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新论文 · v1arXiv:2611.02375

Allen--Cahn Residual-Constrained Topology Optimization via an Augmented Lagrangian Method

Run Du, Xuan Xiao, Wenming Cheng, Zeqiang Zhang

原文摘要

This paper presents a residual-constrained topology optimization framework that controls interface transition bandwidth without requiring external density filters. The steady conserved Allen--Cahn (AC) equation is formulated as an equality residual constraint and solved via a mass-weighted Augmented Lagrangian Method (ALM) coupled with the Method of Moving Asymptotes (MMA). A closed-form chemical potential projection eliminates the constant residual mode, ensuring algebraic compatibility with external volume targets. Variational sensitivity analysis reveals a symmetric-plus-rank-one Jacobian that yields matrix-free sensitivities without additional adjoint PDE solves, reducing residual sensitivity overhead to only 0.11% of the state elasticity solve time in 3D. Systematic 2D benchmarks confirm that interface bandwidth is governed intrinsically by reaction--diffusion parameters across mesh refinements. While diffuse interfaces incur a continuous compliance elevation (1.4%--12.3%), a volume-preserving 0/1 post-processing audit across 48 benchmark cases demonstrates that this gap stems primarily from intermediate-density penalization: binarized AC-ALM designs achieve equal or superior stiffness in 45 configurations (averaging 1.2% stiffer than SIMP). Simultaneously, single-node hinges and strut fragmentation are substantially suppressed compared to unregularized baselines. Three-dimensional benchmarks further demonstrate practical spatial viability for complex structures.

cs.CE · math.OC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.22987

Capsule Network-Based Multimodal Fusion for Mortgage Risk Assessment from Unstructured Data Sources

Mahsa Tavakoli, Rohitash Chandra, Cristian Bravo

原文摘要

Mortgage risk assessment traditionally relies on structured financial data, which is often proprietary, confidential, and costly. In this study, we propose a novel multimodal deep learning framework that uses cost-free, publicly available, unstructured data sources, including textual information, images, and sentiment scores, to generate credit scores that approximate commercial scorecards. Our framework adopts a two-phase approach. In the unimodal phase, we identify the best-performing models for each modality, i.e. BERT for text, VGG for image data, and a multilayer perceptron for sentiment-based features. In the fusion phase, we introduce the capsule-based fusion network (FusionCapsNet), a novel fusion strategy inspired by capsule networks, but fundamentally redesigned for multimodal integration. Unlike standard capsule networks, our method adapts a specific mechanism in capsule networks to each modality and restructures the fusion process to preserve spatial, contextual, and modality-specific information. It also enables adaptive weighting so that stronger modalities dominate without ignoring complementary signals. Our framework incorporates sentiment analysis across distinct news categories to capture borrower and market dynamics and employs GradCAM-based visualizations as an interpretability tool. These components are designed features of the framework, while our results later demonstrate that they effectively enrich contextual understanding and highlight the influential factors driving mortgage risk predictions. Our results show that our multimodal FusionCapsNet framework not only exceeds individual unimodal models but also outperforms benchmark fusion strategies such as addition, concatenation, and cross attention in terms of AUC, partial AUC, and F1 score, demonstrating clear gains in both predictive accuracy and interpretability for mortgage risk assessment.

cs.CE

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2501.14974

Private Minimum Hellinger Distance Estimation via Hellinger Distance Differential Privacy

Fengnan Deng, Anand N. Vidyashankar

原文摘要

Objective functions based on Hellinger distance yield robust and efficient estimators of model parameters. Motivated by privacy and regulatory requirements encountered in contemporary applications, we derive in this paper \emph{private minimum Hellinger distance estimators}. The estimators satisfy a new privacy constraint, namely, Hellinger differential privacy, while retaining the robustness and efficiency properties. We demonstrate that Hellinger differential privacy shares several features of standard differential privacy while allowing for sharper inference. Additionally, for computational purposes, we also develop Hellinger differentially private gradient descent and Newton-Raphson algorithms. We illustrate the behavior of our estimators in finite samples using numerical experiments and verify that they retain robustness properties under gross-error contamination.

math.ST · cs.CR · math.PR · stat.ME · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02374

Submodularity Is Barely Testable

Diptaksho Palit, Yuichi Yoshida

原文摘要

In 2010, Seshadhri and Vondrák asked: is submodularity testable? For functions $f : \{ 0, 1 \}^n \to \mathbb{R}$, they gave a tester that makes $2^{O(\sqrt{n} \log n)}$ queries. We show that every tester, even an adaptive one with two-sided error, must make $2^{Ω(\sqrt{n})}$ queries, thereby concluding: submodularity is barely testable.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02373

Mixed-Type Relational Database Synthesis with Graph-Prompted Language Model

Yuxiang Sun, Mengshi Chen, Jianwei Wang, Wenjie Zhang, Kai Wang

原文摘要

Data access is often limited by privacy or proprietary restrictions, motivating relational database synthesis, which synthesizes realistic contents across related tables. Relational databases couple multi-table structure with mixed-type attributes and intricate semantic dependencies among values, making realistic synthesis challenging. In this paper, we propose REMIX, a unified framework for realistic relational database synthesis with mixed-type attributes. REMIX integrates a small language model (SLM) and a large language model (LLM) through two key phases: Graph-to-Prompt Tuning and Collaborative SLM-LLM Decoding. To capture attribute dependencies within and across tables, Graph-to-Prompt Tuning represents the database as an attributed heterogeneous graph. We project mixed-type record embeddings and aggregated relational context into soft prompts that serve as continuous vectors for an SLM. We jointly optimize the graph-to-prompt mapping and SLM through autoregressive attribute reconstruction. Furthermore, to reduce semantic inconsistencies, Collaborative SLM-LLM Decoding combines SLM-based record generation with repair programs induced by an LLM. The LLM discovers semantic dependencies, iteratively refines them through independent validation with a statistical bound on the overall probability of false acceptance, and translates validated dependencies into tested programs that repair generated values. Extensive experiments on seven real-world relational databases demonstrate the effectiveness of REMIX in generating high-fidelity data with improved semantic consistency and downstream utility.

cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02370

State Synthesis with an NP oracle

Aniruddha Sen

原文摘要

In this note, I describe a quantum algorithm that prepares an arbitrary state $|ψ\rangle$ to inverse exponential precision in polynomial time by making queries to an $\mathsf{NP}$ oracle, assuming one can directly query entries of the state vector corresponding to $|ψ\rangle$. The previous best upper bound on the complexity class required by an oracle for this task was $\mathsf{PP}$. I provide evidence that $\mathsf{NP}$ is the best possible upper bound by showing that any quantum algorithm using a $\mathsf{P}$ oracle, or even a $\mathsf{P/poly}$ oracle, requires exponentially many queries. I also discuss some implications of this result for the $\mathsf{QMA}$ search-to-decision problem and unitary synthesis.

quant-ph · cs.CC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.11806

External Experience Serving in Production LLM Systems: A Deployment-Oriented Study of Quality-Cost Trade-offs

Lin Sun, Heming Zhang

原文摘要

Production LLM systems accumulate reusable operational experience, but the practical deployment issue is not merely whether such experience can help. It is how different serving strategies trade off quality against online cost under realistic constraints. Injecting external experience can improve task quality, yet it also increases prompt burden, latency, and serving pressure. We study \textit{external experience serving} as a deployment-oriented quality-cost trade-off problem. We evaluate this question in a real production moderation setting, with tool-use and GPQA as supporting contrast tasks that expose different output-cost regimes. We compare no-experience baselines, random experience controls, global prompt injection, and retrieval-based selective injection, and analyze both task quality and serving cost. The results show that, once experience becomes case-dependent, selective retrieval provides a stronger operating point than unconditional global injection. They further show that retrieval quality matters more than simply increasing Top-$K$, and that the same serving policy can exhibit substantially different cost-benefit profiles across short-output and decode-heavy regimes. These findings suggest that external experience is best treated as a selective, cost-aware serving decision rather than as a universal add-on. Overall, in the settings studied here, external experience pays off only when both the serving interface and the task-specific cost structure make its quality gains worth the online cost.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2509.06216

Agentic Software Engineering: Foundational Pillars and a Research Roadmap

Ahmed E. Hassan, Hao Li, Dayi Lin, Bram Adams, Tse-Hsun Chen, Yutaro Kashiwa, Dong Qiu

原文摘要

Agentic Software Engineering (SE 3.0) represents a new era where intelligent agents are tasked not with simple code generation, but with achieving complex, goal-oriented SE objectives. To harness these new capabilities while ensuring trustworthiness, we must recognize a fundamental duality within the SE field in the Agentic SE era, comprising two symbiotic modalities: SE for Humans and SE for Agents. This duality demands a radical reimagining of the foundational pillars of SE (actors, processes, tools, and artifacts) which manifest differently across each modality. We propose two purpose-built workbenches to support this vision. The Agent Command Environment (ACE) serves as a command center where humans orchestrate and mentor agent teams, handling outputs such as Merge-Readiness Packs (MRPs) and Consultation Request Packs (CRPs). The Agent Execution Environment (AEE) is a digital workspace where agents perform tasks while invoking human expertise when facing ambiguity or complex trade-offs. This bi-directional partnership, which supports agent-initiated human callbacks and handovers, gives rise to new, structured engineering activities (i.e., processes) that redefine human-AI collaboration, elevating the practice from agentic coding to true agentic software engineering. This paper presents the Structured Agentic Software Engineering (SASE) vision, outlining several of the foundational pillars for the future of SE. The paper culminates in a research roadmap that identifies a few key challenges and opportunities while briefly discussing the resulting impact of this future on SE education. Our goal is not to offer a definitive solution, but to provide a conceptual scaffold with structured vocabulary to catalyze a community-wide dialogue, pushing the SE community to think beyond its classic, human-centric tenets toward a disciplined, scalable, and trustworthy agentic future.

cs.SE · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02368

Beyond Accuracy: How Quantization Affects Coding Agents

Areej Mehboob, Muhammad Ali Shafique, Muhammad Usman Qadeer, Hamza Farooq

原文摘要

Agents are increasingly used for coding tasks, but the inference cost of large language models (LLMs) limits where they can run. Quantization reduces model size, but its effects on coding agents that execute and revise programs remain underexplored. We compare BF16 with 4-bit AWQ and GPTQ for Qwen2.5-Coder-Instruct (7B, 14B, and 32B) and Devstral-Small-2507 (24B) on OJBench coding problems across three seeds. We detect no statistically significant accuracy loss for any of the coding agents. However, final accuracy can conceal substantial behavioral changes: AWQ reduces Qwen-7B one-shot solves from 17 to 0 while increasing loop-based solves from 10 to 25. All Qwen-32B successes involve tool use, whereas Devstral rarely makes a valid executor call. Failure profiles also differ: Qwen-32B produces far more compilation errors than the smaller Qwen models, rising slightly under quantization, while Devstral's failures shift significantly from runtime errors toward wrong answers and timeouts. These findings indicate that quantization robustness in coding agents should be assessed through solution trajectories, tool use, and failure modes, in addition to final accuracy.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.39194

Zero-Complement Extrapolation: Parameter Recovery and Classification in Severely Imbalanced Logistic Regression under Rank-Deficient Regimes

Chongsun Park

原文摘要

Parameter estimation and classification in binary logistic regression deteriorate severely when the number of minority class observations ($n_1$) is smaller than the feature dimension ($p$). Under such rank-deficient rarity ($n_1 < p$), maximum likelihood estimators suffer from quasi-complete separation, penalized likelihood methods exhibit severe intercept bias, and convex minority oversampling techniques such as SMOTE collapse into an $(n_1 - 1)$-dimensional affine subspace. In this paper, we propose a novel \textit{Self-Calibrated Zero-Complement} estimator that shifts the paradigm from minority interpolation to majority fringe extrapolation. Grounded in Stein's Lemma, we prove that the conditional expectation shift of the abundant majority class ($Y=0$) induced by logistic truncation uniquely identifies the true hyperplane normal $\boldsymbolβ$ after Mahalanobis whitening. We further establish that extrapolating synthetic minority instances outward from the high-quantile density fringe of the majority distribution along the conjugate direction preserves full-rank covariance regularity ($\mathrm{rank} = p$). To accommodate both semi-supervised environments (with an unlabeled background mean) and strictly supervised subsamples, we introduce a hybrid estimator incorporating the intrinsic third-moment skewness of the majority class alongside a location-shift-invariant zero-boundary intercept calibration. Extensive Monte Carlo simulations and real-world benchmark applications (Wisconsin Diagnostic Breast Cancer and Banknote Authentication) under extreme rarity ($n_1 \in \{3, 5\}$) demonstrate that our approach prevents parameter divergence, recovers the oracle hyperplane with high fidelity, and substantially outperforms Firth's regression and SMOTE in F1-score and estimation stability.

stat.ML · cs.LG · stat.ME

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02366

Do Visual RL Gains Scale at Test Time? A Quantile View of Post-Training

Austin Wang, Lexing Ying

原文摘要

Reinforcement learning (RL) post-training improves the single-sample quality of visual generative models, but deployment often provides additional inference compute through Best-of-N sampling. Whether RL gains transfer under such test-time scaling, particularly on difficult held-out tasks, remains poorly understood in the visual domain. Our central contribution is to systematically identify and characterize a failure mode of scaling transfer in visual RL: across multiple RL methods, model backbones, and evaluations spanning compositional generation, text rendering, and preference alignment, higher single-sample reward does not necessarily imply a better quality-compute frontier. RL gains may attenuate, saturate, or even reverse at large sampling budgets, with the base model eventually outperforming its post-trained counterpart. We explain these regimes through a quantile-transfer framework, showing that Best-of-N places increasing weight on the upper tail of the reward distribution as N grows. Consequently, RL can improve typical samples while compressing or degrading the rare high-quality outcomes that test-time scaling exploits. As a simple intervention motivated by this analysis, we introduce Quantile Utility Policy Optimization (QUPO), which optimizes a controllable mixture of quantile utilities corresponding to different inference budgets. Experiments on SD3.5-M and BAGEL show that QUPO better preserves upper-tail quality and substantially improves scaling on challenging held-out prompts relative to standard RL post-training. Our results suggest that visual generative models should be evaluated and optimized not only for single-sample quality, but for how post-training reshapes the full reward distribution that inference-time scaling exploits.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.10336

Settling PROPm and PROPavg in Graphical Resource Allocation

Bo Li, Ankang Sun, Ruijie Wang

原文摘要

We study proportional fairness in graphical resource allocation, where agents are vertices, indivisible items are edges, and each item must be allocated to one of its two endpoints. It has been proved that PROP1 orientations always exist and PROPx orientations may not, but it has remained open whether the intermediate relaxations PROPm and PROPavg (both can be satisfied without graphical constraints) can always be satisfied. In this paper, we resolve this gap. We prove that PROPm orientations always exist for goods on multigraphs and can be computed efficiently. The guarantee extends to chores and a mixture of goods and chores. In sharp contrast, we show that PROPavg orientations need not exist, even for simple graphs with binary valuations, and that deciding their existence is NP-complete. We further quantify the efficiency loss of PROPm: for goods, the price of PROPm is exactly $2$, which is one of the few settings where a constant bound on the price of fairness can be obtained. We complement these results with hardness results for welfare optimization under PROPm and for the existence of orientations that are simultaneously PROPm and Pareto optimal.

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02365

SCARF: A Scene-Adaptive Depth-Guided G-3DGS Encoder Accelerator with Semantic Reuse and Fused Dataflow

Zirui Ma, Zhihua Fan, Haibin Wu, Wenxing Li, Fulin Zhang, Dongrui Fan, Xiaochun Ye, Wenming Li

原文摘要

Depth-guided, cost-volume-based Generalizable 3D Gaussian Splatting (G-3DGS) encoders reconstruct scenes as sets of Gaussian primitives directly from multi-view images without requiring per-scene optimization, thereby facilitating feed-forward novel-view synthesis on edge computing platforms. However, their shared encoder architecture presents two primary bottlenecks. First, the per-pixel depth search disregards semantic similarity within the 2D feature space, causing depth search operations to account for over 65% of memory traffic due to redundant exhaustive candidate scans. Second, the sequential pipeline of depth prediction followed by Gaussian generation neglects local 3D geometric continuity, resulting in more than 52% of generated Gaussians being redundant prior to instantiation. In this paper, we introduce SCARF, a dataflow-co-designed accelerator that aligns each bottleneck with a corresponding scene-level regularity. To mitigate the first bottleneck, Feature Similarity Depth Reuse reconceptualizes depth search as a problem of semantic locality: depth consistency is determined by affinity in feature space rather than physical address proximity. It transforms exhaustive per-pixel depth searches into anchor-centered local searches. To overcome the second bottleneck, Scene-Adaptive Early Sparsification disrupts the conventional serial depth-to-materialization boundary by probing each tile to infer local scene regularity. It then selectively activates only the minimally sufficient generation pathway, eliminating redundant Gaussian computations before primitive instantiation. On a 27.8 square millimeter 28 nm post-layout design, SCARF delivers a 2.94x geometric-mean end-to-end speedup with maximum PSNR degradation below 0.072%, 2.23 inferences per joule, and 34.5x GPU energy efficiency.

cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02364

Spectral-spatial vision test-time training for hyperspectral image classification

Xiang Zhao

原文摘要

Hyperspectral image (HSI) classification requires the representation of contiguous spectral signatures and spatial context under limited labeled supervision. Vision test-time training (TTT) offers an alternative to fixed-weight token mixing by performing self-supervised inner-model updates on the input sequence, but its application to center-pixel HSI classification remains insufficiently characterized. We present ViTTT-HSI, a spectral-spatial adaptation of the vision test-time training (ViT3) architecture. A spectral-aware embedding stem extracts ordered-band information using spectral convolution and forms spatial tokens without reducing their spatial resolution. Two ViT3-based TTT encoder blocks perform patch-conditioned token mixing, followed by the fusion of center-pixel and neighborhood-average features for classification. We evaluate the framework on Indian Pines, Pavia University, Salinas, and Houston 2013 using ten random seeds and dataset-specific training fractions of 1% or 5%. The mean overall accuracies are 97.6390%, 98.8753%, 99.3292%, and 98.4283%, respectively. Ablations reveal a substantial contribution from the TTT inner update on Indian Pines and a smaller positive contribution on Pavia University, while the relative benefits of the spectral stem and feature fusion depend on the dataset. The results demonstrate the feasibility of using patch-conditioned vision TTT for spectral--spatial HSI classification and highlight the role of the inner adaptation mechanism.

eess.IV · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.24290

RxGS: Receiver-Generalizable 3D Gaussian Splatting for Radio-Frequency Data Synthesis

Kang Yang, Mani Srivastava

原文摘要

Radio-frequency (RF) data synthesis predicts the received signal given transmitter and receiver positions, and is essential for wireless applications. Recent 3D Gaussian Splatting (3DGS)-based methods achieve efficient synthesis at any transmitter location within a surveyed scene, but require a separate model for each receiver. We therefore present RxGS, a shared 3DGS model that first learns receiver-independent Gaussian geometry and then freezes it while learning directional radiance conditioned on receiver position. Its global conditioning branch captures receiver-dependent effects shared across the scene, while a local branch models per-scatterer variations from the receiver's geometry and occlusion. The shared geometry also supports batched rendering through a multi-receiver rasterizer. Across RF datasets, RxGS predicts at receiver positions excluded from training within the surveyed scene while significantly reducing training, inference, and storage costs. Project page: https://kangyangg.com/projects/rxgs/.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.11352

Cover Semantics for Intuitionistic Modalities

Nachiappan Valliappan

原文摘要

Intuitionistic modal logic (IML) has inspired several developments in programming languages including modal type systems for staging, computational effects and language-based security. IMLs are typically studied using Kripke-style relational semantics, which simplifies proofs of meta-theoretic properties, such as completeness and consistency, by making it easy to construct models. Kripke-style relational semantics, however, relies upon classical reasoning principles, which makes it unappealing from a computational perspective and unsuitable for formalization in a constructive type theory. Goldblatt provides an alternative semantics for IMLs by extending Beth-Kripke-Joyal-style "cover" semantics for intuitionistic propositional logic with relations to support modalities. Goldblatt's "relational cover" semantics overcomes classical reasoning but introduces a new limitation: it relies upon a "modal localization" condition that restricts the class of models and complicates model construction. Goldblatt bypasses this restriction by using intricate order-theoretic completion arguments to prove completeness. In this article, we present a conservative extension of relational cover semantics that alleviates this restriction and is amenable to simpler and standard model construction techniques. We formalize our semantics in Agda and prove completeness constructively in the style of Normalization by Evaluation for a variety of IMLs featuring independent box and diamond modalities.

cs.LO · cs.PL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.11156

Itô maps for any-step SDEs

Zhengkai Pan, Peter Potaptchik, Wenxi Yao, Michael S. Albergo, Jakiw Pidstrigach

原文摘要

Recent one-step generative models accelerate sampling by learning deterministic flow maps of the underlying dynamics. These methods rely on learning from ordinary differential equations, leaving open how to define an exact distillation procedure for stochastic dynamics. We introduce the Itô map, an any-step stochastic flow map that takes an intermediate state and Brownian path and predicts future states in a single pass. The Itô map formulation yields novel estimators for inference-time control by providing cheap, differentiable access to posterior samples. Empirically, Itô maps produce diverse, conditionally valid endpoint samples from fixed intermediate states and support strong steering performance on synthetic and image-generation benchmarks. These results establish any-step SDE integration as a useful primitive for posterior sampling and stochastic control.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.25499

EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming

Xuanhao Yang, Bing Xue, Mengjie Zhang

原文摘要

Time series classification is an important analytical task across diverse domains. However, its practical application is often hindered by the scarcity of labeled data and the requirement for substantial computational resources. To address these challenges, this paper proposes EvoTSC, a novel genetic programming approach designed to automatically evolve lightweight feature learning models for time series classification. The core of EvoTSC is a carefully designed multi-layer program structure that strategically embeds diverse forms of prior expert knowledge into the evolutionary process, effectively guiding the search toward operations known to be highly effective for time series analysis. To mitigate the common overfitting problem in time series classification, a tailored Pareto tournament selection strategy is proposed to favor models that perform consistently well across varying training data subsets, promoting the discovery of highly generalizable models. Extensive experiments conducted on univariate time series classification datasets demonstrate that EvoTSC significantly outperforms eleven benchmark methods in most comparisons. Further analyses verify the contribution of each component and the resource efficiency of the evolved models.

cs.LG · cs.NE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02363

GEAR: Generative Editing for Scene-Conditioned Outfit Retrieval for VR Travel

Gia-Huy Dinh, Thinh-Phuc Nguyen, Thanh-Hai Nguyen, Lam-Huy Nguyen-Ngoc, Hoang-Bach Nguyen-Phan, Nam-Quan Nguyen, Minh-Triet Tran, Trung-Nghia Le

原文摘要

In virtual environments, choosing appropriate outfits requires contextual harmony with surrounding scenes, yet bridging the visual gap between 3D ambient views and catalog garment assets remains challenging. This paper investigates scene-conditioned outfit retrieval for interactive 3D environments, where a user's egocentric view serves as the retrieval signal. We present GEAR (Generative Editing for Asset Retrieval), a framework that bridges the cross-domain gap by synthesizing intermediate visual queries rather than predicting direct compatibility. GEAR uses a vision-language model to describe suitable clothing from a captured scene, employs an instruction-guided diffusion model to render the concept onto a standardized reference character, and utilizes the cropped clothing region to retrieve existing 2D outfit assets via embedding similarity. An interactive Unity prototype demonstrates real-time scene capture, asset retrieval, and in-engine 3D avatar fitting. We benchmark GEAR against direct visual similarity, aesthetic scoring, and VLM-based reranking on a controlled dataset using automated Swiss-system pairwise judgments, where GEAR achieves competitive top-ranked performance (mean Elo@5 of 1012.75 and top-10 overlap of 0.180) alongside 0.539 pairwise agreement. Furthermore, a user study across 20 virtual travel scenes demonstrates that GEAR achieves the highest overall participant preference in both visual appeal (34.5%) and contextual suitability (34.5%).

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02362

Prior Action Control Causally Alters Language-Model Plasticity

Marc-Andre Schulz

原文摘要

Two copies of one language model start from identical weights and receive the same rewards during training. For one copy the rewards depend on its own answers. The other copy is yoked: it receives the first copy's rewards no matter what it answers, a design taken from the learned-helplessness experiments of animal learning. We ask whether the copy whose rewards depended on its answers, the copy with control, later learns a new task faster, that is, whether control changes the model's plasticity. In Experiment 1, copies of Qwen3-0.6B generated their own answers in a two-turn game and afterwards learned a new task. The copies with control learned the new task faster in every pair, and the advantage held on different starting models as well as on the larger Qwen3-1.7B. Copies with no reward signal, trained only on the updates that every copy shared, learned about as fast as the yoked copies. The advantage is therefore mostly a benefit of control, not damage from lacking it. Control had also changed how the copies answered, so the twins practiced different answers. That practice could carry part of the advantage, and Experiment 2 removed the difference. Both copies of a pair trained on the same answers, fixed in advance, in the same order, and received the same number of rewards. Only one copy's rewards followed the answers, and that copy again learned a new task faster. The advantage of the copies with control over their yoked twins could be forecast before the new task began from each copy's weights. In sum, control over rewards during earlier training left models faster at learning a new task later on, and a conventional model evaluation need not show it.

cs.LG · q-bio.NC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02361

Agent Memory as Rate-Limited Filtering: Explicit State Outlasts Long Context Beyond a Reader-Dependent Horizon

Tianhao Guo, Chongyao Guo

原文摘要

Can a language model that reads the full history of an access log say whether a service may act now? On an executable benchmark of grants, withdrawals, reinstatements, expiries, groups and change windows under a known policy, scored on pairs of histories that differ only in the order or one field of the deciding events, open models of 7 to 32 billion parameters fall from 72 to 86 percent after eight or sixteen events to 48 to 55 percent after 256, while reasoning-trained 32B models reading the current state reach 96 and 98 percent. A reasoning model of 120 billion parameters trails the state by four and seven points at 256 and 512 events and by about ten at 1,024, so the length beyond which explicit state outlasts long context grows with the reader. We treat agent memory as recursive filtering. The decision depends on the history only through a state whose size is set by the number of tracked keys. A store holding fewer component records than the state forgets on a time scale set by its capacity, and a reader model predicts its accuracy within 0.02 on average in a registered test. Retaining log lines without regard to time order errs with probability approaching one half, a bound that is attained. A filter in which a model parses each line and an explicit rule applies the updates answers 80 to 89 percent at 256 events and beats relevance retrieval, model-kept ledgers, a reimplementation of StateMem and the Mem0 library. A retriever that selects at query time the lines the queried state depends on matches it at 256 events on the first domain, so what decides is selection by the state's dependencies, which the filter performs when writing, without the query. Applying parsed updates themselves, the models fall to 58 and 59 percent. A frozen replication on a second domain with fresh wording reproduces the horizon.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2506.20533

Global Convergence of Iteratively Reweighted Least Squares for Robust Subspace Recovery

Gilad Lerman, Kang Li, Tyler Maunu, Teng Zhang

原文摘要

Robust subspace estimation is fundamental to many machine learning and data analysis tasks. Iteratively Reweighted Least Squares (IRLS) is an elegant and empirically effective approach to this problem, yet its theoretical properties remain poorly understood. This paper establishes that, under deterministic conditions, a variant of IRLS with dynamic smoothing regularization converges linearly to the underlying subspace from any initialization. We extend these guarantees to affine subspace estimation, a setting that lacks prior recovery theory. Additionally, we illustrate the practical benefits of IRLS through an application to low-dimensional neural network training. Our results provide the first global convergence guarantees for IRLS in robust subspace recovery and, more broadly, for nonconvex IRLS on a Riemannian manifold.

stat.ML · cs.LG · math.OC

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新论文 · v1arXiv:2611.02353

VisCodeBench: a new benchmark dataset for Text-to-Vis reflecting real-world practice

Hy Nguyen, Thomas James Elton, Ella Wilson, Andrew Reeson, Cecile Paris, Josiah Poon, Jonathan K. Kummerfeld

原文摘要

Generating visualisations from natural language requests (text-to-vis) enables users to explore data more efficiently. While LLMs can complete simple visualisation requests, their performance on challenging ones is not well covered by existing benchmarks. We introduce VisCodeBench, an end-to-end text-to-vis benchmark with 2,672 samples from three different domains: educational, scientific, and general audience scenarios with varying levels of complexity. We benchmark three visualisation systems---Codex, Claude Code, and LIDA---paired with a range of open and commercial LLMs across seven metrics covering execution, chart content, and visual quality. While basic execution is near-solved for strong systems, no system comes close to reproducing the reference data, with the best displayed data similarity reaching only $0.81$ and falling to $0.69$ on our hardest subset for this metric. We further find that frontier models separate on chart correctness and efficiency, with ClaudeCode-Opus-4.7 improving chart correctness by around 5 points over Codex-GPT-5 while running roughly 20 seconds faster. Separately, we find that system design matters as much as the underlying model: despite both running on GPT-5, Codex clearly outperforms LIDA, showing that the surrounding prompting, execution, and refinement pipeline can shape outcomes as much as the model itself. Finally, latency remains a practical obstacle, as even the strongest systems take 39-78 seconds to produce results. VisCodeBench is available at \href{https://github.com/SAIPLL/VisCodeBench}{https://github.com/SAIPLL/VisCodeBench} and accessed via a leaderboard; the full dataset is kept private to prevent LLM training use.

cs.CL · cs.HC

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新论文 · v1arXiv:2611.02349

APG: Agentic Static-Analysis Infrastructure on LLVM-IR Graph Databases

Zexin Zhong, Yanjun Zhang, Yulei Sui, Xiao Cheng

原文摘要

LLM-based coding agents increasingly rely on external program knowledge, yet access to static-analysis graphs alone does not ensure appropriate operation selection or evidence interpretation. We present APG, an infrastructure for semantics-grounded static program analysis. Built on SVF and LLVM, it decomposes compiler-derived program analyses into atomic capabilities that LLM agents can select, compose, and orchestrate for goal-directed, cross-graph investigation, with query safeguards and evidence traces preserving inspectable results and provenance. APG provides 38 atomic capabilities. On analysis requests arising during benchmark repair runs, 35 achieve first-attempt query-generation success rates above 50%, including 13 at 100%. Evaluation on 812 C/C++ defects shows higher repair success than the evaluated baselines. Compared with Passive Graph Push, APG improves first-iteration repair success by 17.6 and 18.0 percentage points on the two benchmark collections and reduces average LLM input-token consumption by 90.8%. Our tool is available at https://github.com/JasonZhongZexin/APG-ICSE-Demo and its demonstration video at https://youtu.be/vkuExZUjGnw

cs.SE

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新论文 · v1arXiv:2611.02348

Which Words a Model Fears: From a Refusal Direction to a Vocabulary-Wide Fear Lexicon

Boyuan Zhang, Mohammad Zandsalimy, Shanu Sushmita

原文摘要

Whether an aligned language model refuses depends in part on which words a prompt contains, regardless of whether those words carry the request's meaning. Harm words shift the hidden state at the generation boundary along a component shared across prompts and words, which crossed differencing extracts as a single direction. In eight models, writing along this direction raises and lowers refusal monotonically with dose, ahead of random and KL-matched directions. Used as a ruler over the full vocabulary, the direction gives every word a shift, from "feared" words at one end to "courage" words at the other; a word's shift is predictable from its own representation, and appending a feared word to a benign prompt is enough to make the model refuse it. Two held-out tests validate the ranking in all eight models: refusal after inserting an unseen word rises with its table shift, and replacing a prompt's most feared word by its most courageous meaning-preserving synonym significantly raises the answer rate on benign and harmful prompts alike, by an amount that follows the shift the replacement actually causes.

cs.CL

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新论文 · v1arXiv:2611.02346

Pointwise Score Evaluation from Succinct Boolean Descriptions: SAT Equivalence for Strongly Log-Concave Gaussian Mixtures

Yinzhen Lv

原文摘要

Score-based generative models rely on access to accurate scores, but the cost of computing those scores depends on how the underlying distribution is represented. Can smoothness and strong log-concavity make score evaluation efficient when a distribution is specified by Boolean constraints? I investigate this question by separating geometric encoding from computational extraction. I construct a succinctly specified Gaussian mixture whose score at a fixed query location encodes whether a Boolean formula is satisfiable. The construction has rational component means and uniform strong log-concavity, yet deterministic polynomial-time approximation of the designated score coordinate to constant absolute accuracy is equivalent to polynomial-time solvability of Boolean satisfiability. This equivalence remains valid throughout a neighborhood whose radius is independent of dimension. I further characterize how numerical precision connects bounded witness enumeration to exact counting, and show that independent sample access turns the same target into a dimension-independent mean-estimation problem. An explicit parity construction also separates low-degree approximation from efficient computation. Together, these results show that favorable geometry alone does not resolve the computational cost of extracting scores from compact descriptions. They identify the precise extraction requirement in this formulation while leaving the existence of a general efficient evaluator open.

cs.CC

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新论文 · v1arXiv:2611.02345

MBUndo: Sound, Automatic, and Efficient Measurement-Based Uncomputation for Quantum Circuits

Shengyuan Yang, Charles Yuan

原文摘要

Quantum programs that use temporary values, known as \emph{ancilla} qubits, must explicitly \emph{uncompute} each ancilla back to zero to ensure that the program produces correct outputs. Conventional uncomputation uses unitary quantum gates, whereas leading quantum algorithms now leverage \emph{measurement-based uncomputation} (MBU), which replaces costly unitary $T$ gates with cheaper measurements and phase corrections. Current quantum programming frameworks can use measurement to uncompute specific gates such as Toffoli. No automatic transformation, however, accepts an arbitrary circuit whose ancillas are computed and uncomputed via the standard approach and converts the circuit to use MBU\@. Researchers instead apply MBU to individual circuit designs by hand, which limits algorithm development and compiler optimization. Such a transformation has not existed due to two fundamental unknowns: where MBU applies and which gates realize MBU for an arbitrary computation. To solve these, we first present a static analysis that identifies, for each ancilla qubit, each circuit position where measurement is sound --- subsuming basic assumptions that MBU only applies at the end of a circuit. Next, we present circuit transformations that soundly turn unitary uncomputation gates into efficient phase corrections after measurement --- generalizing rewrite rules from prior literature that are either limited to a single gate or not applicable to a general circuit. We present \textsf{MBUndo}, an integrated compiler pass that automatically converts circuits to measurement-based uncomputation. It requires no user annotations other than which qubits are ancillas, the same as needed by existing uncomputation synthesizers, and can enhance their output circuits. We evaluate \textsf{MBUndo} on 22 benchmarks to reveal \emph{T} gate reductions of ${>}36.5\%$ over uncomputation synthesizers.

cs.PL · quant-ph

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新论文 · v1arXiv:2611.02344

Efficient Proper Agnostic Learning of Halfspaces

Anindya De, Elchanan Mossel, Shivam Nadimpalli, Joe Neeman

原文摘要

We give a proper membership-query algorithm for agnostically learning halfspaces under the uniform distribution on $\{\pm1\}^n$. Given query access to an arbitrary function $f:\{\pm1\}^n \to \{\pm1\}$, our algorithm runs in time $\mathrm{poly}(n)+2^{\mathrm{poly}(1/\varepsilon)}$ and outputs a halfspace with error at most $\mathrm{OPT}+\varepsilon$, where $\mathrm{OPT}$ is the error of the best-fitting halfspace for $f$. Previously, even with membership queries, the best known running time was $n^{\mathrm{poly}(1/\varepsilon)}$, achieved by the sample-based algorithm of Kalai, Klivans, Mansour, and Servedio (FOCS 2005). Our algorithm is the first agnostic halfspace learner under the uniform distribution on $\{\pm1\}^n$ whose running time is polynomial in $n$ with an exponent independent of $\varepsilon$. Combining our algorithm with a boosting argument yields a proper agnostic learner using only random examples. It achieves the same $\mathrm{OPT}+\varepsilon$ guarantee, with running time matching that of Kalai et al.\ up to an additive term depending only on $\varepsilon$. To our knowledge, this is the first sample-based proper agnostic learner in the Boolean setting with running time $2^{o(n)}$ for every fixed $\varepsilon>0$.

cs.DS · cs.CC · stat.ML

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新论文 · v1arXiv:2611.02341

AgiWorld: An Agentic Video World Model for Building Persistent Worlds

Shaoheng Fang, Yiqun Mei, Hanwen Jiang, Xin Sun, Haitian Zheng, Hao Tan, Kalyan Sunkavalli, Georgios Pavlakos 等 10 位作者

原文摘要

Recent interactive video world models generate visually compelling videos from a single image, following user-provided camera motion and text prompts. However, such low-level control demands substantial user effort to realize a high-level intent, and the generated world struggles to remain persistent as the exploration proceeds. In this work, we present AgiWorld, an agentic video world model that explores and builds a persistent world from a single image and a free-form instruction. For high-level planning, our agent AgiWorld-Agent designs the world and the journey through it in a world state and directs the exploration. For low-level realization, our video world model AgiWorld-WM generates the video chunk by chunk and stores it with its geometry in a spatial memory, from which the agent updates its world state. This closed loop keeps the world faithful to the user's intent and keeps the agent's world state consistent with the generated content throughout the exploration. We co-design the two levels around the shared spatial memory and an interface of metric camera trajectories and structured prompts. In AgiWorld-WM, to handle occlusion and accumulated geometric errors in the spatial memory over long rollouts, we propose occlusion-aware history selection, which retrieves past views by surface visibility, and reliability-based memory maintenance, which prefers reliable geometry and earlier observations. AgiWorld-WM achieves state-of-the-art overall performance on world revisiting and exploration benchmarks, and qualitative results and ablations show that AgiWorld builds persistent worlds from free-form instructions over minute-long journeys. Persistence thus arises not from the video model alone but from its closed loop with the agent.

cs.CV · cs.AI

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新论文 · v1arXiv:2611.02340

On Stochastic Continuous-Time Linear System Identification for Adaptive Stabilization

Reza Sadeghi Hafshejani, Mohamad Kazem Shirani Faradonbeh

原文摘要

Learning to stabilize an unknown continuous-time stochastic linear system is a central problem in control theory and reinforcement learning. This paper numerically studies data-driven identification of such systems with possibly unstable dynamics for the purpose of stabilization. We present a method that learns a stabilizing gain from observed trajectories, and examine how the failure probability, namely the probability that the learned gain does not stabilize the system, depends on the amount of data, dimension, noise intensity, excitation, and controllability. Numerical experiments on an unstable batch-reactor benchmark show that the squared identification error decays inversely with the aggregate observation time, that is, the combined length of all trajectories, while the failure probability decays exponentially in the same quantity. Hence, despite instability, a single long trajectory can achieve similar stabilization performance to that of many short trajectories of the same total length. We also observe \emph{signal saturation}: stronger excitation improves stabilization only logarithmically with the excitation strength. Finally, we show experimentally that accurate identification of the full dynamics is \emph{not} necessary for successful stabilization.

stat.ML · cs.LG

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