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新论文 · v1arXiv:2611.16645

Rethinking Actions: Continuity-Aware Priors for World-Model Planning

Shaobo Yang, Zichen Ai, Ziyang Ma, Qian Yang, Bin Fang

原文摘要

Actions in expert demonstrations exhibit temporal structure. An ordered-versus-shuffled analysis across five environments confirms this dependence. We ask whether action proposals that better reflect expert continuity can improve visual world-model planning. Rather than imposing generic smoothness, we learn continuations from the temporal structure of demonstrated actions. Our locality-gated deep-kernel Gaussian-process prior, LG-DKGP, combines recent and longer-range executed action history, predicts a neural continuation, and learns a temporal Gaussian-process (GP) residual. A training-derived exploration scale initializes search around the corrected mean; a frozen world model evaluates progress toward the goal. Training first fits the neural mean, then jointly optimizes the prior with the GP residual using expert future actions. The resulting planner achieves 87.07\% macro success across five simulated tasks spanning native and fragment-level goal attainment, compared with 71.03\% for INTACT, 61.66\% for PRISM, and 58.66\% for LeWM. Component interventions and expert-relative action-structure comparisons support demonstration-informed continuations and reveal task-dependent effects of visual conditioning, history length, and planning horizon.

cs.RO · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16643

Optimized Mirabbasi-Martin Pulse Shaping Filter for Higher-Order Modulation in Zak-OTFS

Naveed Bin Nazir, Ananthanarayanan Chockalingam

原文摘要

The performance of Zak-OTFS modulation depends critically on the delay-Doppler (DD) domain pulse shaping filter, which must support both accurate channel estimation and reliable data detection. Filters reported in the Zak-OTFS literature fall short in bit error rate (BER) performance for higher-order modulation due to sensitivity to model-free channel estimation errors and pilot-data interference. Motivated by this, in this paper, we introduce a separable two-dimensional Mirabbasi-Martin (MM) filter for Zak-OTFS and propose a filter coefficient optimization framework that minimizes the aggregate sidelobe energy while preserving the compact structure of the MM prototype. By trading exact Nyquist nulls for high localization, the optimized MM filter exhibits no sidelobes with the narrowest $-40$ dB DD domain pulsewidth compared to sinc, Gaussian, and GS pulses. This compactness of the optimized filter tightly confines the pilot energy within the pilot region of the embedded frame, ensuring that the full response is captured for model-free channel estimation while significantly reducing pilot-data interference. This yields both an improved estimation performance and a significantly improved BER performance for higher-order QAM. For the proposed filter, we derive closed-form expressions for the effective DD domain channel and the covariance of the receive-filtered noise, which drastically reduce simulation run times. Our simulation results demonstrate that the optimized MM filter achieves significantly better uncoded/coded BER performance for higher-order QAM compared to that of sinc, Gaussian, and GS filters. For example, for 64-QAM with M=32, N=48, rate-1/2 convolutional code, and TDL-A channel model with fractional DDs, the optimized MM filter achieves a coded BER of 3x10^{-4} at an E_b/N_0 of 40 dB, while the sinc, Gaussian, and GS filters exhibit error floors above 10^{-2}.

cs.IT · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16642

Dataset Distillation via Drifting

George Cazenavette, Angelina Quan, Giannis Daras, Antonio Torralba, Vincent Sitzmann

原文摘要

The task of dataset distillation concerns the learning of a small set of synthetic samples that can serve as an effective replacement for a large real dataset when used for downstream tasks. While many solutions have been proposed since the problem's inception, most of them primarily seek to optimize the distilled data with respect to a classifier. In this work, we propose a new distillation method that learns a set of samples such that they directly approximate the per-class data distribution. Specifically, we "drift" our distilled data in the latent space of a feature extractor such that they move both toward the real data and away from each other without using a classification objective. We find that our drifted data cover the real distribution significantly better than existing methods while still serving as powerful training samples for classification. Furthermore, we showcase our method's use as a visualization tool for better interpreting models and extend it beyond images to other modalities. Our work advances the frontier of dataset distillation, offering a novel synthesis method and proposing distribution-based metrics as a complementary evaluation. More broadly, our findings help address the growing need to understand both data and models by uncovering more about how they interact.

cs.LG · cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16634

Machine Learning for Scalable Multi-AP Coordination in IEEE 802.11bn Networks

Maksymilian Wojnar, Szymon Szott

原文摘要

As Wi-Fi deployments become denser, fully distributed random channel access no longer guarantees the required quality of service. The multi-access point coordination (MAPC) framework introduced in IEEE~802.11bn lets neighboring access points (APs) cooperate, but choosing which APs transmit together and how to configure them remains an open, computationally hard problem. We develop and validate a stack of machine learning (ML) coordination methods, progressing from lightweight hierarchical multi-armed bandits (H-MABs) to a generative pipeline built on graph neural networks (GNNs) and flow matching (FM). The methods are evaluated in simulation and on a hardware testbed, improving throughput over legacy access and scaling coordination to dozens of APs where analytical and search-based methods become intractable.

cs.NI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.11963

Can LLMs Fix It Without Code? Toward Automated Verification of No-Code Bug Fixes

Utku Boran Torun, Veli Karakaya, Eray Tüzün

原文摘要

A no-code fix resolves an invalid bug report by directing the user to change a setting, update to a version where the problem is already fixed, or adjust their workflow. Manually verifying whether a proposed no-code fix resolves the reported bug takes considerable developer time. This study proposes an automated, execution-based pipeline for evaluating the capability of large language models (LLMs) to generate no-code fixes in a real browser environment. We evaluate 322 no-code fixes generated by the 12 configurations released with the benchmark of a previous study, covering bug reports categorized as Faulty Configuration, Wrong Version, or External System & Dependency. An executor agent applies each fix by following its natural-language instructions, and an issue-specific checker determines whether the reported bug persists. We repeat the pipeline with three executors: two Computer-Use Agents, OpenCUA-72B and Claude Sonnet 5, and one multimodal agentic LLM, Meta's Muse Glimmer. Only 17.6% of the candidate issues could be set up and passed both sanity gates. Across the 322 fixes, 14.6% to 49.7% resolved the bug depending on the executor, and the strongest configuration, Claude Opus 4.6 in the Vanilla pipeline, resolved up to 74.1% of its fixes under Claude Sonnet 5. Changing only the executor shifted a configuration's resolution rate by 38.8% on average, and the three executors reached the same verdict on only 46.9% of the fixes. Compared with human execution, the executors matched the human consensus for 66.1% to 88.1% of the sampled fixes. Even under the best executor, fewer than half of the LLM-generated no-code fixes resolve the reported bug, so such fixes need verification before they reach users. Execution-based verification can provide this, but the measured capability depends strongly on the executor, which evaluations must report and control.

cs.SE · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16630

When Chekhov's Gun Fires Anyway: Over-Realization in Text-to-Image Models

Jiahao Huang, Kon Woo Kim, Song-Duo Ma, Yuka Kitamura, Felix Wagner, Yunshan Huang, Sunisth Kumar, Xanh Ho 等 15 位作者

原文摘要

In text-to-image generation, a target that the prompt mentions often appears in the generated image in a way the prompt does not call for. We call this failure over-realization. Mental-spaces theory points to the situations where it can happen: under a negation, inside what a character believes or imagines, as the vehicle of a figurative comparison, or behind a viewpoint that hides the target. Prior work has studied only negation in depth, and the conventional alignment and preference metrics do not penalize the failure. We build OverReal to measure it: OverReal-Gen, 698 prompts covering the four situations, on which we measure each generator's over-realization rate; and OverReal-Det, 2,841 generated images human-labeled for whether and how the target is over-realized, on which we verify the automatic judge that produces this rate. Our evaluation of eight text-to-image systems reveals that all of them over-realize, and that conventional metrics such as CLIPScore reward the failure rather than penalize it. Prompt expansion only partly mitigates the failure. Inside the generator, the diffusion backbone attends to a mentioned target almost as much as to a requested one. The cue words that should hold the target back receive less attention than ordinary words.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16628

Safety as a Budget: Calibrating the Control Channel of Constrained GRPO for Vision-Language Models

Bo Han

原文摘要

Reinforcement learning can teach vision-language models (VLMs) to decline harmful image-text requests, but how safe the result becomes is set indirectly, through reward weights found by trial runs. Stating safety as budgets on the rate of harmful assistance and the rate of over-refusal, enforced by Lagrange multipliers inside GRPO with an off-the-shelf scorer as the cost, is the natural fix, and it fails twice. The multiplier compares the scorer's mean with the budget, and scorers built for ranking get the mean wrong: a fixed-point analysis shows that the mean bias, not the budget, then decides whether the multiplier settles, saturates or switches off. And standard GRPO standardizes the penalized reward within each group, which cancels the multiplier before it reaches the gradient. Two-channel constrained GRPO repairs both: the base reward keeps the raw score for the within-group comparison, the penalty and the constraint estimate pass through a monotone map fitted on judge-labeled training responses, and the advantage keeps the multiplier. On a Qwen3-VL-2B policy with a training-free readout of an open 4B model as the scorer, held-out harm on 1,037 adversarial prompts falls from 8.9% under standard GRPO to 4.8%, lower in every seed, where the uncalibrated budget gives 7.6%; over-refusal stays at most 2% on 400 benign queries and MMBench is unchanged. The gain is largest where the intent sits in the image (10.1% -> 5.1%) and grows on the unseen HADES benchmark (28.9% -> 12.8%). Order-preserving distortions of the scores reproduce each predicted controller state in every seed, and the results hold under a map fitted on another judge's labels, an image-reading judge and a human audit.

cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.33097

IRON-Meme: A Benchmark for Structured Understanding of Identities and Roles in Online Meme Narratives

Gabriella Bollici, Stefano De Giorgis, Wouter Van Atteveldt, Filip Ilievski

原文摘要

As polarization grows on social media, understanding how political memes shape online narratives has become increasingly important. Previous research has relied on either qualitative frameworks that are hard to scale or computational methods that lack theoretical grounding. Both approaches treat narrative categories in isolation and focus exclusively on moral portrayals of identity. To address these limitations, we introduce a framework grounded in social science theories, encompassing three categories: social identities (such as gender, politics, and ethnicity), viewpoints (ingroup and outgroup perspectives), and narrative roles (moral character, intelligence, appearance, and power). Building on this framework, we present IRON-Meme, a human-annotated benchmark comprising 1,110 political memes across substantially more templates than existing benchmarks. Our analysis of IRON-Meme shows that most memes assign derogatory roles and that identities, roles, and templates are systematically interconnected. Evaluation of nine vision-language models indicates that frontier-scale and reasoning models achieve the highest performance, while accuracy on social identities and narrative roles decreases for less frequent labels. Incorporating template background knowledge does not enhance narrative role classification, suggesting that meme interpretation relies on how memes are adapted for specific communicative purposes. We make IRON-Meme and our code publicly available.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16627

Folio: Recoverable Multi-Resolution Context Folding for Long-Horizon Language Agents

Yubo Ruan, Tao Peng, Jiao Zhang, Chuanguo Ma, Lei Xu, Han Zhou, Wenwu Wang, Xiafei Lv 等 14 位作者

原文摘要

Long-horizon language agents accumulate tool results and replies until the history no longer fits the context window. Current agents shrink this history by having a model summarize it, so each compaction rereads and rewrites the history it replaces, and cache-aware triggers decide when to compact while taking the size of the result as given. We present Folio, a context manager that records, folds, and recovers history through one set of handles. While the agent runs, Folio records three linked levels: a short turn note the model writes as part of its ordinary reply (L1), an event manifest of every call written by the runtime (L2), and the captured source records (L3). Because these views already exist, the runtime folds the context programmatically, selecting coarser views for older turns. The fold prints the handles that index L2 and L3, so recovery tools that read bounded pages, compute over full text in files, and keep findings in a persistent dossier reach the evidence the agent needs with little added input. The fold trigger prices an explicit landing point that includes the retained budget. On 75 LOCA-bench tasks with DeepSeek-V4-Pro behind every system, Folio solves 66 tasks against 54 for the same runtime with model-written compaction and 49-59 for Claude Code, Codex, and SoL-Pi, in about half their working time, at 2.4 times lower cost than that runtime; each fold takes a median of 0.6 seconds.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16623

DySR-GNN: Dynamic Sensor-Relationship Graph Neural Network for Root Cause Analysis in Automotive HiL Multivariate Time Series

Hamza Ouarrad, Mohammad Abboush, Andreas Rausch

原文摘要

Root-cause analysis in automotive Hardware-in-the-Loop (HiL) systems is challenging because faults can propagate across multiple interacting signals, making the most abnormal sensor different from the actual fault origin. This work proposes DySR-GNN, a Dynamic Sensor-Relationship Graph Neural Network for supervised multi-label root-cause identification in multivariate HiL time series. The approach jointly extracts temporal patterns within individual sensor signals and structural relationships across sensors. Each sensor is processed by a shared temporal encoder and combined with a trainable sensor-identity representation, from which a sparse relationship graph is reconstructed independently for every input window. This window-specific structure provides an explicit representation of the sensor relationships used for graph message passing and direct root-location prediction. Under a recording-disjoint evaluation protocol, the proposed model outperforms CNN--LSTM, Static GCN, Static GAT, and context-based variants, achieving a recording-level Macro-F1 of 0.9524, MCC of 0.9302, and strict exact-match accuracy of 0.8519. Analysis of the learned graphs reveals both persistent and state-dependent relationships among sensor variables. The results demonstrate that extracting adaptive relational structure alongside temporal representations can improve multi-label root-cause identification in complex automotive cyber--physical systems.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16621

Studies on Sign Language Retrieval in Low-Resource Dictionary Settings

Ushnish Sarkar, Vineet Kumar Rakesh, Bhaswar Chattopadhyay, Tapas Samanta

原文摘要

Most sign languages have no annotated corpus, only small dictionaries with one or a few example videos per sign. This study examines how well a pose encoder trained on a single sign language serves such languages without any target-language training. An encoder trained with metric learning on American Sign Language (ASL-Citizen) is evaluated frozen on six datasets of Turkish, Russian, Indian, Argentinian, Pakistani and Sri Lankan sign languages, with one or four clips per sign enrolled and, where signer identities are available, the query's signer excluded from the gallery. With one enrolled clip, the encoder retrieves the correct sign first in 36.4-80.7 % of queries, against 16.0-69.6 % for the best learning-free method (angle-based dynamic time warping) and a six-target mean of 39.3 % for an ASL-trained OpenHands graph network. In a crossed ablation of the encoder (embedding geometry, aggregation of body and hand embeddings, training objective, and attention between articulators), two choices matter: a per-pair InfoNCE objective outperforms supervised contrastive and cross-entropy training, and blocking attention between body, left hand and right hand inside the encoder raises accuracy by up to 5.1 points. The best configuration reaches 68.0 % one-shot accuracy (59.0 % for the reference encoder). On CISLR, an Indian Sign Language dictionary of 4,765 words with one video per word, the encoders find the correct word first for 17.3-19.0 % of queries, above the 16.8 % of the original RGB baseline. Over eight seeds with the joint encoder, spherical, Euclidean and hyperbolic embedding spaces differ by at most 1.6 points, except the hyperbolic token-attention head trained with InfoNCE, which is 1.4-2.0 points worse, so the geometry is a minor factor.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16620

Improved CNN-Transformer Network for Neutron Image Super-Resolution

Yuheng Chu, Hongli Li, Tian Zhang

原文摘要

The spatial resolution of neutron images is of great importance for material defect diagnosis, structural characterization, and nondestructive testing. However, owing to limitations in neutron beam quality, scintillator conversion efficiency and light scattering, detector pixel size, and noise levels, conventional neutron images often suffer from speckle noise, blurred edges, and missing details, making them inadequate for high-precision analysis. To address these issues, an improved super-resolution model combining convolutional neural networks (CNNs) and Transformers is proposed. First, to overcome the difficulty of obtaining high-quality and clear training images for neutron imaging, a pretrained image restoration model is fine-tuned using real neutron images and then employed to enhance existing neutron images, thereby providing high-quality reference images for subsequent super-resolution model training. Second, an improved CRAFT-based super-resolution network is developed by incorporating a hybrid prior-guided channel fusion module (HPCF), a mean-standard deviation dual-statistics squeeze-and-excitation block (MS-SEBlock), and a partial convolution feed-forward network (PCFN), with the aim of improving the reconstruction of important information, such as textures and fine details, in neutron images. Simulation results demonstrate that, compared with bicubic interpolation, SRCNN, SRGAN-VGG, and other methods, the proposed network achieves superior performance in both full-reference metrics, including PSNR and SSIM, and no-reference metrics, including AGM and SMD. The visual quality and structural fidelity of the reconstructed images are also significantly improved. This study provides a new approach to improving neutron image quality.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16612

Unibuc - NLP at "Voight-Kampff" Generative AI Detection PAN 2025

Teodor Marchitan, Claudiu Creanga, Liviu P. Dinu

原文摘要

This paper describes the approach of the Unibuc - NLP team in tackling the "Voight-Kampff" Generative AI Detection task from PAN at CLEF 2025. In the Subtask 1: Voight-Kampff AI Detection Sensitivity we have explored different approaches of leveraging embeddings extracted from LLMs, using either classical ML models or further fine-tuning LLMs with a classification head for the downstream binary classification task. Our experiments showed that using a majority vote ensemble of classical ML models (Light GBM, XGB, Logistic Regression and SVM) is comparable with fine-tuning for downstream task achieving around 99.4% F1 score. For the Subtask 2: Human-AI Collaborative Text Classification we explored a way of combining features at different layers extracted using transformer backbone with layer-wise projection and attentive pooling which proved modest results.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16608

Towards Democratising Low-Data Medical Image Classification: CPU-Trainable ZACH-ViT and Its Geometric Variants

Athanasios Angelakis

原文摘要

Medical image classification in resource-constrained settings must contend with limited annotations and computational capacity. We evaluate four ZACH-ViT variants with linear, Euclidean prototype, Poincare and spherical heads alongside two compact multiple-instance-learning baselines. All models are trained from scratch with fewer than 260,000 parameters across seven MedMNIST+ datasets and three external benchmarks (PCam, ISIC2017 and Shenzhen tuberculosis radiographs). The low-data protocol uses 50 training images per class and five seeds, with validation-selected checkpoints at 23 and 47 epochs; a full-data reference uses one seed. Compact-TransMIL achieves the highest ten-dataset mean test Macro-F1 at 23 epochs (53.34%) and with full data (73.93%). At 47 epochs, ZACH-Linear narrowly leads (57.76% versus 57.54% for Compact-TransMIL). Extending training improves Macro-F1 in 239 of 300 paired comparisons. On ISIC2017, hZACH-Spherical has the highest 47-epoch low-data Macro-F1 (44.09% with SD 4.09%), whereas ZACH-EuclideanProto leads with full data (51.27%). We additionally conduct 40 CPU-only training runs on ISIC2017 and Shenzhen using four CPU threads; model-only single-image inference requires approximately 4-9 ms, while image decoding and preprocessing substantially increase end-to-end latency. The results demonstrate the feasibility of CPU-only training on the tested platform and the task- and regime-dependence of compact medical imaging under strict resource constraints.

cs.CV · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.20455

GraphSkillAA: Attribution-Guided Skill-Graph Updating with Targeted Validation and Rollback

Ziqiao Shang, Ling-Yue Ge, Lan-Zhe Guo

原文摘要

External skills provide domain knowledge and procedures without updating model parameters, but flat text makes their applicability, dependencies, and composition difficult to express. Existing skill graphs organize rules into nodes and relations mainly for retrieval and composition. They give less attention to what should change after a failure, which examples should be retested, and which edits should be reverted. We introduce GraphSkillAA, short for Graph-Skill Abductive Attribution, which connects rule use, error localization, and update validation through the same skill graph. Nodes record applicability, procedures, and exclusion conditions, while edges describe prerequisite and enhancement relations. The student records the nodes and edges used during execution. The teacher contrasts related successful and failed trajectories to identify the fields or relations that need repair, keeping changes as small as possible. Execution lapses or insufficient evidence leave the graph unchanged. A Local Gate retests affected examples, while a Big Gate checks the merged graph on the complete update pool. Rejected edits are rolled back, and model parameters remain frozen throughout. With GPT-5.6-sol, GraphSkillAA reaches 81.5%, 66.7%, and 91.2% on SearchQA, LiveMath, and DocVQA, respectively, and achieves the highest mean performance in every main setting. These results show that graph structure supports skill selection and composition while also providing explicit targets for repair, retesting, and rollback.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16603

Regime-Uncertain Data Transactions: A Game-Theoretic Framework for Self-Adapting and Self-Diagnosing Multi-ML-Agent Systems

Chethana Prasad Kabgere

原文摘要

Autonomous ML agents in large organizations exchange data and model outputs across organizational and legal boundaries. Which rules bind a given exchange is not fixed. It depends on where the data subjects live, where the agents run, who owns them, whom they target, and which version of each rule is in force. Tags that describe the data can be noisy, and a counterparty can overclaim its safeguards. We model one exchange as a \emph{regime-uncertain transfer game} between two ML agents, with an enforcer as a third player. Rules enter as hard constraints with a strictest-wins structure. Agents hold a Bayesian belief over which rules apply, can pay to unlock safeguards or verify claims, and then bargain over the remaining legal bundles.Our method, Legally-Constrained Nash Bargaining (\LCNB), implements this with a Monotonic Concession Protocol using Zeuthen's rule. We prove four properties of the model and test it in a synthetic simulator with GDPR, DPDP, CCPA/CPRA and a stylised stricter regime. In two of three stress cases where residence and citizenship differ, a citizenship-based rule violates in 21--37\% of shared records, and \LCNB{} in under 1\%. The results also show limits. With our default economics, \LCNB{} shares almost only aggregates, and its low violation rate follows partly from that. Violation tolerance, tag noise and enforcement strength matter more than the choice of bargaining solution.

cs.GT · cs.SI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16594

Language Learning through Immersive Multi-User Gamification

Rosalina Ribeiro Silva, Zichen Feng, Rikard Olajos, Tim Rolff

原文摘要

We propose an immersive virtual-reality game designed to enable multi-user, multi-language learning during co-play. We hypothesize that our method will be more engaging, leading to better rhythm, pronunciation, memorization, and speaking confidence. We implement the experience as a fantasy role-playing game by introducing vocabulary and phrases to the user as spells to learn. Players then cast more efficient spells depending on their language skills. For evaluation, we compare the game to a single-user baseline that replicates traditional language learning in a non-immersive setting. We measure short- and long-term retention of vocabulary and phrases, as well as scoring rhythm and pronunciation. Furthermore, we collect user data on engagement and mental workload.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16591

Singularity-Bench: Can Agents Build Agents?

Yang Zhou, Krish Agarwal, Zhuoming Chen, Zerui Wang, Zhenyu Zhang, Wenshuang Li, Xinpeng Wei, Zhichen Zeng 等 19 位作者

原文摘要

Frontier AI agents increasingly succeed on realistic, long-horizon tasks and are beginning to improve the systems through which they operate. Yet existing benchmarks often restrict agents to narrow action spaces, use simplified environments, or rely on verification methods that are unreliable for evaluating arbitrary agent-generated systems. We introduce Singularity-Bench, an open-ended benchmark for autonomous AI development with six end-to-end tasks spanning pre-training, mid-training, post-training, inference, the agent harness, and cluster orchestration. Each task requires agents to implement core systems from a minimal starting point and evaluates them against mature human-engineered baselines on identical hardware. Additionally, through extensive red-teaming covering 9 of the 10 categories in the OWASP Top 10 for Agentic Applications, we expose reward-hacking vulnerabilities in the conventional Harbor framework and develop Breakwater, an extension of Harbor that blocks all attacks tested in our study. Across 11 agents built from 7 frontier models and 2 harnesses, Opus 5.5 with Claude Code matches or outperforms the human baseline on all six tasks with a Singularity-Index of 1.25, suggesting it may have the skills needed to reach the singularity. The remaining agents average only 0.58. Our vision is for Singularity-Bench to track how much of AI development agents can already automate and when they may transition from assisting to taking end-to-end responsibility for building stronger successors.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16590

AstroMemBench: A Benchmark for Memory Systems in Embodied Spacecraft Agents

You Wu, Guhong Zhang, Hongwei Li, Xinyue Hong, Yuan Jia

原文摘要

Long-term spacecraft autonomy requires embodied agents to use operational history under communication delays, time-critical operations, environmental uncertainty, complex physical interactions, power and computational constraints, and safety and reliability requirements. We introduce AstroMemBench and a Scenario-Challenge-Capability design method that maps these requirements to executable memory tests. The benchmark covers 16 memory subchallenges through 140 templates and 2240 instances across nine tasks in planetary surface operations, on-orbit servicing, and deep-space exploration. A deterministic environment scores actions for task completion, state changes, safety, and resource use. Ablations under nine conditions show that the benchmark is sensitive to how accurately agents recover and use historical information and to differences in memory-system design. Main experiments compare six configurations. Mem0, Chain-of-Memory, LightMem, and retrieval-augmented generation (RAG) improve same-session mean scores over native Pi Agent, with no clear advantage of the three memory systems over RAG. After a session restart, Mem0 outperforms RAG by 12.25 points. Benefits favor instruction retention and result reuse over ongoing state tracking and energy reservation. AstroMemBench supports the design, development, and selection of memory systems for embodied spacecraft agents. AstroMemBench 1.0 and its evaluation code are publicly available: https://github.com/flyingView/AstroMemBench-v1.

cs.AI · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.04013

Pending Conflicts Make Progress Impossible

Petr Kuznetsov, Pierre Sutra, Guillermo Toyos-Marfurt

原文摘要

In this work, we study progress conditions for commutativity-aware, linearizable implementations of shared objects. Motivated by the observation that commuting operations can be executed in parallel, we introduce conflict-obstruction-freedom: a process is guaranteed to complete its operation if it runs for long enough without encountering step contention with conflicting (non-commuting) operations. This condition generalizes obstruction-freedom and wait-freedom by allowing progress as long as step contention is only induced by commuting operations. We prove that conflict-obstruction-free universal constructions are impossible to implement in the asynchronous read-write shared memory model. This result exposes a fundamental limitation of conflict-aware universal constructions: the mere invocation of conflicting operations imposes a synchronization cost. Progress requires eventual resolution of pending conflicts.

cs.DC

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新论文 · v1arXiv:2611.16589

STVLA: Spatial-aware and Temporal-memory forVision-Language-Action Models

Zhuo Han, Yeqiang Wang, Xinran Wang, Hongwei Han, Yi Xiong, Zunian Wan, Jianbo Mao, Huan Peng 等 11 位作者

原文摘要

Robotic manipulation often requires both spatial scene understanding and task history. However, many vision-language-action (VLA) policies rely primarily on current observations, without explicitly integrating spatial structure and task history. Appearance-based memories can be sensitive to occlusion and viewpoint changes, while spatial representations of the current scene alone do not preserve how the scene has changed during task execution. We introduce STVLA, a framework that incorporates features from a pretrained visual geometry foundation model into temporal memory alongside visual representations. This allows the policy to recall past scene structure as context for current action decisions. To select informative history, STVLA uses a mathematically grounded scoring rule that balances relevance to the current observation with complementary evidence from memory. Across evaluations on LIBERO, LIBERO-Mem, MIKASA-Robo, and SimplerEnv Bridge, STVLA achieves the highest average success rate among the compared methods on each benchmark. On real-world block stacking, object organization, and phone packaging, it improves success over $π_{0.5}$ by $58.33$, $30.00$, and $35.00$ percentage points, respectively. These results support integrating scene geometry into temporal memory to improve manipulation requiring both spatial precision and task history. Project website: \href{https://stvla.github.io/}{https://stvla.github.io/}

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16588

CrossPloit: A Benchmark for Cross-Version PoC Migration in Python Web Applications

Hung Nguyen-Huu, Nguyen Anh Tuan, Ting Zhang, Yue Liu, Chengran Yang, Lwin Khin Shar, Eng Lieh Ouh, David Lo

原文摘要

Affected-version ranges in vulnerability databases decide which deployments scanners, patching pipelines, and incident responders treat as vulnerable, yet these ranges are often incomplete, so vulnerable releases are silently treated as safe. Auditing them requires executable evidence: given a proof-of-concept (PoC) exploit for a listed version, show that the same vulnerability can be triggered on an earlier, unlisted version. This cross-version PoC migration combines source-code analysis, environment repair, and execution-based verification. We introduce CrossPloit, a benchmark of 127 migration instances across 45 CVEs in the Python web ecosystem. Each instance includes a version-pinned container, a reference PoC, and a vulnerability-specific oracle; two validators check that the observed effect arises from the disclosed vulnerability. Benchmark construction confirms 149 vulnerable prior versions absent from the affected-version lists of four databases, 127 of which require migration. Across 18 agent configurations (nine models and two harnesses), the best configuration resolves 58.3% of instances, compared with 8.7% for an adapted trace-guided baseline. Attack-vector shifts are particularly difficult: no configuration resolves more than 37.9% of these instances. Higher resolve rates are associated with a smaller share of missed-API failures and a larger share of incomplete executions among the remaining failures. In one configuration, withholding the reference PoC and its environment specification reduces resolve rate from 50.4% to 15.0%, highlighting the value of the supplied reference package. CrossPloit provides an execution-based measurement of under-reported exposure and of how far its enumeration can be automated.

cs.CR · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16586

Large Language Models as Digital Twins: a Distribution-Aware Questionnaire-Based Evaluation

Francesco Di Cursi, Chiara Boldrini, Andrea Passarella, Marco Conti

原文摘要

This paper investigates whether Large Language Models can answer a personality questionnaire as a specific individual would. Given a user's social-media posts, the model completes the BFI-2-60 Big Five questionnaire as that user. The predicted responses are converted into trait scores and compared with the user's actual scores. We evaluate GPT-4, Gemma, Llama 3.1, and Mistral on the MyPersonality dataset. To determine whether predictions reflect information from the user's writing, we compare them with answers produced without user text and with values derived from general population tendencies. We measure prediction error across all users and separately for individuals with low, intermediate, and high trait scores. We also test whether an LLM can express an explicit personality profile when given only the target Big Five scores, without user text, and examine how predictions change as more text is provided. The results show that user text does not consistently improve performance. Models perform better near the middle of the trait distribution, while at the lower and upper ends they often remain close to central values or perform well at one end but poorly at the other. When given an explicit profile, GPT-4 most clearly translates trait information into consistent questionnaire responses, particularly for Extraversion and Neuroticism. Increasing context does not systematically reduce error: open-source models fluctuate more, whereas GPT-4 remains comparatively stable. Overall, the evaluated models do not consistently reproduce the questionnaire behavior of specific individuals.

cs.AI · cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.29922

Unintended Side Effects of Value Training in LLMs

Siddhesh Pawar, Chen Cecilia Liu, Anna Korhonen, Isabelle Augenstein

原文摘要

As LLMs are increasingly deployed in value-sensitive contexts, aligning them with human values has emerged as a post-training objective in NLP, aimed at instilling additional values and guiding model behaviour. However, current evaluations remain narrow. Because values are interconnected, it is unclear how fine-tuning for specific values alters unrelated capabilities and behaviours. In this work, we adapt four instruction-tuned LLMs across individual Hofstede's value dimensions and evaluate them on both cross-value transfer and broader behaviours. We find that value-specific training produces unintended side effects on untrained values, risk preferences, social cognition, and safety traits. Consequently, we suggest that value alignment after post-training should incorporate broader behavioral evaluations, and we highlight the need to investigate where additional value training might best fit into the pipeline. We release all 48 value-tuned model checkpoints at https://huggingface.co/sidicity/cultural-value-models

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.10224

Jais 3: Introducing Arabic-centric Agentic AI

Team IFM, Haonan Li, Hadi Abdine, Nikhil Ranjan, Mohamed Anwar, Ahmad Chamma, Amr Mohamed, Ahmed Frikha 等 54 位作者

原文摘要

We present Jais 3, a fleet of six open, Arabic-centric agentic language models ranging from 0.9B to 375B parameters. Jais 3 is built to complete tasks for Arabic speakers, not only to answer questions in plain text: it is trained to plan multi-step tasks, call tools and Model Context Protocol (MCP) servers, operate a terminal, search the web, and read, edit, and produce PDF, spreadsheet, and presentation files, while communicating with the user in Arabic. Instead of pretraining from scratch, we adapt each model of the fully open K2 Horizon fleet to Arabic through continual pretraining, followed by supervised fine-tuning (SFT), final token preference optimization (FTPO), and reinforcement learning with verifiable rewards (RLVR). We combine natural Arabic text with translated, transliterated, and synthetic Arabic, and replay the base model's own training data to keep its reasoning, agentic, and English abilities. Jais 3 covers Modern Standard Arabic and the major regional dialects, follows instructions with Arabic-specific constraints, and inherits the 512K-token context window of K2 Horizon (all models except the 0.9B). To our knowledge, its 375B model is the largest open Arabic-centric language model to date. For evaluation, we revisit how Arabic models are assessed, moving to fully generative evaluation on audited Arabic benchmarks. Across model sizes, Jais 3 matches or outperforms open-weight models of comparable size on Arabic and agentic benchmarks, with particularly strong agentic capabilities and Arabic instruction following, and far fewer hallucinations. We release the weights and the checkpoints from every training stage of all six models, together with samples of the training data, the data recipes and training configurations, the training logs, the audited benchmarks, and the evaluation code.

cs.CL · cs.AI

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新论文 · v1arXiv:2611.16583

ReFineR: Single image to dense point cloud registration at pixel level with point splatting

Ugo Insalaco, Jean-Emmanuel Deschaud, Pierre Dellenbach

原文摘要

Cameras and LiDARs (Light Detection and Ranging) provide complementary observations of the same scene: cameras capture rich appearance cues while LiDAR sensors provide accurate geometric measurements. In this work, we address the problem of fine image-to-point cloud registration from a single image and a roughly estimated camera pose. On robotic platforms, residual reprojection errors of several pixels are commonly observed even after calibration due to temporal desynchronization, mechanical vibrations, or inaccuracies in point cloud reconstruction. Such errors can lead to decimeter-level geometric discrepancies at moderate distances and significantly affect multiple downstream tasks, such as dense cross-modal supervision, texture mapping, and semantic label transfer. To address this challenge, we introduce ReFineR: an offline framework to finely register individual images to pre-reconstructed point clouds. ReFineR builds upon differentiable point splatting rasterization and a smooth Gradient Orientation Measure (GOM) to iteratively refine the camera pose through analysis-by-synthesis optimization. We evaluated the method on a curated dataset of manually calibrated images collected from heterogeneous data sources. Furthermore, we show that an automatic point cloud rescale factor can be estimated prior to optimization, enabling robust refinement across a wide range of scenarios using a single scale-invariant formulation. The project page can be found at https://refiner-project.github.io/

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16581

Do the Details Help the Whole? One-Way Transfer from Person Actions to Group Intent in VLMs on JRDB-Social

Chengguang Gan, Zhixi Cai, Yunhao Liang, Hanjun Wei, QingHao Zhang, Shiwen Ni

原文摘要

Social scene datasets label people at two levels: what each person is doing and what the group is there for. Training on both levels at once is often assumed to help each of them. We test this assumption for vision-language models (VLMs) on JRDB-Social, treating person actions as the details and group intent as the whole. We fine-tune Qwen3.5 4B under eight training arms with three seeds, repeat the core arms on Qwen3.5 9B and Qwen3-VL 4B, and judge every arm with a verdict rule fixed before testing. Writing each member's actions before the intent raises intent macro-F1 from 30.1 to 31.0, and writing the intent first reaches 31.2, while action F1 stays within 0.2 points of an action-only model. Under the selected recipe, no arm trained with intent improves actions. The gain requires correct, image-grounded actions: empty action lists or actions borrowed from another group with the same intent lower intent F1. Forced-prefix tests, occlusion, member masking, linear probes and component grafting show that the intent follows the written actions, relies more on people and partly resides in the vision tower. Supplying actions as input instead of as targets hurts both levels.

cs.CV · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.14198

Multimodal Zero-Shot Prediction for Transaction Data at Scale

Parameswaran Kamalaruban, Viktor Drobnyi, Maeve Madigan, Julia Rozanova, David Sutton, Stuart Burrell

原文摘要

Sequential financial transaction data are increasingly being used by financial institutions to train payment foundation models at scale, producing rich contextual embeddings that support applications such as fraud prevention, credit risk assessment, and offer personalization. However, such models are not designed for flexible instruction-following reasoning and require bespoke downstream models for each application. In contrast, large language models (LLMs) provide a natural-language interface for flexible transaction reasoning, yet naively serializing transaction histories into text representations is inefficient and costly. Multimodal fusion architectures bridge these paradigms, injecting transaction embeddings directly into flexible language reasoning pipelines. In this work, we present a systematic study of how transaction embeddings, pretrained at the scale of a global payments network, interact with LLM-based reasoning through embedding injection, transaction-language alignment, and instruction tuning. To do so, we train the Multimodal Instruction Network for Transactions (MINT), which combines a transaction encoder pretrained on billions of real-world payments with multimodal instruction tuning on millions of transaction question-answering examples. We evaluate predictive and extractive question answering, zero-shot generalization to unseen question types, and inference efficiency. Our results reveal a consistent trade-off: compact transaction embeddings improve predictive QA accuracy while substantially reducing input tokens, latency, and memory relative to a text-serialized baseline, whereas serialized histories remain highly competitive for extractive QA. Overall, our empirical findings provide a set of actionable insights and practical guidance for building multimodal transaction-aware language systems at network scale.

cs.LG · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16579

ActWorld-JEPA: Action-Conditioned JEPA World Models Meet Latent Planning for End-to-End Autonomous Driving

Ziying Song, Shengkai Zhang, Caiyan Jia, Lei Yang, Lin Liu, Yuchen Liu, Ziyang Liu, Zirui Li 等 13 位作者

原文摘要

Joint-Embedding Predictive Architectures (JEPAs) enable future prediction in latent space without pixel reconstruction. However, latent predictive consistency alone does not establish the relative driving quality of candidate plans. We present ActWorld-JEPA, a joint-embedding formulation for candidate-based driving. Where Plan-Conditioned JEPA (PC-JEPA) uses global and step-wise trajectory intentions to guide recurrent latent prediction, then combines the predicted future with the current scene through gated cross-attention; Relational Residual Reranking ($R^3$) models agreement and discrepancy between plan and future representations under planning supervision, yielding bounded, gated residual corrections to the base planning scores. This formulation connects latent prediction to candidate selection using only observed context and proposed trajectories at inference. Benchmark-specific implementations report 94.1 PDMS on NAVSIM v1 navtest, with the final selector calibrated using aggregate NAVTEST feedback, and 91.6 and 42.7 EPDMS on NAVSIM v2 navtest and navhard, respectively. The nuScenes evaluation reports a mean $L_2$ error of 1.20 m and a predicted collision rate of 0.76% over 1--6 s. This work provides an effective path from JEPA's predictive representations to driving decisions informed by anticipated consequences.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.33457

A Free Knob: A Sensitivity Audit for Calibration in Threshold-based Evaluation

Md Tanveer Hossain Munim, Bijoy Ahmed Saiem, Al-Amin Sany, Tanzima Hashem

原文摘要

Many benchmarks for rare events score a prediction in three steps: pool the prediction and the target over spatial blocks, threshold each pooled field, and score the resulting contingency table. We show that at a fixed rare threshold the max-pooled Critical Success Index (CSI) cannot separate two different things: how well a system places events, and how well its amplitudes are calibrated. Sharp observations push many blocks above the threshold, while the attenuated predictions of squared-error regression leave those same blocks below it. A monotone calibration map is fitted on held-out data and applied separately to each system. The map needs no retraining and cannot change the order of any two pixels, so whatever part of a difference it reproduces cannot be evidence of better spatial ranking. We report how much of a reported difference the map removes. On SEVIR, two released checkpoints of one architecture differ by -29.5% in extreme-threshold CSI before the control and by +5.3% after it. Across 450 pairwise contrasts among 6 systems, how far apart two systems sit in pooled frequency bias predicts how far their CSI difference moves under the control (r = 0.896 when the map is fitted after pooling), and 64 contrasts reverse sign. At CasCast's published extreme-event operating point, calibration alone removes 88%-106% of the cascade's gain over its backbone across three controls. Calibration also reveals advantages hidden by a better-calibrated baseline. On geostationary infrared imagery, a larger share of the score can be explained by calibration as events get rarer. The problem therefore needs two things at the same time: a threshold that is fixed in advance, and a model whose output is poorly calibrated at that threshold. We recommend reporting pooled frequency bias and a symmetric held-out FreeKnob Audit alongside rare-event pool-and-threshold scores.

cs.LG

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