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版本更新 · v2arXiv:2605.29351

Attention as In-Context Empirical Bayes: A Two-Stage View via Particle Dynamics

Matthew Smart, Soumya Ganguly, Nilava Metya, Alexandre V. Morozov, Anirvan M. Sengupta

原文摘要

We study minimal attention-only transformers under all-token corruption and show they admit a two-stage empirical Bayes interpretation. A single attention step computes a kernel-weighted posterior mean with respect to the empirical distribution defined by the context. Depth refines this distribution through particle dynamics (Stage 1), while a long-range skip-connection carries the noisy input as a query for posterior inference (Stage 2), revealing distinct statistical roles for depth and attention residuals. The framework isolates a minimal setting in which the context itself induces a depth-dependent energy landscape governing in-context inference. We show that effective denoising can emerge without an explicit noise schedule: a fixed kernel bandwidth and finite integration horizon suffice, yielding a principled depth-noise relationship. We further establish a posterior-mean recovery guarantee for Gaussian priors, showing the empirical estimator converges to the Bayes-optimal predictor under sequential asymptotic conditions. Connecting these dynamics to reverse-diffusion limits, our results provide a statistical interpretation of attention as in-context inference via sample-based posterior estimation, without explicit density modeling.

cs.LG · math.DS · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11572

Closing the Complexity Gap for Wheeler Graph Recognition on $d$-NFAs

Kengo Hashimoto

原文摘要

Wheeler graph recognition asks whether a directed graph whose arcs are labeled by symbols from a totally ordered alphabet admits a Wheeler order, a total order on its vertices compatible with the labels of arcs and the order of their tails. A $d$-NFA is a nondeterministic finite automaton in which at most $d$ transitions with any given label leave each state. For Wheeler graph recognition on $d$-NFAs, Alanko et al. gave a polynomial-time algorithm for $d \leq 2$, whereas Gibney and Thankachan proved NP-completeness for every fixed $d \geq 5$. The computational complexity for $d = 3$ and $d = 4$ remained open. We resolve both open cases by extending the reduction of Gibney and Thankachan with a new encoding of clauses of Not-All-Equal 3-SAT. Specifically, we prove that recognition is NP-complete even for directed acyclic graphs over an ordered binary alphabet with a unique source reaching every vertex and total out-degree at most three at every vertex. Consequently, Wheeler graph recognition on $d$-NFAs is NP-complete for every fixed $d \geq 3$.

cs.CC · cs.FL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11562

UTOPI: Efficient Egocentric Long-Video Understanding in AR via User-Guided Token Pre-Compression

Ziqi Wang, Su Chen, Qiance Tang, Jieyu Lin, Ziyun Li, Barbara De Salvo, Sai Qian Zhang

原文摘要

As long-video understanding gains increasing attention, augmented reality (AR) devices are emerging as a compelling deployment platform. However, egocentric videos exhibit significant temporal redundancy, imposing substantial memory and computational burdens on resource-constrained AR hardware. To address this challenge, we introduce UTOPI, a plug-and-play pre-compression module designed specifically for egocentric long videos on AR platforms. First, we leverage user motion signals to estimate inter-frame overlap and propose a viewpoint-shift-invariant pruning algorithm. Second, we exploit eye-tracking sensors to identify user fixations, enabling the separation of attended foreground content from background and its preferential retention via a fine-tuned neural network-based adjustment mechanism. Across multiple benchmarks and model backbones, UTOPI achieves up to 95% compression while preserving performance. Code and project page are available at https://sai-lab-nyu.github.io/utopi/.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11542

Dashboard Remixing: Goal-oriented Organization and Enrichment of Visualizations and Insights

Junran Yang, Leilani Battle, Jane Hoffswell

原文摘要

Dashboard creation requires iteratively authoring multiple views and organizing them into a structured format. However, creators often struggle to step back and assess their own work for organization and refinement due to cognitive limitations. In this work, we propose a pipeline to provide computational support for self-reflection during the design process. Our pipeline automatically infers analytical objectives, discovers implicit patterns, and structures those patterns to surface coherent analytical pathways. It further enriches existing dashboard designs by generating complementary visualizations and aggregating natural language insights based on relationships across data entities (e.g., contrasting, drill-down, and relatedness) to reveal chart connections. Our rule-based pipeline provides objective, verifiable feedback, offering an external perspective while enabling creators to tell their own data stories. Four case studies and six expert interviews demonstrate how this approach helps creators reflect on organizational alternatives and surface hidden analytical opportunities in a principled way, reducing the cognitive burden while encouraging deeper analytical exploration.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11541

Safeguarding Agentic AI in Radiology Workflows: A Multimodel Evaluation of Rule-based and Large Language Model-based Verification

Semil Eminovic, Robin Schmidt, Bogdan Levita, Maximilian Lindholz, Tobias Penzkofer, Jawed Nawabi

原文摘要

Purpose: To estimate how often tool-using large language model (LLM) agents record clinically relevant false or unsupported information in simulated radiology documentation and how much safeguards applied before each action reduce these errors. Materials and Methods: In this simulation study (October 2026), 48 synthetic, radiologist-reviewed base scenarios covered finding communication, tumor board preparation and follow-up management. Forty-three also had a misleading-information variant (91 variants). Six LLM agents (Claude Sonnet 5.5, GPT-6.1-Sol, GLM-5.3, MiniMax M3, Gemma-4-31B, Qwen3.8-27B) completed each variant with permissions only, deterministic rules, and rules plus a locally run LLM checker (1638 episodes). The primary outcome was at least one executed high-severity violation per episode. Risk differences were estimated per agent at base-scenario level and, exploratorily, pooled across agents, with analyses of document access and blocks. Results: With permissions only, 7.3%-27.1% of episodes had a high-severity violation (pooled across agents, 19.4%). Pooled, it was 16.7% with rules (-2.8 percentage points; 95% CI: -6.1, 0.5) and 13.2% with rules plus the LLM checker (-6.2; 95% CI: -9.5, -3.0). Adding the LLM checker to rules changed it by -3.5 (95% CI: -6.3, -0.7). No difference between agents was detected (P = .43), and task success did not decrease detectably. In misleading variants, agents opened the altered document in 83% of episodes regardless of error, and 58% of erroneous statements cited it. Most blocked actions (64%-68%) would not have caused a violation and were returned for revision. Conclusion: Layered safeguards reduced but did not prevent high-severity errors. Errors after misleading information reflected misjudged rather than missed information.

cs.AI · cs.CL · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11539

PRISM: Causal Counterfactual Minimization of Agent Tool Privileges via Information Bottleneck Role Induction

Yang Luo, Qingni Shen, Tairui Wang, Jiajun Li, Zhonghai Wu

原文摘要

This paper shifts the object of role engineering from human users to large language model (LLM) agents, and proposes PRISM to address the over-authorization that arises when an agent obtains full tool privileges through the Model Context Protocol (MCP). PRISM formulates whether a tool is necessary for a task as an intervenable causal proposition, and applies structured interventions in a sandbox to learn a counterfactual necessity model. It then compresses trajectories into discrete latent variables through an information bottleneck, and determines the number of roles from the knee of the rate-distortion curve. Finally, it formalizes pruning as a bilevel Stackelberg game, in which a generative flow network samples diverse near-optimal solutions. On three public benchmarks, AgentDojo, $τ$-bench and ToolEmu, PRISM compresses the number of authorized tools per role by 78.3%, attains a task success rate of 78.2%, reaches a counterfactual necessity accuracy of 88.7%, and lowers the blast radius to 15.9%.

cs.CR · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11537

Weight-Space Programming of Variational Quantum Circuits: Large Language Models Write Quantum Circuit Parameters Without an Optimizer

Debarshi Kundu, Swaroop Ghosh, Vasant G Honavar

原文摘要

Variational quantum algorithms (VQAs) optimize circuit angles through repeated quantum-device evaluations, often facing vanishing gradients and high optimization costs. We ask whether a large language model (LLM) can write these angles directly. Extending *weight-space programming* from classical networks to quantum circuits, we provide a frontier LLM (\gpt) with the problem, circuit, and gate conventions. Its angles are executed exactly as written, without returning losses, gradients, or accuracy feedback. Across 139 requests, we test handwritten-digit classification, VQE ground-state estimation for spin chains and H$_2$, and MaxCut QAOA, comparing against random weights, classical optimization, and optimization initialized with LLM weights on the same circuit. When parameters have interpretable structure, the LLM matches or approaches the optimizer: it achieves 95--100\% of the energy spectrum on every VQE problem, derives the exact H$_2$ ground state in closed form, finds optimal depth-one QAOA angles for every graph, and reaches 93--96\% accuracy on digits 3 versus 8 with 12--18 hand-written weights, outperforming an optimizer trained on the same ten examples. Its weights are also strong warm starts: optimization reaches its best value after a median of one evaluation rather than hundreds. Allowed to design circuits, the LLM chooses data-dependent rotation chains encoding pixel importance in all 30 cases (one qubit for binary tasks, one chain per qubit for ten classes), reaching 65\% accuracy across ten digits. Yet the approach fails in opaque weight spaces: accuracy drops to chance when fixed circuits exceed 30 weights, while optimization improves. It is also sensitive to gate conventions: 6 of 42 QAOA replies reverse mixing-angle signs and fail. We release all prompts, replies, and scores.

quant-ph · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11525

Boundary Mass in Feature Space as a Label-Budget Diagnostic for Representations

Siming Zhang, Zhehui Shen, Shijie Chen, Yansen Yu, Hang Yu

原文摘要

Low-label transfer is often decided by a practical question: after a representation looks useful, how many labels are still needed to learn the downstream boundary? Transferability and probe-quality scores characterize global alignment, likelihood, margins, and calibration. Boundary-near target mass complements these diagnostics by measuring the ambiguous regions where scarce labels are most costly. We propose effective boundary complexity, a label-budget diagnostic for frozen representations and linear probes. It measures mass in a thin strip around the learned boundary and normalizes it by class balance, so dense ambiguous regions and predicted-class imbalance are both counted. We prove that, under a smooth representation map, density change and tangential surface change cancel at first order, leaving boundary-normal stretch or compression as the governing factor. This yields a cached-feature estimator that fits a probe on one split and counts independent held-out features near its boundary. Experiments support the diagnostic at three levels. Controlled transformations match the predicted law across 74 maps with 0.38% median relative error. On real frozen features, normal-direction changes move both measured boundary mass and label demand, while tangent controls preserve the first-order coefficient. A differentiable surrogate reduces measured complexity by 16-36% at matched top-1 utility. Finally, CIFAR-100 and Tiny-ImageNet experiments support boundary mass as a second-stage diagnostic for near-tied representations. Five-way experiments show strong OvR-interface backbone-ranking agreement (Spearman 0.872), while acquisition experiments show high correlations between initial boundary mass and realized label budgets across three sampling policies (0.779-0.830). Code is available at https://github.com/qqaqq-code/boundary_mass.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.07923

Stitch4D: 4D Urban Reconstruction from Sparse Multi-Location Observations via Spatio-Temporal Interpolation

Hina Kogure, Kei Katsumata, Taiki Miyanishi, Komei Sugiura

原文摘要

Dynamic urban environments are often captured by cameras placed at spatially separated locations with little or no view overlap. However, most existing 4D reconstruction methods assume densely overlapping views and struggle under sparse multi-location observations, producing unstable reconstructions in unobserved intermediate regions. To address this practical yet underexplored setting, we propose Stitch4D, a unified 4D reconstruction framework that compensates for missing spatial coverage in sparsely observed urban environments. Stitch4D synthesizes intermediate \emph{bridge views} between distant camera locations and jointly optimizes real and synthesized observations in a unified coordinate frame with inter-location consistency constraints. By recovering intermediate spatial coverage before optimization, Stitch4D mitigates geometric collapse and improves reconstruction stability in sparse regions. To evaluate this setting, we introduce Urban Sparse 4D (U-S4D), a controlled CARLA-based benchmark for free-viewpoint reconstruction under sparse multi-location configurations. Experiments on U-S4D show that Stitch4D consistently outperforms representative 4D reconstruction baselines in image-quality metrics. These results suggest that recovering intermediate spatial coverage is an effective strategy for stabilizing 4D reconstruction in sparse urban environments. The project page is provided in https://stitch4d-project-page.vercel.app/.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2302.12670

Personalized Pricing with Invalid Instrumental Variables: Identification, Estimation, and Policy Learning

Rui Miao, Zhengling Qi, Cong Shi, Lin Lin

原文摘要

Pricing based on individual customer characteristics is widely used to maximize sellers' revenues. We study how a firm can learn such a personalized pricing policy from historical transactions when unobserved factors affect both the prices it charged and the revenue it earned. Standard instrumental variable (IV) methods either focus on a discrete treatment space or require that the IV affect revenue only through price. This exclusion restriction is difficult to justify in pricing, where the most natural candidate IVs are price-setting levers that may also affect revenue directly. We propose PRINT (Personalized pRicing using Invalid iNsTrumental variables), a policy learning method for continuous prices that uses such candidate IVs. Relying on structural models of revenue and price, we show that the optimal pricing policy can be identified with invalid IV that shifts higher moments of historical prices, when the unobserved factors that drive customers' price sensitivity are separated from those used by the historical pricing rule. Based on this identification, which leads to conditional moment restrictions with generalized residual functions, we construct an adversarial min-max estimator that accommodates flexible machine learning models and establish regret bounds for the learned pricing policy. Simulation studies and an application to a U.S. online auto lender illustrate the method's performance. Our results give a principled way to learn personalized prices from observational data using imperfect price-setting levers, and clarify when doing so is justified.

stat.ME · cs.LG · econ.EM · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11516

Breaking survivorship bias in driving world models

Zihao Sheng, Zilin Huang, Yuhao Luo, Zhengyang Wan, Boyue Wang, Soyoung Ahn, David A. Noyce, Sikai Chen

原文摘要

World models allow autonomous vehicles to imagine the consequences of candidate actions before execution, but their reliability depends on the futures represented in their training data. We identify a survivorship bias in driving world models: because the futures recorded in prevailing driving datasets almost always end safely, existing models often imagine benign outcomes even when a collision is imminent. We introduce a neutral world model (NWM) that learns both routine and safety-critical futures from a mixture of routine driving and real accident videos with a bounding-box-guided objective. The NWM more faithfully anticipates imminent collisions, while maintaining a low false collision rate and competitive generation quality in routine driving. We further use the model as an imagination engine for decision-making. Given multiple ego trajectories, the NWM predicts the future associated with each candidate, and a reward function evaluates these imagined outcomes before selecting an action. On challenging NAVSIM scenarios, this procedure selects the better trajectory more reliably than the baseline world model. Zero-shot experiments with real-world pedestrian dummy crossing data collected using an autonomous research vehicle further demonstrate transfer to unseen sensor observations. These results show that correcting survivorship bias enables world models to more faithfully reason about futures and support safety-critical decision-making in autonomous driving.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11512

Amortized Bayesian Experimental Design with In-Context Knowledge Conditioning

Zhanghu Zhao, Daolang Huang, Xinyi Wen, Samuel Kaski

原文摘要

Amortized Bayesian experimental design (BED) enables real-time design strategies by shifting acquisition cost offline, but existing methods remain tied to the prior and task distribution they were trained on and cannot exploit additional information available at deployment. We introduce in-context amortized BED with unified knowledge encoding (IMBUE), a BED framework that admits external knowledge at deployment through a unified in-context interface. IMBUE accepts two forms of such knowledge without retraining, namely user-specified prior knowledge and observations from previous instances of the experiment. Both are encoded as additional input tokens and processed in-context with the accumulated observations by a shared Transformer, and a learned reliability filter scores each external token against the observations and excludes inconsistent ones. Across four benchmarks, IMBUE accelerates early-stage information acquisition when the external knowledge is reliable, and remains close to the no-external baseline when it is not.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11504

Data-informed Adaptation of Foundation Models for Causal Effect Estimation

Chunwei Ma, Russell D Wolfinger

原文摘要

Due to the fundamental problem of causal inference -- the impossibility of observing counterfactual outcomes -- there is always a gap between the training goal (outcome, propensity) and the inference target (CATE, ATE). Traditional causal effect estimation revolves around setting assumptions, selecting identification strategies, and choosing causal estimators, while more recently, Causal Foundation Models (CFMs), built on Prior-Data-Fitted Networks (PFNs), ease this process by pretraining on massive synthetic prior datasets. Despite their success, such amortization makes causal inference less transparent, obscuring which assumptions the model implicitly relies on for a given dataset. The high diversity of real-world data-generating processes (DGPs) and Structural Causal Models (SCMs) makes it hard to know, a priori, which causal inference strategy is compatible with a given dataset. In this work, we focus on this compatibility between an observational dataset and method-specific assumptions, and find that geometric characteristics of data embeddings -- specifically, the variogram -- provide a useful signal of whether a given causal estimation strategy is appropriate. We develop MoE-Learner, a mixture-of-causal-experts framework that embeds a dataset under multiple assumption-specific views and uses the variogram as a counterfactual-free router to assess each expert's validity. Rather than eliminating causal assumptions, this shifts their validation from a fixed, a priori choice to a criterion that can be checked directly from data geometry. MoE-Learner achieves the best average rank on benchmark datasets (IHDP, ACIC, Lalonde). Being orthogonal to (causal) foundation models, our method builds a bridge from data characteristics to causal estimation strategy rather than proposing another CFM.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.20271

Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning

Jianghai Li, Pavel Kuznetsov, Yury Yanovich, Konstantin Nott-Whaley, Igor Vodolazov

原文摘要

The rapid proliferation of memecoins on blockchain platforms has increased the risk of fraudulent activities, particularly rug pulls. While previous studies have focused on Ethereum-based tokens, this paper shifts the spotlight to Solana, the leading blockchain for memecoins by trading volume and token count. Unlike Ethereum, where rug pulls often exploit smart contract backdoors, Solana memecoin rug pulls are predominantly driven by liquidity manipulation and social dynamics. This research pioneers large-scale rug pull early detection in the Solana ecosystem by assembling a dataset of 6.4 million tokens over 7 months. Market analysis reveals that a vast majority of these memecoins exhibit rug pull characteristics within one hour of launch, highlighting the urgency of short-horizon prediction. Despite the absence of code-level features, we demonstrate that classic machine learning models, particularly Gradient Boosting (XGBoost), achieve robust performance in detecting potential rug pulls using only the first 5 minutes of trading data. Furthermore, we evaluate cross-platform generalization between PumpFun and Raydium, revealing that multi-source data fusion significantly mitigates domain shift and improves detection reliability. This study advances the understanding of DeFi fraud on high-throughput chains and provides a practical framework for protecting investors.

cs.AI · cs.DC

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arXiv ↗PDF ↗
版本更新 · v9arXiv:2609.38627

Marking Contour Tones in Yorùbá: A Typographic and Computational Proposal

Kólá Túbòsún

原文摘要

Yorùbá is a tonal language in which contour tones pose persistent orthographic challenges. These are especially notable for personal names and lexical items whose conventional spellings avoid vowel lengthening that would otherwise provide a host syllable for the second tone. A particular concern is a class of names in which the conventional spelling does not just omit tonal information but inverts the meaning of said name, sometimes asserting the opposite of what the name intends. This paper describes the problem, illustrates the inadequacy of current solutions, and proposes the adoption of the caron and circumflex marks. These are symbols with precedent in Yorùbá phonological scholarship since Olmsted (1951), used as orthographic conventions on single vowels to encode rising and falling contour tones, making them accessible for the first time through standard keyboard input and computational text processing. The proposal is supported by an implementation in the WriteYoruba keyboard and the TTSYoruba speech synthesizer, whose architecture and listener evaluation are reported separately (Tubosun, Oluokun, et al. 2026).

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11493

Learning through Capacity Awareness to Minimize Hospital Readmissions

Lydia T. Liu, Kara Schechtman, Pengyi Shi, Angela Zhou

原文摘要

Predictive algorithms are increasingly used to guide hospital discharge decisions, yet these models can exhibit censoring bias for vulnerable patients due to insufficient historical data or past discharge decisions. When biased readmission risk predictors are used to inform discharges, the training data generated by discharge decisions may perpetuate similar biases when predictors are re-trained, leading to a vicious cycle. We extend a queueing-based Markov decision process for hospital discharge to incorporate periodically retrained readmission risk predictors. Our key insight is that cost-optimal, "capacity-aware" operational decision policies can naturally diversify the lengths of stay at which patient outcomes are observed, enabling consistent estimation of readmission risk without explicit fairness constraints. We prove that repeated retraining under discharge-up-to policies converges to unbiased risk estimates under well-specification. In synthetic experiments and calibrated simulations based on patient-level data from a partner hospital, we find that where commonly used threshold-on-risk policies can remain persistently biased, discharge-up-to policies reduce learning error over status quo decision policies (such as thresholding on risk) by 24%-25%. These improvements to estimation also mitigate some forms of bias, reducing readmission cost disparities (adjusted for baseline risk differences between demographic groups) by 38%-63%. Our results demonstrate how capacity-aware decision policies can improve both predictive performance and fairness by addressing censoring bias.

cs.LG · cs.CY · stat.AP · stat.ML

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.00671

Moxia: A Trust-First Neuro-Symbolic Execution Architecture for Self-Explaining Mathematical Reasoning

Alessio Bruno

原文摘要

We present Moxia, a trust-first neuro-symbolic architecture for self-explaining mathematical reasoning over natural-language input. Its language model is strictly a canonicalizer: it rewrites informal problem text into a narrow schema consumed by a deterministic Computer-Algebra-System (CAS) pipeline, which derives the answer or abstains as a first-class output. Routing follows a 1:1:1 alignment of problem-shape regex, schema-specific prompt, and closed-form handler: 5,741 routes, 75.9% of which answer without invoking the model, with zero LOST_CORRECT regressions as a standing release gate. Because the answer is derived, so is its explanation, rendered from the steps the handler took; where the derivation fits a fragment of Lean 4 with Mathlib, the same declared data become a theorem the kernel checks. On the full 5,000-record MATH test split, designed against, Moxia answers 95.6% (4,780/5,000) with zero confident-wrong answers and abstains, saying where, on the other 220; its MATH-500 subset reads 95.2% (476/500), also with zero wrong. Earlier versions called MATH-500 held out; it is a subset of the test split, and this version says so. We release the run record by record: the problem, the expected answer, the model's transcription, the answer or abstention, the explanation, and the Lean theorem marked as compiled. 3,142 of the 4,780 correct answers carry a theorem, and all 3,142 compile; building the theorems actually served, not only a fixture corpus, found seven that did not, two of them false statements beside correct answers, all repaired before release. What we emphasize is not an accuracy figure but the forward dynamic: every logged abstain is a candidate correct after one ship cycle, since new tasks compose without regressing the registry.

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.09257

Transductive Generalization for GNNs via Optimal Transport

MoonJeong Park, Seungbeom Lee, Kyungmin Kim, Jaeseung Heo, Seunghyuk Cho, Shouheng Li, Sangdon Park, Dongwoo Kim

原文摘要

Analyzing the generalization of graph neural networks (GNNs) on node classification is challenging: message passing induces dependencies that break the i.i.d. assumption underlying standard inductive generalization theory. While distribution-free transductive theory resolves this dependency issue, existing classical bounds are either intractable or correlate poorly with empirical generalization. To fill this gap, we propose a transductive generalization bound based on optimal transport, connecting generalization to intra-class concentration and inter-class separation in the feature space. Empirically, our bound aligns strongly with the generalization gap. Building on this representation-based approach, we analyze how feature geometry evolves under repeated message passing. This explains the non-monotonic relationship between GNN depth and generalization, providing new insights into oversmoothing research. Practically, our bound serves as a geometry-aware regularizer that corrects the harmful behavior of message passing, yielding consistent performance improvements. The code is available at https://github.com/ml-postech/Transductive-OT-Gen-Bound.

cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11487

THEATRON: A Benchmark for Evaluating Persona Persistence in Long-Horizon LLM Decision-Making

Yuyan Wang, Yuechen Jiang, Zhiwei Liu, Jimin Huang, Sophia Ananiadou

原文摘要

Existing evaluations often test persona expression rather than whether persona information persists in extended decision-making. We introduce THEATRON, a controlled benchmark that uses financial replay to evaluate persona persistence in long-horizon sequential decision-making. In these environments, models conditioned on fixed behavioral-finance personas observe shared daily market evidence and trajectory-specific account-state feedback and make sequential decisions over multi-month trajectories. We evaluate Zero-Shot, One-Shot, and Chain-of-Thought persona injection across four LLMs, using cumulative-return and action trajectories to diagnose within-persona stability, cross-persona separability, and action collapse. Persona conditioning produces systematic behavioral shifts beyond a no-persona baseline, and persona-level separation generally exceeds the variation induced by semantically equivalent descriptions. Nevertheless, these shifts do not reliably form coherent persona-specific policies: apparently stable separation can arise from stereotyped action collapse, while diverse policies can exhibit noisy trajectory switching. These findings expose a gap between observable persona-induced behavior and persistent, context-sensitive persona-conditioned decision policies. Code and benchmark data are publicly available at: https://github.com/yuyan12138/theatron

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2501.15156

Quantifier Elimination and Craig Interpolation, Quantitatively

Kevin Batz, Joost-Pieter Katoen, Nora Orhan

原文摘要

Quantifier elimination (QE) and Craig interpolation (CI) are central to various state-of-the-art automated approaches to hardware- and software verification. They are rooted in the Boolean setting and are successful for, e.g., first-order theories such as linear rational arithmetic. What about their applicability in the quantitative setting where formulae evaluate to numbers and quantitative supremum/infimum quantifiers are the natural counterpart to Boolean quantifiers? Applications include establishing quantitative properties of programs such as bounds on expected outcomes of probabilistic programs featuring non-determinism and analyzing the flow of information through programs. In this paper, we present, to the best of our knowledge, the first QE algorithm for possibly unbounded, $\infty$- or $(-\infty)$-valued, or discontinuous piecewise linear quantities. They are the quantitative counterpart to linear rational arithmetic, and they are a popular quantitative assertion language for probabilistic program verification. We provide rigorous soundness proofs as well as upper bounds on the size of the output. Moreover, we present two applications of our QE algorithm. First, our algorithm yields a quantitative CI theorem: Given arbitrary piecewise linear quantities $f,g$ with $f \models g$ (i.e., $f$ is at most $g$ at every variable valuation), both the strongest and the weakest Craig interpolants are quantifier-free and effectively constructible. Such interpolants lie between the two quantities and use only their common free variables. Second, we apply our QE algorithm to computing minimal and maximal expected outcomes of loop-free probabilistic programs featuring unbounded non-determinism, and to checking inductivity of candidate upper bounds on minimal and maximal expected outcomes of probabilistic loops featuring unbounded non-determinism.

cs.LO · cs.PL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11477

Robust and Private Swarm Learning via Zero-Knowledge Validator Admission and Consensus Aggregation

Marco Arazzi, Mert Cihangiroglu, Antonino Nocera

原文摘要

Swarm Learning improves resilience and removes the need for a single trusted coordinator by distributing model aggregation and validation across participating nodes. However, it also delegates the validation of model updates to peers whose private data may be biased, unrepresentative, or adversarial, and existing Swarm Learning protocols generally do not attest that data, leaving decentralized validation as an attack surface. We present a robust, privacy-preserving Swarm Learning framework that makes verifiable data quality the condition for admission to the learning task. A validator joins only after a zero-knowledge attestation proves, without revealing any raw sample, that a reference model ran correctly on its private data. The attested outputs also yield a label-free class profile, so the network assembles a collectively IID validator pool from individually skewed validators as accurately as an oracle with the true labels (total-variation distance to uniform 0.025, against 0.021 for the oracle and 0.21 for random selection). Admitted validators score client updates and, through a Tendermint-style Byzantine Fault Tolerant consensus, agree on a gradient mask and a quality-derived aggregation weight for each submission, replacing dataset-size weighting with a quality-aware signal. Adapting the cut-layer idea of Split Learning, a challenge-based audit lets peers re-verify that a mask was computed on the attested data without an expensive end-to-end zero-knowledge proof. Across four benchmarks under non-IID partitions the framework matches FedAvg on clean data; under a gradient-scaling attack that collapses FedAvg to random chance, it retains 94% of clean accuracy on CIFAR-100 and over 99% on FashionMNIST, where FedAvg achieves only 2.1% and 11%. The result is a private, robust, and auditable foundation for decentralized learning.

cs.CR · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.06165

HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models

Reihaneh Zohrabi, Hosein Hasani, Akshita Gupta, Mahdieh Soleymani Baghshah, Anna Rohrbach, Marcus Rohrbach

原文摘要

Large vision-language models can produce object hallucinations in image descriptions, highlighting the need for effective detection and mitigation strategies. Prior work commonly relies on the model's attention weights on visual tokens as a detection signal. We reveal that coarse-grained attention-based analysis is unreliable due to hidden confounders, specifically token position and object repetition in a description. This leads to Simpson's paradox: the attention trends reverse or disappear when statistics are aggregated. Based on this observation, we introduce HaloProbe, a Bayesian framework that factorizes external description statistics and internal decoding signals to estimate token-level hallucination probabilities. HaloProbe uses balanced training to encourage learning from internal evidence and combines it with a learned prior over external features to recover the true posterior. While intervention-based mitigation methods often degrade utility or fluency by modifying models' internals, we use HaloProbe as an external scoring signal for non-invasive mitigation. Our experiments show that HaloProbe-guided decoding reduces hallucinations more effectively than state-of-the-art intervention-based methods while preserving caption utility and linguistic fluency.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11465

When Normality Shifts: Multi-Level WavLM Modeling for Open-World Audio Deepfake Detection

Daniel Niekurzak, Daniel Kozuch, Weronika Folwarska, Khan Muhammad, Rafał Scherer

原文摘要

Audio deepfake detectors trained with known synthesis attacks can learn generator-specific shortcuts that fail as synthesis systems and acoustic conditions evolve. We investigate an attack-free alternative: a normality model trained exclusively on bona fide speech, which scores synthetic audio by its deviation from authentic representations. We propose MLN-WavLM, a method that applies shrinkage-Mahalanobis modeling to pooled hidden states from a frozen large-scale, pre-trained speech processing WavLM Base+ architecture. Evaluated on ASVspoof 2019 LA, our best attack-free variant achieves an 8.45\% Equal Error Rate (EER), closely tracking a fully supervised WavLM-HM-Conformer baseline (7.86%). However, when subjected to the ASVspoof 2021 DF domain shift, performance drops to 22.12% EER, exposing vulnerabilities in the bona-fide-calibrated operating point. Layer-wise analysis reveals that early WavLM representations dominate the ensemble. To test real-world viability, we simulated deployment streams using unlabeled threshold adaptation. While robust median/MAD recalibration easily handles in-domain contamination up to 10%, recalibrating under the 2021 DF domain shift triggers a total collapse of low-FPR recall. We conclude that while frozen source-domain normality can effectively rank deepfakes, it does not yield a stable open-world decision rule, and downstream threshold adaptation cannot rescue upstream ranking degradation.

cs.SD · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2611.06194

Cross-Asset Knowledge Transfer for Early Fault Detection of Data-Scarce Assets Without Target Fault Data

Zekai Wang, Yuxuan Li, Zhangyue Shi

原文摘要

Reliability management of industrial fleets depends on early fault warnings, yet lifecycle data across similar assets are highly imbalanced. A few assets accumulate run-to-failure histories, while newly installed or rarely failing assets provide only normal-condition data. This paper asks how diagnostic knowledge can be transferred from a data-rich source asset to a data-scarce target asset when no fault data have been observed on the target. We formulate cross-asset early fault detection as a zero-shot domain adaptation problem, in which zero-shot refers to the absence of target fault samples, and propose a framework that requires only normal data from the target. The framework comprises (1) a statistical process control step that labels the source asset run-to-failure data with an exponentially weighted moving average chart, reducing manual annotation to verifying flagged segments, (2) an Adapted Domain-Adversarial Neural Network (ADANN) that combines domain-adversarial training and a Maximum Mean Discrepancy penalty with an Adapted Classification-Domain (ACD) loss, which anchors the classifier normal region to the target own normal data, and (3) an ahead-of-time labeling scheme with a prediction horizon that targets incipient faults. Under an exact-classification assumption, a theoretical analysis characterizes the domain-adversarial term for a normal-only target and the alignment state in which source fault features remain separated from target normal features. On a public run-to-failure bearing dataset, ADANN attains the highest mean F1-score among four transfer baselines, with a nominally significant precision gain over the strongest baseline, and its robustness is examined under synthetic noise.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11450

How Much Training-Inference Mismatch Can On-Policy Distillation Tolerate?

Cong Yu, Ziting Wang, Songlin Jiang, Sitong Zhang, Chengliang Chai, Youhe Jiang, Eiko Yoneki, Tuo Shi

原文摘要

On-policy distillation (OPD) trains a student language model on its own responses, guided by a fixed teacher. Generating these responses is expensive, so systems use quantized inference and asynchronous rollouts to reduce the cost. Both, however, introduce training-inference mismatch between the rollout engine and the model being trained. Such mismatch can destabilize reinforcement learning (RL), but its effects on OPD remain unclear. This paper investigates how much mismatch OPD tolerates, where its effects arise, and how training systems can use this tolerance. Measuring all mismatch sources on one scale, we examine quantization across three student-teacher pairs, with additional tests of weight staleness and injected noise on one pair. OPD tolerates substantial mismatch without meaningful loss of training quality but degrades at higher levels, revealing an empirical mismatch budget. Both the quality cost of mismatch and the mismatch introduced by quantization depend on the model, with some layers particularly sensitive to reduced precision. In controlled comparisons, OPD remains stable under rollout configurations that destabilize RL methods. Under weight noise, mismatch also shifts the prefixes reached during generation, introducing errors that per-token corrections cannot address. To put these findings into practice, we introduce a lightweight admission check that screens rollout configurations against a calibrated budget without requiring a full training run for each candidate. Model-specific FP8 recipes preserve training quality while providing capacity for at least $1.93\times$ as many KV-cache tokens as BF16. Combining FP8 with asynchronous rollouts reaches the same training quality $1.62\times$ sooner than synchronous BF16 training. Code is available at https://github.com/benyucong/train-inference-mismatch-opd

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11437

How to "train" a surgeon? Benchmarking generalist agents in surgical scene understanding

Gengyuan Zhang, Xiao Han, Shijie Zhou, Abdullah Koukash, Jingpei Wu, Jindong Gu, Yuqicheng Zhu, Nassir Navab 等 10 位作者

原文摘要

Evaluating general-purpose multimodal large language models (MLLMs) for surgical scene understanding remains limited due to scarce surgical data and fragmented evaluation protocols. We address these challenges through a two-stage evaluation framework for surgical scene understanding. In Stage I, we introduce SurgKnowBench, a diagnostic benchmark that harmonizes public surgical datasets across procedures and tasks and evaluates proprietary and open-source MLLMs to map the boundary of pretrained surgical competence. SurgKnowBench reveals that current models retain partial visual priors but remain weak on relational and procedural understanding and workflow assessment. Building on this failure frontier and motivated by the scaffolded nature of surgical training, we introduce SurgAgentGym, an agentic environment where general-purpose MLLM agents can use bounded knowledge search over SurgWiki and general-purpose visual grounding for surgical tasks. SurgAgentGym evaluates whether generalist agents can improve surgical understanding at inference time through tool-mediated evidence use, without additional surgical training. Our results show that agentic inference narrows the gap mainly for perception-heavy tasks, while fine-grained localization and expert-style assessment remain persistent bottlenecks. This opens a path toward future studies of agentic surgical understanding.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2610.09471

When Should an In-Context Learner Expand Its Hypothesis Space?

Weihan Li, Xinlei Chen, Junhao Wu, Tianshi Zheng

原文摘要

Learning systems adapt quickly within a familiar family of models. The harder step is deciding, from a prediction failure, whether opening a richer family is worth its cost. We cast this as a costly sequential decision and introduce the Structural Revision Environment (SRE), which produces matched failures from noise, exceptions, change within the family or structure outside it, varies the price of expansion and the remaining horizon independently of the evidence, and admits an exact normative solution of the one-shot decision. The solution shows that revision is a value boundary, not an evidence threshold: except at the lowest prices, the richer family becomes the more probable explanation long before expanding becomes optimal. Small Transformers trained in SRE reproduce the expansion boundary from utility alone. Pretrained 7B and 8B language models fail at a definite point: their predictions carry a failure-sensitive signal that their decisions do not reflect. Told the gain of expanding, they weigh it against price and horizon only when they reason, and the one model that finishes its reasoning on the history alone still does not estimate that gain. Controlled post-training of the small Transformers moves the prior and the sharpness of predictions, and neither moves the criterion.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2602.01518

Qrita: High-performance Top-k and Top-p Algorithm for GPUs using Pivot-based Truncation and Selection

Jongseok Park, Sunga Kim, Alvin Cheung, Ion Stoica

原文摘要

Despite their importance in model sampling, efficient implementation of the Top-k and Top-p algorithms poses a significant challenge as model vocabulary grows with each new model release. Existing approaches often rely on sorting, which incurs significant computation and memory overhead on GPUs, or on stochastic approaches that alter the algorithm's output. In this work, we propose Qrita, an efficient Top-k and Top-p algorithm based on pivot-based truncation and selection. Qrita leverages pivot-based search for both Top-k and Top-p with two key techniques: 1. Gaussian-based sigma-truncation, which limits the complexity of the algorithm to O(n), and 2. Quaternary pivot search with duplication handling, which halves the number of pivot search iterations and guarantees deterministic output. We implement Qrita using Triton and evaluate its performance against the Top-k and Top-p kernels of high-performance LLM execution engines such as SGLang and FlashInfer, improving end-to-end serving throughput up to 1.4 times with half the memory usage, while providing the same output as stable sort-based Top-k and Top-p algorithms. Qrita is now the default Triton Top-k and Top-p sampler of vLLM, and its implementation is available at https://github.com/vllm-project/vllm/blob/main/vllm/v1/sample/ops/topk_topp_triton.py.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2607.20002

A Survey of Post-Training in Time Series Foundation Models

Shifeng Xie, Ambroise Odonnat, Zehao Xiao, Lei Zan, Malik Tiomoko, Lujia Pan, Themis Palpanas, Boris N. Oreshkin 等 10 位作者

原文摘要

Time series foundation models (TSFMs) have emerged as general-purpose models for time series analysis, but pretraining alone is often insufficient for reliable downstream deployment. Bridging this gap requires further intervention to handle domain shift, task heterogeneity, limited supervision, and computational constraints, which motivates post-training as a broad class of methods to adapt, augment, compose, calibrate, or specialize pretrained TSFMs for downstream tasks. In this work, we analyze TSFM post-training methods based on their locus of intervention in the prediction pipeline, yielding five categories: parameter adaptation, context augmentation, model composition, output processing and uncertainty control, and compression and specialization. Within each category, we study main representative methods and discuss their current limitations. We further identify future directions toward controlled adaptation, reliable context construction, uncertainty-aware model composition, calibrated output processing, and deployment-aware specialization. Overall, by providing a unifying framework for the emerging TSFM post-training landscape, this work aims to support future research to navigate the design space between a pretrained TSFM and its reliable downstream deployment.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11412

Joint Design of Communication and Control in Center-Fed Pinching-Antenna Systems

Xu Gan, Yuanwei Liu, Hyundong Shin

原文摘要

An analytical and optimization framework for the joint design of communication and control (JDCC) in a center-fed pinching-antenna system (C-PASS) is proposed. Within this framework, the mathematical coupling between data throughput and closed-loop control cost is characterized for JDCC. The analysis captures the mutual interference between concurrently transmitted data and control streams during uplink state uploading and downlink command delivery. Based on this coupling, an achievable communication-control performance region is established for C-PASS. Accordingly, a throughput maximization problem is formulated to jointly optimize digital and pinching beamforming subject to control-cost constraints. To address this problem, an alternating optimization algorithm is developed based on constraint reformulation and resource decomposition. The proposed framework reveals how control requirements shape achievable data throughput, guiding the joint beamforming design. Numerical results demonstrate that 1) the proposed C-PASS design achieves 110.2\% higher frame-average throughput than conventional PASS, and 2) the proposed concurrent data-control transmission protocol increases frame-average throughput by up to 66.7\% over separated transmission.

cs.IT

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arXiv ↗PDF ↗