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版本更新 · v4arXiv:2602.16681

VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detection

Yingyuan Yang, Tian Lan, Yifei Gao, Yimeng Lu, Xuming An, Wenjun He, Meng Wang, Chenghao Liu 等 9 位作者

原文摘要

Time-series anomaly detection (TSAD) requires identifying both immediate Point Anomalies and long-range Context Anomalies. However, existing foundation models face a fundamental trade-off: 1D temporal models provide fine-grained pointwise localization but lack a global contextual perspective, while 2D vision-based models capture global patterns but suffer from information bottlenecks due to a lack of temporal alignment and coarse-grained pointwise detection. To resolve this dilemma, we propose VETime, the first TSAD framework that unifies temporal and visual modalities through fine-grained visual-temporal alignment and dynamic fusion. VETime introduces a Reversible Image Conversion and a Patch-Level Temporal Alignment module to establish a shared visual-temporal timeline, preserving discriminative details while maintaining temporal sensitivity. Furthermore, we design an Anomaly Window Contrastive Learning mechanism and a Task-Adaptive Multi-Modal Fusion to adaptively integrate the complementary perceptual strengths of both modalities. Extensive experiments demonstrate that VETime significantly outperforms state-of-the-art models in zero-shot scenarios, achieving superior localization precision with lower computational overhead than current vision-based approaches. Code available at: https://github.com/yyyangcoder/VETime .

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.07417

Bayesian Optimization on Function Spaces via Sparse RKHS Manifolds

Davide Sartor, Meghan E. Huber, Donghyun Kim, Nathan Wycoff

原文摘要

Bayesian Optimization (BO) has become an established methodology for minimizing black-box functions of a vector input. Often, however, this parameter vector arises from the discretization of an inherently functional relationship. Several recent articles have considered the Functional Bayesian Optimization (FBO) setting, in which the variable to be optimized is not a member of a finite dimensional vector space, but rather an infinite dimensional function space. In this work, we propose $L^0$ Manifold Optimization (L0MO), a simple approach to FBO which searches the subset of a Reproducing Kernel Hilbert Space (RKHS) consisting of functions with a sparse representation in the kernel functions, optimizing both the kernel locations and their coefficients. We discuss in detail the relationship between our method and existing ones, providing a unifying lens through which to view prior works. To assess our method against the state of the art, we conduct an extensive computational study, and along the way develop a novel set of benchmark test functions which port standard finite-dimensional ones to the infinite dimensional domain. Our experiments demonstrate that, on balance, the proposed method achieves superior performance across a wide range of test benchmarks.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12458

Causal discovery from Bayesian symbolic regression

Roger Guimerà, Kai-Hendrik Cohrs, Sergio Pedreira, Marta Sales-Pardo, Gustau Camps-Valls

原文摘要

Causal discovery from observational data is central to scientific reasoning, yet established constraint-based and score-based methods often recover only a Markov equivalence class and provide no signal in bivariate or strongly autocorrelated regimes typical in some areas of science. Here, we recast causal discovery as a Bayesian symbolic regression problem. Given a set of candidate parents for each target variable, the Bayesian symbolic regression samples closed-form expressions from a posterior over symbolic models. The fraction of sampled expressions in which each candidate variable appears defines a posterior inclusion probability that we use as the causal score. This replaces pointwise model selection by an ensemble of plausible mechanisms. We evaluate the method on coupled nonlinear bivariate dynamics, on the CauseMe benchmark suite spanning linear, chaotic, nonlinear, higher-dimensional and high-noise settings, and on a four-variable biogeoscience structural causal model under Gaussian, heavy-tailed and mixed-noise regimes. Our approach is competitive with, and often outperforms, specialized causal discovery algorithms. Additionally, the method returns closed-form mechanisms leading to explicit causality and interpretability.

stat.ML · cs.LG · physics.comp-ph · physics.data-an

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.20827

Swim2Real: Vision-Language Models for Simulator Calibration of a Soft Robotic Fish

Kevin Qiu, Kyle Liam Walker, Mike Yan Michelis, Marek Cygan, Josie Hughes

原文摘要

Calibrating a soft aquatic robot simulator requires matching coupled deformation and propulsion from limited pool recordings. Swim2Real uses a vision-language model (VLM) to compare real and simulated swimming videos and propose updates to 16 simulator parameters. A backtracking line search tests each direction and accepts updates that reduce marker error. To test whether the proposals carry useful directions, we compare them with random and signed-permuted directions under the same line search and simulator budget. In an independent, preregistered fifty-seed replication, Swim2Real reduces the mean best-so-far error over 40 simulator evaluations by 63.4 mm against random directions and 53.2 mm against permuted directions. Both paired comparisons pass Holm correction ($p<10^{-5}$), conditional on run completion. A 24-seed experiment with a second VLM supports the same direction effect. Backtracking averages 16.1 VLM proposals per run versus 39 for Vid2Sid, which attains lower error at the same simulator budget. Ten hardware trials demonstrate open-loop execution of simulator-generated motor commands. Project page and videos: https://swim2real.github.io.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.08786

PRIM: Meta-Learned Bayesian Root Cause Analysis

Christopher Lohse, Anish Dhir, Amadou Ba, Bradley Eck, Marco Ruffini, Jonas Wahl

原文摘要

Root Cause Analysis (RCA) aims to identify the main drivers of anomalies in complex multivariate systems. The task is challenging due to the need for structural causal knowledge for some methods, and the computational cost of inference at test time. This paper introduces PRIM (Prior-fitted Root cause Identification with Meta-learning), a causal meta-learning approach that frames RCA as a Bayesian inference task over a prior of causal models. By marginalising out structural uncertainty, PRIM detects changes in the data-generating mechanism between normal and anomalous periods, infers distributional differences without explicit statistical testing, and implicitly learns causal structure without model fitting at test time. Following the simulation-based meta-learning paradigm of prior-fitted networks (PFNs), PRIM uses a transformer neural process (TNP) that jointly attends over normal and anomalous samples and the structural dependencies of nodes, enabling fast zero-shot inference in systems with up to 100 variables. Across synthetic benchmarks and two realistic benchmark datasets, PetShop and CausRCA, PRIM is competitive with other graph-free methods.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.08932

Shape-based Figure-Ground Organization in Vision Transformers

Matthias Tangemann, Benjamin Lo, Zygmunt Pizlo, Kaleem Siddiqi, Dirk B. Walther, Sven Dickinson

原文摘要

Rich perceptual structure emerges in modern vision encoders. Several studies have shown that the internal representation in many vision transformers separates foreground objects from the background, however, little is known about the visual cues that have been learned by these models. In this work, we study whether figure-ground organization in a broad range of 25 vision transformers relies on generalizable shape cues, rather than on superficial texture statistics. To do so we train linear probes to classify patches as figure or ground, and evaluate them zero-shot on synthetic stimuli where individual cues are systematically manipulated. For a broad range of vision encoders, we find that probes are robust to the random replacement of textures in the input image, suggesting that the emerging figure-ground organization relies on shape cues. Probes also generalize in a zero-shot fashion to synthetic images where individual shape cues, recognizability and spatial priors are controlled for. Our experiments reveal a strong influence of surroundedness and convexity cues, while results for parallelism and symmetry are more nuanced. More detailed analyses reveal stronger evidence for shape-based cues in larger models, that models trained for object-recognition tend to be less robust to texture changes and that figure-ground information emerges most prominently in middle layers. Taken together, our study demonstrates that emerging figure-ground organization in recent vision encoders relies strongly on generalizable shape cues, advancing our understanding of the internal processing in vision transformers, and opening an exciting avenue for computational research on human visual perception. Code and data is available at https://github.com/mtangemann/mlvbench.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12452

V3DSAM: Semantic-Geometric Loop Verification and Geometry-Anchored Persistent Instance Mapping for Feed-Forward Dense SLAM

Maolin Ye, Dongqing Zhao, Linyang Li, Shuai Xue, Yuan Liu, Yiyuan Wang, Wen Li

原文摘要

To address unreliable cross-view association in long-sequence feed-forward dense SLAM, this letter presents V3DSAM, an open-vocabulary semantic dense SLAM system that establishes physical correspondences at both the scene and object levels. The system combines feed-forward geometric estimates from VGGT-SLAM 2.0 with open-vocabulary semantic observations from SAM3. We introduce semantic-geometric dual-scale loop verification, which identifies false loop closures using pixel-level bidirectional reprojection and object-level 3D spatial correspondences, without accessing backbone-internal features. We further propose geometry-anchored persistent instance mapping, which defines physical instances through class-constrained 3D connectivity in an optimized global coordinate system and assigns global IDs without inter-frame identity propagation. The proposed verifier rejects all four false loop candidates in our TUM RGB-D evaluation, while long-sequence ScanNet experiments show improved instance identity consistency. Thus, V3DSAM reduces dependence on internal backbone features and temporal ID propagation for robust long-term cross-view association.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12446

Pricing Symbolic Rules by What GNNs Leave Unlearned

Satabarto Sarkar, Prithwineel Paul

原文摘要

Symbolic knowledge (motif counts, ring counts, label constraints) is routinely added to graph neural networks (GNNs) and justified by their limited expressivity. We replace this yardstick with an exact one. The largest log-loss reduction a rule $S$ can give a model family is $I(Y;S\midσ)$ given what the family computes; every realised gain splits exactly into this term plus repaid neural debt; and for a trained network with representation $T$ the value is $I(Y;S\mid T)$, the label information training failed to extract. Rule design thus becomes rule discovery: we score candidates on the network's out-of-fold residual against calibrated permutation nulls with family-wise error control. On ZINC, discovery recovers the hidden ring term $\max(0,\ell_{\max}-6)$ of the target in three independent runs ($p_{\mathrm{adj}}=0.001$) and beats expert cycle counts; frozen and applied to five unseen datasets it is never significantly worse than the plain network or the expert rule, and on BBBP it significantly beats both.

cs.NE · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12444

Sampling Colorings on $G(n, d/n)$ via Glauber Dynamics

Leon Schiller

原文摘要

We study the mixing time of Glauber dynamics for sampling uniformly from the proper $q$-colorings of sparse Erdős-Rényi random graphs $G(n,d/n)$. Our main result establishes that with probability $1-o(1)$ over the draw of $G$, Glauber dynamics is rapidly mixing for every $q\geq d+d^{1/2+\varepsilon}$, given any fixed $0<\varepsilon\leq1/2$ and any sufficiently large fixed $d$. In particular, the surplus of colors above $d$ is sublinear and close to $\sqrt{d}$. The achieved color range therefore approaches the tree uniqueness threshold and improves over the previously best achievable number of colors $q > 1.763 d$ due to Efthymiou, Hayes, Stefankovic, and Vigoda [SODA 2018]. Moreover, our mixing-time bound is $O(n^{2+o_d(1)})$, and hence almost quadratic as $d\rightarrow\infty$. Our proof is based on establishing a spectral gap for block dynamics resulting from a suitable decomposition of $G(n,d/n)$ into blocks and singleton vertices, ensuring that every high-degree vertex is encapsulated within a block. Our main technical contribution is a novel way of establishing rapid mixing of block dynamics via an integrated Bochner-Bakry-Émery approach, which we expect to have applications beyond sampling colorings. We further devise a new block decomposition of $G(n,d/n)$ that strengthens and simplifies similar constructions used in previous work. Finally, to compare block and Glauber dynamics, we establish a novel recursive separator decomposition of tree-like blocks, together with a coupling argument that avoids the need for nearly uniform color marginals at vertices adjacent to hubs. This comparison argument might be of independent interest.

math.CO · cs.DS · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12445

LongSCOP: Semantically Consistent Long Video Outpainting

Yu Tang, Zhiyuan You, Zhixiao Wang, Fanghua Yu, Qingyu Zhang, Xiang Yin, Chao Dong, Jinjin Gu

原文摘要

Video out-painting is an important problem, yet existing research focuses primarily on short clips, leaving long video out-painting largely underexplored. Extending video out-painting to long videos poses substantial challenges. A natural solution is iterative out-painting, i.e., generating videos chunk by chunk, but this introduces two major issues: generation instability or even failure caused by inter-chunk inconsistencies and error accumulation, and semantic drift over long temporal horizons. In this paper, we propose LongSCOP, short for Semantically Consistent long Video Out-Painting. First, we introduce a training recipe with three components for smooth and stable long-horizon generation: self-forcing training, latent calibration, and future references. Beyond generation stability, we recognize that the rich temporal context of long videos provides valuable references for maintaining semantic consistency. We therefore develop a VLM-based agent that automatically selects the most informative reference frames from the entire video to maintain consistent subjects, scenes, and backgrounds throughout long-horizon generation. We also construct a high-quality training dataset of 62K video clips. To facilitate research on this underexplored problem, we introduce LVO-Bench, a benchmark curated by human experts to assess long-horizon stability and semantic consistency. Extensive experiments demonstrate that our method outperforms existing approaches in both generation stability and semantic coherence.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12443

MoSTE: Mixture of Spatio-Temporal Experts for Deepfake Detection in Videos

Charbel Yahchouchi, Nabyl Quignon, Noemi Roggero, Laurent Saroul, Antitza Dantcheva

原文摘要

Generalizable deepfake video detection remains challenging because forgery artifacts vary substantially across manipulation types and often combine diverse spatial and temporal inconsistencies. Existing parameter-efficient adaptation methods typically rely on a single shared representation, rendering them prone to manipulation-specific bias and limiting transfer to unseen forgeries. To address this challenge, we propose Mixture Of Spatio-Temporal Experts (MoSTE), an adaptation framework that enables conditional specialization of forensic representations. Instead of forcing one adapter to model all forgery patterns, MoSTE decomposes adaptation into parallel spatial and temporal expert pools and dynamically routes tokens to the most relevant experts through an unsupervised token-level routing mechanism. This allows experts to naturally specialize in complementary forgery cues without requiring explicit manipulation labels or multi-stage training, while effectively transferring representations from CLIP to the video domain. Extensive experiments across cross-dataset and cross-manipulation benchmarks demonstrate that MoSTE sets a new state of the art in average generalization while preserving strong performance across diverse manipulation families. Additional analysis of expert routing and attention behavior reveals the emergence of expert diversity, suggesting that MoSTE learns complementary spatio-temporal forensic features. Code is available at https://github.com/charbelyah/MoSTE

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12442

When Handshakes Go Dark: An Adaptive Semantic-Sequential Fusion Framework for Encrypted Traffic Classification

Tong Guan, Yunchun Li, Minyuan Xiao, Mingyuan Xia, Yuchen Zhao, Wei Li

原文摘要

Encrypted traffic classification is essential for network security monitoring and traffic management. Self-supervised pre-training can learn reusable representations from large-scale unlabeled traffic, but TLS 1.3, QUIC, and privacy mechanisms such as Encrypted Client Hello (ECH) increasingly conceal application, handshake, and service-identifying information. Observable upper-layer bytes are therefore dominated by ciphertext, weakening the link between raw-byte patterns and application semantics and limiting model generalization. Moreover, the availability and discriminative value of observable information vary across protocols, protection profiles, and tasks, complicating classification across diverse scenarios. To address these challenges, we propose FLARE, a Field-aware Learning and Adaptive Representation Fusion framework for encrypted traffic classification. FLARE pre-trains a BERT-based packet encoder through Masked Field Prediction, which reconstructs masked tokens from field-structured context. During downstream fine-tuning, the parameters of the pre-trained packet encoder remain frozen, and a packet-level gated module adaptively combines protocol-semantic representations with directional packet-length representations. A flow-level encoder then models inter-packet dependencies for classification. We evaluate FLARE on eight datasets spanning diverse encrypted-traffic tasks, including CipherSpectrum and VisQUIC for strongly encrypted scenarios. FLARE achieves superior performance over baselines across the eight datasets, exceeding the strongest pre-trained baseline by 10.92 percentage points on QUIC website fingerprinting. These results demonstrate the effectiveness of field-aware pre-training and adaptive semantic-sequential fusion under constrained observability.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12440

Exploring public-private data sharing practices through the lens of Open Data Ecosystems: A multiple case study on municipal and regional freight logistics

John Larsson, Johan Linåker, Per Runeson

原文摘要

Background: Freight transport is expected to increase, contributing to air pollution. There is, therefore, a critical need for increased efficiency that can reduce emissions. Data sharing is one approach, where actors within Open Data Ecosystems (ODE) collaborate and share data among stakeholders to plan and operate more efficient and sustainable freight transport. Aim: In this study, we explore how public-private data sharing practices in municipal and regional last-mile freight logistics in Sweden may improve efficiency. Method: Following a prolonged participation process, we conducted a multiple-case study involving three different cases, built upon initial meetings, semi-structured interviews, supported by a thematic analysis, and validated through a follow-up workshop. Results: The findings show that data sharing in public-private freight logistics (ODE-PPF) is shaped by interrelated socio-technical aspects, including platform design, roles of actors, handling of sensitive data, data quality, and procurement processes. We synthesize our findings in an actor model and a conceptual model, highlighting how these aspects contribute to the functioning of ODE-PPF, and derive seven proposition sketches as a foundation for future research. Conclusion: Data sharing is a central mechanism enabling integration and value creation in last-mile logistics. It depends on the interaction between technical and organizational aspects. Public actors can undertake a key role as a neutral keystone actor by orchestrating the ecosystem and facilitating data sharing through governance practices such as procurement.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.22698

FogDrive: A Multi-Modal Synthetic Driving Dataset for Perception under Graded Fog

Vansh Panwar, Naveen Varshney, Prashant W. Patil

原文摘要

Fog degrades autonomous-driving perception across modalities: it lowers image contrast, attenuates and scatters LiDAR returns, and shifts the input distribution of every learned module downstream. Choosing between restoring the degraded input and training on it directly requires the same scenes observed with and without fog at known severities, and enough foggy data to train on. Real-world capture cannot supply this: fog arrives entangled with lighting and traffic, at severities recorded rather than chosen; existing synthetic datasets narrow the sensor suite, the scale, or the severity range. We present FogDrive, a CARLA dataset of ~660 scenes (~133k frames), each released as a clear reference plus three calibrated fog densities, with physics-based fog on camera and LiDAR and 2D/3D annotations consistent across all variants. Every frame carries four cameras (RGB, depth, semantic), LiDAR, semantic LiDAR and radar -- spanning fog's response spectrum from heavily degraded cameras to largely unaffected radar. Experiments trained on FogDrive show that mixing fog densities improves 3D detection at no extra data cost, that PSNR and SSIM poorly predict a dehazer's detection benefit, and that radar improves camera-LiDAR fusion at every density. The dataset and its generation framework will be released.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12437

Occupancy Modelling of Biased Landslide Inventories: When Does Separating Occurrence from Recording Improve on Neutralisation?

Abdirashid Omar, Abdikadir Ugaas

原文摘要

Landslide susceptibility models are trained on inventories that record where landslides were seen, not where they occurred. The established remedy fits bias-describing variables and neutralises them when mapping. We adapt occupancy-detection modelling from ecology, treating several inventories as imperfect observers of one event and separating occurrence from recording, and ask when this improves on neutralisation. We use near-complete inventories of the 2018 Hokkaido Eastern Iburi, 2015 Gorkha and 2010 Haiti earthquakes with simulated reporting bias, and three real Gorkha inventories. Compared with standard practice, the occupancy model reduces road bias in 24 of 25 paired draws and raises AUC by 0.08-0.14 on Gorkha and 0.03 on Haiti, but only 0.01-0.02 on Iburi. Its median predicted-to-true landslide rate is 0.99 (range 0.44-1.89), against 0.24-0.30 for the baselines. Rescaling the baselines with the same anchor area corrects their median but leaves larger errors (range 0.20-4.34). Cell-level probabilities remain too extreme (calibration slope 0.59). Compared with the best neutralised model, AUC is higher on Iburi (by 0.03-0.07) and lower on Gorkha and Haiti (by 0.01-0.05). Transplant experiments tie the Iburi advantage to the structure of its true landslide pattern, not to its terrain or road network. In all eight settings with a low interaction gap, computable from biased records, neutralisation was at least as accurate, including a prespecified test on Haiti; a high gap did not guarantee an advantage. The real Gorkha inventories are slope-biased, not road-biased, and there the model brings no meaningful gain. Report-based catalogues remain the main untested case.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12436

Patching Up Circuit Learning via Parameter Sharing

Charles Bricout, Gennaro Gala, Viacheslav Borovitskiy, Antonio Vergari

原文摘要

Probabilistic circuits (PCs) have emerged as generative models guaranteeing tractable probabilistic inference. However, tractability comes at the price of larger model sizes than in other generative models. As such, to scale PCs to high-dimensional data such as images, previous work has devised several parameter sharing techniques as well as fast evaluation protocols that consider learning PCs on independent image patches. In this work, we revisit this practice to highlight a surprising learnability issue: models that assume independence counterintuitively can yield better likelihoods than models that do not. To break this apparent bottleneck and to scale PCs further, we introduce a recursive parameter sharing construction (RePA) that fosters efficiency without assuming independence. We show empirically that RePA can greatly reduce time and memory consumption while yielding competitive likelihoods. Inspired by these results, we further generalize the approach to data modalities that do not inherently exhibit patch-structure (NoPA), such as tabular data, showing similar efficiency improvements, with marginal changes in likelihoods and sample quality on seven tabular datasets. Code available at https://github.com/april-tools/RePA.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12435

To SIMD or Not to SIMD: Comparing RVV vs. CGRA SoCs for Non-Linear Functions in AI

Sameer Deshmukh, Naoya Mizuki, Takumi Honda

原文摘要

Non-linear functions are an important class of operations in modern large language models. However, chip vendors building for AI have so far relied on traditional vector or scalar processing units for non-linear functions. The performance of these units has not kept pace with matrix multiplication units (MMUs), which can be found in both high-performance and edge-computing devices from almost every vendor. Reconfigurable units such as FPGAs and CGRAs have been proposed in the literature, but have not been systematically compared with traditional units backed by MMUs. In this paper, we build two SoCs: one using an RVV vector processor and the other using a latency-insensitive (\emph{elastic}) CGRA, both coupled with an MMU and driven by a single-core CPU. We then propose mappings for common feed-forward networks on our SoCs and find that the CGRA reaches a maximum speedup in clock cycles of 7.12x over RVV on the activation stage of simple point-wise non-linear functions like SiLU, whereas the RVV reaches a maximum speedup of \textbf{9.8x} over the CGRA on complex non-linear functions such as $\mathrm{softmax}$. Over the enclosing feed-forward block the corresponding maxima are 3.16x and 4.24x.

cs.AR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12434

Frontier AI Safety and Governance 2026: Risk Understanding and Coordinated Governance

Zeng Yi, Seán Ó hÉigeartaigh, Cao Gongce, Lu Enmeng, Chen Yu, Han Kaiyu, Li Kai, Wang Zhengqi 等 110 位作者

原文摘要

Frontier AI Safety and Governance 2026: Risk Understanding and Coordinated Governance is a key outcome of the AI Safety and Governance Program of World Internet Conference Specialized Committee on AI in 2026. Drawing on the latest developments in frontier AI capabilities and applications, the report systematically examines the evolving risk landscape, analyzing key risk areas, their severity and response priorities. Building on a review of existing global governance practices and an assessment of frontier AI governance readiness, it identifies key governance gaps and weaknesses and proposes directions and practical mechanisms for coordinated responses. The report provides a reference for government departments, international organizations, businesses, research institutions and other stakeholders. It aims to strengthen the international community's understanding of and consensus on frontier AI risks, enhance preparedness and collective response capacity, and advance the safe, reliable and responsible development of frontier AI to better serve the common well-being of humanity.

cs.CY

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.15874

LLM-as-Code: Agentic Programming for Agent Harness

Junjia Qi, Zichuan Fu, Jingtong Gao, Wenlin Zhang, Hanyu Yan, Xian Wu, Xiangyu Zhao

原文摘要

Every major LLM agent framework gives the LLM the role of orchestrator; the model decides what to do next, when to call tools, and when to stop. We argue that token explosion, control-flow hallucination, and unreliable completion are not implementation bugs but architectural consequences of assigning the deterministic work of looping, branching, and sequencing to a probabilistic system. A better prompt or a stronger model cannot guarantee the reliability of the LLM agent. We therefore propose Agentic Programming, in which the program governs all control flow, and the LLM is itself part of it, an adaptive component we call LLM-as-Code and invoke only where a task calls for reasoning or generation. Within each call the model keeps full flexibility, but it cannot alter the program's execution path. With control in the program, the LLM's context is built from the execution history's call tree and forms a directed acyclic graph (DAG). Each call's context length is then determined by its call depth rather than by accumulation over steps. A case study of computer-use agents shows that the design is practical, not just a theoretical stance, substantially improving the stability of long visual operation sequences.Code is available at https://github.com/FuzzyKuji/OpenProgram.

cs.AI · cs.SE

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2505.23086

Equivariant Spherical Transformer for Efficient Molecular Modeling

Junyi An, Xinyu Lu, Yunfei Shi, Peijia Lin, Licheng Xu, Qianwei Tang, Chao Qu, Fenglei Cao 等 9 位作者

原文摘要

Equivariant Graph Neural Networks (GNNs) have significantly advanced the modeling of 3D molecular structure by leveraging group representations. However, message passing built only from exactly equivariant operations can have restricted practical expressiveness at a fixed representation degree. We introduce the Equivariant Spherical Transformer (EST), a plug-and-play module that applies Transformer attention to spherical spatial representations obtained from group representations. EST explicitly mixes information across sampled orientations, unlike prior orientation-wise spherical operations, while retaining a strong approximate equivariant inductive bias through near-uniform spherical sampling. As demonstrated by our experiments on challenging benchmarks like OC20, MPtrj and QM9, EST-based models achieve state-of-the-art performance. For the complex molecular systems within OC20, small models empowered by EST can outperform some larger models and those using additional data. We characterize the ideal closed-domain condition under which the continuous operation is exactly equivariant and empirically quantify the finite-sampling accuracy--equivariance--memory trade-off used in practice.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12429

Can Frontier Auto Researchers Reconstruct Human Research Decisions in AI?

Shengyun Si, Junjie Chu, Ye Leng, Yang Zhang

原文摘要

Scientific research has long been driven by substantial human effort, from reviewing prior work and identifying research gaps to designing specific solutions. Recent advances in harness-based agent systems have given rise to increasingly capable Auto Researchers, which can autonomously carry out these tasks and, increasingly, entire end-to-end research workflows. Yet despite their rapidly growing capabilities, how closely the research decisions and outputs produced by auto researchers align with those of human experts remains underexplored. We investigate this gap through the lens of research reconstruction and study: Given comparable research context, how similar are the research decisions and outputs produced by Auto Researchers to those of human researchers in AI research? To answer this question, we introduce AutoResearcher-Human Research Similarity (AutoHRS), a stage-wise evaluation framework for assessing how faithfully auto researchers reconstruct human research artifacts across different stages of the scientific workflow. Using 152 ICLR oral papers as our corpus, we generate 456 research trajectories spanning different stages of the research process. Applying AutoHRS to these trajectories, we find that current Auto Researchers frequently fail to recover key research decisions and their resulting artifacts across multiple stages of the research process.

cs.CL

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版本更新 · v2arXiv:2610.19011

PMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization

Gor Simonyan, Tatevik Abrahamyan, Narek Abelyan, Tigran Fahradyan, Hrant Khachatrian

原文摘要

The Practical Molecular Optimization (PMO) benchmark standardized evaluation in molecular optimization, but it is built on simple property-based oracles that do not capture structure-based drug design. As the field has moved to docking-based objectives, evaluation practice has become unstandardized, and different methods report results on different docking tasks, often without a shared protocol for fair comparison or sample-efficiency constraints. To address this, we introduce Practical Molecular Optimization for Docking (PMO-Dock), a benchmark and protocol for docking-based optimization consisting of 25 tasks covering hit generation, lead optimization, and a new specificity task requiring strong on-target binding while penalizing off-target interactions. The protocol separates model development from final evaluation: validation tasks are used for hyperparameter tuning, and test tasks are held out for final evaluation and are not used during development. Evaluation on the test tasks is done under a strict oracle budget to reflect realistic drug-discovery settings. We benchmark four diverse high-performing methods spanning different optimization paradigms, Saturn (reinforcement learning), GenMol (discrete diffusion), Genetic-guided GFlowNet, and ChemLlama (LLM-based evolutionary search). Our results show no universally best method across tasks, additional analyses of hyperparameter transfer (cross-task and cross-category) and sensitivity show that transfer is often limited and varies by method, motivating method-dependent selection strategies. This benchmark provides a concrete standard for evaluating generalization to unseen tasks and sample efficiency under a limited test-time oracle budget.

cs.LG · q-bio.QM

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新论文 · v1arXiv:2611.12427

UCB-min: Combining Bandits Under Variance Heterogeneity (Extended Version)

Lilia Izri, Benoit Groz, Silviu Maniu

原文摘要

Multi-armed bandits are a natural framework for many real-world sequential decision problems, from recommender systems and clinical trials to educational test recommendation and online advertising. The performance of these algorithms depends on how they explore different arms, and exploration strategies range from variance-independent (e.g., UCB1) to variance-aware (e.g., UCB-V). In practice the reward variance of each arm is rarely known and varies widely across arms, and the reward support is often underspecified. In these cases no single rule is uniformly best: variance-independent methods are robust but loose when variances are small, while variance-aware methods are tight only when their variance estimates and the assumed support bound are reliable. Such heterogeneous, unknown variances arise in many settings, for instance when multiple objectives are scalarized into a single reward. We propose UCB-min, a per-arm adaptive meta-algorithm that selects, for each arm, the tighter of a variance-independent and a variance-aware exploration bonus, without prior knowledge of the variance regime and without reward normalization. We prove that, with UCB-V as variance-aware base, its regret is bounded by the sum of the two base bounds at the standard exploration constant, and by their per-arm minimum, up to a constant, for a larger one. We validate it on synthetic data, an educational tutoring task, and two group-recommendation benchmarks (MovieLens, Steam), showing that UCB-min consistently tracks the best base algorithm across diverse variance regimes and datasets.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2611.03412

DeliverBench: Benchmarking AI Agents on Delivering Realistic Multi-Stage Professional Projects

Yumou Liu, Mingju Chen, Zhoufutu Wen, Qianyu Yang, Zijie Wang, Sihan Chen, Yeqian Liu, Chenghao Yang 等 24 位作者

原文摘要

LLM agents are judged by what they deliver, and in professional work what gets delivered is a project, not a task. A project pays off only if the whole package holds together, and one weak part can limit the value of the rest. Yet existing benchmarks mostly test agents on one task at a time, leaving a gap between the capability we measure and the outcome that makes agents useful. To address this gap, we introduce DeliverBench, which tests whether agents can carry a project from brief to final delivery. Reconstructed from real materials with 13 domain experts, its 11 projects span settings such as financial due diligence, legal contract drafting, and drug safety reporting. They comprise 150 subtasks, with 2-15 subsequent releases of new materials and client feedback per project. Later subtasks build on the agent's own earlier deliverables, so unresolved errors can carry into downstream work. Expert rubrics assess each subtask submission and the final delivery package. Across seven model-harness systems, the best scores 65.0/100 on final delivery and passes 50.0% of subtasks at a 90-point threshold. Together, these results suggest that task-level capability does not simply compose into delivery; delivery also depends on keeping the work coherent as it evolves.

cs.AI

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新论文 · v1arXiv:2611.12426

dNaViT: Discrete Codes are General-Purpose Visual Representations

Xiaotong Li, Zhengrong Yue, Xu Huang, Jialv Zou, Minhao Jing, Wenhao Yan, Haodong Li, Hai Huang 等 12 位作者

原文摘要

Discrete autoregressive modeling, notably through next-token prediction, has become a cornerstone of modern language models. Extending this paradigm to vision allows visual and language models to converge on a unified framework, thereby fostering shared methodology and infrastructure. This work investigates a central question: how far can a finite discrete vocabulary go in representing the richness of the visual world? We present dNaViT, a Discrete Native-resolution Vision Transformer that produces discrete visual primitives analogous to language tokens. Built upon a frozen pretrained encoder, dNaViT applies lightweight residual modulation with a dual-branch objective to inject reconstruction-aware cues into semantic features. By leveraging grouped product quantization, it scales discrete capacity without inflating spatial sequence lengths, while a balanced training recipe maintains both semantic and pixel-level information. Consequently, dNaViT functions as a versatile discrete visual tokenizer, enabling unified encoding across downstream tasks alongside high-fidelity image decoding. Extensive evaluations show that dNaViT matches or exceeds task-specific baselines across 12 visual understanding benchmarks, 4 generation benchmarks, and an image editing benchmark. These findings demonstrate that a compact discrete vocabulary can serve as a universal visual currency and unlock the full potential of fully unified, native multimodal methodology.

cs.CV · cs.AI

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新论文 · v1arXiv:2611.12424

Physics-informed neural networks in the small-data regime: A controlled comparison with classical PDE surrogates

Kirill Katsuba, Oleg Rudenko, Egor Bugaev, Alexander Tarakanov

原文摘要

Neural surrogates for engineering models are often trained on large collections of simulated solutions. We investigate whether physics-informed neural networks (PINNs) can achieve accurate predictions from scarce labelled observations by incorporating governing equations and prescribed initial and boundary conditions. We introduce hierarchical loss weighting (H-PINN) and its gradient-direction-aware extension (HO-PINN), which adjust the emphasis on physical constraints according to higher-priority losses. Hierarchical PINNs are compared with Vanilla PINN, tuned gradient boosting, support-vector regression, and Gaussian-process regression on a heat equation and three heterogeneous two-phase-flow benchmarks. Experiments use 20--1000 labelled states and ten paired seeds. Each label is one coordinate--state observation, not a complete simulation. At least one PINN variant achieves the highest mean primary-target \(R^2\) in each interpolation and equation-assisted forecasting comparison at 20 and 50 labels. The advantage for water saturation persists across all tested budgets. Hierarchical weighting reduces variability across runs in selected settings. These results demonstrate that known physics can support accurate neural surrogate predictions from very few solution observations, at substantially higher fitting cost than the tested classical regressors.

cs.CE · cs.AI · math.NA

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新论文 · v1arXiv:2611.12422

Two-Phase Rapid Simulation-Based Inference with Differentiable Simulators

Uros Zivanovic, Rahul Srinivasan, Roberto Trotta, Andre Scaffidi

原文摘要

We introduce Rapid Simulation Based Inference (RSBI), a diffusion-based variational approach to likelihood-free Bayesian inference that achieves high sampling efficiency under large prior-to-posterior volumes. RSBI builds on advances in Schrödinger Bridge (SB) diffusion sampling to handle multi-modal posteriors, with an initial likelihood supplied by a distance measure via a differentiable simulator. The key innovation of this initial surrogate likelihood is that it gives a highly efficient proposal distribution, replacing sequential rounds of other methods with an efficient one-shot proposal. Subsequent refinement then transports the proposal to the true posterior as specified by the simulator and the original prior. The SB sampler is warm-started from the proposal, and its energy is corrected by a neural likelihood ratio that is learned only where the proposal places mass. As a variational method, RSBI does not suffer from prior leakage commonly observed in standard sequential posterior estimation methods. To improve mode coverage we optionally utilize the well-tempered meta-dynamics framework, which also encourages exploration of the prior volume. We observe strong performance on standard SBI benchmarks, particularly when the prior volume is increased relative to the ground truth posterior, achieving a performance degradation of only $\sim5\%$ on Two Moons under a highly constrained simulation budget. Additionally, we evaluate RSBI's performance for gravitational wave ring-down posterior estimation, in a real-world physics benchmark inspired by black hole spectroscopy.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.15551

Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis

Pedram Bakhtiarifard, Sophia N. Wilson, Mahmoud H. A. Afifi, Jonathan Wenshøj, Raghavendra Selvan

原文摘要

Training large-scale deep neural networks (DNNs) is resource-intensive, making model compression a practical necessity. The widely accepted ''learning as compression'' hypothesis posits that training induces structure in network weights, which enables compression. Measuring this structure through Kolmogorov-Chaitin-Solomonoff (KCS) complexity is appealing, but existing estimators based on the Coding Theorem Method (CTM) and the Block Decomposition Method (BDM) are limited to small binary objects and do not scale to modern DNNs. We introduce the Quantized Block Decomposition method (QuBD), which extends algorithmic complexity estimation to any $k$-ary object. QuBD first quantizes the network weights to a finite alphabet, then estimates the KCS complexity by aggregating per bit-plane CTM estimates. We show theoretically that QuBD yields a tighter upper-bound on the estimation gap for ideal prefix KCS complexity under bit-plane independence than binarization-based methods. Using QuBD, we study how the algorithmic complexity of neural network weights evolves during training, showing that it decreases as models learn, scales with data budget, increases during overfitting, follows the delayed generalization observed during grokking, and correlates with generalization performance. We further show that algorithmic information resides predominantly in the most significant bit-planes, which can serve as a practical diagnostic for determining appropriate post-training quantization levels. This work offers novel insights into learning mechanisms in DNNs by providing the first scalable, tractable estimates of KCS complexity for large, non-binary objects such as DNN weights.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12420

Path-Level Structural and Semantic Alignment for Hierarchical Multi-Label Text Classification

Xindi Wang, Aifen Xiong, Baiyang Wang, Xuri Ge, Robert Mercer, Frank Rudzicz, Yifan Peng, Zhumin Chen 等 9 位作者

原文摘要

Hierarchical multi-label text classification (HMTC) requires models to capture both structural dependencies among labels and the fine-grained semantics of labels at different hierarchy levels. Existing methods often model hierarchy through prediction constraints or representations of individual labels and local relations. Structural and semantic supervision, however, are usually formulated separately. We instead use complete root-to-node label paths as a shared unit for both. Our framework introduces two auxiliary training objectives while retaining a standard multi-label classifier at inference. PathSimNCE aligns document representations with hierarchical paths using hierarchy-aware contrastive targets. These targets provide graded supervision for structurally related paths while preserving explicit supervision for the ground-truth paths. The complementary semantic objective uses LLM-generated, document-conditioned descriptions of the same positive paths. It thereby provides context-specific label semantics beyond static label representations. Experiments on three benchmarks show consistent improvements: structural alignment is particularly beneficial in deeper hierarchies, while semantic alignment provides additional gains for fine-grained labels.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12419

Optimal Rates for Adaptive Private $k$-PCA

Johanna Düngler, Amartya Sanyal

原文摘要

Given $n$ i.i.d. random matrices $A_i \in \mathbb{R}^{d \times d}$ with common expectation $Σ$, the goal of \emph{Differentially Private Stochastic PCA} is to identify a $k$-dimensional subspace capturing the leading variance directions of $Σ$, while preserving differential privacy~(DP) for each individual sample $A_i$. \citet{dungler2025iterative} introduced \kdppca, the first algorithm to simultaneously (1) achieve sample complexity $n=\widetilde O(d)$ for sub-Gaussian data, (2) adapt its privacy noise to the intrinsic randomness of the data, and (3) extend seamlessly to any target dimension $k\le d$. However, its sample complexity has a suboptimal dependence on $k$. We propose the first algorithm that achieves optimal sample complexity in both $d$ and $k$, while retaining properties (2) and (3) of \kdppca. Additionally, prior utility guarantees require a minimum sample size with exponential dependence on the eigengap. Our method removes this dependence. It also improves the dependence on spectral parameters, matching known lower bounds for the spiked covariance model. Unlike deflation-based approaches like \kdppca, our algorithm updates the full $d\times k$ subspace jointly rather than one eigenvector at a time. This non-deflation structure simplifies the algorithm, reduces the number of hyper-parameters, and improves computational efficiency, particularly when using block linear algebra libraries.

cs.LG · stat.ML

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