash as storage: UFS starves per-token weight streaming, and mobile High-Bandwidth Flash (HBF) needs a redesigned SoC and cannot absorb continuous KV writes. We show that a non-volatile memory can join an unmodified LPDDR6 bus as a plain rank only if its access latency fits the DRAM timing window, a condition no NAND tier meets. We present NORmal, to our knowledge the first main-memory architecture for 3D ferroelectric NOR flash. We have fabricated and experimentally characterized back-end-of-line (BEOL) 3D FeNOR arrays that co-integrate dense 3-bit MLC and fast, high-endurance in-place-writable SLC with sub-7.5 ns sensing. NORmal turns these co-fabricated arrays into a byte-addressable, deterministic-timing load/store rank on an unmodified LPDDR6 interface through an under-array DRAM-emulation bridge, staggered bank reads with zero-cycle weight swizzling, and posted SLC writes. Across this rank and DRAM, NORmal places agent state by mutability and lifetime rather than by hotness, serving 200B-class models and persistent planner contexts in place while dynamically partitioning subchannels across agent phases. In serving-level simulation on production agent traces, NORmal is 8.9$\times$--28.0$\times$ faster than UFS offloading, within 0.4% of an unlimited-DRAM ideal, and 2.8$\times$--6.3$\times$ faster than HBF on sessions beyond 256K tokens, with 8.1$\times$--17.8$\times$ less memory energy per token.

cs.AR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.15085

Edge AI and TinyML for Resource-Constrained Agriculture: Hardware Platforms, Model Optimization, and Deployment Strategies

Riya Samanta, Bidyut Saha, Soumya Kanti Ghosh

原文摘要

Deploying artificial intelligence at the edge is increasingly important for systems that must operate under strict limits on memory, computation, energy, connectivity, and hardware cost. Tiny Machine Learning (TinyML) represents the most resource-constrained regime of this shift, enabling inference on microcontroller-class devices with tightly bounded flash and SRAM budgets. This study examines recent deployment trends in Edge AI and TinyML using precision farming as a representative real-world environment in which sensing nodes, gateways, UAVs, and embedded processors must often operate with intermittent connectivity and limited power. The analysis emphasizes the deployment stack rather than predictive accuracy alone, covering hardware classes, software toolchains, model- and system-level optimization, intelligence placement, and hardware-resource reporting. Across the analyzed studies, microcontroller-class platforms such as ESP32, STM32, and ATmega are prominent endpoint targets, while single-board computers and embedded accelerators support more compute-intensive workloads. Among the studies included in the optimization comparison, almost 50% report quantization, making reduced-precision inference the most frequently identified strategy within this subset. In contrast, structured pruning, hardware-aware neural architecture search, and coordinated cross-layer optimization remain comparatively limited. Resource profiling is also inconsistent as flash, peak RAM, MAC count, latency, and energy per inference are not reported uniformly, limiting reproducibility and comparisons across deployments. A recurring systems-level pattern is an inference-training asymmetry in which inference moves toward local devices while training and model lifecycle management remain predominantly centralized.

cs.ET · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.10571

SPERA: Spherical Prior EEG Foundation Model with Geometry- and Frequency-Aware Latent Prediction

Minsu Kim, Ye-Sung Kim, Hyeseong Jeon, Wooseok Hyung, Joshua Lee, Chang-Hwan Im

原文摘要

Electroencephalography (EEG) provides a non-invasive measure of ongoing neural activity, but building general-purpose EEG models remains challenging due to the heterogeneity of subjects, devices, and electrode montages. Existing EEG foundation models predominantly rely on reconstruction-based objectives defined on the observed signal, which contains both neural and non-neural components. We introduce the Spherical Prior EEG Representation Architecture (SPERA), an EEG foundation model that adopts the joint-embedding predictive architecture (JEPA) to predict in latent space. SPERA introduces a Legendre-polynomial spatial prior, incorporated into attention to encode varying scalp electrode geometries. Two further components adapt the model to EEG: factorized temporal and spatial attention interleaved with periodic full-attention blocks, and a relational spectral regularizer aligning latent similarity structure with spectral views. Pretrained on approximately 80,000 hours of EEG from 29,048 subjects across 106 datasets, SPERA achieves the highest average balanced accuracy across nine downstream tasks spanning clinical, cognitive, and BCI applications. SPERA further exhibits strong parameter efficiency under linear probing and robustness across varying recording conditions, suggesting its potential as a general-purpose backbone for diverse EEG analyses.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05436

Constraint-Preserving Flow Matching for Spectral Graph Generation

Anita Yang, Ken-ichi Kawarabayashi, Yohan Jung

原文摘要

Spectral graph generation models graphs through the eigenvalues and eigenvectors of their Laplacians, providing a compact and continuous alternative to direct graph construction. However, these eigensystems must retain the structural properties of graph Laplacians. Existing approaches preserve eigenvector orthonormality but do not enforce the Laplacian nullspace constraint, potentially producing eigensystems inconsistent with Laplacian structure. To address this, we introduce Laplacian-Constrained Stiefel Flow Matching (LaSFM), which preserves the nullspace condition alongside eigenvector orthonormality and positive eigenvalues during generation. We characterize the underlying constrained Stiefel manifold and formulate flow matching on it. We further extend LaSFM to property-guided generation, steering samples toward desired graph properties while maintaining these constraints. Experiments demonstrate improved performance on both spectral and graph-level metrics, along with better alignment with target properties.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04594

Rondo: Controlling Reordering in High-Throughput Packet Spraying

Xizhi Zhang, Zhongjie Chen, Yang Zhang, Fengyuan Ren

原文摘要

Modern datacenter applications demand high network bandwidth, motivating packet-level multipath transport to exploit the aggregate capacity of multiple paths. However, packet reordering under packet spraying poses challenges for hardware transports. Excessive packet reordering makes it difficult to infer packet loss, imposes substantial pressure on NIC resources, and ultimately degrades performance. Existing transports either prioritize throughput without controlling packet reordering, or mitigate reordering by restricting transmissions, which leaves available path capacity unused. In this paper, we identify two fundamental sources of packet reordering: transient inter-path queueing differences caused by control dynamics, and persistent path-delay differences due to path heterogeneity. These observations motivate a joint design of a multipath controller and a packet scheduler to address transient and persistent delay differences, respectively. We present Rondo, a packet-level multipath transport that achieves both high throughput and low packet reordering. Its multipath controller uses ACK-based per-path capacity estimation and probing to maintain stable path loads, while its packet scheduler adjusts packet transmission order to compensate for persistent delay differences. Testbed experiments and large-scale simulations show that Rondo reduces the maximum out-of-order degree by up to 97.5% while maintaining high throughput. With constrained receiver buffers, Rondo achieves up to 8.51 times higher goodput.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04592

System-level performance in massive ISAC networks

Emanuele Mengoli, Nahuel Soprano-Loto

原文摘要

We propose a dynamic stochastic geometry framework to model and evaluate large-scale integrated sensing and communication (ISAC) networks. Base stations are distributed according to a homogeneous Poisson point process, while user equipments and sensing objects undergo localised random motion around their serving base station. Full spectrum reuse with a common waveform generates mutual interference that couples the two functionalities. Communication performance is characterised through downlink queues whose service rates are governed by the signal-to-interference-plus-noise ratio (SINR) via the Shannon capacity. Sensing performance is captured by a Kalman filter (KF) whose measurement noise covariance depends on the sensing SINR, yielding a non-classical state-dependent filtering problem. We define system-level performance metrics under the Palm distribution of the base station process and prove the consistency of their empirical estimators. We then prove that, for a typical base station, communication and sensing metrics are associated, so that operating regimes favourable to one functionality tend to benefit the other. The proof combines the monotonicity of the queue and filter recursions in the configuration of interferers with the Harris--FKG inequality for Poisson functionals. We also show that the excess association ratio of the two interference levels decays as the inverse of the network density. Simulations based on a ray-tracing channel model corroborate the analysis.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04591

SparseGraph-I2P: Consistency-Guided Hypothesis Selection for Efficient Image-to-Point-Cloud Registration

Ziyang Yu, Xiang Li, Qiong Chang, Jun Miyazaki

原文摘要

Coarse-to-fine image-to-point-cloud (I2P) registration risks discarding correct but ambiguous patch pairs, while retaining more candidates repeats fine matching over overlapping patches. SparseGraph-I2P aims to improve registration recall by increasing the inlier ratio at the pose solver, using sensor or monocular depth. A Coarse Hypothesis Graph combines appearance with depth-derived first- and second-order compatibility to select a fixed budget of patch pairs. Sparse Fine Matching decodes each queried pixel once and skips pixel--point comparisons covered by an ancestor patch paired with the same point-cloud region. With sensor depth, depth-aware filtering ranks correspondences by SC2 spectral support for a single EPnP solve. With monocular depth, depth or inverse depth is calibrated from correspondence geometry before filtering. SparseGraph-I2P achieves the highest registration recall and lowest latency among the compared methods on RGB-D Scenes V2 and 7-Scenes with sensor depth. With predicted depth, it retains the highest recall on RGB-D Scenes V2 at an additional calibration cost. Code will be released.

cs.CV · cs.MM

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arXiv ↗PDF ↗