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版本更新 · v2arXiv:2609.04325

The microscope is the mask: privileged views and labels from a cryo-ET forward model

Bogdan Toader, Kiarash Jamali, Tanmay A. M. Bharat, Sjors H. W. Scheres

原文摘要

We explore the use of simulated data for training a model for protein annotation in crowded cryo-electron tomography volumes reconstructed from images collected at limited tilt angles and severely corrupted by the measurement operator. Firstly, we leverage the corruptions imposed by the forward model to generate domain-specific augmented paired views of the exact same scene for an invariance objective integrated into the LeJEPA self-supervised training framework. Secondly, we use additional information from the simulation pipeline such as the positions and identity of proteins in the simulated volumes to inform the architecture of the model and the loss function, so that semantic information is localised at protein positions in the resulting dense feature volume. The resulting model, CARNIVAL, is evaluated without finetuning on classification and detection tasks in real tomograms, using a benchmark dataset containing multiple protein types and two tomogram processing types. We show that CARNIVAL outperforms a state-of-the-art model trained using a contrastive objective on simulated data but without forward model-based paired views or privileged information.

cs.CV · cs.LG · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16831

A Modular Framework for ESG Prediction through Domain-Specific Language Modeling and Graph Learning

Sofia Baiocco, Gianluca Bonifazi, Christopher Buratti, Michele Marchetti, Camilla Mazzoli, Federica Parlapiano, Giulia Quaglieri, Davide Traini 等 10 位作者

原文摘要

Environmental, Social and Governance (ESG) scores are increasingly used to evaluate corporate sustainability, supporting investment decisions, ensuring regulatory compliance and assessing risk. However, current ESG evaluations largely rely on proprietary methodologies that often produce inconsistent ratings. This motivates the development of data-driven prediction frameworks. In this paper, we present ESGPred, a modular framework for predicting ESG scores by combining domain-specific language modeling with graph-based learning over a network that models the relationships among the companies being evaluated. In addition to presenting the rationale behind ESGPred and detailing its ESG score prediction process, we show that ESGPred can effectively predict both overall ESG ratings and their individual Environmental, Social, and Governance components. We evaluated ESGPred using a dataset of 5,266 sustainability reports published by 2,134 publicly listed companies between 2020 and 2023. In this evaluation, we used ESG ratings provided by LSEG Data & Analytics as ground truth. Our experimental results demonstrated that integrating graph-based relational learning improves prediction performance over purely text-based baselines.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.25735

Pointing the Way, Hiding the Destination: Practical Private Dense Retrieval at Scale

Peichun Hua, Danyang Chen, Junan Zhang, Haifeng Sun, Jingyu Wang, Diwen Xue, Mingyu Li, Yunming Xiao

原文摘要

Hosted retrieval-augmented generation (RAG) and semantic search allow users to query valuable provider-held corpora, raising two competing demands: to hide each query and chosen result, yet reveal only the documents that the user is authorized to receive. Existing cryptographic approaches either make this costly by processing the entire corpus for every query, or sacrifice quality for efficiency by scanning a few clusters. We repurpose learned deep hashing as a private filter: a randomized binary code points the provider to a short candidate list, while encrypted reranking and oblivious key transfer protect the precise query and final selection. This shortlist short-circuits full-corpus cryptographic search without sacrificing retrieval quality: with 200-500 candidates, it closely matches full-corpus retrieval across five zero-shot corpora spanning 25K to 5.4M documents. On the full 2.68M-passage NQ corpus over a 10-Gbps link, our protocol only adds 0.38 seconds, or 6.5%, to a 128-token Qwen3-32B RAG pipeline. The released code satisfies directional metric differential privacy (DP) and substantially reduces embedding-inversion and property-inference leakage, demonstrating that a carefully learned shortlist can make private dense retrieval both accurate and practical.

cs.CR · cs.AI · cs.IR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16822

A Class of Bayesian Conjugate Distributions on $SE_n(3)$ for Motion Estimation

Shijie Wang, Haichao Gui, Rui Zhong

原文摘要

This paper proposes a novel class of Bayesian Conjugate Distributions (BCDs) on extended special Euclidean groups SE_n(3) for global motion uncertainty representation and estimation. BCDs are constructed by restricting matrix Gaussian distributions on ambient Euclidean space to SE_n(3), avoiding singularity of local coordinates. The unique feature of BCDs is their conjugacy to a broad class of sampling distributions arising in motion estimation, enabling exact closed-form Bayesian inference. Optimal estimation with respect to BCDs is further investigated for implementing motion estimation. Two means are extracted as random state estimates, which maximize probability and minimize mean squared error, respectively. Moreover, within the BCD class, moment matching is shown to minimize the Kullback-Leibler divergence, and maximum likelihood estimation is also derived, which provide analytical methods to approximate underlying distributions with the optimality guarantee. As an application, a Bayesian estimator for pose and velocity estimation of an Unmanned Aerial Vehicle (UAV) is developed within an assumed density filtering architecture. The propagation and estimate extraction leverage the optimal estimation related to BCDs, and the landmark measurements are exactly fused with the BCD prior in closed form due to the conjugacy. Numerical simulations of a UAV flight indicate the advantages of BCD-based estimators over two classic Kalman filters on Lie groups with various initial uncertainties and measurement noise.

stat.ME · cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.04779

Tree-Based Formalization of Multi-Agent Complementarity in Human-AI Interactions

Andrea Ferrario

原文摘要

Predictive tasks constitute subprocedures within human--AI interactions (HAIs) involving professionals and AI systems. Complementarity occurs when an HAI outperforms a benchmark defined by its constituent agents. Existing formalizations, however, focus on a single human and a single AI, leaving multi-agent interactions insufficiently characterized. We introduce a tree-based mathematical framework for multi-agent complementarity. Ordered agent-role configurations decorate the leaves of rooted planar binary trees and local aggregation rules generate protocol outputs evaluated against either a best-average or a pointwise-min benchmark. We establish four results. First, under linear pooling, every protocol tree provides a bijective coordinate system for the interior of the simplex of global leaf weights. Arbitrary trees with the same ordered leaves admit output- and complementarity-preserving reparameterizations, realized locally through path-independent Tamari rotations. However, HAI constraints can violate admissibility preservation between equivalent protocols. Second, for regression under squared loss, we characterize complementarity through residual correction and derive optimal linear-pooling weights. We also characterize protocol-indifference loci under identical local parameters. Third, selector-based protocols cannot achieve complementarity relative to the pointwise-min benchmark, independently of task and loss. Fourth, in binary classification, HAIs cannot achieve complementarity under endpoint-monotone losses and the pointwise-min benchmark, with an analogous obstruction for multiclass cross-entropy. Numerical illustrations examine the main claims. We provide a mathematical foundation for analyzing multi-agent predictive subprocedures within HAIs, establishing a first step toward understanding how interaction, aggregation, and real-world constraints affect HAI performance.

cs.AI · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16816

Outcome-Mass Alignment: Controlling Length-Dependent Trajectory Influence in Multi-Turn LLM Agent Reinforcement Learning

Dawei Zhang, Yubin Huangfu, Jessica Fan, Boyu Wei, Likang Wu, Hongke Zhao, Ming He, Zhu Teng

原文摘要

To keep long interaction contexts tractable, stepwise group-relative training for multi-turn LLM agents materializes each source trajectory into multiple response rows and valid tokens. This expansion creates an outer-accounting problem distinct from within-trajectory credit assignment: row-based outcome statistics can count one episode outcome multiple times, while token-level reduction can assign unequal aggregate objective mass across trajectories. We term these effects outcome-statistics multiplicity and objective-mass imbalance. We introduce Outcome-Mass Alignment (OMA), a two-component method that makes both accounting choices explicit. Outcome Alignment computes outcome statistics over unique source trajectories, while Mass Alignment controls aggregate objective mass after within-trajectory aggregation of optimizer-native token objectives. Neither redesigns the base optimizer's credit assignment, importance ratios, or clipping. Across matched ALFWorld GRPO comparisons at two model scales, where failed trajectories typically expand much more than successful ones, OMA improves matched-horizon endpoint success by 7.0-9.4 percentage points. Ordinary WebShop exhibits weak or reversed expansion-outcome coupling and correspondingly mixed effects, whereas in a success-oriented WebShop setting, WebShop-SOL, OMA improves success nAUC from 0.399 to 0.511. Component ablations and GiGPO and GSPO comparisons further support component separability and cross-optimizer compatibility. These results show that trajectory expansion always defines an implicit weighting rule, but its practical effect depends on how expansion couples to outcomes and optimizer-native updates.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.02545

Reward Inflation: A Healthy Stimulus for Reinforcement Learning

Ganghun Lee, Minji Kim, Minsu Lee, Byoung-Tak Zhang

原文摘要

Reward serves as the primary learning signal in reinforcement learning (RL). However, while reward magnitudes are typically held fixed throughout training, their temporal modulation remains underexplored. In this paper, we propose reward inflation, a gradual scaling of rewards over the course of training, and show that it can act as a healthy stimulus for RL. Theoretically, reward inflation induces an implicit recency weighting that upweights recent transitions during policy updates, enabling faster adaptation. We further show that reward inflation suppresses the emergence of dormant neurons by sustaining gradient signals as the policy saturates, suggesting a possible contribution to plasticity preservation. Empirical results on ALE games and MuJoCo tasks corroborate these findings, showing that an appropriate level of reward inflation benefits a broad range of tasks. Finally, we introduce Fed, an adaptive variant that adjusts the inflation level on the fly, and find that it often improves upon fixed inflation. Source code is available at https://github.com/northadventure/RewardInflation.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16803

Detection Is Not Early Warning: Evaluating Token-Level Alarms on Language Model Failures

Marco De Negri, Luca Sartori, Lorenzo Baggi, Moritz Reihs, Eduard Frank Ďurech, Mennatallah El-Assady, Valentina Pyatkin, Anna Hedström

原文摘要

Many failures of language models begin at a specific token, the onset. A monitor that reads the model during generation can classify the finished response, detect the failure once it is under way, or warn before it begins, and only a warning leaves time to stop or steer the generation. Monitors are usually evaluated with response-level metrics such as AUROC, which ignore when the monitor fires. We introduce PREWARN, an evaluation protocol for any failure with an annotated onset. It calibrates each monitor's threshold on validation data to an error rate chosen for the application, runs the monitor, and scores its alarms relative to the onset on all three tasks. As a case study we apply it to repetitive degeneration in three open language models, with early warning as the main task, using linear probes on activations and six untrained baselines. Response-level metrics saturate on this failure, and a rule that reads only the response length scores almost perfectly, while probes that AUROC ranks as equal warn very differently under PREWARN. Probes warn before the onset on most degenerate responses of two of the three models, low-rank adapters below the probe more than double the share of tokens just before the onset that it flags, largely by flagging degenerate responses long before the onset, and no untrained baseline warns much more often than random alarms. We release the probes with calibrated thresholds at https://huggingface.co/collections/marcodene/prewarn-degeneration-probes

cs.LG · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.07314

TEMPO: Semantic-Action Decoupled RL Post-Training for Vision-Language-Action Models

Ziheng Liu, Quantao Yang

原文摘要

Vision-language-action (VLA) models are commonly adapted to downstream manipulation tasks via supervised fine-tuning (SFT) or online reinforcement learning(RL) post-training. SFT is prone to distribution mismatch, and existing RL approaches typically optimize all trainable components within a single RL loop at a uniform update rate, although these components play distinct functional roles. We propose TEMPO, a semantic-action decoupled, two-timescale RL post-training framework for VLA models. TEMPO freezes the pretrained vision-language backbone and adapts two components with dedicated RL loops: the semantic projection layer, which produces the latent action conditioning the action expert, and the low-level action expert itself. The semantic projection layer is updated infrequently and the action expert frequently, which aims to limit latent-action drift while allowing the action expert to learn from online feedback. Our analyses show that, compared with equal update frequencies, less frequent semantic updates reduce latent-action drift and improve policy performance. Experiments on the CALVIN benchmark and real-world manipulation tasks demonstrate that TEMPO outperforms both pretrained state-of-the-art VLA models and the RL post-training baseline, while reaching and maintaining higher evaluation rewards on three real-world tasks.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16795

KuaiRecV2: Benchmarking Large-Scale Continual Learning for Diversified and Multi-task Recommendation

Chenxu Li, Shuchang Liu, Hantao Shu, Wei Yuan, Zhe Xu, Guoqing Hu, Yan Wang, Biao Yang 等 18 位作者

原文摘要

Recommender systems, which retrieve items of interest to users based on their interaction histories, have recently witnessed several key technological advancements, benefiting from the semantic representation space and model-data scaling. Nevertheless, practitioners and researchers have frequently encountered critical inconsistencies between simple offline evaluations and complex online results, and this gap still exists in the era of large models. In this work, we address several key factors that contribute to this inconsistency: 1) the \textit{large-scale continual learning challenge} that assumes a practical demand to maintain long-term model effectiveness under dynamic item pools and distributional shifts; 2) the \textit{combinatorial diversity challenge} that requires the generation strategy to solve the accuracy-diversity trade-off in both the finer token level and the holistic item level; and 3) the \textit{multi-task reward balancing challenge}, which is particularly critical for modern generative models that can accurately model the sequence distribution but are less controllable for the multi-task demands. To address these challenges and further pave the way for the development of more realistic recommendation solutions, we introduce KuaiRecV2, a deliberately constructed benchmark with industrial-level datasets from Kuaishou. Specifically, we provide a large-scale interaction dataset with million-scale multi-modal item information and long-term interaction records across 30 days, with a dynamic video candidate pool. Additionally, we provide a rigorous benchmark that evaluates retrieval accuracy, diversity, and multi-task performance in one rubric system, where an effective advancement occurs if and only if the new method improves all metrics. The code is available at https://github.com/cxli2333/KuaiRecV2

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16792

Systematic Evaluation of Training Data Generation Strategies for Machine Learning-Based Molecular and Materials Property Prediction

Minh Nghia Nguyen, Beate Paulus, Akhil S. Nair

原文摘要

Machine-learning (ML) models for molecular and materials property prediction are often trained on large datasets curated through arbitrary or generic sampling strategies. Such approaches can lead to the over- or under-representation of regions of chemical and materials space that are most relevant to the prediction task. An important challenge is therefore how to efficiently curate training data to maximize predictive performance while minimising the number of labelled examples required. Here, we systematically compare training data acquisition strategies for molecular and materials property prediction, ranging from random sampling to various active learning approaches. We evaluate their performance across different model classes, representations, datasets, and target properties. Our results show that the effectiveness of data acquisition strategies depend strongly on the interplay between the model, dataset, and prediction task, although uncertainty-based sampling generally outperforms alternative strategies. Based on these findings, we derive guidelines for selecting data acquisition strategies under a given labelling budget.

cond-mat.mtrl-sci · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16789

ScrewSense: Mapping the Invisible - Anomaly Detection and Error Classification in Screwdriving Processes

Tobias Lettner, Tatyana Ivanovska, Anwar Al Assadi, Christian Bergler

原文摘要

The growing volume of process data generated by screwdriving systems, combined with advancements in computing power, data storage, and algorithms, is facilitating the application of artificial intelligence in this field. Accurately detecting and classifying screwdriving errors is crucial for ensuring product quality, avoiding costly rework in manufacturing, and supporting sustainability by facilitating the maintenance, reuse, and recycling of screw-connected products. In this context, machine learning approaches offer a promising alternative to traditional monitoring techniques, such as window-based methods or envelope curves. This study examines the use of deep learning techniques for anomaly detection and error classification in screwdriving processes. The data corpus consists of multivariate time series capturing process parameters, such as torque and rotation angle. For anomaly detection, the embedding space of a vector quantized variational autoencoder was analyzed. In terms of error classification, four different model architectures were evaluated. The results demonstrate the effectiveness of anomaly detection and confirm that deep learning models can distinguish between multiple error categories beyond traditional binary classification. This extends the capabilities of conventional monitoring approaches, achieving accuracies of up to 100%. Additionally, real-world data material was examined in order to further demonstrate model robustness. Overall the results indicate strong generalization capability and highlight the practical potential of the developed models for use in industrial screwdriving processes, eliminating the need for expert knowledge in error identification and significantly reducing manual effort and analysis time.

cs.LG · cs.RO · eess.SP

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2611.05355

Reading Why: Born-Measured Attention with Exact Word Evidence on Light and Qubits

Teerawat Tongloy, Jiraporn Pongsopa, Rangsan Jomtarak

原文摘要

When a language model answers, we can usually only estimate why. We build a transformer in which every nonlinearity is a quadratic detection - an interference, a balanced reading or a camera - and the output is a Born probability: attention |<q|k>|^2, the form of the Born rule, normalised per row, a measured product for the feed-forward nonlinearity, and a Born readout; between detections, classical control rescales and re-encodes. With its denominators held at the input, each class probability is a polynomial in the word amplitudes, so its integrated-gradient evidence is computed by a finite quadrature with no quadrature error: the words' contributions sum to the class probability above its no-information share to 10^-13. On e-SNLI natural language inference it reaches 77.45 +- 0.91 % accuracy, level with a same-size softmax transformer (76.62 +- 0.36 %) and an order-free bag of embeddings (77.79 %). Exactness buys the sum, not the best ranking. Deleting the word its evidence ranks first changes the answer in 44 % of pairs, of the 53 % some deletion changes, against 31-32 % for the softmax model's integrated gradients; the model's own gradient x input does as well (43 %), as does a bag of embeddings' (43 %, seed 0), and leave-one-out does better. Holding the denominators helps the low end: the five words ranked last change the answer in 1 %, against 6 % free. The same weights compile exactly to simulated optoelectronics, and their attention to qubits. On ibm_kingston, reading only the attention, the device gave the model's answers in all three Hadamard-test runs (the two shortest pairs and one without its deciding word) and both compute-uncompute runs (one circuit per overlap); uniform attention answers otherwise on two of the three inputs. With the device's row sums the evidence correlated with the exact one at 0.997 and 0.962 by Hadamard tests; with compute-uncompute, 0.971 and 0.48.

quant-ph · cs.CL · cs.LG · physics.optics

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2509.03244

FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization

Yiming Yao, Fei Liu, Liang Zhao, Xi Lin, Yilu Liu, Qingfu Zhang

原文摘要

Expensive multi-objective optimization is a prevalent and crucial concern in many real-world scenarios, where sample-efficiency is vital due to the limited evaluations to recover the true Pareto front for decision making. Existing works either involve repeatedly fitting Gaussian process surrogates from scratch for each newly encountered problem, or rely on computationally demanding hypervolume-oriented policy learning for amortized optimization, making it challenging to achieve scalable pre-training and efficient yet robust adaptation for diverse emerging real-world applications. To address these challenges, we propose FoMEMO (Foundation Models for Expensive Multi-objective Optimization), which adopts a decomposition-based training paradigm that converts multi-objective optimization into preference-wise aggregated posterior learning tasks, enabling scalable pre-training on hundreds of millions of diverse synthetic datasets without relying on extensive real-world domain experiments. At test time, given observed trajectories from unseen problems and user preferences, the foundation model performs in-context posterior prediction and optimizes the derived acquisition functions for efficient candidate generation, achieving strong generalization and optimization performance without any subsequent model training or updates.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.11789

costream: A Streaming Evaluation Protocol for Wearable Fall Detection

Timilehin B. Aderinola, Ilaria D'Ascanio, Luca Palmerini, Lorenzo Chiari, Jochen Klenk, Clemens Becker, Brian Caulfield, Georgiana Ifrim

原文摘要

Wearable fall detectors are commonly evaluated on presegmented windows, where a fall window is extracted at an annotated impact and non-fall windows are sampled from fall-free signal. However, a deployed detector processes a continuous sensor stream in which the location of a fall is unknown. We specify a streaming evaluation protocol in which a detector traverses an unsegmented recording, aggregates window-level scores into a temporal confidence map, and emits timestamped events that are evaluated against the annotated fall. Holding the dataset, participant-wise splits, window construction and fitted models fixed, we compare presegmented and streaming evaluation on clinically verified real-world falls. The two protocols identify the same best-performing method overall, but select different methods in 6 of 20 model-window configurations, with the divergence associated with differences in the temporal clustering of false alarms. Streaming evaluation also exposes deployment-relevant quantities, including false alarm rate over elapsed monitoring time, detection delay, and the effect of the decision threshold, that are not captured by per-window evaluation. These results show that evaluation protocol can materially affect conclusions about wearable fall detectors and should be considered when assessing their suitability for continuous monitoring.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16772

Class-Competence Aggregation: Output-Layer Routing for Federated Multiclass DDoS Detection under Class-Concentrated Non-IID Data

Tapu Datta, Hafiz Imtiaz

原文摘要

Federated learning over distributed intrusion-detection sensors naturally produces \emph{class-concentrated} non-IID data, where an entire attack class is held by a single client and absent elsewhere. We show this collapses federated averaging in a specific, localized way: the output layer holds one decision boundary per class, and when a class's aggregation weight comes overwhelmingly from clients that have never seen it, its neuron receives an active anti-signal rather than mere dilution, collapsing to zero. On CIC-DDoS2019, standard FedAvg drives the concentrated LDAP class to $0.00 \pm 0.00\%$ F1 across every seed. We propose Class-Competence Aggregation (CCA), which aggregates the output layer per class using each client's own locally reported validation F1 as the weight, while hidden layers remain under standard size-weighted FedAvg. CCA requires no server-held auxiliary data and adds only one F1 scalar per class per round beyond the feature-scaling and class-count exchanges common to any federated pipeline. On our primary CNN-LSTM architecture, CCA alone raises LDAP F1 from $0\%$ to $84.83 \pm 1.65\%$, and the full method reaches $91.64 \pm 0.57\%$ macro F1, closing the federated-to-centralized gap from $-42.53$ to $-4.05$ points. Benchmarked against four other federated methods under identical partitions and seeds, CCA is the only one that recovers the concentrated class substantially and reliably; a $178\times$ smaller MLP shows the same qualitative recovery, though less completely, confirming the effect holds across architecture scale even as its magnitude varies. Results are validated across three seeds and replicated on a second dataset, CICIDS2017.

cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16764

Efficient Periocular Recognition through Model Scaling and Cross Spectral VIS NIR Verification

Fabian Stockhard, Juan E. Tapia, Christoph Busch

原文摘要

Periocular recognition offers an efficient alternative to full-face verification when facial regions are occluded or image resolution is limited. We present AdaEye, a systematic study of input and backbone scaling for periocular verification in visible-spectrum (VIS) and cross-spectral (VIS/NIR) settings. Using an AdaFace-style framework, we compare full-face, masked-face, and dedicated 80x40 periocular inputs while varying backbone width and embedding dimensionality. Across four VIS/VIS and VIS/NIR protocols, we assess verification accuracy, computational cost, inference throughput, training memory, template size, and training stability. Direct periocular processing reduces recognition cost from 6.30 to 1.64 GMACs per image and increases measured throughput by approximately threefold. Its effectiveness depends on the evaluation protocol: periocular models perform better on native UFPR periocular images, whereas face-based protocols favor retaining more input information. Moderate backbone width reductions maintain comparable mean verification performance on CASIA and ND-NIVL while reducing computation. Embedding compression decreases template-storage requirements, primarily benefiting deployment. Multi-seed experiments reveal that some configurations require numerical stabilization, particularly when embedding and backbone reductions are combined. These findings support selecting compact periocular architectures according to the target protocol and deployment constraints, balancing accuracy, efficiency, storage, and robustness.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16756

Beyond Binary Classification: Multiclass Machine Learning for Diabetes and Prediabetes Risk Prediction Using NHANES

Aman Mujahid, Anita Penkova

原文摘要

Detecting diabetes risk early is important for preventing disease progression. Most existing machine learning approaches focus on binary classification, which limits their ability to capture intermediate risk states such as prediabetes. We develop a supervised learning framework to classify adults as normal, prediabetic, or diabetic using National Health and Nutrition Examination Survey data. The framework uses routinely collected demographic, clinical, and lifestyle variables from 15,356 adults, with glycated hemoglobin (HbA1c) used only to define labels and excluded from input features to prevent leakage. We compare conventional machine learning and transformer-based models and examine two binary formulations: normal versus prediabetes, and normal versus combined prediabetes and diabetes. Models are evaluated on a held-out test set using precision, sensitivity, specificity, and F1 score. In the multiclass setting, performance is broadly consistent across models, with accuracy up to 0.72 and macro-averaged F1 of 0.70. Prediabetes remains the most challenging class, with F1 scores of 0.54 to 0.56 for conventional models and 0.53 to 0.55 for transformers, reflecting its overlap with both neighboring states. When the task is reduced to normal versus prediabetes, F1 scores rise modestly to 0.62 to 0.64. A more substantial improvement is observed under a broader screening formulation combining prediabetes and diabetes, with F1 scores of 0.73 to 0.74 across all models. These findings suggest that feature overlap along the biological continuum between normal and diabetic states contributes more to the difficulty of prediabetes detection than model architecture. Multiclass prediction provides a more clinically informative representation of metabolic risk progression but highlights a trade-off between clinically meaningful stratification and predictive accuracy.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2609.39329

PatchKV: Weight-Space Compensation of KV Cache

Chanryeol Lee, Chanhyuk Lee, Yeonwoo Choi, Donggyun Kim, Seunghoon Hong

原文摘要

Long-context inference with Large Language Models (LLMs) is bottlenecked by the linearly growing memory of the key-value (KV) cache. Existing compression methods reduce the cache through token eviction or approximation, but degrade sharply at aggressive compression budgets. We propose PatchKV, a training-free framework that compensates KV cache compression methods by carrying part of the context in the model's weights. PatchKV pairs an off-the-shelf compressed KV cache with a context-specific weight patch, which is computed once at context-loading time and served for downstream queries for the context. The weight patch is derived in closed form via ridge regression, by aligning the block-wise activations of context-derived reference query tokens under the full cache and the compressed cache. Once merged into the model, the patch leaves the forward graph and per-query inference cost unchanged in the single-context, multi-query setting. Across long-context QA (SCBench with up to 170K tokens, SQuAD, NIAH) and math (GSM8K) benchmarks on three model architectures, PatchKV consistently improves cache compression methods, suggesting an alternative direction to compensate them at aggressive budgets. Code is available at https://github.com/cusasak/PatchKV.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16752

AgriSense: Vision-Language Decision Support and Robotic Intervention in a Virtual Greenhouse Copy title

Lyes Saad Saoud, Oualid Doukhi, Reza Ghorbani, Moussa Ayyash

原文摘要

Autonomous greenhouse management increasingly requires robotic systems that integrate perception, navigation, environmental regulation, and plant-level intervention. This paper presents AgriSense, a framework combining an Isaac Sim virtual greenhouse, a quadruped mobile manipulator, RGB and LiDAR-inertial sensing, environmental control, and supervised vision-language decision support. Frozen DINOv2 and BERT encoders, together with structured environmental inputs, support joint prediction of twelve environmental references, a target zone, and an intervention flag. Checkpoint replay reproduces 41/43 zone decisions. Across five seeds on a source-grouped test set, the full, image-removed, and language-removed configurations each achieve 43/44 correct zone decisions, providing no evidence of incremental multimodal zone-accuracy improvement. The full model achieves mean absolute errors of 0.249 degrees C for temperature, 2.030 percentage points for humidity, and 0.997 percentage points for soil moisture. Sensor-only inputs match full-model zone accuracy, while language-only inputs achieve 68.64%. External real-image benchmarks reveal distribution-shift limitations. Five offline Point-LIO routes yield rigidly aligned trajectory errors of 5.22-17.32 mm. Of 40 virtual-arm targets, 32 satisfy geometric acceptance criteria and eight are rejected at boundaries. Six long-horizon climate cases and fault protocols characterize integration limits. Navigation and integrated treatment rely on simulator-state inputs; physical greenhouse performance and agronomic treatment efficacy remain unvalidated. The contribution is an evaluated decision-support and robotic-intervention framework for a virtual greenhouse.

cs.RO · cs.AI · cs.CV

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新论文 · v1arXiv:2611.16750

Interpretable machine learning diagnosis of degradation mechanisms in lead-free $RbSn_{0.5}Ge_{0.5}I_{3}$ solar cells from temperature- and illumination-dependent electrical measurements

Nabanita Sarker, Hafiz Imtiaz

原文摘要

Machine learning coupled with device simulation is widely used to optimize thin-film solar cells, but almost exclusively in the forward direction, from device parameters to predicted performance. The inverse problem encountered after fabrication, namely identifying the mechanism that limits a given device, is still addressed by manual inspection of characterization data. In this work, both directions are examined for lead-free $RbSn_{0.5}Ge_{0.5}I_{3}$ solar cells using approximately 42,000 SCAPS-1D simulations. Replacing the Spiro-OMeTAD hole transport layer with CuI increases the simulated power conversion efficiency from 19.6\% to 21.6\%, almost entirely through the fill factor. Surrogate models trained and tested on a factorial parameter grid substantially overstate their predictive accuracy. An XGBoost regressor attains $R^2 = 0.9997$ on held-out grid points but only 0.58 on independently sampled devices, whereas training on a Latin hypercube design of comparable size restores $R^2 = 0.995$. For the inverse problem, a multi-label classifier identifies which of six degradation mechanisms are present from simulated temperature- and illumination-dependent open-circuit-voltage measurements. It achieves a macro $F_1$ score of 0.938, compared to 0.687 for a calibrated physics-rule baseline. The classifier does not, however, generalize to combinations of mechanisms absent from the training set.

cs.LG · math-ph

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新论文 · v1arXiv:2611.16749

ORCA:Orthogonal Residual Consensus Alignment for Multi-View Clustering

Xiaojian Ding, Xian Li, Xiaoying Zhu, Lin Zhao, Kaixiang Wang

原文摘要

Deep multi-view clustering often suffers from redundant information accumulation during fusion and indiscriminate alignment across representation levels. We propose Orthogonal Residual Consensus Alignment (ORCA) to address these challenges. Its semantic incremental recurrent fusion module extracts cross-view orthogonal residuals as non-redundant increments and adaptively weights recurrent states through divergence-guided gating. Its consensus-calibrated hierarchical contrastive alignment module uses the global consensus to weight contrastive alignment of view-level, pairwise, and recurrent representations. Together, these modules balance view-specific structure with cross-view consistency. Experiments on five benchmarks against fifteen competing methods demonstrate ORCA's effectiveness, including a 5.25 percent accuracy improvement over the strongest baseline on 100leaves. Ablation studies validate both modules. Code is available at https://github.com/XiaojianDing/2026-NeurIPS-ORCA.

cs.CV · cs.AI

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新论文 · v1arXiv:2611.16748

PhysLoop: Harnessing LLMs' Physics Awareness for Self-Improving Video Generation Models

Yuxuan Chen, Xiaojie Li, Hao Chen, Juncheng Ma, Kewei Zhang, Zhongjing Du, Guoqiang Wei, Fangfang Wang 等 14 位作者

原文摘要

Video generation models produce visually realistic videos but still struggle with physical dynamics and causal consistency. Learning from generated videos requires fine-grained physical supervision, yet reliable quality judgments, correction pairs, and controlled variations are difficult to obtain. Large language models (LLMs) possess physical knowledge, but struggle to reliably guide video generation models in producing their training videos. We present PhysLoop, a unified harness designed to support physically grounded self-improvement in video generation models. The harness guides and constrains LLMs throughout video generation, selection, and correction, distilling their physical knowledge into physically consistent videos. The framework supports controllable training-data construction and inference-time video repair. By identifying and correcting failures, PhysLoop marks a key milestone toward recursive self-improvement (RSI) in video generation models with limited human intervention. We release 100K videos spanning diverse, realistic indoor and outdoor scenes, along with generation tools and two evaluation datasets: PhysLoop-Gen and PhysLoop-Repair. We evaluate controllable generation and localized repair on these datasets, respectively, and assess physical behavior on the external Physics-IQ Verified benchmark.

cs.CV · cs.AI

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版本更新 · v2arXiv:2609.34924

Audit the Scaffold, Not the Checkpoint: A Stationarity Dichotomy for Recursive Self-Improvement in Agentic Coding

Sebastian Bobadilla-Suarez, Bob Suh, Ryan Fortin

原文摘要

Rules for AI self-improvement often key on the model itself: has it been retrained, or are its weights frozen? We argue that, for AI coding agents, this check is incomplete, and we propose, but do not run, the experiment that would test it. An agent that keeps revising its own work runs into diminishing returns as long as the set of changes it can reach stays fixed, and can escape them only if that set grows. Our hypothesis is that the set can grow with the model untouched, because the software around the model, its scaffold of tools, tests and task breakdown, can be rewritten. If so, a frozen model guarantees a ceiling eventually but not steady behavior on the way there, and audits should track changes to the scaffold, not only to the model. The same criterion separates three things often lumped together: more search within a fixed setup, retraining the model during use (which can raise the ceiling), and rewriting the scaffold, which sits between them. Adding more agents does not lift the ceiling. Picking the best of several agents' answers reaches the best single agent's ceiling exactly, and only gets there faster. A majority vote could do better, but only if the agents fail on different tasks. In our tests, 30 agents from one model family failed together: some task defeated all of them, and a majority failed 23/55 (42%) of tasks. Our evidence is descriptive. On a public coding benchmark, the gain from each extra round of revision shrinks toward zero, though the unsolved tasks left to measure are also fewer and harder. Across 401 development sessions, the amount of code changed falls off from commit to commit, but programmers working before AI tools show the same pattern, so the data show that current practice levels off without showing why. We reach the criterion by treating repeated revision as a known machine-learning method, boosting, and note where real agents depart from it.

cs.LG · cs.AI · cs.SE

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新论文 · v1arXiv:2611.16744

Scale-Induced Semantic Shift in Visual Prompt Injection Against VLMs and Web Agents

Yiheng Zhao, Runze Ji, Chenyu Yi, Yi Yu, Bingquan Shen, Alex Kot, Xudong Jiang

原文摘要

Visual content in multimedia systems may be resized, compressed, or re-rendered before it is interpreted by a vision-language model. We study a security failure mode in which these transformations change the machine-readable semantics of an image: a textual instruction is not recovered at a $512\times512$ presentation resolution under our evaluation protocol but becomes recoverable after aggressive downsampling. We formulate this behavior as Scale-Induced Semantic Shift (SISS) and develop a closed-loop black-box framework that combines dual-scale verification, selective self-hardening, shallow-layer low-rank adaptation, and pixel-space noise regularization. On a fixed 300-case benchmark, the third SISS refinement round obtains a $58.75\%$ average attack success rate across four closed-source vision-language models, compared with $41.08\%$ for the strongest generation baseline. A controlled sweep bounds the effect to aggressive compression: average success decreases to $17.68\%$, $6.85\%$, and $5.60\%$ at $128\times128$, $224\times224$, and $336\times336$, respectively. In a downstream web-agent study, the generated images increase hidden-instruction perception, but absolute behavioral compromise remains about $4\%$. Generic input transformations provide little protection in a separate defense protocol, whereas the proposed ScaleGuard consistency detector reduces non-adaptive success from $61.4\%$ to $24.3\%$ and one-round defense-aware success from $42.5\%$ to $15.0\%$, with no observed rejection among matched clean controls. The results characterize a bounded but reproducible semantic inconsistency in multimedia preprocessing and motivate scale-consistent visual-language inference.

cs.AI

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新论文 · v1arXiv:2611.16742

Fog-FWL: A Large-Scale Full-Waveform LiDAR Dataset for Recovering Fog-Hidden Objects

Kazuma Ikeda, Kotaro Oishi, Ryosei Hara, Ryo Yoshida, Takahiro Kado, Ibuki Fujioka, Taro Beppu, Mariko Isogawa 等 9 位作者

原文摘要

LiDAR is a core sensor for autonomous driving, yet it is highly vulnerable to fog. Fog scattering produces strong backscatter near the sensor and attenuates reflections from objects behind it, so weak object returns are often discarded by the peak detection that converts received signals into point clouds. Full-waveform LiDAR (FWL), which records the received optical signal before peak detection, retains these weak returns, but the lack of suitable data has prevented research on recovering fog-hidden objects from FWL. We present Fog-FWL, a dataset foundation for this task that consists of a real-world benchmark, Fog-FWL-Real, and a large-scale synthetic training set, Fog-FWL-Sim. Fog-FWL-Real contains 7,527 real foggy FWL frames from 8 scenes and 58 sequences, which is 579x larger than the only existing real foggy FWL data, together with histogram-level Object/Fog/Noise labels, 10,719 3D bounding boxes, and three difficulty levels defined by the attenuation of object reflections. Fog-FWL-Sim contains 12,000 frames generated by our physics-based simulator, Fog-FWL-Simulator, which integrates spatially heterogeneous fog density fields, time-resolved light transport including multiple scattering, and a photon-counting SPAD receiver model. The two subsets share the same sensor format and annotation protocol, enabling a strict sim-to-real evaluation in which no real waveforms are used for training. Our analysis shows that, under the hardest condition, object returns are included in the three strongest peaks for only 34.9% of object rays, underscoring the need for waveform-level recovery. Fog-FWL-Real, Fog-FWL-Sim, and the Fog-FWL-Simulator will be publicly released to facilitate research on fog-robust LiDAR perception.

cs.CV

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版本更新 · v2arXiv:2607.07652

AI Search as an Information Intermediary: Clickstream Evidence on Web Traffic

Qiaoni Shi, Kai Gu, Kai Zhu

原文摘要

AI search retrieves web information to ground generated answers. We study this new information intermediary using U.S. desktop clickstream data observing use alongside referrals during ChatGPT Search's rollout. Referrals accompany 5.4% of ChatGPT sessions versus 31.1% of Google queries. Ad-supported sites receive a smaller share of ChatGPT than Google referrals, while reference and technical sites receive larger shares. Conventional searches (Google, Bing, Yahoo) decline after ChatGPT adoption, sooner when adoption follows ChatGPT Search's expansion to users without accounts. Among existing users, that expansion raises ChatGPT use; conventional searches show no detectable overall change but fall among households with lower prior search activity. These findings suggest AI search can reshape open-web traffic through its own referrals and changes in conventional search.

cs.CY · cs.AI · econ.GN

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新论文 · v1arXiv:2611.16736

On the Difficulty of Sequence-Level Supervised Learning for Autoregressive Language Models

Mathieu Blondel, Michaël E. Sander, Germain Vivier-Ardisson, Tianlin Liu, Vincent Roulet

原文摘要

During the supervised learning phase, autoregressive language models are typically trained by minimizing the negative log-likelihood of the training data, which decomposes as a sum of token-level cross-entropies via teacher forcing. This objective is simple and efficient but potentially suffers from so-called exposure bias: a mismatch between the way the model is trained and the way it is used at inference time. Can we instead design sequence-level loss functions that incorporate inference-time decoding procedures, such as greedy decoding or autoregressive sampling? We show that two natural attempts lead to loss functions that are fundamentally flawed: unbounded below. We further show that these two flawed loss functions are intimately connected: using autoregressive Gumbel tricks, the sampling based loss is the expected greedy loss under Gumbel perturbations. Since the pathology stems from the dependence of the decoded sequence on the model parameters, we analyze the update direction obtained by applying a stop gradient on the sampling procedure, leading to a moment matching direction. While it is not the gradient of any loss function, we prove that it defines a valid descent direction in function space for the autoregressive negative log-likelihood. Despite these theoretical insights, the moment matching direction suffers from high variance. We conclude that teacher forcing may well be ``all you need'' for supervised learning of autoregressive models.

cs.LG · stat.ML

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版本更新 · v2arXiv:2610.34390

Controlled Block Proximal Linearized Method for Nonconvex and Nonsmooth Optimization

Weifeng Yang

原文摘要

In this paper, we consider multiblock nonconvex and nonsmooth optimization without assuming bounded iterates or coercivity of the original objective. We propose the Controlled Block Proximal Linearized (CBPL) method, whose control potentials establish boundedness while retaining the original objective, constraints and variables. We prove finite length and whole-sequence convergence to an effective critical point of the original objective, where one limiting subgradient annihilates all effective feasible directions. We establish finite total variation of the original objective and block functions, summable original effective residuals, and finite termination and linear or sublinear convergence rates for the original objective values. Scale orthogonality further gives full original criticality and, for homogeneous balancing controls, summable absolute objective errors with an $o(1/K)$ best-iterate bound. We establish a complete degeneracy theory for graph-regularized nonnegative factorizations: scale criticality is equivalent to graph collapse, and full criticality eliminates graph action; our controls prevent this collapse while retaining genuine reconstruction changes. Furthermore, our proposed method provides the practical, simple, and effective schemes for these applications, combining guaranteed convergence with explicit closed-form updates that can be applied directly to solve the original problems. We apply CBPL to graph-regularized sparse nonnegative matrix factorization with $\ell_0$-constraints ($\ell_0$-GSNMF), graph-regularized sparse nonnegative canonical polyadic (CP) decomposition with $\ell_0$-constraints ($\ell_0$-GSNCP), and graph-regularized sparse nonnegative independent-core matrix tri-factorization with $\ell_0$-constraints ($\ell_0$-GSNMTF). Numerical experiments demonstrate effectiveness, convergence and structural control.

math.OC · cs.LG

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新论文 · v1arXiv:2611.16727

From Foundation VLM to Versatile Driving VLM: A Triplet Incremental Training Approach

Shengtao Wu, Zichao Dong, Weixuan Qiao, Xiaolu Liu, Xin Zhan, Junbo Chen, Jianke Zhu

原文摘要

Adapting general Vision-Language Models (VLMs) to Autonomous Driving (AD) is thought to require heavy 3D spatial priors or intrusive architectural overhauls to compensate for a lack of spatial grounding. In this work, we present a counterintuitive pilot study: off-the-shelf general VLMs already exhibit remarkable adaptability across diverse driving tasks, while precise metric depth remains the primary missing link, a bottleneck readily addressable via onboard sensors and depth foundation models. To this end, we introduce the Triplet Incremental Training Framework, a non-intrusive paradigm that seamlessly instills 3D geometric awareness into a general VLM backbone while fully preserving its open-world capabilities. Driven by automatically constructed (image, depth, QA pair) triplets, our framework operates via a unified tri-stream training pipeline: an unfrozen primary VLM stream to adapt to driving tasks, an auxiliary depth stream that guides the visual backbone to implicitly imagine 3D scene geometry via explicit geometric constraints, and a frozen twin VLM stream employing logit-level KL distillation to prevent semantic drift. Crucially, the auxiliary depth branch is discarded during deployment, ensuring the enhanced geometric awareness comes with zero inference overhead. We evaluate our framework across four major benchmarks targeting four diverse capabilities spanning 3D spatial perception, driving-oriented language reasoning, end-to-end planning, and general multimodal understanding. Our approach outperforms state-of-the-art driving VLMs, rivals domain-specific expert models in 3D perception, and successfully retains its original general reasoning capabilities. This demonstrates that Triplet Incremental Training offers a unified, deployment-ready paradigm for transforming general foundation models into robust driving intelligences.

cs.CV · cs.AI

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