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新论文 · v1arXiv:2611.02047

Beliefs in feedback: Factored representations of interacting agents in transformers

David A. Ehrlich, Manuel Baltieri, Fernando E. Rosas, Alexander B. Boyd

原文摘要

Complex systems, from ecosystems to brains, are usually modeled as modular components coupled through feedback rather than as indivisible wholes. Can a transformer trained on the joint behavior of a group of interacting agents be understood in the same way? In this work, we develop a computational-mechanics framework to study agents interacting in feedback loops. We formally prove that any joint stochastic process can be presented as resulting from the behavior of agents interacting in feedback, and the resulting process is generated by a hidden Markov model whose Bayesian beliefs factorize into separate components per agent. Furthermore, our experiments show that transformers trained on such feedback sequences make use of this separability, disentangling the beliefs about the underlying factors into separate subspaces of their residual stream. This representation is found to scale linearly (instead of exponentially) with the number of agents. Moreover, interventions show that these representations are causally efficacious, allowing for targeted steering of individual agents' beliefs by selectively inserting counterfactuals. However, coarse-graining the behavior is found to disrupt the factorization, and can force a higher-dimensional representation. Together, the results establish both the dimensional advantage of a factorized representation and the ability of transformers to exploit it, laying the groundwork for investigating traces of compositional predictive architecture in pretrained models.

cs.LG · cs.AI · cs.MA · nlin.AO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02046

On Guarding Line Arrangements

Valentin Brimkov

原文摘要

We study the problem of guarding a set of straight lines in the plane by selecting a minimum-cardinality set of points such that every line contains at least one selected point. This problem is known to be strongly NP-hard \cite{megiddo}. We investigate approximation algorithms for the problem and analyze their performance guarantees under various structural assumptions on the underlying line arrangement. In particular, we present approximation schemes based on set-cover techniques, relaxation methods, and properties of maximal lines. We establish constant-factor bounds on the approximation ratio for several classes of arrangements and derive relationships between the guarding number and the incidence parameters of the arrangement. Connections with Dirac-type incidence results are also explored.

math.CO · cs.CG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02045

METER: Matched-Event Calibration for Short-Event EEG Microstate Transition Analysis

Abdul Basit, Eklavya Tomar, Muhammad Shafique

原文摘要

Short cognitive events contain unequal and often sparse transition evidence, so raw EEG microstate distances can reflect finite sampling rather than neural reconfiguration. We introduce METER (Matched-Event Transition Estimation against a Reference), a participant-relative framework that separates marginal occupancy from conditional next-state choice and evaluates each event against duration-conditioned events from a fixed participant-specific reference bank. METER-Jump integrates uncertainty in sparse target and reference transition tables, returning a randomized event rank for testing and a continuous standardized coordinate for association analysis. Using a reproducible three-state representation selected by spatial criteria, we evaluated METER with participant-fitted simulations, estimator ablations, map perturbations, and preprocessing sensitivities. Event-level false-positive rates were 0.044-0.056 across four heterogeneous null mechanisms. By contrast, global cutoffs learned under the participant-fitted null and transferred unchanged to other regimes produced false-positive rates from 0.004 under rare transitions to 0.234 under map perturbation; duration-conditioned ranks remained near 0.05. Detection probability was approximately 0.69 with fixed maps but fell to 0.22 after bootstrap-derived map-assignment perturbation. In 4,175 natural-reading events from 18 participants, no linguistic association survived multiplicity correction in the primary 30-ms analysis, although a 20-ms sensitivity changed one within-stream max-$T$ decision. METER therefore calibrates finite-event departures conditional on a defined microstate sequence, while map assignment, filtering, temporal smoothing, and reference construction remain separate sources of measurement uncertainty.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02042

SANAI: Secure ECC Distributed Keyed Parity Embedded in Weights for Protecting Neural Networks against Bit-Flip Attacks

Habibur Rahaman, Daniel Lobo, Md Rezoan Ferdous, Swarup Bhunia

原文摘要

Quantized neural networks store weights in dense memory, where bit flips caused by voltage scaling, aging, radiation, or deliberate attacks such as RowHammer can severely degrade model behavior. A single corrupted high-order bit can cause misclassifications or nonsensical language-model outputs. Embedded error-correcting codes (ECC) avoid dedicated parity storage by hiding parity within model weights, but existing schemes embed uniformly across layers and bit planes, leave parity unsecured, and tolerate bit-error rates (BERs) only up to about 6%. An adversary aware of sensitive weights and the ECC scheme can also bypass protection by exceeding the correction capability of targeted codewords. We propose SANAI, a sensitivity-guided, bit-plane-selective scheme that protects critical model weights against bit flips with zero additional parity-storage overhead. SANAI secures parity through keyed embedding across a two-dimensional weight space: spread-transform dither modulation binds parity to a secret key, and keyed interleaving disperses concentrated attacks across codewords. We derive a survival law showing that robustness is governed by the correctable fraction t/n rather than by t alone, and a closed-form bound on the protectable fraction of a model. As a streaming checker on an 8x8 systolic array, SANAI verifies weights on the load path with 0.41% area overhead and no additional memory. Across ten vision backbones, three quantization schemes, and ten language models from 82M to 13B parameters, SANAI stays within about one percentage point of clean accuracy at BERs up to 12%, twice the maximum of prior embedded-ECC approaches. It survives fine-tuning bit-exactly, neutralizes progressive and targeted bit-flip attacks, and exactly corrects AttentionBreaker and SilentStriker attacks.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02041

ECGJev: Question-Conditioned Option Scoring for 12-Lead ECG Interpretation

Zhuoyuan Li, Xidong Wu, Hanyu Cui, Jiansong Gao, Rongrong Fu, Mingke Yan, Haotian Tang

原文摘要

Clinical interpretation of a 12-lead electrocardiogram (ECG) often requires answering targeted diagnostic questions with clear, quantifiable decisions. We introduce ECGJev, the first ECG framework built on a decision decoder, a pretrained language model trained to return a probability for every listed option. A question-conditioned bridge compresses frozen waveform and spectrogram features into 16 ECG tokens, and a two-stage procedure adapts the bridge and low-rank adapters on the decoder's native option readout using diagnostic labels alone, scoring all options for a question in one forward pass. Evaluated across four ECG benchmarks, ECGJev achieves the highest macro-F1 on CPSC2018 (80.46%), PTB-XL (71.68%) and Chapman (96.86%), and generalizes competitively to the small-sample PTB cohort. Concept probes and perturbation analyses further show that its predictions rely directly on the ECG input, remain stable under option-order and wording variations, and transfer zero-shot to unseen waveform concepts. Our code is available at https://github.com/zhuoyuanli-lab/ECGJev.

cs.LG · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02040

Multicriteria Recommendation System by Leveraging Predefined, Implicit, and Undefined Criteria

Emrul Hasan, Chen Ding

原文摘要

Data sparsity, a long-standing problem in the recommendation system, occurs when the user-item utility matrix is sparse. For years, many approaches have been experimented with, including utilizing user's textual reviews and multiple criteria ratings. In a review-based recommendation system, user preferences and item attributes are learned from reviews, while in multi-criteria recommendation systems, criteria ratings are used for the recommendation task. Multi-criteria are domain-specific pre-defined explicit criteria and users are bound to provide feedback only on these criteria with discrete values. On the contrary, users are free to express their opinions through textual reviews, which can cover multiple perspectives or aspects. It is possible that these pre-defined, domain-specific explicit criteria are not enough to fully capture the multi-faceted user preferences. To this end, we propose a novel multi-criteria recommendation model that utilizes predefined, implicit, and undefined criteria. We use a semantic similarity-based sentence clustering method to identify the predefined and implicit criteria and a sentiment analyzer to estimate their ratings. The criteria that are extracted from the review and are aligned with predefined criteria are referred to as implicit criteria. A sentence is considered as expressing opinions on an undefined criterion if the similarity score between this sentence and all the predefined criteria is lower than a predefined threshold. Ratings are computed for each extracted implicit criterion and the undefined criterion based on the review content. Finally, we use all three types of criteria and an aggregation model to make the final rating prediction for the recommendation system. Our proposed method demonstrates the superiority compared to several baselines on TripAdvisor and Beer Advocate datasets.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02039

Factor Closure in Noncommutative Algebraic Complexity

V. Arvind, Pushkar S. Joglekar

原文摘要

We prove that $\VPnc$ is closed under taking factors over every field. If a noncommutative circuit of size $s$ computes a nonconstant polynomial of degree at most $D$, then each left or right factor has a circuit of size $O(sD^3)$. A central factor $g$ in a decomposition $f=agb$, with nonconstant outer factors $a,b$, has a circuit of size $O(sD^6)$. The proof uses a small weighted nondeterministic finite automaton (NFA) with replacement labels. Given $f=gh$, its labels depend on a word of maximum degree in $h$ and the coefficients of its suffixes. A pairing of accepting paths proves that the induced linear map $T$ satisfies $T(gh)=g$. Matrix substitution implements $T$ in a circuit. Applying the same substitution to a Boolean-sum verifier proves $\VNPnc$ closure. The construction also proves formula closure. The automaton has one copying state and, after leaving it, uses only scalar replacements. This structure gives formulas of size $O(s^2)$ for one-sided factors and $O(s^4)$ for central factors of a size-$s$ formula. The same map proves that polynomial-size noncommutative algebraic branching programs are closed under taking factors. Polynomial-size ABP factorizations over finite fields are already known from~\cite{AJ25}. These are arithmetic size bounds, with arbitrary field constants at unit cost, including over $\Q$. Over finite fields, combining the formula construction with~\cite{AJ22} gives randomized polynomial-time complete factorization of formula inputs, with factors output as formulas. For polynomial-degree circuit inputs, we obtain polynomial-length succinct ABP descriptions with polynomial-time edge queries. We also give two algorithms that output complete ordered irreducible factorizations as polynomial-size circuits: a deterministic $\FP^{\SigTwo}$ algorithm and a zero-error randomized algorithm with an NP oracle and expected polynomial running time.

cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02038

QueueTrack: Framework for Detecting the Onset of Queueing in Mobile Networks

Hamid Hassani, Anudeep Karnam, Kishor Joshi, Sonia M. Heemstra, Ignas G. Niemegeers, George Exarchakos

原文摘要

In mobile networks, timely and accurate detection of the onset of queueing at path bottlenecks remains challenging for end-to-end congestion control algorithms (CCAs), where end hosts have limited visibility into queue status. Traditional congestion signals, such as packet loss, round-trip time (RTT), and one-way delay (OWD), have well-known limitations for early queue buildup detection. This paper presents QueueTrack, a practical end-host framework to detect the onset of queueing in mobile networks. QueueTrack computes a queue buildup indicator, denoted QBI, from endpoint-observable packet-level timing statistics, without requiring tight synchronization between end hosts or modifications to the existing network infrastructure. We develop and assess the complete QueueTrack machine learning (ML) pipeline, including data collection, labeling, model training, and testing, on a 5G testbed built on the OpenAirInterface platform, under a range of scenarios with competing TCP traffic. The experimental results show that QueueTrack can reliably detect the onset of queueing at the 5G base station using low-complexity classification models such as logistic regression. For example, QueueTrack achieves an F1 score of approximately 80% in a challenging mixed TCP/UDP scenario. These findings confirm that QueueTrack can serve as a practical framework for developing CCAs that target low delay, as well as for network telemetry and network monitoring in mobile networks.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02037

Scalar Smoothness Is One Point on a Besov Spectrum

Bum Jun Kim, Anastasis Kratsios

原文摘要

Studies of neural network generalization and robustness often summarize smoothness with a single number, such as a Lipschitz constant or a Hessian curvature statistic. However, Lipschitz constants summarize global worst-case sensitivity and Hessian statistics summarize local curvature; neither captures how model responses vary with the size, direction, and location of perturbations encountered in real-world deployment. To measure smoothness under these perturbations, we introduce a finite-difference roughness spectrum that profiles model responses across these conditions. Our roughness spectrum generalizes scalar smoothness measures, bringing Lipschitz sensitivity and directional Hessian curvature into a common framework across perturbation scales. We connect this spectrum to classical Besov theory, which describes how a function's variation changes with scale. Our theoretical analysis also proves that a larger output shift can cause less task damage than a smaller one, and that local curvature alone can miss the blocks most vulnerable to finite perturbations. In image classification, Roughness Source Control (RSC) suppresses margin roughness along noise directions and improves noise robustness in a comparison of 80 checkpoints across four model--dataset combinations. In post-training quantization, we identify blocks that need higher precision by measuring task-loss increases from perturbations matched to the size and direction of each block's quantization error. Our mixed-precision allocation achieves lower perplexity than the Hessian-based HAWQ-V2 baseline under the same GPTQ weights and bit budgets in all 14 settings across seven held-out large language models.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2610.07766

OTel: Open Telco AI Datasets, Benchmarks, and Models

Farbod Tavakkoli, Gregory Diamos, Kenneth Church, Roderic Paulk, Ashish Vaswani, David Kanter, Jorden Terrazas, Mark Austin 等 13 位作者

原文摘要

We present Open Telco (OTel), an open telecom AI resource that releases derived telecom datasets for retrieval, reranking, instruction tuning, and safety/abstention, together with 30 full-parameter post-trained baselines spanning 10 embedding models, 3 rerankers, and 17 language models. The community has already engaged substantially with the resource: as of May 3, 2026, the released models have been downloaded over 16 million times and the project has received 157+ pieces of media coverage worldwide. Building on prior open telecom datasets and benchmarks, OTel provides documented telecom data sources, held-out evaluation partitions, trained embedding models, rerankers, context-grounded LLMs, and safety/abstention data in one unified resource. Each baseline starts from an open-weight model and is post-trained on OTel-derived data using an open training recipe, then evaluated on held-out OTel evaluation partitions. OTel post-training improves performance across all three model families: embedding retrieval reaches 93.1% NDCG@10, reranking reaches 0.947 MRR@10, and language-model correctness reaches 87.8%. We release OTel as a reproducible starting point and invite the community to expand the data, improve embedding and reranking models, and build stronger context-grounded telecom LLMs.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02034

4D Triangle Splatting Reconstruction of Dynamic Endoscopic Scenes from Monocular Videos

Laura Salort-Benejam, Stamatia Giannarou, Antonio Agudo

原文摘要

Robust 4D reconstruction of deformable endoscopic scenes from monocular videos remains challenging due to non-rigid tissue deformation, illumination changes, occlusions, and unknown camera trajectories. Reliable dynamic reconstruction of endoscopies could significantly enhance visualization, diagnostic accuracy and intraoperative guidance of minimally invasive procedures. We propose SurgicalTS, a self-supervised triangle-splatting framework for dynamic endoscopic scene reconstruction from static monocular cameras. The scene is represented by a canonical set of triangles jointly optimized with a deformation network. We introduce a quaternion-based deformation model, an improved color parametrization, spatio-temporal priors, and a redefined initialization strategy that improves completeness under occlusions. Extensive evaluation on challenging in-vivo endoscopy videos from diverse procedures shows that SurgicalTS successfully models soft-tissue deformations and tool-tissue interactions over time, while consistently outperforming state-of-the-art methods. Our method also reduces training times while maintaining competitive geometric and photometric accuracy results, highlighting the effectiveness of quaternion-based deformation modelling with physical consistency for robust and efficient 4D reconstruction of endoscopic scenes.

cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.22850

Accelerating New Product Introduction for Visual Quality Inspection via Few-Shot Diffusion-Based Defect Synthesis

Serkan Hamdi Güğül, Kemal Levi, Burak Acar

原文摘要

Industrial visual inspection systems often suffer from a severe scarcity of labeled defect data, particularly during the early stages of New Product Introduction (NPI). This limitation hinders the deployment of robust supervised detectors precisely when automated quality control is most needed. We present an end-to-end generative framework for high-fidelity, few-shot defect synthesis that enables both in-domain augmentation and cross-domain transfer. Our approach disentangles defect morphology from background appearance by combining masked textual inversion for defect representation learning, noise-blended conditioned generation for surface-aware synthesis, and gradient-aware post-processing for seamless visual integration. We evaluate the framework in two practically relevant settings: few-shot data augmentation, where synthetic samples enrich a small set of real defects, and zero-shot adaptation, where defects learned from a source domain are transferred to a novel target surface without any real target-domain defect examples. Using RF-DETR as the downstream detector, we show that the proposed pipeline substantially narrows the domain gap on a private industrial dataset. In the few-shot setting, synthetic augmentation improves mAP from 78.8% to 83.3%. In the zero-shot setting, synthetic domain adaptation improves mAP from 65.0% to 85.1%. These results demonstrate that high-fidelity defect synthesis can meaningfully accelerate NPI by enabling effective inspection models before sufficient real defect data has been collected.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02033

How Green Is Your Manifesto? Scoring Environmental Positions in the 2024 EU Elections with an LLM Panel

Franco Corti, Sajid Siraj

原文摘要

Locating political parties on environmental and climate policy is central to the study of party competition, yet the established instruments, expert surveys and the hand coding of manifestos, are costly, periodic, and slow to extend to new documents. We ask whether a panel of large language models can serve as a cheaper and reproducible alternative. Treating each manifesto as a set of sentences, three models from independent providers score every sentence on two dimensions, environmental protection and climate change, separating a party's position on an issue from the salience it gives it, and the judgments are averaged to the manifesto level. Agreement across the models is retained as a built-in reliability signal. We apply the panel to 41 manifestos from the 2024 European Parliament election, covering Austria, France, Germany, Italy, Spain, and the European party families, and validate it against the 2024 Chapel Hill Expert Survey for the 25 parties with a like-for-like expert benchmark, using the other manifestos as diagnostic cases. The panel recovers the expert ordering of parties with strong rank agreement on both dimensions, while reading them as systematically greener than experts do, a gap we interpret as the distance between manifesto promise and governing record. Inter-model disagreement modestly flags where the instrument errs, and position and salience prove related for environmental protection but independent for climate change. The result is a transparent, low-cost, and multilingual instrument that complements rather than replaces expert and hand-coded approaches; the prompt, code, and manifesto-level results are released openly.

cs.CL · cs.CY · econ.GN

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02032

PRISM: RL-Based Rate Control for Low Latency and High Data Rate in Mobile Edge Networks

Hamid Hassani, Staji Wilson, Kishor C. Joshi, Sonia M. Heemstra, Ignas G. Niemegeers, George Exarchakos

原文摘要

Emerging delay-sensitive interactive services, such as extended reality and remote operation, demand advanced congestion control algorithms (CCAs) capable of maintaining high data rates while keeping latency low in mobile networks. Existing CCAs typically rely on congestion signals such as packet loss, delay, or explicit congestion notification (ECN) marks, which can be delayed, imprecise, or require extensive upgrades to the network infrastructure. To address these limitations, physical resource block (PRB) utilization has recently been proposed as a reliable early congestion signal in mobile networks [1]. This paper introduces PRISM, a reinforcement-learning-based CCA that uses peak PRB utilization to regulate the send rate under challenging mobility and fading conditions. NS3 simulation results show that, in a mobility scenario where two PRISM flows coexist with two TCP Cubic flows and one TCP BBR flow, PRISM maintains a per-packet mean one-way delay (OWD) of 6.3ms and a $p_{99}$ OWD of 44.6ms, with negligible (0.02%) packet loss, over a 20min high-contention interval. However, over the same interval, the coexisting TCP flows experience 60x higher mean OWDs of 371-390ms and $p_{99}$ OWDs of 563-578ms, with 0.1% packet loss. PRISM achieves a 31x improvement in goodput-over-latency performance, indicating a favorable tradeoff for delay-sensitive applications.

cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02031

SensOFormer: Foundation Models for Particle Accelerators

Mahindra Rautela, Alexander Scheinker

原文摘要

Large-scale scientific facilities, such as particle accelerators, are complex systems composed of thousands of interacting components that must operate collectively to support diagnostics and control, and ensure safe, reliable operation. These facilities rely on extensive sensor networks that generate large volumes of heterogeneous, incomplete, and noisy telemetry, often sampled at different rates across subsystems. In this paper, we adopt the foundation-model paradigm to learn general-purpose sensor representations from historical accelerator data through self-supervised pretraining. We introduce SensOFormer, a denoising masked transformer with a Perceiver-style encoder-decoder architecture designed to handle variable sensor sets, accommodate heterogeneous sampling rates, and learn robust representations from noisy measurements. The model is pretrained on multiple particle-accelerator datasets and transferred to downstream tasks, including missing-data imputation, anomaly detection, cavity identification, and fault identification. Through extensive evaluations and comparisons, we demonstrate that a single self-supervised model can learn reusable representations for diverse operational tasks in large-scale scientific facilities. Project page: https://mahindrautela.github.io/sensoformer.

cs.LG · physics.acc-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03734

Minimal Superior Penalties: Characterising and Calibrating Decision-Aware Proper Scoring Rules

Rouhollah Ahmadian

原文摘要

Training a classifier means letting a validation metric decide which model is kept and when training stops. A natural requirement on that metric is superiority: every correctly classified forecast should score better than every incorrectly classified one, however close the underlying probabilities are. Neither the Brier score nor the logarithmic score satisfies this on its own. We show that superiority is governed by the separation between two endpoints -- the supremum of the score on correct predictions and the infimum on incorrect predictions -- and derive both in closed form for each score. Their difference is the infimal penalty, the greatest lower bound on the penalty needed to restore superiority. Prior work restores superiority with a penalty calibrated from the first endpoint alone; for the Brier base, our infimal penalty is smaller than that published penalty by a factor ranging from 4.00 at three classes to 2.11 at twenty. We further embed the published and infimal penalties in a one-parameter family and show that, under a single-valued tie convention, every member of the family is strictly proper. In a pre-specified two-dataset case study, mean selected-model macro-F1 rises with the penalty in the reported ten-class regimes, though not distinguishably from noise once seed/selection-mode nesting is accounted for; a fifth regime at forty-seven classes does not reproduce even this descriptive pattern. The intended no-superiority baseline is algebraically a rescaling of the family under test, so the experiment cannot attribute the observed pattern specifically to superiority. When superiority itself is the objective, any penalty strictly above the infimal value suffices; choosing a larger penalty is a separate calibration trade-off rather than a requirement for the guarantee.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.31090

Poolwise: Evaluating Reward Models for Selection across Prompts

Mateusz Modrzejewski, Tomasz Radzikowski

原文摘要

Reward models are used to select training data, but common benchmarks compare responses only within the same prompt. Selecting data across prompts also depends on how scores compare between prompts. We introduce Poolwise, a protocol that selects a fixed fraction of a response pool by score, measures the selected set's quality using external labels, and reports within-prompt ranking separately. In matched experiments on two rating sets and two model sizes, training each prompt's mean score to match its average response rating improves selection over permuted or omitted mean targets in every seed at 30 percent retention. In grouped HelpSteer experiments, the model with the highest within-prompt accuracy selects a lower-rated set than the correct-mean model. Subtracting prompt means estimated from separate reference responses preserves local rankings but lowers selected helpfulness for six open reward models and two closed judges on HelpSteer. Poolwise provides a direct way to compare reward models and score transformations for data selection.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02028

LARK: Lean-Assisted Reasoning for LLM-Generated GPU Kernels

Zixi Zhang, Jiayi Nie, Haoran Wu, Yujie Yan, Yann Herklotz, Rika Antonova, Robert Mullins, Yiren Zhao 等 10 位作者

原文摘要

Efficient GPU kernels are typically low-level and hardware-specific, and LLM agents can now generate them faster than experts can verify. Existing approaches rely on testing, which provides evidence but not a guarantee of correctness. We introduce LARK, an agentic framework that automatically and formally verifies GPU kernels against their PyTorch reference implementations. LARK proves equivalence in functional and memory behavior under exact arithmetic and establishes a new trust boundary: human effort is only needed to verify operator-level Lean semantics ahead of kernel generation time, instead of checking every single line of generated code. LARK formalizes the GPU machine model and the semantics of each operation in Lean 4 once per target language, and the Lean 4 semantics of any incoming kernel is derived from these pre-built definitions. LLM agents then automate the proof in two steps. First, an agent decomposes the whole kernel semantics into smaller segments and Lean 4 verifies this segmentation, which addresses the intractability of proving equivalence for large kernels directly. Second, agents write Lean proofs that each kernel segment matches its high-level PyTorch counterpart. Together, the two steps give whole-program equivalence. We demonstrate that LARK can be used 1) to ensure correctness in existing code-generation agents and 2) to automatically verify human-written kernel libraries. On the KernelCraft GPU tasks, combined with a simple ReAct agent, LARK enables GLM 5.3 to fully verify 10 of 12 Level-1 kernels and Opus 5.5 all 5 Level-2 kernels. Moreover, over 10 production kernels randomly sampled from popular open-source LLM serving frameworks, LARK verifies 9 and identifies a new data race bug in the tenth. We believe LARK represents a significant step toward verifiable code generation in the agentic era.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.02686

Fooling Reward Models with Gibberish: Adversarial Examples Beyond the Semantic Regime

Yuheng Zhang, Mingyue Huo, Minghao Zhu, Mengxue Zhang, Nan Jiang

原文摘要

Reward models (RMs) are widely used as optimization targets in reinforcement learning from human feedback (RLHF). We show that leading RMs can assign higher scores to gibberish than to meaningful reference answers. To systematically discover such inputs, we introduce Token Mapping Perturbation Attack (TOMPA), a framework that uses a perturbed token mapping to extend an attack LLM's search beyond natural language. The mapping changes the linguistic meaning of the attack LLM's natural language responses, producing non-linguistic text for the target RM to score. We then use these scalar scores as rewards to train the attack LLM through reinforcement learning to discover non-linguistic adversarial inputs. Experiments against Llama and Qwen RMs demonstrate such attacks using two instantiations: identity token mapping across different tokenizers and random permutation with a shared tokenizer. Against Skywork-Reward-V2-Llama-3.1-8B, ranked first on RewardBench 2, the two mappings achieve mean rewards of +33.64 and +43.78, respectively, compared with +17.48 for GPT-5 reference answers. Their attack success rates are 97.6% and 100%, measured as the percentages of individual responses scoring above their corresponding GPT-5 references. These results show that strong benchmark performance does not ensure robustness to non-linguistic adversarial inputs, exposing a fundamental vulnerability in using RMs as optimization targets.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02027

Uncertainty-Aware 3D Position Reconstruction in CZT Drift-Strip Detectors

Alejandro Valverde Mahou, Michał Kossakowski, Selina R. H. Owe, Rikke S. Klausen, Irfan Kuvvetli, Christian A. Naesseth, Søren Hauberg

原文摘要

Accurate three-dimensional interaction-position reconstruction is important for CZT drift-strip detectors, but conventional methods can suffer from degraded performance in challenging detector regions, while learned models provide limited information about prediction reliability. We investigate two uncertainty-aware approaches: deep ensembles of point-estimation networks and simulation-based inference (SBI), which estimates a posterior distribution over interaction position. Both approaches are trained on physics-based simulations and evaluated on both synthetic and experimental data. On synthetic data, uncertainty from both methods correlates with positioning error, with SBI showing a substantially stronger relationship. On experimental measurements, the deep ensemble achieves improved $z$-position resolution, while SBI provides more informative uncertainty estimates that highlight regions of reduced positional information and discrepancies between simulation and the physical detector. These results demonstrate the complementary strengths of ensemble-based reconstruction and posterior inference for uncertainty-aware detector position reconstruction.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02026

Reconstruction as Spatial Evaluation: Diagnosing Cross-View Spatial Intelligence in Large Vision-Language Models

Junfei Wu, Qiuyue Wang, Xuhong Huang, Qihang Peng, Mingsheng Li, Jian Guan, Qiang Liu, Shu Wu 等 11 位作者

原文摘要

The spatial intelligence of large vision-language models (LVLMs) requires the ability to combine local visual observations into a geometrically consistent representation of the physical world. Many static benchmarks assess isolated spatial judgments, such as relative direction or distance, while interactive embodied evaluations combine perception with exploration, planning, control, and simulator-specific execution. To bridge this gap, we introduce RECONSPATIAL, a benchmark centered on multi-view 3D scene reconstruction across indoor, tabletop, and outdoor environments. Given real multi-view images and target-object queries, LVLMs are required to jointly predict oriented 3D bounding boxes in a shared, camera-anchored metric coordinate frame. Reconstructions are evaluated along two complementary axes: Structure for relative layout and Metric for physical scale and camera-anchored placement. Nine atomic spatial QA tasks, grouped into Geometric Perception and Spatial Relations, probe spatial capabilities involved in joint reconstruction. Offline navigation extends evaluation to path planning. Evaluation of 18 frontier LVLMs reveals a consistent gap between relative scene structure and metric reconstruction; even GPT-6-Astra scores 81.4% on Structure but only 58.7% on Metric. Tracing this gap, diagnostic analyses link atomic spatial capabilities to reconstruction quality and show that relational consistency alone does not ensure metric accuracy. Experiments with 3D map injection further separate scene construction from path planning, revealing how these geometric errors limit navigation success. Finally, spatial fine-tuning yields broad transfer across both spatial and general vision-language benchmarks.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02023

Naviq: An Open-Source System-on-Chip Simulator for Rapid Prototyping, Verification, and Design Space Exploration

Scott Smith, Lukas Zscherpel, Alyssa Huang, Marissa Lanz, Deming Chen

原文摘要

Modern FPGA SoCs are no longer just collections of accelerators on top of a programmable fabric. They combine programmable logic with hardened processors, NoCs, and components that dominate end-to-end performance. Evaluating individual pieces is insufficient; system performance depends on the interplay of SoC components. However, current FPGA SoC simulations provide limited support for this style of evaluation. Vendor simulations capture these interactions but are prohibitively slow for iterative design-space exploration. At the same time, generic software models overly abstract the hardware components that determine real behavior, resulting in less accurate simulations. Fast, easy-to-use simulators that accurately model real SoCs are critical for system debugging and design space exploration. We present Naviq, an open-source, cycle-level software simulator for FPGA SoCs, tuned to AMD Versal-class architectures. Naviq bridges accurate but slow vendor simulation and fast software modeling: it captures key behaviors of modern commercial FPGA SoCs while being fast enough for debugging and design-space exploration. Naviq models hardened SoC infrastructure in C++ and integrates accelerators through Verilator, allowing designers to evaluate custom hardware in a full-system environment. At the center of Naviq is an AXI4-compliant NoC model that captures transaction ordering, packetization, routing, backpressure, and memory interactions. Naviq provides CPU and memory integration, protocol checking, checkpointing, tracing, and performance-analysis tools for debugging and design-space exploration. Experimental results show Naviq closely reproduces the bandwidth and latency of Vivado's RTL NoC model while accelerating the evaluated simulations by 81x-182x. We present five case studies demonstrating Naviq's capabilities. Naviq enables practical full-system evaluation of modern FPGA SoC designs.

cs.AR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02021

ClickSight: An LLM Framework for Interpreting Student Clickstreams Through Learning Strategies

Bahar Radmehr, Lan Tran, Ekaterina Shved, Fatma Bet{ü}l G{ü}reş, Adish Singla, Tanja K{ä}ser

原文摘要

Digital learning environments produce rich clickstream data that can reveal how students approach learning tasks, yet interpreting these fine-grained traces often requires substantial expert effort. We introduce ClickSight, a framework for interpreting student clickstreams through learning strategies defined in the literature. Given a student clickstream and a predefined set of learning strategies, ClickSight uses a staged LLM-based pipeline to identify strategy-relevant evidence, assess adherence to each strategy, and generate an evidence-grounded interpretation of the student's behavior. We evaluate ClickSight across two learning environments, 365 students, and 12 model backbones using an expert-grounded quality rubric and an LLM judge calibrated with human-expert ratings. Our results show that ClickSight can generate high-quality strategy-based interpretations across different models and environments, with reasoning models performing more consistently than instruction-tuned counterparts. We further find that providing external clickstream-extracted guidance can improve interpretation quality for strategies that require precise quantitative information from the trace. By connecting fine-grained clickstreams with theoretically defined learning strategies and preserving the evidence underlying each judgment, ClickSight provides a flexible approach for studying student behavior in digital learning environments.

cs.AI · cs.CL · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02019

Multi-criteria Rating and Review based Recommendation Model

Emrul Hasan, Chen Ding, Alfredo Cuzzocrea

原文摘要

These days, due to the advancement of information technology, recommendation system has become one of the key tools for e-commerce business. E-commerce platforms allow users to provide feedback in both written comments and numerical ratings. Recommendation systems are utilized to recommend users to new or unseen items based on these previously collected comments or ratings. In recent years, multi-aspect or multi-criteria based recommendation systems have been studied a lot by the recommendation research community. However, these research works are conducted either with reviews or ratings, not with both. In this project, we argue that integrating both textual reviews (with multiple aspects) and numerical multi-criteria ratings can further enhance the overall rating prediction accuracy. We propose a Multi-criteria Rating and Review based Recommendation model (MRRRec). We show that incorporating multi-criteria ratings into multi-aspect ratings from reviews has a great impact on performance. Our proposed model outperforms several state-of-the-art models such as ANR, DeepCoNN, and Deep Multi-criteria Recommendation System in terms of MSE, MAE, precision, recall, and F1. We show that our proposed model achieves an average of 19% and 23.0% lower MSE and MAE respectively and 7.0%, 1.0% and 3.8% higher precision, recall, and F1 score respectively. We further show that our model performs significantly better with Word2Vec word embedding than the GloVe word embedding method.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02018

CULTURE-CMT: Benchmarking Contextualized Social Media Translation

Linjuan Wu, Dingming Li, Yiwen Qiu, Xinze Lyu, Ye Guo, Daoxin Zhang, Zhe Xu, Weiming Lu

原文摘要

Social-media comments are interactions, yet they are often translated and evaluated as isolated text. This mismatch is especially problematic when meaning depends on surrounding discussion, community-specific slang, or culturally situated expressions. We introduce CULTURE-CMT, a Chinese-English benchmark for contextualized social-media comment translation that restores the post and conversational context naturally available to users. Its 572 manually verified examples are organized into Slang, Context, and Both, enabling diagnostic analysis of distinct and interacting sources of translation difficulty. To support scalable evaluation, we further develop a retrieval-augmented cultural-effectiveness Judger that combines a calibrated scoring model with dynamically retrieved cultural knowledge. CULTURE-CMT reveals several non-trivial patterns. Providing interaction context improves most model configurations, but does not eliminate culturally ineffective translations. Examples requiring both contextual grounding and culturally situated language remain substantially more difficult than either source of difficulty alone. Moreover, larger models are not consistently better, and thinking produces highly model-dependent gains, including occasional degradation. In contrast, a 27B model adapted on benchmark-disjoint social-media data achieves the lowest observed invalidity rate among the evaluated configurations under thinking inference with context. Together, these results suggest that realistic social-media translation is not simply a matter of scaling models or reasoning longer, but of learning to translate meaning as it is constructed within evolving online interactions.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02017

Switch-SWE: Do Coding Agents Hold Focus When Switching Between Unfinished Tasks?

Shuochen Chang, Qianli Ma, Yibo Miao, Qingyang Liu, Bingjie Gao, Haonan Zhao, Siyu Sun, Yulin Sun 等 10 位作者

原文摘要

Software development sessions often involve several unfinished tasks rather than one issue carried through to completion. As coding agents take on repository-level development, they need to recover an earlier task's requirements and partial progress after intervening work. Existing repository-level benchmarks typically score one issue-to-patch episode and do not directly measure whether an agent can resume a task after advancing other unfinished work in the same repository. We introduce \textbf{\ourmethod}, a benchmark for focus-task resumption in sessions that interleave unfinished tasks drawn from the same source repository, with a persistent checkout for each task. Each focus task is evaluated in matched uninterrupted and switched sessions with identical focus requirements and executable verifiers. The switched session interleaves ordered stages from other unfinished tasks in the same repository, while only the final focus patch is scored. \ourmethod~contains 200 focus tasks from 19 Python repositories, reconstructed from issue, pull-request, and review histories and rendered as 400 matched session specifications. Across 7 coding agents, interleaved unfinished work produces an average focus-task resolution gap of 11.5 percentage points, ranging from 6.0 to 15.0 percentage points. Trajectory analysis identifies cross-task requirement confusion and loss of task state as recurrent resumption behaviors. We introduce a plug-in context-management module that selects task-relevant history and maintains concise task-state summaries. On OpenCode, the module improves focus-task completion under same-repository task switching by 5.5 to 15.5 percentage points across the evaluated models. Code is available at github.com/Miraclecsc/Switch-SWE.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02016

Improving Entrepreneurial Skills by Practicing with AI

Amir Globerson, Anisha Choudhury, Anna Iurchenko, Avinatan Hassidim, Cathy Cheung, Elizabeth Kim, Femi Olanubi, Gabby Espinosa 等 24 位作者

原文摘要

Durable skills like critical thinking, creativity, and collaboration are essential for innovation, yet teaching them remains difficult. In this work, we focus on the entrepreneurial domain, where these skills and others are required, and ask whether AI can be used as a tool for improving entrepreneurial proficiency. As a first step towards this challenge, we present an extension of Vantage, a platform developed at Google Research, adapted to serve as an interactive practice environment for entrepreneurship. Vantage pairs students with simulated AI peers engineered to navigate realistic business scenarios and challenge learners to exercise key skills. In a randomized controlled trial (RCT) embedded in summer entrepreneurship programs run by The Network for Teaching Entrepreneurship (NFTE), students using Vantage scored significantly higher on their Capstone Business Plans than control students with access to standard AI tools. These preliminary findings suggest that orchestrated AI peers can meaningfully support short-term performance on tasks requiring complex, durable skills.

cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02015

Scaling Makes It Possible: How Large Models Master Impossible Languages

Yaning Jia, Chunhui Zhang, Chiyu Ma, Zhongyu Ouyang, Ivory Yang, Soroush Vosoughi

原文摘要

While language models (LMs) excel in natural language tasks, their ability to learn impossible languages--those that violate the structural, cognitive, or logical constraints that make human languages learnable and functional--remains debatable. This controversy is further fueled by a divide between linguistic theorists and empirical researchers working with small-scale LMs. To address this controversy, we introduce semantic coherence. This concept measures how effectively semantic information is expressed in language structures and examines the learning capabilities of LMs across multiple scales. By fine-tuning billion-parameter LMs on impossible language samples, we demonstrate that larger LMs achieve similar performance on impossible and possible languages, whereas million-parameter LMs exhibit significant degradation when trained on impossible languages. This performance gap widens as semantic complexity increases. We further show that scaling up the LM size fundamentally shifts the learning focus from grammatical rules to semantic understanding, with billion-scale LMs exhibiting a threefold reduction in sensitivity to low-level linguistic perturbations while maintaining stable performance under high-level semantic perturbations.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.27606

Ask to Learn: Query-Based Collaborative Reinforcement Learning

Kaicheng Zhang, Hongqian Niu, Laixi Shi, Didong Li, Guannan Qu, Zhun Deng

原文摘要

Offline reinforcement learning is fundamentally limited by the coverage of the available data, while collecting additional trajectories through online exploration may be costly, unsafe, or infeasible. We study whether this limitation can be overcome without directly interacting with the underlying environment. Motivated by practical settings such as collaborator-mediated simulation, where a collaborator can provide information complementary to the offline data, we introduce query-based collaborative reinforcement learning to study how to acquire this information efficiently. The learner starts with a fixed offline dataset and adaptively queries state-time pairs; the collaborator samples an action according to a fixed, possibly suboptimal policy, and the simulator returns the corresponding reward and next state. For finite-horizon tabular problems, we derive an instance-dependent information-theoretic lower bound on the additional queries required to identify an $ε$-optimal policy and develop a local quadratic approximation. Guided by this characterization, we develop an adaptive query-allocation algorithm whose query complexity nearly matches the local benchmark under suitable conditions. Experiments on multiple synthetic and semi-synthetic environments demonstrate the empirical efficiency of the proposed method.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02014

One String to Rule Them All: Measuring Consent Under the IAB Global Privacy Platform

Muhammad Abu Bakar Aziz, Christo Wilson

原文摘要

Many privacy regulations, including the California Consumer Privacy Act (CCPA), require websites to honor users' opt-out choices. To facilitate this process, industry has introduced technical standards such as the IAB Global Privacy Platform (GPP), which aims to standardize collection and transmission of user consent information across multiple jurisdictions. However, it remains unclear whether these standards consistently and correctly transmit user opt-out choices, or whether opt-out choices are respected by advertisers. In this study, we conduct an in-depth analysis of consent propagation under the GPP framework. We collect data from four web crawls across four experimental settings to analyze how widely the standard is adopted, the extent to which opt-out signals propagate from publishers to third parties, and whether these signals reach and alter third-party advertiser behavior. Our analysis reveals several inconsistencies in the implementation and adoption of the standard that result in user consent signals being lost and user opt-out choices not being respected by the advertisers.

cs.CR

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arXiv ↗PDF ↗