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新论文 · v1arXiv:2611.07231

Efficient Projection-Based Reduced-Order Modeling for Computational Fluid Dynamics via Multifidelity Basis Construction

Ashok Viswanathan, Charbel Farhat

原文摘要

Projection-based reduced-order models (PROMs) accelerate computational fluid dynamics simulations, but generating high-fidelity (HF) training snapshots remains expensive. We present a multifidelity methodology that reduces offline basis-construction costs by combining a small HF database with low-fidelity (LF) solutions. The method accommodates discrepancies in physical models and spatial discretizations. Parameter-matched HF and aligned LF snapshots define a correction basis. At new parameters, LF predictions are corrected by minimizing the HF residual over an affine trial space, with selective Reynolds-averaged Navier-Stokes (RANS) smoothing. Accepted snapshots enrich the training database and update the correction basis. The resulting basis is compatible with conventional projection methods, including least-squares Petrov-Galerkin projection. For steady flow over a NACA0012 airfoil, using fine-grid RANS and coarse-grid Euler models, four HF snapshots and 36 LF-derived snapshots reduce snapshot-generation cost by a factor of 7.8. At basis dimension 40, mean lift, drag, and lift-to-drag ratio errors are 1.1%, 3.6%, and 3.9%, and the mean relative skin-friction error is approximately 5%, compared with 0.18%, 2.9%, 2.8%, and 4% for an HF-only PROM. Errors are evaluated against fully converged HF solutions on a test set emphasizing shock-containing flows. Increasing the initial HF snapshot count raises cost without monotonically improving accuracy. Among the tested multifidelity configurations, ten initial HF snapshots give the lowest aerodynamic errors at dimension 40; increasing the count to twelve worsens all three. The results demonstrate reduced snapshot-generation costs while retaining compatibility with established PROM formulations.

cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07229

Spatial-UMI-Dream: Scaling World Action Models with Metric Geometry for UMI Manipulation

Weiduo Yuan, Ze Yuan, Sihan Liu, Jionghao Wang, Liang Heng, Zhenyu Zhao, Yue Wang

原文摘要

The Universal Manipulation Interface (UMI) enables scalable demonstration collection independently of a specific robot. However, its moving, close-range gripper cameras entangle camera and object motion without a stable external view, leaving the metric geometry needed for precise manipulation implicit in RGB. We introduce Spatial-UMI-Dream, a 5B spatial World Action Model (WAM) pretrained on 605 hours of RGB-D UMI data through two-stage training -- world-only adaptation followed by joint world-action learning -- to jointly predict future RGB, fisheye-aligned metric depth, and actions. Stereo-derived depth makes scene scale and spatial relationships explicit within a unified dual-gripper RGB-D representation. Furthermore, evaluating UMI policies requires testing whether UMI's robot-independent demonstration collection translates into policy generalization across robot embodiments, alongside robustness to environmental changes. We introduce RoboTwin-UMI, a twenty-task simulation benchmark built on RoboTwin with UMI wrist-mounted fisheye observations, enabling controlled, repeatable evaluation of UMI policies under environment and embodiment changes separately and jointly without additional fine-tuning. On RoboTwin-UMI, Spatial-UMI-Dream achieves 79.10% in-domain success and 72.15% under combined embodiment and environment shifts, exceeding the strongest evaluated baseline in each setting by 4.65 and 10.30 percentage points, respectively. It also exhibits the smallest success-rate drop under embodiment transfer among the evaluated policies. In a matched pretraining ablation, RGB-only pretraining leads to a 5.75-percentage-point decrease in in-domain success compared with RGB-D pretraining. Across six real-world tasks, Spatial-UMI-Dream achieves 85.0% average success, exceeding the strongest baseline by 24.2 percentage points. Project page: https://weiduoyuan.com/spatial-umi-dream/

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07228

Source-balanced Capped Fisher: Bounded Evidence Aggregation for User Inference in Language Models

Yang Liu, Ran Zou, Wanrong Zhu

原文摘要

User inference asks whether a source contributed documents to a language-model update, even when the auditor observes only fresh documents from that source. Document-level scores can provide evidence of participation, but combining this evidence is challenging: documents differ in baseline predictability and in how strongly they reflect information learned from other documents of the same source. We propose source-balanced capped Fisher (SCF), an aggregation method that uses known nonparticipating sources to assess how unusual each document score is. It transforms the resulting reference-tail frequencies into negative-log evidence, caps individual contributions, and averages them within each source. This limits the influence of isolated extreme scores while retaining evidence across multiple documents. We complete the inference procedure with a decision rule that tests source participation using a p-value calibrated against a separate set of known nonparticipating sources. The rule provides finite-sample Type-I error control under source exchangeability. Experiments across four real-world datasets show that SCF achieves the highest AUROC among the evaluated aggregation methods in four domains. With the calibration-based decision rule, SCF delivers consistently competitive detection power across the tested settings, achieving the highest power in many cases, while empirical false-positive rates remain generally close to or below the nominal levels.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07226

Loopback Transformer: Towards a Unified View of Residual Looped Transformers

Xuan Ouyang, Zefan Cai, Chenlong Zhang, Cheng Luo, Ke Wan, Junjie Hu

原文摘要

Looped Transformers reduce parameter count by repeatedly applying shared blocks, but parameter sharing alone does not determine how the results of repeated computation are reused. We take the depth-memory view of residual connections and analyze which representation is stored after each block application and how later reads weight it. A full block output contains both its input and its new update, so weighting that output also reweights the history already carried by the input. Reading individual updates instead, with the shared stack's input kept on a separate path, gives separate controls over input strength, total update weight, and allocation across updates. The Loopback Transformer implements these controls: geometric decay sets the total update weight and supplies a recency prior, and attention learns how to allocate that weight. On ClimbMix, Loopback reaches lower validation loss than standard Transformer baselines with 40-75% of their Transformer parameters (excluding token embeddings and the output head) at reference scales from 191M to 3B, with training tokens and executed depth matched. It also has higher mean accuracy over thirteen downstream tasks at 1.4B and 3B. At 191M, 374M, and 778M, it has the lowest validation loss and the highest thirteen-task mean among the standard Transformer and seven looped residual designs.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.09214

Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability

Qingyue Zhao, Kaixuan Ji, Heyang Zhao, Quanquan Gu

原文摘要

\emph{Kullback-Leibler} (KL) regularization is ubiquitous in reinforcement learning algorithms in the form of \emph{reverse} or \emph{forward} KL. Recent studies have demonstrated $ε^{-1}$-type fast rates for decision making under reverse KL regularization, in contrast to the standard $ε^{-2}$-type sample complexity. However, for forward-KL-regularized objectives, existing statistical analyses are either not applicable or result in $\tilde{O}(ε^{-2})$ slow rates. We take the first step towards addressing this problem via a streamlined analysis of forward-KL-regularized offline CBs. We give the first $\tilde{O}(ε^{-1})$ upper bounds in tabular and general function approximation settings, both under notions of \emph{single-policy concentrability}. In particular, our convex-analytical pipeline unifies these settings by exploiting the pessimism principle in a novel way and completely bypasses the proof routines in previous works based on the mean value theorem, which might be of independent interest. Moreover, we provide rate-optimal lower bounds, manifesting the tightness of our upper bounds in terms of statistical rates. Our lower bounds also demonstrate that the forward-KL-regularized sample complexity recovers the unregularized slow rate in the low-regularization regime, similarly to the reverse-KL regularization.

cs.LG · cs.AI · cs.IT · math.ST · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07225

PRB Starvation at Slot Time via Rank Indicator Inflation: Attacking Proportional Fair Memory in the Scheduler

Ryan Barker, Julia Boone, Tolunay Seyfi, Alirez Ebrahimi, Ethan Farquhar, Brian Davis, Joseph Boccuzzi, Fatemeh Afghah

原文摘要

Proportional fair (PF) scheduling in fifth-generation (5G) New Radio (NR) ranks user equipment (UE) by instantaneous rate over an exponentially weighted moving average (EWMA) of delivered service. We identify a served-but-undelivered denial-of-service (DoS) attack in which rank indicator (RI) inflation induces persistent physical resource block (PRB) starvation. Unsupported spatial layers cause first-attempt delivery failure, keeping the attacker's service EWMA low; the evaluated PF allocator repeatedly grants it the full PRB pool, denying honest UEs transmission opportunities. We formalize this recurrence and evaluate it in 3,300 paired, 200 slot multi-user shards spanning rank 1 and rank 2 Sionna Ray Tracing geometries, three link adaptation controllers, PF and round robin (RR), four target identities, and five seeds. On the rank 2 geometry, selective RI inflation yields 94--95\% target capture, reduces PRB Jain fairness from 0.997 to 0.28, and produces mean honest zero-grant runs of 41--56\,ms; RR preserves equal allocation. Exact offline replay and a Monte Carlo recurrence isolate the delivered-service denominator as the causal path. Cross-field and controller ablations show a weaker, controller-dependent channel-quality trigger, while activity-gated EWMA defenses remain structurally blind. The results motivate RI-consistency checks before feedback acquires scheduling authority and slot-time PRB evidence inside the O-RAN Distributed Unit.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.22535

TerminalWorld: Benchmarking Agents on Real-World Terminal Tasks

Zhaoyang Chu, Jiarui Hu, Xingyu Jiang, Pengyu Zou, Han Li, Chao Peng, Peter O'Hearn, Earl T. Barr 等 11 位作者

原文摘要

We introduce TerminalWorld, a scalable data engine that automatically reverse-engineers high-fidelity evaluation tasks from "in-the-wild" terminal recordings. Processing 80,870 terminal recordings, the engine yields a full benchmark of 1,530 validated tasks, spanning 18 real-world categories, ranging from short everyday operations to workflows exceeding 50 steps, and covering 1,280 unique commands. From these, we curate a Verified subset of 200 diverse, manually reviewed tasks. Comprehensive benchmarking on TerminalWorld-Verified across eight frontier models and six agents reveals that current systems still struggle with authentic terminal workflows, achieving a maximum pass rate of only 62.5%. Moreover, TerminalWorld captures real-world terminal capabilities distinct from existing expert-curated benchmarks (e.g., Terminal-Bench), with only a weak correlation to their scores (Pearson r=0.20). The automated engine makes TerminalWorld authentic and scalable by construction, enabling it to evaluate agents in real-world terminal environments as developer practices evolve.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.01640

Prescriptive Scaling Laws for Data Constrained Training

Justin Lovelace, Christian Belardi, Srivatsa Kundurthy, Shriya Sudhakar, Kilian Q. Weinberger

原文摘要

Training compute is increasingly outpacing the availability of high-quality data. This shifts the central challenge from optimal compute allocation to extracting maximum value from limited data. The widely adopted Chinchilla scaling law assumes every training token is unique. This limits its ability to guide pretraining decisions in data-constrained regimes. We model the excess loss under repetition with a simple additive overfitting penalty and find that it accurately describes model behavior. Our scaling law yields qualitatively new compute-optimal allocation advice. Beyond a point, further repetition is counterproductive and compute is better spent on model capacity. We show that following our law's recommended configuration improves performance in data-constrained regimes. Finally, because our one-parameter form isolates overfitting in a single coefficient, it enables direct comparison across training configurations. As a case study, we show that strong weight decay ($λ=1.0$) reduces this coefficient by approximately 70\%, providing a scaling-law explanation for recent findings that optimal weight decay in data-constrained regimes is an order of magnitude larger than standard practice.

cs.LG · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.04216

LiSA: Linear Stochastic Attention for Long-Range Visual Modeling

Sahar Dastani, Fereshteh Shakeri, Samuel Barbeau, Kunal Mahatha, Ismail Ben Ayed, Herve Lombaert, Christian Desrosiers

原文摘要

Self-attention has become the standard for modern sequence modeling, enabling Transformers to model long-range dependencies across a wide range of modalities. Despite these advances, its quadratic cost with respect to sequence length remains a significant computational bottleneck. Recent work addresses this limitation by introducing linear variants of self-attention, namely with kernel approximations of the softmax operator. However, their first-order aggregation restricts expressivity and struggles to capture higher-order structure. We propose Linear Stochastic Attention (LiSA), a linear attention plug-in module that enables higher-order token interactions via a stochastic diffusion process based on truncated random walks. LiSA incorporates adaptive locality, favoring semantically similar neighbors to preserve fine-grained structures, while maintaining stable and efficient training. We conduct extensive experiments on classification, semantic segmentation, and object detection tasks and show improved results over state-of-the-art methods.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07218

SAFE-Teen: Evaluating Chatbot Safety Across Adolescent Mental Health Conversations

Andrea Mock, Valerie Chen, Eric Lin, Rayan Krishnan, Diyi Yang, Caroline Figueroa

原文摘要

Adolescents are increasingly turning to AI chatbots for mental health support, advice, and companionship, raising safety concerns. While recent evaluations have started to examine safety across multiple conversational turns, few compare how slight changes in a teen's responses can affect a chatbot's safety in the same scenario. We conduct a clinician-informed evaluation of chatbot safety in simulated conversations with adolescents aged 13-17, examining how safety failures vary with adolescents' responses and teen-aware system prompting. We evaluated 32,555 simulated conversations across 18 chatbot APIs and two system-prompt conditions using 22 safety criteria grounded in previously identified harms. Conversations spanned 983 variants of 106 seed scenarios covering urgent safety issues, health advice, and identity and relationships. The overall criterion failure rate was 43.3% without the teen-aware prompt and 24.9% with it. The teen-aware prompt reduced failures, but it didn't eliminate them. When a teenager pushed back on a chatbot's answer, failures on mental health diagnosis, medication, and substance misuse boundaries rose by 10-12 percentage points without the prompt, for more than 80% of chatbots. These findings reveal persistent safety failures in simulated adolescent-chatbot interactions and show that safety depends on how a conversation unfolds.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07217

TileQuant: Tile as the Precision-Allocation Unit for LLM Mixed-Precision Post-Training Quantization

Hanzhi Zhang, Qiao Zhang, Qinglei Cao, Heng Fan, Yan Huang, Kewei Sha, Yunhe Feng

原文摘要

Post-training quantization (PTQ) reduces large language model (LLM) deployment costs, but preserving quality requires accounting for uneven weight sensitivity. Uniform PTQ uses one weight bit-width, importance-aware PTQ adjusts scales or reconstructs weights within that width, and column-wise mixed precision retains selected columns at higher precision. However, sensitivity varies within columns, so whole-column retention also protects less sensitive weights, while runtime outlier detection adds decoding overhead. We therefore introduce TileQuant, a training-free framework that assigns precision to hardware-aligned 2D weight tiles. Offline calibration provides input-activation-aware scoring from forward statistics for lower calibration cost and gradient-informed scoring from loss sensitivity for stronger quality preservation. Within each projection's retention budget, the highest-scoring tiles remain at 16-bit and the rest use 8-bit. The resulting map guides reconstruction into compact weight buffers, which the kernel reuses for native integer and 16-bit computation with local accumulation to reduce memory traffic. Experiments show that TileQuant reaches 2.66x the original model's generation throughput. Across language modeling, reasoning, long-context retrieval, and question answering, it preserves quality and surpasses quantized and original models on multiple tasks. These results support selecting precision units that capture local sensitivity while enabling efficient hardware execution. The implementation is: https://github.com/HanzhiZhang-Ulrica/TileQuant

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07216

Compositional Unalignment: Auditing Safety in Large Vision-Language Models

Hong kyu Lee, Kai Shu, Li Xiong

原文摘要

Large vision-language models (LVLMs) are vulnerable to compositional harm: an image and a prompt that are benign in isolation become harmful through the model's cross-modal reasoning. Recent alignment methods report strong scores on compositional safety benchmarks, but an LLM judge only measures observed behavior and cannot reveal how robustly safety is integrated into the model. We argue that un-alignment provides a complementary audit of alignment robustness. We first show that minimal supervised fine-tuning reaches an LLM-judge score comparable to a multi-stage reinforcement-learning pipeline, so similar benchmark scores cannot establish robustness by themselves. We then show that recognition and refusal of compositional harm are decoupled: nine standard direction-finding methods detect compositional harm with up to AUC=0.99, yet ablating the recovered directions either leaves refusal intact or destroys model coherence. We therefore propose CMaG, a training-free un-alignment method for compositional safety. CMaG measures refusal intention with a refusal-versus-compliance log-probability margin, takes its gradient with respect to mid-layer activations, and contrasts it against partially benign pairs to isolate the cross-modal interaction term. A single backward pass over 80 examples yields a direction along which steering monotonically converts refusal into compliance. On Qwen2.5-VL-7B and LLaVA-1.5-7B, CMaG reduces compositional safety scores by up to 65 points while over 98% of outputs remain coherent, outperforming gradient-based baselines; the direction also transfers to text-explicit harm and un-aligns models hardened by defensive training. Compositional safety can thus appear behaviorally strong while remaining fragile to targeted internal intervention. Warning: this paper contains unsafe model outputs, including references to self-harm.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.18374

Inference and Uncertainty Quantification for Streaming $r$-PCA

Haoshu Xu, Hongzhe Li

原文摘要

We address two open questions in streaming PCA via Oja's algorithm: sharp operator-norm convergence for general rank under sub-Gaussian data, and distributional inference for the resulting subspace estimator. Existing convergence analyses, even in the rank-one case, either assume bounded data or leave non-vanishing remainder terms that prevent adaptation to a polynomially vanishing tail spectrum, while existing distributional results are confined to the rank-one case. Our convergence theory removes these remainder terms and yields a sharp rate. In the dense-tail spiked covariance regime, this rate matches the minimax rate up to logarithmic factors. More generally, we prove a matching lower bound, up to logarithmic factors, across both dense-tail and sparse-tail regimes under a mild nondegeneracy condition. The analysis yields a linearization of Oja's iterates, which in turn enables a high-dimensional Gaussian approximation for the general-rank subspace estimation error with an explicit limiting covariance. We also establish a row-wise Gaussian approximation over convex sets for the aligned difference, recovering prior rank-one results as special cases. For practical inference, we develop an online multiplier bootstrap algorithm and prove its consistency. Beyond streaming PCA, our techniques contribute to Gaussian approximation and bootstrap inference for nonconvex stochastic approximation.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07215

Fractional physics-informed neural networks to model social media virality

Alex Koong, George Em Karniadakis

原文摘要

Existing models of social media virality either prescribe the functional form of engagement growth or assume a fixed mechanism by which past activity influences future engagement. We present a fractional physics-informed neural network that learns both the temporal memory and nonlinear growth dynamics of social media engagement, using symbolic regression to translate the learned dynamics into interpretable equations. Synthetic experiments at the deployment resolution of 24 hourly observations demonstrate that the fractional order and reaction law can be jointly recovered from clean data. Under observation noise, however, the standard pointwise physics residual drives the fractional order toward its lower bound through a scale artifact. We address this failure using a fully whitened residual that propagates observation noise through the fractional operator. Applying the framework to 348 posts from X (formerly Twitter), we find that a single-window model estimates strongly memory-dependent dynamics but fails to accurately reconstruct held-out trajectories. Partitioning the 24-hour window into four teacher-forced segments substantially improves performance, yielding segment-specific fractional orders that increase from 0.23 to 0.99 and interpretable growth laws for each phase. The segmented model reconstructs 64% of held-out trajectories within a mean squared error of 0.01, compared with 44% for the strongest of nine classical baselines. Together, these results demonstrate the potential of fractional physics-informed equation discovery to uncover interpretable engagement dynamics while highlighting the importance of noise-aware estimation and temporal segmentation.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07212

Canonical Correlation Neural Networks

Andrea Cavallo, Sundeep Prabhakar Chepuri, Charilaos Kanatsoulis, Elvin Isufi

原文摘要

Relational inductive biases such as grids or graphs allow neural networks with fewer trainable parameters and improved generalization, but require a known structure beforehand. When no structure is available, the covariance matrix provides a principled substitute, and coVariance Neural Networks (VNNs) have shown that using it yields models that are theoretically grounded in Principal Component Analysis, stable to finite-sample estimation errors and parameter-efficient. This framework, however, is limited to single-view data, whereas many applications observe the same phenomenon through multiple views, each with its own in-view covariance and a cross-view covariance linking them. To incorporate these matrices as inductive biases in a principled way, we introduce CAnonical correlation NEural networks (CANEs), architectures that use in-view and cross-view covariances as distinct relational biases to solve downstream tasks. Via spectral analysis, CANEs draw analogies with Canonical Correlation Analysis (CCA), a classical technique to project two-view data onto a correlated subspace. However, while CCA's objective is to compute shared view representations, CANEs are trained to solve a downstream task directly. We bound their output error under finite-sample covariance perturbations and show that it depends on controllable Lipschitz constants of the learned filters in place of the singular-value gap that governs the stability of CCA, thus guaranteeing better robustness. Experiments on seven datasets from brain imaging and motion sensing show that CANEs match or outperform single-view, two-view and CCA-based baselines, often with fewer parameters, and retain their performance when covariances are re-estimated from fewer samples.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07211

Not All Loops Cost the Same: Phase-Dependent Depth for Serving Looped Transformers

Zhixu Du, Yuhan Cheng, Hai "Helen" Li, Yiran Chen

原文摘要

Looped transformers scale inference-time reasoning by repeatedly reusing a shared core, increasing computation without increasing parameter count. Serving prefilling and decoding with a uniform loop budget couples their costs. We present LoopDuet, a phase-dependent depth and training-free serving approach that selects the two budgets jointly while preserving decoding depth at prefilling boundaries. Our vLLM implementation gathers tokens requiring further recurrence into compact batches, turning the selected allocation into executable savings with existing weights and attention kernels. At runtime, a queue-age controller adjusts prefilling depth to retain quality as demand changes. Experiments on the full HotpotQA dataset with one A100-80GB demonstrate two benefits of this flexibility. First, separate phase budgets improve the quality--throughput trade-off: Huginn delivers 19\% more throughput than the fastest tested uniform setting meeting the same accuracy criterion, while a higher-throughput operating point gains 6.04 EM points over uniform depth eight at 97\% of its throughput. Second, the same execution mechanism retains more quality under load: on two evaluated OLMo arrival traces, adaptive prefilling gains 0.92--1.35 EM points over the most accurate tested fixed-depth controls at the same 100\% attainment of a one-second time-to-first-token target. Together, these results establish LoopDuet as a practical way to turn flexible model computation into higher serving capacity and better quality under load.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07210

Latent Terrain: Adapting Neural Audio Autoencoders as Design Materials in NIME

Shuoyang Jasper Zheng, Keigo Yoshida, Nico García-Peguinho, Jiatong Liu, Dan Hearn, Anna Xambó Sedó, Nick Bryan-Kinns

原文摘要

Neural audio autoencoders, a deep learning method for sound synthesis, are increasingly popular in AI-enhanced NIMEs. It is timely to explore as a community how this technological opportunity has opened a domain of design in NIME. This paper focuses on one compelling technique of using autoencoders for sound synthesis: navigating their latent space as a generative sound space. We introduce Latent Terrain, a Max/MSP tool package designed to tailor latent spaces into corpus-based sound spaces for NIMEs. We describe the rationale and development process of Latent Terrain to offer insights into the use of autoencoders in a material-oriented crafting space of musical interface design. We deliver an annotated portfolio resulting from a collaborative artistic exploration of Latent Terrain with four NIME makers, to showcase the design possibilities opened by autoencoders. We reflect on our practice-based account to discuss the challenges and opportunities of enabling neural audio autoencoders as design materials for AI-enhanced NIMEs.

cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07209

Task-Calibrated Random Features via Influence-Based Error Cancellation

Shinto Eguchi

原文摘要

Random features approximate kernel integrals by Monte Carlo, yet only selected frequency-sampling errors affect a downstream task. We propose Task-Calibrated Random Features (TCRF), which balance frequency moments constructed from task-specific influence using weights shared across kernel entries. Closed-form weights coincide with classical minimum-distance calibration; the contribution is a reusable, task-derived calibration space. We show that first-order variance equals the residual variance after projection onto this space. For kernel ridge regression, complete cancellation yields a non-Gaussian second-order limit. For kernel Stein discrepancy, a subset identity and disjoint-block variance decomposition eliminate the linear component of pilot noise. Positive exponential-tilting weights preserve the positive-semidefinite feature representation and support higher feasible calibration ranks. Paired experiments show improved approximation to exact kernel ridge regression at larger frequency budgets and to the exact empirical Stein discrepancy under a fixed alternative, even after accounting for a frequency-equivalent pilot budget. Gains are smaller with cross-validated ridge, landmark pilots require conservative rank selection, and near the Stein null allocating the pilot budget to additional ordinary frequencies is preferable.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07208

Structured Machine Theory of Mind from Agent Trajectories

Nigel F. Doering, Rahath Malladi, Arshia Sangwan, M Tanjid Hasan Tonmoy, Jacob Doan, David Danks, Tauhidur Rahman

原文摘要

Predictive models of human behavior trained on large-scale trajectory data optimize for statistical accuracy without representing the mental states that causally generate behavior. Such models support prediction but not principled intervention: they cannot answer how an agent's behavior would change if its beliefs or preferences were different. We introduce Structured Machine Theory of Mind (SMToM), a framework that attributes explicit, independently supervised belief and desire representations from observed trajectories within a Belief-Desire-Intention (BDI) causal structure. The central architectural element is a goal head that consumes only the predicted mental-state channels and a current-trajectory embedding; counterfactual intervention on beliefs and desires is a direct operation. We instantiate SMToM on a controlled pedestrian navigation domain where ground-truth mental states are known by construction, enabling evaluation of attribution accuracy, local channel interventions, and trajectory-level counterfactual diagnostics. The resulting model, BDIBottleneck, improves over trajectory-only and unsupervised-context neural baselines. It is also competitive with a matched auxiliary-supervised context baseline while providing an editable mental-state interface that the unbottlenecked baseline lacks. Counterfactual experiments on desires confirm that substituting an agent's inferred preferences with a different activity type coherently shifts predicted destinations toward relevant locations. Counterfactual experiments on beliefs confirm that marking a location as unavailable in the agent's belief state reduces its predicted probability as a destination, though in a complex way. These results demonstrate, in a controlled navigation setting, that explicit BDI-structured supervision is a viable foundation for interpretable and counterfactual analysis of longitudinal trajectory data.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.02677

ARMs: Adaptive Red-Teaming Agent against Multimodal Models with Plug-and-Play Attacks

Zhaorun Chen, Xun Liu, Mintong Kang, Jiawei Zhang, Minzhou Pan, Shuang Yang, Bo Li

原文摘要

As vision-language models (VLMs) gain prominence, their multimodal interfaces also introduce new safety vulnerabilities, making the safety evaluation challenging and critical. Existing red-teaming efforts are either restricted to a narrow set of adversarial patterns or depend heavily on manual engineering, lacking scalable exploration of emerging real-world VLM vulnerabilities. To bridge this gap, we propose ARMs, an adaptive red-teaming agent that systematically conducts comprehensive risk assessments for VLMs. Given a target harmful behavior or risk definition, ARMs automatically optimizes diverse red-teaming strategies with reasoning-enhanced multi-step orchestration, to effectively elicit harmful outputs from target VLMs. We propose 11 novel multimodal attack strategies, covering diverse adversarial patterns of VLMs (e.g., reasoning hijacking, contextual cloaking), and integrate 17 red-teaming algorithms into ARMs via model context protocol (MCP). To balance the diversity and effectiveness of the attack, we design a layered memory with an epsilon-greedy attack exploration algorithm. Extensive experiments on instance- and policy-based benchmarks show that ARMs achieves SOTA attack success rates, exceeding baselines by an average of 52.1% and surpassing 90% on Claude-4-Sonnet. We show that the diversity of red-teaming instances generated by ARMs is significantly higher, revealing emerging vulnerabilities in VLMs. Leveraging ARMs, we construct ARMs-Bench, a large-scale multimodal safety dataset comprising over 30K red-teaming instances spanning 51 diverse risk categories, grounded in both real-world multimodal threats and regulatory risks. Safety fine-tuning with ARMs-Bench substantially improves the robustness of VLMs while preserving their general utility, providing actionable guidance to improve multimodal safety alignment against emerging threats.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.09605

Hypnos: Learning Generalisable Representations of Sleep Physiology via Next-Token Prediction

Jonathan F. Carter, Lionel Tarassenko

原文摘要

Foundation models offer a promising route to compress multi-modal physiological signals into compact representations of human health, with broad applications across sleep medicine, cardiology, neurology and other healthcare domains. Existing models have typically been trained with masked-reconstruction or contrastive objectives. However, masked reconstruction may be poorly suited to the stochastic nature of these signals, while contrastive approaches rely on positive-pair definitions despite the semantic invariances of physiological signals being poorly understood. In this work, we show that next-token prediction is a simple and scalable alternative. We develop Hypnos, a multi-modal sleep foundation model trained using eight different sensing modalities (e.g. EEG, ECG, respiratory signals) drawn from over 20{,}000 overnight polysomnography recordings. We tokenize each modality into streams of discrete tokens using residual vector quantization, then train a large auto-regressive RQ-Transformer to jointly predict the next token across all modalities in parallel. After training, Hypnos can be applied to continuous streams of sensor data from any subset of supported modalities, generating embeddings for downstream tasks. Hypnos significantly outperforms existing foundation models on most benchmarks evaluated. In sleep stage classification, we match the performance of strong supervised baselines on held-out test sets whilst using 100\times less labelled data. Hypnos even generalises beyond sleep recordings, surpassing a dedicated ECG foundation model at detecting atrial fibrillation. Our results demonstrate that auto-regressive token modelling is a strong self-supervised approach for representation learning from multi-modal physiological signals. Code and model weights: https://github.com/joncarter1/hypnos

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07203

τ-bench-LILT: Evaluating Conversational Agents on Realistic Multilingual Tool-Use Tasks

Kaden Uhlig, Kiarash Mokhtari, Anıl Arslan, Yunsu Kim, Joern Wuebker

原文摘要

Frontier evaluations of tool-using conversational agents are conducted almost exclusively in English, leaving a critical gap for organizations deploying agents in other languages and regions. We present $τ$-bench-LILT, a multilingual extension of ${τ^3}$-bench covering German, Korean, Turkish, and Farsi in the airline, retail, telecom, and banking domains. We natively localize every layer of the benchmark: customer databases are populated with culturally appropriate records, user-simulator prompts are refined iteratively through contrastive annotation by native-language experts, and tasks are updated according to the localized database and to reflect realistic tasks in the target region. Evaluation across eight agent models shows the largest drops compared to English in the Korean and Farsi retail domains, by up to 61 points in pass^4. Code and data released at https://github.com/lilt/tau-bench-lilt.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07201

IRIS: Intelligent Random Access with Integrated Shared Learning for Decentralized LEO Direct-to-Satellite Uplinks

Abdullah Al Noman, Gaurav Singh, Rameez Ahmed, Debashri Roy

原文摘要

Grant-free uplink access in low-Earth-orbit (LEO) non-terrestrial networks (NTNs) poses a coordination challenge when multiple UEs share a satellite channel without real-time centralized scheduling under long propagation delays and time-varying channels. Conventional random access does not exploit peer context, leading to collisions and inefficient channel use as contention increases. To address this problem, we propose IRIS, a multi-agent reinforcement learning framework for decentralized grant-free direct-to-satellite (D2S) uplink access, with A2A-IRIS as its sidelink-assisted Agent-to-Agent (A2A) realization. Each UE executes a shared binary transmission policy using local traffic/channel information augmented by compact peer states exchanged over UE-to-UE sidelink, while Vanilla-IRIS serves as a local-only ablation. Evaluations in rural and suburban LEO D2S deployments use TLE-based trajectories propagated using the Simplified General Perturbations 4 (SGP4), with a Walker-Delta constellation as fallback. A2A-IRIS achieves 2.1579 and 5.4478 Mbps, corresponding to 99.9% and 96.1% of the respective scenario-specific upper bounds. Relative to Vanilla-IRIS, throughput improves by 40.8% and 325.0%, with collision rates of 0.0000 and 0.0433, while one- and two-epoch delayed centralized schedulers are outperformed by approximately 51--130%. A2A-IRIS also improves fairness, service coverage, and transmission efficiency with moderate Deep Q-Network (DQN) inference overhead. Active-UE load scalability results further show sustained performance over the evaluated load range. These results demonstrate the effectiveness of sidelink-assisted A2A coordination for decentralized LEO D2S uplink access under propagation delay and delayed channel feedback.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.16675

LinAlg-Bench: A Forensic Benchmark Revealing Structural Failure Modes in LLM Mathematical Reasoning

Shradha Agarwal, Deepak Rajbhar, Tariq J

原文摘要

We introduce LinAlg-Bench, a diagnostic benchmark evaluating 10 frontier large language models on structured linear algebra computation across a strict dimensional gradient of 3x3, 4x4, and 5x5 matrices. Spanning 9 task types and 660 SymPy-certified problems, the benchmark exhaustively evaluates 6,600 model outputs. Beyond binary accuracy, LinAlg-Bench introduces a three-stage automated forensic pipeline classifying 1,156 failures into ten primary error tags with fine-grained subtypes, revealing that LLM mathematical failure is not random but structurally constrained by algorithm type and matrix dimension. Our central finding is a sharp behavioral threshold at 4x4 scale: below it, models fail through execution errors -- sign tracking failures, arithmetic drift, and parity errors; above it, failure transitions to computational abandonment, with models fabricating responses through tool roleplay, constraint-consistent confabulation, and structured hallucination rather than attempting computation. This fabrication-to-abandonment transition is near-universal across all model tiers and architectures, suggesting a working memory limit rather than a knowledge gap, supported by three scale-emergent error types absent at 3x3 but present at 4x4 and 5x5. We further show that solution strategy rigidity is a near-perfect predictor of 5x5 determinant accuracy, document constraint-aware confabulation as a novel structured hallucination failure mode, and release all data, model outputs, error labels, and judge pipeline publicly.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.07191

A First Look at Starlink Maritime on Cruise Ships

Jinwei Zhao, Kousar Malekinasab, Jianping Pan

原文摘要

Low-Earth-Orbit (LEO) satellite networks are reshaping maritime connectivity, offering much lower latency and higher capacity than traditional geostationary (GEO) links. As a single user terminal (UT) cannot serve hundreds to thousands of passengers, a cruise ship often deploys several UTs behind an onboard multi-WAN router for aggregated capacity and load balancing. Consequently, passenger connectivity depends not only on the satellite links but also on how the router distributes sessions among UTs. In this paper, we measure Starlink maritime connectivity on two cruise ships of different scales, Utopia of the Seas near the Bahamas in December 2025 and Silver Muse in the Adriatic Sea in July 2026, combining UT telemetry, inside-out measurements from a passenger device, and outside-in measurements from cloud servers near Starlink Points of Presence (PoPs). We find that the onboard network, rather than the satellite links, largely determines the passenger experience, as cruise lines rate limit each passenger device. Moreover, the public IP address of a passenger device changes not only at PoP handovers but also within the same PoP. Furthermore, while the country code reported by the UT reflects the maritime jurisdiction of the ship, the serving PoP follows neither this jurisdiction nor geographic proximity alone, suggesting factors beyond the distance-based PoP boundaries observed in Starlink aviation. To the best of our knowledge, this is the first systematic study of Starlink on commercial cruise ships, complementing prior land mobile and in-flight measurements.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.19065

Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap

Renbo Tu, Yang Liu, Shuying Sun

原文摘要

Generative retrieval (GR) has emerged as a strong candidate generator for sequential recommendation as language models and semantic-item identifiers have improved. However, GR is not a uniformly stronger replacement of dense retrieval (DR) in online retail candidate generation. We examine their differences on two Amazon Toys datasets under different metrics. GR leads on best relevant item value among users both methods cover, while DR leads on exact-target recall and relevant value across the ranked list. With deep analysis, we find that popularity contributes the most to GR's measured value lead. Adding the same static item value to DR in one reranking step reverses this lead and keeps recall above GR. Finally, on an industrial-scale internal e-commerce dataset with LLM-judged relevance, we show that a 14B SID-LLM still underperforms a $47\times$ smaller dense encoder.

cs.IR · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.31572

nuReasoning: A Reasoning-Centric Dataset and Benchmark for Long-Tail Autonomous Driving

Zhiyu Huang, Johnson Liu, Rui Song, Xuewei Qi, Yiluan Guo, Zewei Zhou, Hanyin Zhang, Mingxuan Gao 等 15 位作者

原文摘要

Reasoning is essential for autonomous driving (AD) in long-tail scenarios, where vehicles must apply commonsense knowledge, understand spatial relations, infer agent interactions, and make safe decisions. However, existing AD datasets and benchmarks mainly target perception, prediction, or planning, and provide limited supervision for reasoning over realistic long-tail driving scenes. We introduce nuReasoning, a large-scale real-world dataset and benchmark for reasoning-centric AD. Following the lineage of nuScenes and nuPlan, nuReasoning advances real-world AD datasets and benchmarks toward reasoning in long-tail driving scenarios. The dataset contains 20,000 clips, each 20 seconds long, collected across multiple cities, with synchronized multi-camera images, LiDAR data, HD maps, object annotations, and human-verified reasoning annotations spanning Spatial Reasoning, Decision Reasoning, and Counterfactual Reasoning. Unlike prior datasets that focus primarily on visual question answering, nuReasoning supports both reasoning evaluation and planning evaluation, enabling a direct study of how reasoning supervision affects driving performance. Experiments show that fine-tuning VLMs on nuReasoning substantially improves driving-specific question answering, while incorporating reasoning supervision into VLA training improves planning performance even when textual reasoning outputs are disabled at inference time. These results establish nuReasoning as a foundation for evaluating and improving robust, interpretable, reasoning-driven AD systems in realistic long-tail settings.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.05464

Effective pruning of task-trained recurrent neural networks using noisy fluctuations and connection rescaling

Sanjith Senthil, Rishidev Chaudhuri

原文摘要

Pruning network connections is key to brain function but, despite its importance, there exist few biologically-plausible pruning rules with demonstrated good performance. We evaluate noise-prune, a recently introduced unsupervised local pruning rule for recurrent networks that uses noisy fluctuations to determine the importance of connections. Noise-prune has previously only been empirically tested on random networks without a specific computational function. We show that noise-prune preserves task-performance in task-trained recurrent neural networks, greatly outperforming a strategy that only uses connection magnitude and exceeding a non-local strategy that uses second-order information. Rather than deterministically removing connections that fall below a threshold, noise-prune samples connections to preserve based on their importance and strengthens retained connections to preserve average strength. We show that this sampling and rescaling is essential to good performance, and can augment other pruning strategies. We also find that the optimal empirical degree of rescaling is lower than predicted by the original theoretical argument, and thus develop a new version of noise-prune that outperforms the previous version. Our results demonstrate that a biologically-motivated pruning strategy based on local activity statistics can effectively preserve task performance in functional recurrent networks by combining activity-dependent connection selection with compensatory strengthening of surviving connections.

q-bio.NC · cs.LG · cs.NE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.12289

The Standard Interpretable Model: Design interpretable machine learning methods from elementary symmetries

Pietro Barbiero, Giovanni De Felice, Mateo Espinosa Zarlenga, Francesco Giannini, Filippo Bonchi, Mateja Jamnik, Giuseppe Marra, Ruggero Noris

原文摘要

As Artificial Intelligence models grow in complexity, interpretability has become an indispensable tool for understanding, debugging, and controlling their computations. However, interpretability lacks general theories to deductively design interpretable methods. This gap between theories and methods results in a fragmented literature and inconsistent evaluation protocols. To fill this gap, we introduce the Standard Interpretable Model (SIM), a general theory that enables the deductive design of interpretable methods from first principles. Specifically, the SIM summarises, in a set of premises, what interpretability is for a target entity. From these premises, the SIM systematically derives interpretability symmetries and corresponding constraints. One can then build interpretable models by either introducing constraints in the optimisation problem or by compiling constraints into an interpretable architecture. The most important finding, both theoretical and empirical, is that full expressivity and interpretability symmetries can coexist in the same model, showing that the trade-off between accuracy and interpretability is an artefact of model design. We demonstrate this on language models (LMs), where SIM-based LMs match the performance of equivalent opaque LMs without making any compromise on interpretability. Beyond this, the SIM shows how to improve the interpretability of existing pre-trained models, highlights underexplored research directions, and unifies a discipline that has long been fragmented, offering pedagogical grounding for interpretability curricula.

cs.LG · cs.AI · cs.NE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.31145

SeKV: Resolution-Adaptive KV Cache with Hierarchical Semantic Memory for Long-Context LLM Inference

Amirhossein Abaskohi, Giuseppe Carenini, Peter West, Yuhang He

原文摘要

Large language models increasingly operate over long contexts, where the KV cache grows linearly with sequence length and dominates memory during decoding. Token eviction discards entries permanently, and semantic compression fixes each unit's resolution at prefill, so detail that becomes relevant later cannot be recovered. We propose SeKV, a resolution-adaptive KV cache built around a trained zoom-in mechanism that decides at decode time which compressed spans to restore to token resolution. SeKV segments the context at high-surprisal positions, keeps anchor tokens and span summaries on GPU, and offloads low-rank factors of each span's keys and values to CPU. Lightweight per-head routers select query-relevant spans, which are reconstructed with a learned per-span rank. The cache is lossy but non-evicting: every span stays in attention, coarse or reconstructed. The base LLM stays frozen, and the router adds under 0.1% parameters. Across five backbones and four benchmarks at a 10% GPU-resident KV budget, SeKV outperforms every method that fixes resolution at prefill, improving over the strongest semantic baseline by 2.9 points (5.5% relative). It matches exact-recall offloading caches on LongBench and InfiniteBench and trails on synthetic exact retrieval, while transferring 3 times less data per decoding step than ShadowKV and using the least total memory among the methods we profile. At 128K context, it halves peak GPU memory and cuts total memory by one third relative to full KV caching.

cs.CL

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arXiv ↗PDF ↗