arxiv arXiv cs.LG · 7d ago

TESSERA and AlphaEarth Embeddings Enable Fine-scale LCZ Mapping in Swiss Cities

A study across five Swiss cities compares TESSERA and AlphaEarth embeddings with traditional Sentinel data to upscale Local Climate Zone maps to 10-meter resolution using an attention-based U-Net. TESSERA consistently outperforms both Sentinel-1/2 and AlphaEarth, achieving IoU scores of 0.59–0.69 and 0.77–0.82. The results show embeddings reduce manual preprocessing and support scalable, reproducible LCZ mapping, though improved reference data is key for further accuracy gains.

arxiv arXiv cs.LG · 7d ago

Optimal Coarse Correlated Equilibria in Mean Field Games

The paper introduces optimal coarse correlated equilibria for continuous-time mean field games. It presents a linear programming formulation, proves existence, and develops a no-regret learning algorithm with explicit convergence rates, supported by numerical examples.

arxiv arXiv cs.LG · 7d ago

Federated Conformal Risk Control via Risk-Curve Shrinkage

A new federated conformal risk control method addresses coverage failures in hospital-level predictions. On real brain tumor data from 20 institutions, pooled calibration fails 40% of sites, with one exceeding false-negative targets by 7.8 percentage points. The proposed shrinkage-based protocol uses empirical risk curves and a hyperparameter n0=19 to achieve 2.7/20 coverage violations at 2.0x prediction set stretch, while preserving marginal guarantees and ensuring no patient-level data leaves any site.

arxiv arXiv cs.LG · 7d ago

PASQA: Pitch-Accent-Focused Speech Quality Model

PASQA is a speech quality assessment model designed to evaluate pitch-accent correctness in synthetic Japanese speech. It uses a dataset with controlled accent errors and achieves high accuracy in ranking accent-error severity, outperforming conventional models and aligning better with human judgments.

arxiv arXiv cs.LG · 7d ago

Effective Dimension Governs Generalization in Quantum Vision Models

Quantum vision models exhibit better generalization with more entanglement or quantum noise, phenomena unified by the effective dimension of the noise-shaped quantum feature kernel. This dimension acts as a regularization mechanism in overfitting regimes, with amplitude damping improving test accuracy by up to 13% along an inverted-U sweet spot.

arxiv arXiv cs.LG · 7d ago

Learner-based Concept Drift Detection: Analysis and Evaluation

This study analyzes and evaluates concept drift detection algorithms across multiple categories using synthetic and real-world streaming datasets. It examines drift characteristics and evaluates detector performance under abrupt and gradual drift scenarios to improve understanding of drift behavior and detector applicability.

arxiv arXiv cs.LG · 7d ago

QCPIKAN: Quantum-Classical Physics-Informed KAN for PDEs

QCPIKAN is the first quantum-classical physics-informed Kolmogorov-Arnold network designed to solve partial differential equations. It uses Chebyshev-polynomial KAN layers and parameterized quantum circuits to embed physical constraints into training, achieving exponential error convergence and reduced numerical dispersion. Validated on seepage scenarios in porous media, it outperforms existing quantum-classical neural networks in prediction accuracy, error control, and dynamic tracking.

arxiv arXiv cs.CL · 7d ago

Introducing P-CHR AUC and CRR for Semantic Caching

We introduce Precision-Cache Hit Ratio (P-CHR) AUC and Calibration Retention Rate (CRR) to address the calibration gap in semantic caching. These metrics evaluate precision across cache utilization levels and measure how offline ranking quality persists in deployment. Our analysis shows the gap is driven by training objectives, not data scale, and post-hoc calibration only partially resolves it.

arxiv arXiv cs.CL · 7d ago

Multi-Agent Transactive Memory Framework

Multi-Agent Transactive Memory (MATM) enables population-level storage and retrieval of agent-generated trajectories. It allows producer agents to share procedural knowledge with consumer agents, improving task performance and reducing interaction steps in interactive environments like ALFWorld and WebArena without coordination or joint training.

arxiv arXiv cs.CL · 7d ago

Tool-Intent Stabilization in Streaming RAG

A study measures tool-intent stabilization in Streaming RAG, defining when speculative tool queries converge to correct answers. On the CRAG benchmark, 73.9% of queries allow substantial latency hiding, with early stabilization observed in questions with verbatim retrievable evidence. Question type significantly predicts early versus late stabilization, informing when speculative triggers are effective.

arxiv arXiv cs.CL · 7d ago

PASQA: Pitch-Accent-Focused Speech Quality Model

PASQA is a speech quality assessment model designed to evaluate pitch-accent correctness in synthetic Japanese speech. It uses a dataset with controlled accent errors and incorporates self-supervised learning, mora-conditioned fusion, ranking loss, and accent-error localization to achieve high accuracy in detecting accent errors across speakers, outperforming conventional models in alignment with human judgments.

arxiv arXiv cs.CL · 7d ago

Token-Operations-Oriented Inference Optimization Techniques

This paper introduces a four-layer technical architecture for token-oriented inference optimization, including Multi-model Fusion, Model Optimization, Compute-Model Fusion, and Compute-Network-Model Fusion. It reviews key technologies and industry status, analyzing their real-world application value in reducing token costs, enhancing service efficiency, and ensuring stable token supply.

arxiv arXiv cs.CL · 7d ago

CATCH-ME if you RAG: Multilingual Counterspeech Dataset for Hate and Misinformation

CATCH-ME introduces the first large-scale, multilingual dataset of contextually annotated, multi-turn counterspeech dialogues targeting hate and misinformation. The dataset covers five languages and focuses on seven marginalized groups, with dialogues grounded in verified fact-checking sources and including document- and chunk-level span annotations for RAG systems.

arxiv arXiv cs.LG · 7d ago

Cross-dataset AUC for Realistic Deepfake Detector Evaluation

A new metric, Cross-dataset AUC (Cross-AUC), addresses limitations of traditional AUC evaluations by averaging per-domain AUCs and incorporating prediction polarization via Wasserstein Distance. It better reflects real-world performance under domain shifts and provides interpretable insights into detector degradation.

arxiv arXiv cs.LG · 7d ago

XGBoost-Forget for Machine Unlearning in Network Intrusion Detection

XGBoost-Forget enables efficient machine unlearning for XGBoost models on tabular network intrusion datasets. It maintains model performance while achieving faster unlearning compared to full retraining, addressing a gap in unlearning research for tabular data in network intrusion detection.

arxiv arXiv cs.LG · 7d ago

Chandra-Gaia Catalog Uses Machine Learning to Resolve X-ray and Optical Source Matches

A machine learning framework resolves ambiguous matches between Chandra X-ray and Gaia optical sources by using magnitude, color, and distance data. It identifies counterparts for 113,000 of 254,000 Chandra sources, finds plausible multiple counterparts for 7,000, and validates its performance on the COUP survey with 95% accuracy without positional data.

arxiv arXiv cs.LG · 7d ago

LOCUS: A Local Ordinance Corpus for the United States

LOCUS provides machine-readable access to U.S. municipal and county ordinances, covering 9,239 cities and counties. It includes a county-harmonized layer for 2,309 of 3,144 U.S. counties, serving the majority of the population. The corpus, built with OCR and metadata, enables research on legal opacity and paternalism using ModernBERT-based models.

arxiv arXiv cs.AI · 7d ago

XGBoost-Forget for Machine Unlearning in Network Intrusion Detection

XGBoost-Forget enables efficient machine unlearning for XGBoost models on tabular network intrusion datasets. It maintains model performance while achieving faster unlearning compared to full retraining, addressing a gap in unlearning research for tabular data in network intrusion detection.

arxiv arXiv cs.AI · 7d ago

Taxonomy Links Caregiver Needs to Mental Health Tech

A new taxonomy connects Alzheimer's and dementia caregiver mental health needs with technology interventions. It identifies gaps in support for issues like relational strain and compassion fatigue, and offers a shared framework for designing person-centered, clinically grounded technologies.

arxiv arXiv cs.CL · 7d ago

Dango: A Strictly L1-Only LLM for SLA Research

Dango is a 1.8B-parameter LLM designed to study Japanese-to-English second language acquisition. It uses a filtering method to minimize English contamination in monolingual pretraining, preserving realistic L1 exposure. Fine-tuned on LLM-generated lessons, Dango produces human-like L2 outputs, outperforming unfiltered and standard multilingual models.