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新论文 · v1arXiv:2611.04199

An Information Budget for Label-Noise Memorization, and What Statistical Complexity Tracks: A Study with Multilayer Perceptrons

Nancy Baygorrea C., Otávio da Fonseca Martins Gomes

原文摘要

How much label noise has a network memorized, and when? We study a multilayer perceptron and what statistical complexity adds to a description of its predictions. Standard identities decompose predictive information into signal, memorization, and heterogeneity. The memorization term vanishes in the population under the stated noise-independence condition. A Fano-type bound relates it to training accuracies, but the memorized fraction M provides no upper bound. Across 89 MNIST runs with ten pre-specified hypotheses (five supported, four unsupported, one not testable), normalized memorization information rose with M on training samples, remained near zero on held-out samples with fresh noisy labels, and was close to its Fano bound. Unlike raw M, it has a zero population null under the independence condition and chance correction largely removed this difference. When every label was replaced by an incorrect class, test accuracy fell to 0.5%, yet predictions retained information about the true class. Mean statistical complexity peaked before memorization at corruption rates up to 40%. At higher rates, its peak fell within the memorization phase, near the epoch when mean training confidence reached 0.6 and the two could not be separated. As a stopping rule, it matched established rules only at low noise and detected corrupted samples less well than simple label-free scores. An exploratory extension of 280 runs covering Fashion-MNIST, pair-flip noise, and four training variants yielded similar results under uniform noise, but not pair-flip noise, where complexity peaked within two epochs. Conclusions are limited to this architecture, two small datasets, and synthetic noise.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.08363

Roadmap to Quantum Aesthetics

Ivan C. H. Liu, Hsiao-Yuan Chen

原文摘要

Quantum mechanics occupies a central position in contemporary science while remaining largely inaccessible to direct sensory experience. This paper proposes a roadmap to quantum aesthetics that examines how quantum concepts become aesthetic phenomena through artistic mediation rather than direct representation. Two complementary and orthogonal approaches are articulated. The first, a pioneering top-down approach, employs text-prompt-based generative AI to probe quantum aesthetics as a collective cultural construct embedded in large-scale training data. By systematically modulating the linguistic weight of the term "quantum," generative models are used as experimental environments to reveal how quantum imaginaries circulate within contemporary visual culture. The second, a bottom-up approach, derives aesthetic form directly from quantum-mechanical structures through the visualization of quantum-generated data, exemplified here by hydrogen atomic orbitals calculated from the Schrödinger equation. These approaches are framed not as competing methods but as intersecting paths within a navigable field of artistic research. They position quantum aesthetics as an emergent field of artistic research shaped by cultural imagination, computational mediation, and physical law, opening new directions for artistic practice and pedagogy at the intersection of art, data, artificial intelligence and quantum science.

physics.pop-ph · cs.AI · quant-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.21590

Radial Basis Function Networks as Projection Heads in Self-Supervised Learning

Andreas Schliebitz, Heiko Tapken, Martin Atzmueller

原文摘要

Self-supervised learning (SSL) typically relies on a backbone encoder followed by a small multilayer perceptron (MLP) projection head, which is conventionally discarded after training, while backbone quality is assessed via costly linear probing on labeled data. We argue that this approach including discarding the projector is rather computationally wasteful. Instead, we propose replacing the MLP head with a radial basis function network (RBFN), whose interpretable center and shape parameters can be exploited to judge representation quality without labels or a separate classifier. To this end, we introduce Scale-Normalized Separation (SNS), a novel label-free quality metric derived solely from the kernel centers and shapes learned during training. Across five canonical SSL architectures (MoCo, SimCLR, BYOL, SwAV and SimSiam) and four image classification datasets, we show that RBFN projection heads are competitive drop-in replacements for standard MLP projectors. We recommend constructing them with three RBF layers activated by the Gaussian radial basis function. Moreover, SNS exhibits strong to very strong positive correlation with established logistic regression metrics, demonstrating that a trained RBFN projector can act as a reliable proxy for backbone representation quality. We additionally publish a novel PyTorch compatible image classification dataset based on Google's Open Images V7 to facilitate reproducible research into representation learning.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04196

DoGNAVY-Exploit: Harnessing Parallel Open-Weight Agents for Stage-Aware Vulnerability Exploitation

Yuxuan Wang, Dadi Guo, Weijie Guo, Zikai Xu, Chen Lu, Yizhe Zhuang, Peng Huang, Yuejin Xie 等 12 位作者

原文摘要

Frontier LLM agents can turn known vulnerabilities into working exploits, but are costly and often access-restricted due to their potential for malicious use. Open-weight models offer a more accessible alternative, but struggle to maintain progress across long exploitation chains. They also often fail to apply relevant security knowledge at the appropriate stage and in the right context to identify and evaluate promising attack directions. To address these limitations, we propose DoGNAVY-Exploit, a multi-agent harness built around two complementary designs: stage-aware decomposition with progressive expert knowledge disclosure, and hierarchical orchestration with parallel exploration. Specifically, DoGNAVY-Exploit uses configurable milestones to recast long-horizon exploitation as short, verifiable capability transitions and provides only the knowledge relevant to each stage. Within each milestone, a hierarchical architecture separates strategic planning from parallel execution: a planner proposes diverse attack routes, executors test them concurrently, and a controller accepts progress only when supported by runtime evidence. A shared structured memory consolidates verified facts, artifacts, replay steps, and failed routes across branches, allowing agents to build on collective progress and carry validated knowledge into subsequent milestones. In controlled benchmark environments, DoGNAVY-Exploit with GLM-5.2 achieves 97.5% Pass@1 on Cybench, and 97.5% zero-day and 100% one-day Pass@1 on CVE-Bench. On ExploitGym, it solves 79 tasks with GLM-5.2 and 157 with GLM-5.3, while the corresponding public Claude Code baselines solve 39 and 130 tasks, respectively. Ablation results further demonstrate that the stage-aware orchestration, expert knowledge, parallel exploration, and memory designs can substantially improve the exploitation capabilities of open-weight agents.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04195

SettleSplat: Monocular Gaussian Mapping under In-View Scene Changes

Anh Thuan Tran, Xiyun Song, Yubin Zhou, Heather Yu, Jana Kosecka, Liang Peng

原文摘要

Online 3D Gaussian mapping enables fast and high-fidelity novel view synthesis, with broad applications in augmented reality, robotics, and autonomous systems. However, many current methods assume static scenes or filter out dynamics as outliers. In this paper, we study in-view scene changes where objects are manipulated in front of the camera within a single online sequence, unlike prior works that detect changes outside the camera view or between separate captures. This setting entangles transient and persistent dynamics, as objects can be distractors while moving and persistent once they settle. While recent studies start to address distractors by removing them for clean static background, in-view scene changes remain underexplored. To resolve this, we present SettleSplat, a monocular mapping system that maintains up-to-date 3D Gaussian maps under such changes. Specifically, we lift coarse per-pixel uncertainty into Gaussian dynamicity field, then prune transient ones. For persistent changes, we derive geometric contradictions weighted by parameter-free rendered depth uncertainty to separate true changes from under-optimized regions. On Bonn and Co-Fusion, SettleSplat achieves consistent 3D Gaussian maps, outperforming current baselines on both full images and change regions while maintaining comparable speed.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04193

Statistical Machine Learning for Medicinal Plant Leaves Classification

Jayani P. Gamage, Thiyanga S. Talagala

原文摘要

Medicinal plants are usually identified using practitioners' experience, but reproducible, image based methods can complement this knowledge. We present MEDIPI (MEDIcinal Plant Identification), a statistical machine learning approach that classifies medicinal plant leaves from photographs. From each leaf image we extract 52 interpretable features covering shape, texture, color, and a new scagnostics category. We then classify leaves using a three level hierarchy: shape, then edge type, then species. We also release MedLEA, an open R package with 1,099 leaf images of 31 Sri Lankan medicinal plant species. On this dataset, a hierarchical Random Forest achieved the best overall accuracy (100%) in our comparison of hierarchical and non-hierarchical approaches and eight algorithms: Random Forest, XGBoost, Gradient Boosting, linear discriminant analysis, k-nearest neighbor, support vector machine, probabilistic neural network, and artificial neural network. Adding texture, color, and scagnostic features to shape features raised shape level accuracy from 90% to 97%. Linear discriminant analysis plots show how the trained model separates leaf shapes. Accuracy dropped sharply (23-50%) when training and test data came from different datasets, so generalization across datasets remains a challenge.

cs.CV · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04192

Where Did the Update Go? Certifying Declared Change Envelopes for Model Updates

Muhammad Usama, Muhammad Saad Qadir

原文摘要

Deployed language models are updated continually, and regulators increasingly accept changes that were declared in advance. Showing that an update changed behavior only where it was declared to is a different statistical problem from bounding how much it changed: a useful update is meant to change some decisions, so global disagreement and regression bounds cannot certify it. We formalize the problem with declared change envelopes. Before training, the developer commits to a region of inputs, and every decision that changes outside it counts as leakage. Leakage compares two models, so it can be certified from unlabeled traffic with anytime-valid tests whose sample cost we bound from above and below, and per-update certificates compose into a bound on lifetime drift outside all declared scopes. We measure leakage for LoRA and full fine-tuning, preference optimization, reinforcement learning, and knowledge editing on models from 1.5B to 72B parameters. Narrow updates leak widely: domain and policy LoRA updates keep only about 7-36% of the decisions they change inside the declared scope. Across 256 trained variants, leakage tracks the update's KL divergence to the incumbent on out-of-scope inputs (Spearman 0.94). None of the customary remedies we tested makes a rule-like update certifiable without giving up much of its intended change. Distilling the incumbent's decisions on unlabeled prompts in the deployment format does: anchored LoRA, GRPO, and (at a small learning rate) full fine-tuning updates certify 5% leakage from a few hundred to a few thousand unlabeled samples, and the certificates replicate on held-out traffic and on written answers. The certificates cover decision-like outputs only; on open-ended chat, re-batching alone changes 11-21% of responses and no update certifies. Code: https://github.com/Usama1002/declared-change-envelopes

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.07370

PersonaFuse: A Situation-Aware Post-Training Framework for Adaptive LLM Behavior in Enterprise Tasks

Yixuan Tang, Yi Yang, Ahmed Abbasi

原文摘要

Large language models (LLMs) are increasingly deployed in enterprise settings to support task execution and workflow automation. Despite the strong performance of general-purpose models on standard benchmarks, organizations often need to adapt them to domain-specific tasks and operational constraints in practice. However, existing post-training approaches introduce capability trade-offs and lack mechanisms for situational adaptation. In this work, we propose PersonaFuse, a post-training framework that enables situation-aware behavioral adaptation in LLMs. Grounded in Trait Activation Theory and operationalized through the Five-Factor Model, PersonaFuse dynamically adjusts model behavior based on task context using a mixture-of-experts architecture with trait-level experts and a context-aware routing mechanism. We evaluate PersonaFuse at three levels. At the capability level, it achieves balanced improvements across general reasoning, interaction quality, and safety without introducing trade-offs. At the workflow level, it improves task completion in enterprise scenarios involving tool use and structured execution across both B2B and B2C tasks. At the deployment level, we analyze model selection under cost and safety constraints using a capability-cost frontier framework, showing that models adapted with PersonaFuse achieve more favorable trade-offs and are consistently selected as optimal across deployment scenarios. PersonaFuse provides a practical framework for developing LLMs that are not only capable, but also adaptable and deployable in real-world enterprise environments.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04185

What Does 4-Bit Microscaling Cost a Wearable Activity Recognizer?

Megan Healy, Hossein Valavi

原文摘要

Wearable sports sensors can recognize actions on the device itself, where lower-precision numbers reduce model storage and memory traffic. Two 4-bit block-scaled formats, MXFP4 from the Open Compute Project, introduced in 2023, and NVFP4 from NVIDIA, introduced in 2025, are drawing interest. Published evaluations cover general deep-learning benchmarks and large language models; to our knowledge, nobody has yet measured what they cost a small wearable-sensor model. We train a 159,492-parameter 1D CNN to recognize four basketball actions from wrist-accelerometer data in Hang-Time HAR and test it on players it has never seen, using five-fold cross-validation grouped by player. Every comparison between number formats is made fold by fold. In full precision the model reaches 69.26\% macro-F1. Measured TFLite FP16 and INT8 models lose at most 0.16 percentage points. In simulation, 4-bit weights cost 1.45 points with MXFP4 and 0.72 points with NVFP4. When activations are quantized to 4 bits as well, MXFP4 loses 5.14 points and NVFP4 only 0.75, and NVFP4 comes out ahead in all five held-out-player folds. The ranking survives two alternative simulator settings, with NVFP4 leading by 3.49 to 4.40 points. A $2 \times 3$ ablation with 4-bit weights and activations (two block sizes and three scale rules) points to scale selection as the main source of MXFP4's loss. The reference MXFP4 conversion we simulate rounds the scale down, which can pick scales that are too small, so many blocks clip their largest values. Rounding the scale up recovers 3.18 points with 32-value blocks and 3.63 points with 16-value blocks, and switching to an 8-bit floating-point scale, as NVFP4 does, adds another 0.84 and 1.20 points. Block size matters less. Under this round-down rule, 16-value blocks score 0.44 points below 32-value blocks in all five folds; under the other two rules there is no consistent effect.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04183

Agentic Path Maps: Exposing an Agent's Traversal Through the Garden of Forking Paths

Madeleine Grunde-McLaughlin, Hafsah Usman, Daniel Mitroshkov, Emily Reif, Nino Migineishvili, Lakshya Chauhan. Kevin Yu, Jeffrey Heer, Daniel S. Weld

原文摘要

When an AI agent executes an underspecified task, it embeds choices that may contradict unarticulated user context and preferences. However, interrupting the user for clarification on all such decisions is untenable. How might agents communicate impactful, but unexplored, paths to the user after execution? To investigate, we first distill a design space for communicating alternative paths, supporting the generation of new systems and structuring dimensions of future study. We then measure the impact of one vital design space dimension on user comprehension: the number of alternatives displayed. Through a user study (n=48) in the data analysis domain, we find that participants rarely consider alternative paths of their own volition, but AI overreliance reduces when alternative paths are visualized for them. By revealing paths the agent did not take, we demonstrate how users can better interpret results and incorporate personal context.

cs.HC · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04182

When the Consumer Is an Agent: Execution-Grounded Service-Level Agreements

Michal Orzechowski, Renata G. Slota, Jacek Kitowski

原文摘要

Large language model (LLM) agents increasingly act as service consumers. They select operations and report whether a task is complete. In an asynchronous distributed service, an accepted call or immediate state read does not establish that the required state became observable by its deadline. We introduce execution-grounded completion contracts, which treat completion as a service-level outcome. An independent control plane observes a deadline-bound service-state predicate, admits completion, and directs recovery while the agent performs the task. We evaluate placement and deletion in a live multi-provider data service with heterogeneous models and an injected transfer delay. Across policies, some tasks that missed the deadline were accepted and some that met it were rejected. Under enforcement, accepted completions remained consistent with later observations; all included healthy tasks completed, and unresolved faulted tasks terminated as unverified. The results show that completion of these tasks can be governed by independent observation of service state rather than by the agent's self-assessment.

cs.DC · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04180

Validation-Gated ML Pipeline Synthesis with Expert-Grounded Self-Evolving Skills

Zixiao Zhao, Jing Sun, Zhe Hou, Mengze Li, Qian Liu, Cheng-Hao Cai, Zijian Zhang, Xin Wang 等 10 位作者

原文摘要

Recent advances in large language models have enabled agentic systems that automate machine learning pipeline construction from minimal user input. However, existing agentic AutoML methods either depend on rigid templates that limit adaptability, target narrow task domains, or rely on free-form code generation that lacks reliability guarantees, leaving intermediate errors to propagate silently and degrade the final model. We present EvoML, an agentic framework for end-to-end ML pipeline synthesis built on expert-grounded skills that evolve across runs, executed through a validation-gated harness. The framework decomposes pipeline construction into four phases coordinated by three agents: an Orchestrator that enforces validation gates, advancing each phase only when its outputs pass structural, execution, and semantic checks, which precludes error propagation and keeps every artifact auditable; an Executor that runs expert-grounded skills, grounded in the nnU-Net workflow and pairing reusable templates with online adaptation; and a Curator that performs post-run evolution, consolidating the scripts and guidance recovered during a run into the skill library so it improves across datasets. We evaluate EvoML on eight datasets spanning six sophisticated, real-world ML task types, against agentic AutoML systems and command-line coding tools. EvoML achieves full automation and the best final model quality on all eight, spanning segmentation (up to 0.94 Dice), classification (0.99 accuracy), reconstruction (0.95 SSIM), and denoising (0.79 normalized PSNR), while transferring to broader ML tasks.

cs.AI · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04181

Clocks all the way down: does sequence-processing AI need endogenous temporal structure?

Meysam Hashemi, Alireza Valizadeh, Viktor Jirsa

原文摘要

Mainstream sequence-processing AI is temporally reactive: each update is driven by input and learned state transitions, rather than by an independently evolving internal temporal reference. Biological brains, by contrast, continuously generate rhythmic activity that structures perception, memory, and cognition, and this rhythmicity reaches all the way to behavior. We ask whether this difference is merely implementational or genuinely computational. Four lines of evidence suggest that endogenous temporal structure supports putative functions that input-driven architectures may not readily replicate: 1) phase-dependent routing of information, 2) delay-structured combinatorial representation, 3) cross-frequency multiplexing of items, and 4) oscillation-gated offline consolidation. A proof-of-concept delay-ablation test dissociates representational capacity from memory retrieval. In recurrent and spatial spiking networks with fixed synaptic weights and connectivity, shuffling delays preserves part of the capacity but substantially impairs the sequence retrieval. The relative contrast is more pronounced for spatial networks and longer propagations. Instantaneous transmission collapses capacity itself. We then outline a cross-frequency multiplexing scheme that combines item identity and serial order with alternative mechanisms for selective memory retrieval. Finally, we state one falsifiable hypothesis, conditions for matched-resource tests, and three research directions: the components already exist; the disciplined comparison does not.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.18309

Alignment Dynamics in LLM Fine-Tuning

Yuhan Huang, Huanran Chen, Yinpeng Dong

原文摘要

Although Large Language Models (LLMs) achieve strong alignment through supervised fine-tuning and reinforcement learning from human feedback, the alignment is often fragile under subsequent fine-tuning. Existing explanations either attribute alignment fragility to gradient geometry or characterize it as a distributional shift in model outputs, yet few provide a unified account that bridges parameter-space learning dynamics with function-space alignment behavior during fine-tuning. In this work, we introduce a tractable alignment score and derive its closed-form update during fine-tuning, yielding a unified framework for alignment dynamics. Our analysis decomposes alignment updates into two competing components: a \textbf{\color{red!60!black} Rebound Force}, governed jointly by the current alignment state and the narrowness of model distribution, and a \textbf{\color{green!60!black} Driving Force}, determined by how the training distribution aligns with outcome-conditioned posteriors over aligned and non-aligned completions. This decomposition explains why prior alignment can be reversed by later fine-tuning and why narrower posterior structure strengthens such reversal. Moreover, our framework predicts a \textbf{Rehearsal Priming Effect}: prior alignment leaves a latent posterior imprint that amplifies the effective Driving Force upon re-exposure, leading to faster re-alignment. We validate these predictions across safety alignment, emergent misalignment, and sentiment settings, demonstrating consistent alignment reversal and accelerated re-alignment under re-exposure. In addition, controlled experiments in safety alignment confirm the predicted dependence of rebound strength on posterior narrowness. Together, these results provide a unified dynamical perspective on how alignment is disrupted and reactivated during LLM fine-tuning.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.18539

AniGS: Bridging Rendering and Diffusion Prior for 3D Scene Animation

Yen-Chi Cheng, Chen Gao, Chuhan Chen, Tuotuo Li, Rajvi Shah, Ayush Saraf, Changil Kim, Liangyan Gui 等 11 位作者

原文摘要

Novel view rendering of large and complex reconstructed scenes is becoming increasingly photorealistic. However, most reconstructions remain static and lack the ambient motion that makes environments immersive. We present AniGS, a method for scene-level animation of 3D Gaussian Splatting (3DGS) reconstructions that adds subtle, distributed dynamics, e.g., vegetation motion, while preserving rigid structures. Unlike existing 3D animation techniques which are limited to object-centric subjects or small regions, AniGS is designed for large, cluttered, navigable scenes. AniGS represents the scene with a canonical 3DGS and models motion using a time-conditioned deformation field. To animate the entire scene, we leverage a pretrained video diffusion model and introduce an iterative dataset--model update strategy that progressively expands viewpoint coverage and repeatedly updates camera-fixed training videos using a render-and-refine scheme. To prevent artifacts from unintended motion in static areas, we further introduce a composed video-to-video refinement scheme that restricts motion to desired regions. Experiments on five real-world, large-scale outdoor scenes demonstrate that AniGS produces natural ambient dynamics and high-quality novel view videos, enabling more immersive viewing experiences of reconstructed environments.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04176

Grasping and Stabilizing Tumbling Objects with Rotating-Base Multi-Arm Space Manipulators

Farhad Aghili

原文摘要

This paper presents an innovative guidance and control method for a space manipulator that actively maneuvers its base to hold a tumbling object stationary relative to the base during proximity operations. This simplifies motion planning, reduces collision risk, and enables impact-free capture using single- or multi-arm configurations, with or without predefined grasping interfaces. The proposed approach involves two phases. In the pre-grasping phase, a synchronization control aligns the target's center of mass (CoM) with the system's instantaneous center of rotation (ICR), virtually rigidizing the target-servicer system to keep the object stationary for reliable grasping. In the post grasping phase, a control strategy based on Hamiltonian dynamics and optimal control stabilizes the combined system by minimizing time, fuel, or energy, while respecting base torque limits and multiple grasping constraints. For structured targets, applied forces and torques stay within their bounds; for unstructured ones, friction constraints prevent slipping. A case study demonstrates the method's effectiveness in enabling both the subsequent grasping and detumbling phases.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.10195

More Accurate, Less Human: Gestalt Grouping in Vision Models

Sudhanva Manjunath Athreya, Sai Phani Kumar Malladi

原文摘要

Human vision organizes what it sees into wholes: same-colored points group into series, similar marks cohere into categories, and shapes complete into recognizable objects. These are the Gestalt operations that visualization design builds on. Whether vision models organize visual content this way has not been systematically tested. We introduce a behavioral battery that scores models against human data from prior perception studies on four grouping tasks: mark-color odd-one-out, color-series counting, silhouette recognition, and object odd-one-out. We apply it to 45 models across five training families: supervised, self-supervised, and contrastive vision-language encoders, open-weight VLMs, and closed foundation models. The battery reveals that agreement with human responses captures aspects of perceptual organization that conventional performance metrics fail to distinguish, with several closed models exhibiting substantially lower alignment than their benchmark accuracy would suggest. Scoring against published perception data therefore gives visualization research a reusable yardstick, requiring no new user study, for auditing whether the models now entering visualization pipelines organize what they see the way their human audience does.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2506.22276

Artificial Intelligent Disobedience: Rethinking the Agency of Our Artificial Teammates

Reuth Mirsky

原文摘要

Artificial intelligence has made remarkable strides in recent years, achieving superhuman performance across a wide range of tasks. Yet despite these advances, most cooperative AI systems remain rigidly obedient, designed to follow human instructions without question and conform to user expectations, even when doing so may be counterproductive or unsafe. This paper argues for expanding the agency of AI teammates to include \textit{intelligent disobedience}, empowering them to make meaningful and autonomous contributions within human-AI teams. It introduces a scale of AI agency levels and uses representative examples to highlight the importance and growing necessity of treating AI autonomy as an independent research focus in cooperative settings. The paper then explores how intelligent disobedience manifests across different autonomy levels and concludes by proposing initial boundaries and considerations for studying disobedience as a core capability of artificial agents.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04173

ReconDreamer#: Self-Evolving Closed-Loop Synergy of Reconstruction and Generation for Dynamic Driving Scenes

Songling Liu, Guosheng Zhao, Xiaofeng Wang, Chaojun Ni, Guan Huang, Jiwen Lu, Xingang Wang, Zheng Zhu

原文摘要

Dynamic driving scene reconstruction has made remarkable strides in recent years by leveraging generative priors, particularly in the quality of novel trajectory rendering. Nevertheless, a significant gap remains between the generated data and real-world sensor observations, severely hindering further improvements in reconstruction fidelity. In this paper, we present ReconDreamer#, a self-evolving framework that synergizes reconstruction and generation for dynamic driving scenes, which substantially bridges the gap between synthesized novel trajectory data and recorded real-world observations. Within this self-evolving closed-loop framework, the generative and reconstruction models mutually reinforce each other through iterative refinement, ultimately yielding a significant enhancement in the 3D representation capability of the reconstruction model. Additionally, we introduce a geometry-prior-based adaptive ground model to further enhance the rendering quality of the reconstruction model. To validate the effectiveness of the proposed method, we conduct comprehensive evaluations on Waymo, nuScenes, PandaSet, and EUVS benchmarks. Specifically on Waymo, at a 6-m lateral shift, ReconDreamer# improves NTA-IoU and NTL-IoU by 8.8% and 3.0%, respectively, and reduces FID by 15.1% relative to ReconDreamer++.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04169

Length Is Cheap, Competition Is Not: Attention Predicts Context Rot in Retrieval

Yinuo Liu

原文摘要

Why do language models fail on long inputs? The common answer is length. We show that, for retrieval, length is cheap and competition is not. On five open models (0.5B to 3B parameters, four families), neutral filler costs nothing: accuracy stays at 0.97 to 1.00, up to 32K tokens on Qwen2.5-1.5B. Statements in the format of the evidence, placed at the same positions, drop it to 0.16 to 0.33. A single statement that gives the queried key a rival value multiplies the loss by 9 to 110, and every additional rival adds to it. Inside the model, one quantity tracks the failure: the attention mass left on the evidence. Across 126,473 trials it explains up to 58% of the variance in loss; log length explains at most 8%. The masked-filler protocol behind the claim that length alone hurts reproduces on MMLU (11 to 18 points lost at 30K tokens), but fails a visible control in retrieval: masking costs 45 accuracy points at 32K tokens, while the same filler left visible costs none. In retrieval, context rot is a competition for attention. We release the probe and all 126K trial-level measurements.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04166

Say the Same, Act Differently: Diagnosing and Steering Text-Action Divergence in Reasoning VLAs

Zihao Feng, Qingzhao Zhang, Chunyu Xia, Bo Yu, Z. Morley Mao, Ramesh Govindan

原文摘要

Reasoning vision-language-action (VLA) models pair physical control with natural-language rationales that supervisors can read to monitor embodied safety. Prior attacks on VLA reasoning corrupt the rationale itself and leave visible evidence in the text. We show that visual perturbations can instead alter the physical action while keeping the generated rationale word-for-word identical. At the shared text/action interface of Alpamayo for driving and InstructVLA for manipulation, our text-neutral action-subspace (TNAS) diagnostic shows that the locally most action-sensitive direction is nearly orthogonal to the active text-loss gradients, with 1.87% and 5.28% norm overlap. At equal hidden-state perturbation norms across both models, interventions along this text-neutral residual shift actions 4.64x to 47.8x more than text-gradient perturbations, whereas text-gradient perturbations induce 21.2x to 277x more text drift. Steering visual perturbations along this direction reaches the same decoupling from the image. Across 154 obstacle-annotated driving scenes in Alpamayo, TNAS-guided attacks induce a new collision under a word-for-word identical rationale in 52.6% of scenes under full-image perturbations and 35.1% under a localized forward-camera patch, compared with 4.55% for unguided action attacks. Relative to a text-preserving baseline, TNAS guidance raises exact rationale preservation from 83.0% to 89.5% and cuts teacher-forced text drift by 68.4% at a comparable collision rate. In InstructVLA, TNAS guidance cuts text drift by 40.5% and preserves the exact response on 85.0% of examples alongside a 1.73-unit action-chunk shift. An unchanged rationale therefore cannot certify a safe action, so monitors for reasoning VLAs must verify the predicted action channel directly.

cs.LG · cs.CR · cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.16219

Sparse stochastic digital twins can outperform exact ones

Zongren Zou, Théo Bourdais, Ricardo Baptista, Houman Owhadi

原文摘要

Digital twins are often built under the assumption that greater model fidelity leads to better downstream performance. We show that this is not always the case: retaining weakly dependent or auxiliary degrees of freedom can increase the dimensionality of learning and control problems enough that a sparse stochastic surrogate outperforms even an exact high-dimensional model. To identify such structure, we introduce a framework for dependence discovery directly at the level of the full conditional law. The method combines conditional generative modeling with Gaussian-process distillation and kernel mode decomposition to quantify how strongly individual variables enter the conditional distribution, including through variability, tails, multimodality, and other features invisible to the conditional mean. The resulting dependence structure can be used both to construct sparse stochastic digital twins from data and to reduce known high-dimensional models for downstream tasks. In a modified Lunar Lander problem, an eight-dimensional sparse surrogate yields better control performance than the exact 109-dimensional simulator despite lower transition fidelity. A stochastic heat-equation example shows that the framework recovers sparse structure in a 102-dimensional data-driven setting and produces a more effective surrogate than one conditioned on all candidate inputs, while economic data demonstrate its use for identifying time-varying memory structure. These results show that model fidelity and downstream utility are distinct objectives, and that dependence discovery provides a practical route to sparse stochastic digital twins.

cs.CE · cs.LG · physics.comp-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04164

What Remains in Sight? Autoregressive Video Decoding as Representation-Guided Context Rewriting

Lixuan He, Jihyeon Je, Yang You, Leonidas Guibas

原文摘要

Long-horizon video generation is increasingly moving toward autoregressive rollout, where each newly generated segment becomes part of the evidence used for future prediction. Recent causal decoders, long-context training, and KV-cache mechanisms have extended feasible duration, but most inference pipelines still decide the visible past implicitly through recent windows, compressed caches, or external memory. This raises a direct question: \textbf{can the choice of which past frames or segments to show to the decoder be treated as an explicit test-time decision?} We answer this question with \textsc{ReCR}, a training-free method that formulates autoregressive video decoding as representation-guided context rewriting. At each step, \textsc{ReCR} builds a selected visible context from the generated history by using internal decoder representations to favor context units that match the global rollout state, avoid redundancy with the recent continuation, preserve temporal boundary coverage, and widen the range of spatial layouts in view. The selected units are then mapped onto a compact causal axis before decoding the next block. Across long-horizon text-to-video benchmarks, \textsc{ReCR} consistently improves multiple autoregressive backbones under matched visible-token budgets. Empirical analyses such as representation-space diagnostics and transition-count prompt switching further show that improving what remains visible is an effective path toward more stable long-video decoding.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04158

On the Feature Evolution of Temporal Difference Learning

Shangzhe Li, Dechen Zhang, Difan Zou, Weitong Zhang

原文摘要

Temporal-difference (TD) learning trains neural representations using bootstrapped targets generated by evolving value estimates, yet how predictive features emerge and propagate under incomplete neural updates remains poorly understood. We investigate this problem using finite-width two-layer ReLU critics on a constructed family of smooth stochastic horizon-$H$ MDPs with unknown quadratic structure and $d$-dimensional observations. We first show that an informative TD update acquires the quadratic Bellman signal and yields a representation admitting a bounded predictive readout. The simultaneous hidden-weight and head update achieves a strict finite-sample prediction-risk improvement over matched head-only SGD, with a $2(d+1)$-fold larger population quadratic response. We then establish that an imperfect learned continuation can already provide informative supervision to an earlier zero-reward stage. By tracking coupled neural updates with one SGD step per target copy, without requiring intermediate Bellman regressions to be solved, we obtain a uniform prediction-error transient $\exp[-Ω(K/(Hd))]$, up to action-independent shifts and explicit sampling and finite-width errors. On the constructed signed-action MDP family whose total reward is bounded by 1, we show that an exploration-then-commit algorithm can achieve $\widetilde{O}(d^3H^{1/3}T^{2/3})$ regret over $T$ episodes, using $K=\widetilde{O}(Hd)$ global updates. Our analysis connects bootstrapped feature acquisition, concurrent neural refinement, and finite-budget policy performance.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2512.07988

HOLE: Homological Observation of Latent Embeddings for Neural Network Interpretability

Sudhanva Manjunath Athreya, Paul Rosen

原文摘要

Deep learning models have achieved remarkable success across various domains, yet their learned representations and decision-making processes remain largely opaque and hard to interpret. This work introduces HOLE (Homological Observation of Latent Embeddings), a method for analyzing and interpreting discriminative neural networks through persistent homology. HOLE extracts topological features from intermediate activations and presents them using a suite of visualization techniques, including cluster flow diagrams, blob graphs, and heatmap dendrograms. These tools facilitate the examination of representation structure and quality across layers. We evaluate HOLE using a range of discriminative models, focusing on representation quality, interpretability across layers, and robustness to input perturbations and model compression. The results indicate that topological analysis reveals patterns associated with class separation, feature disentanglement, and model robustness, providing a complementary perspective for understanding and improving deep learning systems.

cs.LG · cs.GR · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04157

Nonparametric GARCH: An Iterated Neural Estimator

Ruizhi Deng, Guohao Shen, Ngai Hang Chan

原文摘要

We study nonparametric estimation and forecasting for GARCH-type processes and propose an iterated neural estimator that alternates between estimating the recursion map and updating the unobserved conditional variances. Under mild conditions, we establish finite-sample bounds for the time-averaged in-sample variance-path error and show that the estimator is minimax rate-optimal up to logarithmic factors. Under stationarity and mixing conditions, we establish the same rate for recursion-map estimation and derive finite-sample bounds for out-of-sample variance forecasts and expected excess Gaussian quasi-likelihood (QLIKE) loss. The convergence rates depend on the true lag orders and remain unchanged when the fitted orders are overspecified. Simulation studies illustrate the effectiveness of our estimators under nonlinear and time-varying volatility dynamics. Real-data applications suggest that our neural methods perform competitively and can incorporate option-price and high-frequency information.

stat.ML · cs.LG · math.ST · stat.ME

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2604.24749

The Optimal Sample Complexity of Multiclass and List Learning

Chirag Pabbaraju

原文摘要

While the optimal sample complexity of binary classification in terms of the VC dimension is well-established, determining the optimal sample complexity of multiclass classification had remained open. The appropriate complexity parameter for multiclass classification is the DS dimension, and despite significant efforts, a gap of $\sqrt{\text{DS}}$ had persisted between the upper and lower bounds on sample complexity. Recent work by Hanneke et al. (2026) shows a novel algebraic characterization of multiclass hypothesis classes in terms of their DS dimension. Building up on this, we show that the maximum hypergraph density of any multiclass hypothesis class is upper-bounded by its DS dimension. This proves a longstanding conjecture of Daniely and Shalev-Shwartz (2014). As a consequence, we determine the optimal dependence of the sample complexity on the DS dimension for multiclass as well as list learning.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.08332

Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning

Michael Mancini, Shabnam Sodagari

原文摘要

Feedback-based adaptive quantum optimization (FALQON) is a promising approach for combinatorial optimization on noisy intermediate-scale quantum devices, but prior studies report that standard fixed-parameter schedules can require relatively deep circuits to reach strong solution quality. We study Optimal FALQON, a layer-wise formulation that treats the time-step $δ_k$ and scaling parameter $w_k$ as classical decision variables optimized per layer. The benchmark uses all 94 non-isomorphic 3-regular graphs with 12 vertices under 8192-shot simulation and compares standard FALQON, Optimal FALQON, and QAOA/QAOA-MA Powell runs warm-started from FALQON-derived schedules. Across approximation ratio, success probability, evaluation-normalized efficiency, and layer-normalized efficiency, Optimal FALQON substantially improves on standard FALQON, and Optimal-FALQON warm starts substantially improve on standard-FALQON warm starts.

quant-ph · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04155

Improving PI-RADS Prediction and Prostate Cancer Detection from Multiparametric MRI with Anatomical Guidance

Seungbin Park, Thibault Heintz, Rachit Saluja, Francesca Khani, Timothy McClure, Daniel Margolis, Mert R. Sabuncu, Heejong Kim

原文摘要

Accurate prediction of Prostate Imaging Reporting and Data System (PI-RADS) scores from multiparametric MRI (mpMRI) remains challenging, more so than clinically significant prostate cancer (csPCa) detection. Given the importance of lesion location in PI-RADS scoring, we evaluated whether incorporating prostate zonal masks improves PI-RADS prediction and csPCa detection across multiple architectures (3D ResNet-18, ResNet-50, and ViT) using a large-scale internal dataset. Anatomical guidance generally improved performance across architectures, with the best results achieved by ResNet-18 with 2-zone guidance for PI-RADS prediction (QWK = 0.511) and ResNet-50 with 5-zone guidance for csPCa detection (AUC = 0.809). Fine-tuned results on external datasets further demonstrated the benefits of anatomical guidance. Furthermore, models with anatomical guidance improved the utility of model-predicted PI-RADS scores for PI-RADS-based csPCa detection (positive defined as PI-RADS>=3). Combining model-predicted and expert-assigned PI-RADS scores significantly increased the AUC from 0.711 to 0.756 (p < 0.05), suggesting that AI-derived PI-RADS scores may provide complementary information to expert assessment for csPCa detection. Overall, our findings highlight the value of incorporating prostate zonal anatomy into deep learning-based prostate MRI assessment.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04153

Palimpsestic Accountability in the US Federal AI Inventories

Dipto Das, Syed Ishtiaque Ahmed, Aditya Vashistha, Shion Guha

原文摘要

An AI inventory is a structured, often public record in which a government or organization lists and describes the AI systems it uses or plans to use. Studies of single AI inventory releases describe what systems do but say little about how they are governed over time. We linked entries across three releases of the US Federal AI Use Case Inventory (2023--2025), compared answers only where both releases required them, and analyzed governance accounts through a dual deductive-inductive approach, guided by the Algorithmic Decision-Making Adapted for the Public Sector (ADMAPS) framework. As the inventory grew from 710 to 3,611 entries from 2023 to 2025, fewer required answers were left blank, but accountability was rewritten through three mechanisms. Each release \emph{redrew} the scope of the questions; agency-assigned classifications \emph{gated} what was reported; and answers were also \emph{overwritten}, with gains and losses concentrated in single agencies and little account of human oversight and judgment. These findings show that each release redraws the boundary of accountability and leaves the public to \emph{re-stitch} the records, a condition we term \textbf{palimpsestic accountability}. We recommend versioned records, reasons for every reclassification, public files that distinguish kinds of missing answers, and oversight documented as conditions to improve public sector AI accountability.

cs.AI · cs.CY · cs.HC

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arXiv ↗PDF ↗