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新论文 · v1arXiv:2611.01566

Principled Human-AI Collaboration in Agentic Workflows

Young Hyun Cho, Will Wei Sun

原文摘要

Organizations adopting AI face the challenge of deciding which outputs can be released without human review while meeting the quality standards of human experts. We develop a statistical framework for delegating tasks between experts and black-box generate-verify workflows, in which AI-generated answers are verified and revised over multiple rounds. Before deployment, human experts evaluate selected outputs, and their judgments are used to calibrate the verifier threshold and number of attempts. Our method selects the policy with the highest estimated automation rate among statistically approved candidates, without modifying the generator or verifier. We establish finite-sample control of the error rate among released outputs, characterize the expert evaluations sufficient to attain a target automation rate, and derive a minimax lower bound establishing rate optimality in the quality margin and required confidence. In a MultiHiertt financial question-answering case study, average held-out automation increases from 2.50% to 24.17% as expert evaluations accumulate, while released-output error rates remain around 1.20%. In contrast, heuristics that release answers after consistently high verifier scores either violate the 5% error tolerance or automate at most 1.30% of tasks. Synthetic experiments further examine these theoretical predictions under controlled settings.

stat.ML · cs.AI · cs.LG · stat.ME

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01565

Beyond Validation Loss: Coverage and Reliability Dynamics in Robot Policy Pretraining

Zhanyi Sun, Max Simchowitz, Shuran Song

原文摘要

In modern robot learning, generative robot policies are first pretrained through supervised learning from demonstrations, then refined through reinforcement learning, supervised finetuning, or other forms of posttraining. Yet conventional pretraining metrics, such as validation loss and average task success, provide an incomplete account of which policies support both effective direct deployment and subsequent posttraining. In this work, we study closed-loop pass@k, which evaluates the probability that k rollouts from the same initial condition attain at least one success. Thus, pass@1 measures mean task success; large-k pass@k measures whether success remains reachable under repeated sampling. Across a wide range of posttraining recipes, we show that large-k pass@k reveals posttraining differences not captured by pass@1. In particular, checkpoints with similar pass@1 but different large-k pass@k have different posttraining performance. Moreover, we show pass@k peaks at different checkpoints during supervised pretraining, revealing a coverage-reliability tradeoff over the course of training. Based on these observations, we propose two practical interventions: (1) a purely offline checkpoint-selection criterion that balances coverage and reliability without closed-loop evaluation, and (2) a generative data augmentation procedure that expands supervision beyond the finite demonstration set, preserving large-k pass@k while maintaining or improving pass@1 and raising the observed coverage-reliability Pareto frontier. Together, our results motivate evaluation, checkpoint selection, and training procedures that explicitly account for posttraining. Blog post, visualizations, and code are available at https://zhanyisun.github.io/robot-policy-learning-dynamics/.

cs.LG · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01564

Meta-memorization and memorization scaling laws in transformers

Alex Nguyen, Kenneth A. Norman, Gautam Reddy

原文摘要

Larger transformers can memorize more data, with a capacity (in bits) that scales roughly linearly with the number of parameters. Counterintuitively, larger models also memorize faster, requiring fewer repetitions of a sequence during training before it is memorized. We reproduce both memorization scaling laws in transformers trained on random binary sequences, showing that rapid memorization can arise even when data contain no semantic or compositional structure. Rapid memorization is due to a meta-memorization effect, where models trained to memorize a large set of random sequences learn to memorize novel sequences more quickly. The rate $r(t)$ at which models memorize grows with training time $t$ as a power-law $r(t) \sim t^β$ with a fixed exponent $β$ but an increasing upper cutoff across model scales. Meta-memorization enables even one-layer transformers to few-shot memorize a 100-bit sequence. This effect is partly explained by the formation of structured attention heads that convey position-invariant sequence information to MLPs where memorized sequences are stored. Meta-memorization is recapitulated in a simplified model of a dense associative memory where retrieval is constrained by an attentional bottleneck. Our results suggest transformers, through pre-training, acquire reusable mechanisms that enable highly efficient storage and retrieval of memories.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.04007

Boost+: Equitable, Incentive-Compatible Block Building

Mengqian Zhang, Sen Yang, Kartik Nayak, Fan Zhang

原文摘要

Block space on the blockchain is scarce and must be allocated efficiently through block building. However, Ethereum's current block-building ecosystem, MEV-Boost, has become highly centralized due to integration, which distorts competition, reduces blockspace efficiency, and obscures MEV flow transparency. To improve equitability and economic efficiency in block building, we propose $\textsf{Boost+}$, a system that pools submitted order flows inside an attested execution environment and gives all participating block-building algorithms equal access to them. The core of $\textsf{Boost+}$ is the mechanism $M_{\textsf{Boost+}}$, built around a baseline block-building algorithm. $M_{\textsf{Boost+}}$ aligns incentives for both searchers (intermediaries that generate or route transactions) and builders: Truthful bidding is a dominant strategy for all builders. For searchers, truthful reporting is dominant whenever the baseline algorithm dominates competing builders, and it remains dominant for all conflict-free transactions, even when builders may win. We further show that even if a searcher can technically integrate with a builder, non-integration combined with truthful bidding still dominates any deviation for conflict-free transactions. We also implement a concrete baseline algorithm informed by empirical analysis of real-world transactions and evaluate its efficacy using historical data.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01563

\textsc{ChatDify}: Automating End-to-End Dify Workflow Synthesis from Natural Language via Multi-Agent Collaboration

Tao Fan, Guoqiang Ma

原文摘要

The proliferation of Large Language Models has catalyzed powerful application development platforms like Dify, which enable users to build sophisticated LLM-powered workflows through modular node orchestration. However, manually configuring these workflows requires deep platform expertise and remains time-consuming and error-prone, creating a significant barrier for non-expert users. To address this challenge, we propose \textsc{ChatDify}, a novel multi-agent framework that automates end-to-end Dify workflow synthesis from natural language descriptions. \textsc{ChatDify} employs an Actor-Critic architecture comprising three specialized agents: the \textit{Planner Agent} that decomposes user requests into structured execution plans, the \textit{Executor Agent} that performs context-aware parameter synthesis and schema-augmented DSL compilation, and the \textit{Reviewer Agent} that ensures reliability through hybrid verification with closed-loop refinement. We evaluate \textsc{ChatDify} on a curated benchmark of 100 production-inspired tasks across three state-of-the-art LLM backends, achieving 0.96, 0.91, and 0.75 success rates with Qwen-3.7, GLM-5.1, and DeepSeek-v4, respectively. These results demonstrate its robustness and practical applicability for lowering the technical barrier to creating sophisticated LLM applications.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.23056

Bridging Static and Agentic RAG for Taiwanese Historical Question Answering

Kai-Hsin Chen, Wei-Yu Chen, Xuanjun Chen, Su-Ling Yeh, Jyh-Shing Roger Jang

原文摘要

Agentic retrieval-augmented generation (RAG) enables language models to adapt retrieval based on previously retrieved evidence, but it remains unclear whether such adaptive orchestration consistently outperforms well-designed static pipelines. We conduct a controlled comparison of agentic and static RAG for Taiwanese historical question answering, sharing the same generator and hybrid retrieval backend. Despite similar aggregate performance, the two pipelines differ on 70.83% of questions, with their advantages largely canceling out when averaged. An oracle that selects the better response per question improves the composite score by 0.2417 over the better individual pipeline, revealing substantial headroom for question-level selection. We therefore introduce a post-hoc selector that compares the two responses and their cited evidence, significantly outperforming either individual pipeline and recovering 60.34% of the oracle headroom. These results show that aggregate comparisons can obscure meaningful question-level differences between retrieval strategies, suggesting that exploiting their complementarity may be more fruitful than seeking a universally superior pipeline.

cs.CL · cs.AI · cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.38311

Detecting Incorrect LLM Explanations for Automated Program Repair

Lam Nguyen Tung, Chenxi Gu, Xiaoning Du, Haoye Tian, Aldeida Aleti, Neelofar Neelofar

原文摘要

LLM-generated explanations help developers understand automated program repair (APR) rationale and assess patch quality. Incorrect explanations can mislead developers about a repair's reliability, making their detection important. However, manually inspecting the growing volume of generated repairs is impractical, motivating automated detection. Existing methods mainly target incorrect answers from general question-answering LLMs, often by sampling multiple responses and detecting contradictions. Applied to APR, they face two limitations: (1) they may depend on external verifiers that are unavailable or lack APR-specific reasoning; and (2) sampling strategies such as paraphrasing or re-prompting the same input often yield superficial, redundant responses that repeat the original error rather than expose it. We propose MetaReJ, the first method designed to detect incorrect APR explanations using metamorphic testing. Inspired by debate and viva examinations, MetaReJ challenges explanations through an argumentative strategy without requiring external verifiers. It applies novel metamorphic relations (MRs) to recursively question the LLM under equivalent and non-equivalent repair scenarios and detect contradictions. To obtain diverse samples, MetaReJ transforms the buggy program into variants that preserve or alter the underlying bug, then asks the LLM how to repair each variant. Contradictions among responses are encoded as MR violations, which indicate that the original explanation is incorrect. We evaluate MetaReJ on a manually labelled dataset of 1,756 explanations from multiple LLM-based APR systems. MetaReJ achieves an MCC of up to 0.43, outperforming prior methods by up to 333.3% in MCC.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01562

Finite-Time Online Sample Complexity for Conditional Diffusion Models

Mudit Gaur, Prashant Trivedi, Sasidhar Kunapuli, Amrit Singh Bedi, Vaneet Aggarwal

原文摘要

We study the finite-time training and sampling error of conditional diffusion models trained by online stochastic-gradient denoising score matching. Rather than assuming access to an exact empirical or population risk minimizer, we analyze the score network produced after a finite number of stochastic-gradient updates under an explicit localized Polyak--Łojasiewicz (PL) condition, stochastic-gradient regularity, and a trajectory-containment assumption. Our analysis separates the network approximation complexity from the online training-sample budget and propagates both errors through the reverse diffusion process. Under anisotropic smoothness, a joint intrinsic-dimensional structure for the data and conditioning variables, sub-exponential tails, and explicit approximation-class compatibility, we obtain a conditional score approximation error of order \[ \widetilde{\mathcal O}\!\left(M^{-η_{\mathrm{app}}}\right), \qquad η_{\mathrm{app}} = \frac{2α_x} {2α_x+d_x'+(α_x/α_y)d_y'}, \] where \(M\) denotes the approximation complexity, \(d_x'\) and \(d_y'\) are the intrinsic response and conditioning dimensions, and \(α_x,α_y\) are their corresponding smoothness exponents. For \(N=RB\) online denoising samples used by SGD, the finite-time optimization error is controlled explicitly in terms of the PL, smoothness, gradient-noise, and initialization constants. Combining these ingredients with reverse-process stability yields a total-variation bound that decomposes into terminal-initialization, time-discretization, approximation/localization, and finite-time optimization errors.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01559

Generative Modeling with Continuous Flows: Sample Complexity of Flow Matching

Mudit Gaur, Prashant Trivedi, Shuchin Aeron, Amrit Singh Bedi, George K. Atia, Vaneet Aggarwal

原文摘要

Flow matching has emerged as a promising alternative to diffusion-based generative modeling, offering a continuous-time ordinary differential equation formulation with fast sampling and a simple regression-based training objective. Despite its empirical success, some theoretical analyses idealize the learning procedure by assuming that the velocity field is obtained by exact empirical risk minimization. This assumption does not reflect the practical finite-dataset setting, where the empirical flow-matching loss is only approximately minimized by a finite number of stochastic-gradient updates. We develop an empirical-risk-minimizer-free analysis for flow matching that explicitly accounts for both finite-sample statistical error and finite-time empirical optimization error. Our analysis considers a finite dataset of \(N\) flow-matching samples and a learned parameter \(θ_T\) obtained after \(T\) stochastic gradient updates on the empirical objective. Under regularity assumptions on the interpolation path, sub-exponential source and target distributions, approximation by a bounded-parameter smooth neural velocity class, empirical-loss optimization, and uniform flow stability, we prove a guarantee that holds with high probability over the finite training dataset and in expectation over the stochastic-gradient randomness. The resulting bound separates approximation error, statistical error from the finite dataset, tail error from unbounded source and target distributions, and empirical optimization error from approximate minimization of the finite-sample objective.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01558

Text-Assisted Event Stream Visual Place Recognition via Multi-Scale Spatiotemporal Optimal Transport

Lan Chen, Xingxing Xiong, Xiao Wang, Ziyan Zhang, Zikang Yan, Yuanhao Zheng, Bo Jiang, Yonghong Tian

原文摘要

Benefiting from the high dynamic range, high temporal resolution, and low energy consumption of event cameras, low-altitude economic research leveraging event cameras has become a popular topic. Several existing attempts introduce natural-language descriptions into visual place recognition (VPR) to mitigate the sparsity of structural spatial information inherent to event cameras. Nevertheless, these methods still fail to fully address two core challenges: effectively encoding event streams and semantically aligning event streams with text. In this paper, we propose a multi-scale spatial-temporal optimal transport approach, MSTOT-EPR, to address these challenges. Specifically, given event streams and scene text information, we first adopt a visual encoder and a text encoder to extract respective feature representations. We then apply optimal-transport constraints to achieve cross-modal alignment. We then design a cross-modal bottleneck fusion module to further fuse bimodal features. Furthermore, we develop multi-scale spatial optimal transport and inter-frame temporal optimal transport modules to mine high-quality spatial-temporal event features for scene recognition. Extensive experiments on the NYC-Event-VPR and EPRBench datasets fully validate the effectiveness of our proposed framework for event-based VPR. The source code of this paper will be released upon acceptance.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.36624

Adapting Video Models for Robot Control via Motion-guided Asymmetric Prediction

Taeyoung Kim, Junyoung Sung, Heeseung Kwon, Minseop Kim, Jinwoo Shin

原文摘要

World Action Models (WAMs) leverage pretrained video generative models for robotic control by jointly predicting future visual observations and actions. The visual prediction objective plays a key role in adapting pretrained video representations to the target robot domain. Yet standard visual prediction inherited from video pretraining treats all visual regions uniformly, despite their differing relevance to robotic control. We therefore investigate the spatial allocation of visual supervision in WAMs and show that focusing supervision on dynamic regions can substantially improve policy performance. Building on this finding, we introduce Motion-Guided Asymmetric Prediction (MoGAP), which augments WAM training with motion-focused visual prediction. MoGAP allocates noise asymmetrically across regions, training the model to reconstruct more heavily corrupted dynamic regions using less corrupted static regions as context. MoGAP is trained alongside standard video-action prediction through a training-only auxiliary prediction path within the shared video diffusion backbone. It uses motion cues derived directly from training videos, requiring neither an external teacher nor additional modalities. Across robot manipulation benchmarks and WAM architectures, MoGAP consistently improves policy performance, increasing success rates on the GR-1 tabletop benchmark from 38.8% to 47.5% for Fast-WAM (Wan2.2-5B) and from 52.3% to 62.3% for Cosmos3-Nano.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01553

A Deterministic O(log m)-Competitive Algorithm for Online Minimum Cost Bipartite Matching with Delays

Tung-Wei Kuo

原文摘要

In the online Minimum cost Bipartite Perfect Matching with Delays (MBPMD) problem, $m$ requests and $m$ servers arrive over time in a metric space. An online algorithm has to match each request to a server and can postpone a matching decision by paying the delay cost. The objective is to minimize the total distance and delay cost. Azar, Chiplunkar, and Kaplan gave a randomized $O(\log n)$-competitive algorithm for metric spaces with $n$ points that are known in advance. For deterministic algorithms, however, the best known competitive ratio in general metric spaces was $O(m^{0.59})$, due to Azar and Jacob-Fanani. On the line, Kuo proposed the Virtual Robust Matching (VRM) algorithm and showed that it is $O(\sqrt{m}\log^2 m)$-competitive. We prove that VRM is $O(\log m)$-competitive for the MBPMD problem in any metric space. VRM is deterministic and does not need to know the metric space or $m$ in advance. It is the first deterministic polylogarithmic-competitive algorithm for the MBPMD problem in general metric spaces. Unlike the previous analysis of VRM, our analysis does not rely on a geometric decomposition of the metric space. Instead, it charges the waiting time of requests over time to the edges of a fixed optimal matching. We also extend VRM to the non-bipartite version and prove that the resulting deterministic algorithm is $O(\log m)$-competitive. This improves the $O(\log^5 m)$ bound of Dufay and Wattenhofer and matches a concurrent result of Ahmadian, Chawla, Kumar, Purohit, and Zhang. The proof that VRM is $O(\log m)$-competitive and its non-bipartite extension were found by GPT-6 Astra (OpenAI) with almost no human input. We used Claude Opus 5.5 (Anthropic) to simplify and present the results. See the AI Usage and Methodology section for details. We take full responsibility for this paper.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01552

Can Large Language Models Extend Their Agentic Capabilities to Goal Pursuit in Open-Ended Multi-Turn Interaction?

Mingyu Chen, Jingkai Lin, Xiaofen Xing, Liankai Huang, Ruiqi Xiong, Xiangmin Xu

原文摘要

Goal pursuit in open-ended dialogue requires agents to adapt their actions to interaction feedback across successive exchanges. We investigate whether agentic capabilities developed for multi-step tool use carry over to dialogue organized through the model's native tool interface, and how this changes interaction behavior. We define Tool-Mediated Interaction (TMI) as a training-free protocol that represents dialogue actions as tool calls and counterpart responses as tool results, maintaining a continuous reasoning-action-observation loop with retained reasoning and interaction history. We evaluate TMI across social interaction, emotional support, persuasion, and price negotiation using two models and 15,678 main-experiment episodes. Across both models, TMI improves social goal attainment, emotional-support preference, donation amounts, and buyer negotiation margins over conversational baselines with and without thinking. A Qwen ablation further shows that TMI outperforms ordinary conversation with retained reasoning on all four primary outcomes. Behavioral analyses associate the improvements with task-specific patterns in initial proposals and responses to interaction feedback. Alongside these gains, TMI reduces mean reasoning tokens per decision by 42.0-81.5% relative to the thinking baseline and lowers mean cross-turn phrase reuse across all eight model-environment pairs. These findings show that interaction organization shapes both goal attainment and reasoning across dialogue turns.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01551

TopoDPO: Dense Preference Alignment for Structure-Sensitive Aerial Referring Segmentation

Huaiyuan Peng, Jisheng Dang, Wencan Zhang, Jingwen Zhao, Hong Peng, Wenjun Ma, Bin Hu, Qi Tian 等 9 位作者

原文摘要

Preference optimization for multimodal segmentation faces a granularity mismatch: dense masks contain spatially heterogeneous preference signals, whereas image-level objectives assign one shared nonlinear weight to all spatial margins. We introduce TopoDPO, a topology-aware dense preference alignment framework for structure-sensitive aerial referring segmentation. Topo-Prior mines informative preference pairs using connected-component discrepancy and macro-corner recall, while the dense objective applies the preference penalty to each local margin before spatial aggregation against a frozen supervised reference. On Topo-RS-Hard, a 4590-instance structural stress set, TopoDPO improves intersection over union (IoU) from 50.91% to 51.24% over image-level direct preference optimization (DPO) and reduces Betti-0 error from 1.34 to 0.90; paired image-cluster bootstrap intervals against image-level DPO exclude zero for all five reported metrics. Matched supervised controls and a complete factorial analysis separate the effects of topology-aware pair mining, localized correction, and dense reference-relative preference weighting. An unfiltered stratification over all 39,244 validation records further shows the clearest structural gains on samples with moderate or severe pre-alignment component-count errors. The code is publicly available at: https://github.com/phy709/TopoDPO.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01550

Detecting Generated-Depth Injection Attacks on RGB-D Place Authentication

Seigo Kawachi, Satoshi Matsuura

原文摘要

Advances in monocular depth estimation (MDE) now let an attacker generate depth from publicly available RGB images and submit forged RGB-D queries. Such attacks break a core assumption of RGB-D place authentication: that depth comes from a sensor. We study a presentation attack detection (PAD) classifier that uses geometric consistency between the enrollment and query views, as a software-layer defense. A three-stage evaluation shows how far this classifier detects software-injected depth, depending on how accurately the attacker knows the camera intrinsics. In the first stage, the attacker has only an estimate of the intrinsics, and the PAD classifier separates forgeries generated by all three MDE models with a validation area under the receiver operating characteristic curve (AUC) of 0.997-0.999. In the second stage, the attacker obtains the exact intrinsics, and the original PAD classifier falls to near chance (AUC 0.54-0.61); a retrained PAD classifier, however, still has the registration residual between enrollment and query as a cue. In the third stage, an oracle evaluation, in which the evaluator fits warps using the ground-truth 3D geometry, shows that detection falls to near chance only in its strongest case, where every valid query keypoint is given its ground-truth 3D coordinate. Of the detection cues, the reprojection error captures the attacker's misestimate of the intrinsics, whereas the registration residual remains even when the attacker knows the exact intrinsics.

cs.CR · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01549

Revisiting LLM Homogeneity Through a Multidimensional Lens

Ziliang Qiu, Renfen Hu, Yi Ding, Ted Underwood

原文摘要

Concerns about LLM homogeneity are typically quantified with embedding similarity, which reduces two texts to a single score. We revisit homogeneity with 25 interpretable metrics spanning five dimensions: semantic, lexical, syntactic, structural, and stylistic & presentational. A human study shows that perceived similarity depends on both content (what responses say) and form (how they say it), yet embedding similarity correlates less with human meaning judgments than sentence-level matching ($ρ=0.417$ vs. $0.606$) and not significantly with form judgments ($ρ=0.140$). Across 18 contemporary LLMs, models say similar things in different words: they share global semantics and broad linguistic profiles but remain distinct in lexical characteristics, structural patterns, and interactional conventions. Across releases from 2024 to 2026, models become more like themselves but not more like one another: among significantly changing metrics, intra-model distances decrease for 17 of 22, and inter-model distances increase for 14 of 21. Across two post-training pipelines, model-specific conventions emerge during SFT and are selectively reshaped by preference optimization. These results suggest that homogeneity is multidimensional and evolving, and that embedding similarity alone can overstate cross-model homogeneity while concealing diverging intra- and inter-model trends.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01548

Band2Net: Frequency-to-Network Transport Distillation for fMRI-Privileged, EEG-Only Session-Condition Decoding

Nishi Kanta Paul, Md Shihabul Islam Shovo, Dwarikanath Mahapatra

原文摘要

Functional MRI resolves large-scale brain networks but is expensive and stationary, whereas EEG is portable but poorly localised. We treat fMRI as privileged information, available during training and absent at deployment, with EEG and fMRI acquired from the same participants under matched conditions but not simultaneously. Band2Net encodes EEG into five frequency-specific connectivity graphs and fMRI into seven canonical network tokens from a frozen teacher, and solves a balanced entropic transport problem between the two token sets that gives each EEG band its own fMRI target. In subject-grouped cross-validation on the PsiConnect psilocybin data set, Band2Net reaches 0.801 ROC-AUC for EEG-only session-condition decoding, 5.0 AUC points (95% CI 1.8-8.2) above an EEG-only student. Although its margins over a single shared plan, a KL-relaxed plan and label-free network descriptors remain unresolved, secondary comparisons favour band-specific transported targets over logit, global-feature, contrastive and pooled-target distillation, though not after Holm correction.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.04434

Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks

Ali Rabeh, Suresh Murugaiyan, Adarsh Krishnamurthy, Baskar Ganapathysubramanian

原文摘要

Developing neural surrogates for unsteady flow that generalize across complex geometries and maintain accuracy over extended rollouts remains a critical challenge for accelerating flow simulations. We present a time-dependent, geometry-aware Deep Operator Network that predicts velocity fields for moderate-Re flows around parametric and non-parametric shapes. The model encodes geometry via a signed distance field (SDF) trunk and flow history via a CNN branch, and is trained using 841 high-fidelity simulations from the FlowBench dataset (https://baskargroup.bitbucket.io/). The model attains $\sim 5\%$ relative L2 single-step error and up to 1000x speedups over CFD on 262 test geometries not used for training. We provide physics-centric rollout diagnostics, including probe-based phase lag and Strouhal frequency analysis, to quantify long-horizon fidelity. These reveal accurate near-term transients but systematic error accumulation in fine-scale wakes, most pronounced for sharp-cornered geometries. We analyze failure modes and outline practical mitigation strategies including physics-informed regularization and diffusion-based refinement. Code, splits, and scripts are openly released at: https://github.com/baskargroup/TimeDependent-DeepONet to support reproducibility and benchmarking.

physics.flu-dyn · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01546

MDS Algebraic Geometry Codes of Square-Root Length on Hyperelliptic Curves

Chuangqiang Hu, Guorui Ma

原文摘要

For every fixed genus $g\geq2$ and every $0<ρ<1$, we construct maximum distance separable (MDS) algebraic geometry codes over $\F_q$ for all sufficiently large odd squares $q$. Their parameters are $\bigl[n,\lfloorρn\rfloor,n-\lfloorρn\rfloor+1\bigr]_q$ with $n=\sqrt q+1+O_g(q^{1/4})$. The codes use full Riemann--Roch spaces on genus-$g$ hyperelliptic curves. They are not monomially equivalent to generalized Reed--Solomon (GRS) codes when $q$ is sufficiently large. An infinite subfamily has exact length $n=\sqrt q+2gq^{1/4}+1$.

cs.IT · math.AG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.10302

How to make the most of your masked language model for protein engineering

Calvin McCarter, Nick Bhattacharya, Sebastian W. Ober, Hunter Elliott

原文摘要

A plethora of protein language models have been released in recent years. Yet comparatively little work has addressed how to best sample from them to optimize desired biological properties. We fill this gap by proposing a flexible, effective sampling method for masked language models (MLMs), and by systematically evaluating models and methods both in silico and in vitro on actual antibody therapeutics campaigns. Firstly, we propose sampling with stochastic beam search, exploiting the fact that MLMs are remarkably efficient at evaluating the pseudo-perplexity of the entire 1-edit neighborhood of a sequence. Reframing generation in terms of entire-sequence evaluation enables flexible guidance with multiple optimization objectives. Secondly, we report results from our extensive in vitro head-to-head evaluation for the antibody engineering setting. This reveals that the choice of sampling method can have a substantial impact, motivating future research into this under-explored area.

cs.LG · q-bio.QM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01544

Secrecy Sum-Rate Maximization for RSMA-Enabled Pinching-Antenna Systems

Code Kebe, Saeid Pakravan, Wessam Ajib

原文摘要

This paper investigates secrecy sum-rate (SSR) maximization in a downlink rate-splitting multiple access (RSMA)-enabled single-waveguide pinching-antenna system (PASS) with an external eavesdropper. The RSMA power allocation, secure common-rate allocation, and pinching-antenna (PA) positions are jointly optimized subject to transmit-power, per-user secrecy-rate, and PA-placement constraints. To tackle the resulting nonconvex problem, an alternating-optimization framework is developed, combining successive convex approximation for RSMA resource allocation with feasibility-preserving projected gradient ascent for PA-position optimization. Numerical results show that the proposed design consistently outperforms the considered benchmarks in terms of average SSR, demonstrating the benefits of joint RSMA resource and PA-position optimization for secure PASS transmission.

cs.IT · eess.SP

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新论文 · v1arXiv:2611.01543

GSExplorer: Interactive World Generation with a Persistent 3D Gaussian World State

Sicheng Yu, Liu Liu, Xinjie Wang, Xinjiang Wang, Taojun Ding, Wenhao Hu, Beizhen Zhao, Puxin Yan 等 10 位作者

原文摘要

Interactive world generation requires both accurate local control and stable scene consistency over long rollouts. Existing methods improve local generation through camera or geometry conditioning and extend historical context with memory mechanisms. However, long-horizon exploration remains challenging as target-view guidance becomes incomplete and inconsistencies accumulate across repeated scene updates. We present \textbf{GSExplorer}, a generation--reconstruction framework built around a persistent 3D Gaussian world state. At each step, the current 3DGS scene provides multimodal rendering conditions for a condition-driven video generator, while camera geometry is injected through a separate Relative Ray Encoding branch. Generated videos are then fed back to a SLAM-based reconstruction backend, where inconsistency filtering prevents conflicting content from corrupting the scene and cross-chunk bundle adjustment improves spatial alignment across generation steps. We further design a data-construction pipeline that reproduces realistic 3DGS degradation during scene extrapolation, yielding 293K training pairs of degraded multimodal renderings and real target videos. GSExplorer achieves the best overall performance among the compared methods on both single-step and three-step WorldScore evaluation, with strong camera control and consistency across both settings, including a Camera Control score of 99.07 in single-step extrapolation. On 481-frame leave-and-return rollouts, it also shows strong revisit consistency, demonstrating the value of maintaining a reliable 3D world state for continued exploration.

cs.CV · cs.AI

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版本更新 · v2arXiv:2610.12830

Cross-Behavior Policy Optimization: Unified Learning from Offline Demonstrations and On- and Off-Policy Rollouts via a Shared Regression Target

Du Cheng, Junyu Lu, Jingfan Zhang, Yuzhuo Zhang, Haoxuan Jia, Shichao Weng

原文摘要

Reasoning post-training draws on offline demonstrations and policy-generated rollouts, yet conventional supervised fine-tuning (SFT) followed by reinforcement learning (RL) separates their objectives and limits continued reuse of collected responses. We propose \textbf{Cross-Behavior Policy Optimization} (CBPO), which makes these sources compatible with one regression loss through a fixed-reference target. Because the generating policy does not enter this target, positive and negative offline completions, reference responses, and current and historical rollouts can share a common reward-labeled record format and replay buffer. As new responses enter the buffer, optimization retains the same regression objective without coordinating separate SFT and RL objectives or requiring stored behavior-policy probabilities, importance weighting, or a learned critic. Restricting the available sources gives offline, on-policy, and off-policy variants, while CBPO-Unified combines them. Across two model scales and four mathematical reasoning benchmarks, CBPO delivers leading \texttt{avg@32} and \texttt{pass@32} performance across benchmarks and data regimes. On Qwen3-1.7B, CBPO-Unified improves benchmark-averaged \texttt{avg@32} and \texttt{pass@32} over SFT DAPO by 2.33 and 4.44 percentage points, respectively. In the replay ablation, retaining historical rollouts improves these metrics by 1.87 and 3.72 percentage points over the no-history configuration.

cs.LG

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新论文 · v1arXiv:2611.01541

MoPort: A View-Aware 2D Interface for Motion Language Models

Lixing Tan, Qing Xia, Yuting Guo, Shuai Li, Aimin Hao

原文摘要

Motion language models (MoLMs) encode 3D human motion as discrete tokens for language-based understanding. Deploying them on monocular video raises two problems. Pose detection yields 2D joint sequences, leaving a dimensional gap from the 3D motion used in training. Moreover, the 2D observation changes with the camera viewpoint, while the depth lost in projection makes the 2D pose ambiguous across viewpoints. We propose MoPort, a view-aware 2D interface that enables MoLMs to take 2D observations as input. We introduce Paired View Alignment (PVA), which gives two views of the same motion a shared 3D teacher target and directly constrains the consistency of their latents, so that 2D observations from different viewpoints are mapped to a consistent semantic representation. Camera-Modulated Encoding (CME) estimates the camera extrinsics from the 2D sequence and uses them to modulate encoding, providing the 2D latents with geometric information about the viewpoint. MoPort achieves R@1 of 0.5674 on HumanML3D and 0.3286 on KIT-ML, outperforming all evaluated baselines. On HumanML3D, its R@1 decreases only from 0.5763 on in-distribution viewpoints to 0.5585 on out-of-distribution viewpoints. We validate portability on three downstream MoLMs. Furthermore, on the real-world RGB video dataset Fit3D, MoPort achieves 80.36\% accuracy. Code will be released upon acceptance.

cs.CV

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新论文 · v1arXiv:2611.01539

The Structure of Disagreement in Emotion Annotation

Emily Öhman, Anna Koufakou, Eugenia Diegoli

原文摘要

Calculating inter-annotator agreement is an established practice in NLP, and disagreement-aware work increasingly retains the distribution of human judgments rather than reducing annotations to a single gold label. Keeping the distribution, however, does not explain why annotators disagree. This paper examines the types of disagreement that occur in emotion annotation, starting from multilabel annotations of 1,677 sentence-level units from Ernest Hemingway's The Old Man and the Sea and testing the patterns they reveal against individual-level annotations from BRIGHTER and GoEmotions. Across the three datasets, we find three main recurring disagreement structures. Emotion categories overlap systematically; annotators differ both in how broadly they apply labels and in how they treat particular category boundaries, while sparse participation and explicit abstention can look like disagreement. Where intensity is rated, disagreement over whether an emotion is present can be separated from disagreement over its intensity once recognized. Similar aggregate disagreement can therefore arise from qualitatively different annotation processes. Hard aggregation removes most of this structure, while annotator vote-share representations keep marginal label frequencies but not the co-occurrence, annotator-specific, or procedural patterns that produced them. We argue that disagreement should be understood in terms of its underlying patterns before it is summarized, aggregated, or used as a modeling target, and propose a vocabulary for disagreement structure.

cs.CL

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新论文 · v1arXiv:2611.01537

Task-driven sparse-view planar X-ray computed tomography for axial metrology

Yibin Tian, Shuming Yang, Wolfgang Heidrich

原文摘要

Computational imaging can reduce the acquisition burden when the system is designed around task-relevant information rather than complete image recovery. We investigate this framework for sparse-view planar X-ray tomography (XCT) of layered objects, using measurement of the axial termination gap (ATG) of back-drilled printed-circuit-board (PCB) vias as a representative task. Instead of using 3D reconstruction, the proposed method estimates ATG directly from polychromatic projection data through a physics-based sensitivity model. A gated hierarchical estimator first fits ATG together with radiometric perturbations and selectively introduces global geometry and specimen-warp models only when a reduced chi-squared adequacy test and Bayesian information criterion justify the added degrees of freedom. The same low-dimensional perturbation model is incorporated into marginalized Fisher-information analyses, yielding task-specific conditional and calibration-aware Cram'er-Rao bounds. Numerical experiments show that, under a fixed total photon budget, task-driven metrology changes little over 12-64 views, while reconstruction-driven metrology is strongly view-dependent with much larger errors. The direct estimator also follows the expected photon-limited scaling. These results demonstrate a computational sensing strategy in which XCT acquisition, physical modeling, and inference are evaluated according to the quantitative information required by the task rather than for general-purpose volumetric image fidelity.

eess.IV · cs.CV

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新论文 · v1arXiv:2611.01534

AbacusGS: Gaussian Splatting without Per-Splat Sorting

Jiacheng Ding, Yanliang Qi, Cong Guo, Peihua Mai

原文摘要

3D Gaussian Splatting sorts every splat by depth in every frame. Viewers that draw splats with the hardware rasterizer and sort them on the CPU, as most web and mobile viewers do, spend most of the frame time in this sort. We present AbacusGS, which stores the blending order of a 3DGS scene in the scene layout. AbacusGS groups the splats into octree boxes of at most 128 splats and stores each box in order along an axis chosen at write time. In each frame, AbacusGS sorts only the visible boxes, and one dot product per box decides whether the box is read forward or backward. A short order-aware fine-tuning converts an existing 3DGS model, and the over operator of 3DGS stays unchanged. On 13 standard scenes, AbacusGS loses 0.13 dB PSNR on average against an equally fine-tuned 3DGS model. In an OpenGL viewer that orders splats on the CPU, AbacusGS renders frames 4.5 times faster than per-splat sorting. We also present a compact format of 17 bytes per splat and a CUDA tile renderer that use the same order. The code will be released.

cs.CV · cs.GR

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新论文 · v1arXiv:2611.01530

Learning Gradients of Homogeneous Functions

Mayur S. Gowda, Srinivasa Pranav, Shreyas Chaudhari, José M. F. Moura

原文摘要

Homogeneous gradient functions have widespread significance, including applications in maximum energy subspace selection and retrieval problems. Euler's homogeneous function theorem specifies how the potential associated with a positively k-homogeneous gradient field can be recovered directly from the field itself. We use Euler's theorem to build on GradNets and propose a homogeneous gradient network architecture that is guaranteed to correspond to the gradient of a homogeneous function. Then, we describe how monotone homogeneous GradNets universally approximate the gradients of k-homogeneous convex functions. The inductive bias of homogeneous GradNets enables them to outperform baseline ICNNs by up to 7dB in our max energy subspace projection experiment and by up to 13% higher accuracy, 98% fewer parameters, and 70% fewer FLOPS in our amortized inner product search experiment.

cs.LG · eess.SP · math.OC

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新论文 · v1arXiv:2611.01529

AR-RRG: Anatomy-Guided Retrieval-Driven Radiology Report Generation

Xiang Zhang, Shijie Song, Wanqing Zhao, Wei Zhou, Rui Cao, Dekui Wang, Qiyao Hu, Xianlin Peng 等 11 位作者

原文摘要

Radiology report generation (RRG) streamlines clinical workflows and alleviates the burden of radiologists' documentation. Standard vision-language models capture global visual cues and lack fine-grained alignment between anatomical regions and diagnostic text. Most retrieval-augmented methods retrieve complete historical reports, introducing redundant noise and severe vision-text mismatches that trigger clinically risky hallucinations and missed lesions. In this paper, we introduce AR-RRG, an anatomy-guided retrieval-driven method for the generation of radiology reports. AR-RRG consists of two core modules: the anatomy-aware self-training lesion detection network (ASLDNet) and the retrieval-driven radiology report generation (RRRG). ASLDNet leverages anatomy-aware spatial verification (ASV) and class-adaptive dynamic thresholding (CDT) via self-training, delivering accurate lesion localization to support subsequent generation. As the generative branch of AR-RRG, RRRG eliminates clinical hallucinations through dual-granularity retrieval (DR) and complements missing lesions with a neighborhood consensus (NC) mechanism. A parallel continuous visual perception (PCVP) stream converts radiographs into visual tokens through fixed medical encoding and resampling. Multi-scale text and visual features are fused, followed by LoRA-efficient large language model tuning to generate clinically consistent, visually grounded radiology reports. Extensive evaluations on MS-CXR, MIMIC-CXR, and IU X-Ray demonstrate that AR-RRG consistently outperforms state-of-the-art methods across mainstream metrics. Our code will be made publicly available.

cs.CV

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新论文 · v1arXiv:2611.01527

From Tool State to Scientific Action: Auditing Decision Provenance in Biomedical Research Agents

Xingyu Yi, Xinya Zhang, Yunshen Zhou, Haoyu Bao

原文摘要

A scientific agent can give the same verdict while reading and citing different evidence. BioAgent-DF tests this separation using paired reference and candidate values saved at the same scientific computation site. It restores or transplants those values at their original coordinates, then runs the agent on healthy hardware. Across eight SciFact batches, Qwen3.5-4B shows repeatable citation changes in three capture-claim combinations, and Gemma 3 4B IT shows repeatable read and citation changes in four. These seven combinations cover two claims. Full restoration recovers the reference evidence sets while verdicts remain unchanged. Verdict changes observed under the baseline prompt did not recur under the explicit-example prompt. Complementary tool and optimizer experiments distinguish recovery of numerical state, recovery of ranked lists, and restoration of an agent's visible history. These results show that an audit should track both the evidence an agent uses and its final answer. In hERG cardiac-safety assay allocation, silent numerical faults changed testing decisions while the fixed-budget shortlist kept the same number of compounds labeled as blockers. After the agent had used faulty values, restoring its earlier execution state and supplying certified scores recovered all 39 changed decisions, whereas replacing the prediction file recovered none; no incorrect action was committed. A streaming implementation of the arithmetic checks reduced the total time for forward execution and verification by 42.4% relative to the original FP64 implementation, with identical flags and recovery outputs.

cs.AI · cs.LG · q-bio.QM

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