China People's Public Security University

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researchsquare.com Nov 12, 2026
Monocular Gaussian SLAM stops painting every keyframe: Sparse-Mono reads the existing map first and uses 89.3% fewer Gaussians than VINGS-Mono on Replica
Monocular Gaussian SLAM predicts a full set of Gaussians for every mapping keyframe, so regions already represented get re-predicted and the map keeps growing. Sparse-Mono encodes the current observation, the persistent map rendered from the current viewpoint, and their photometric and geometric discrepancies, then decides where additions are needed and how many before decoding complete Gaussians only at the selected locations. On Replica it reaches an average PSNR 1.05 dB above MonoGS and uses 89.3% fewer Gaussians than VINGS-Mono, at comparable online processing latency.
Monocular Gaussian SLAM predicts a full set of Gaussians for every mapping keyframe, so regions already represented get re-predicted and the map keeps growing. Sparse-Mono encodes the current observation, the persistent map rendered from the current viewpoint, and their photometric and geometric discrepancies, then decides where additions are needed and how many before decoding complete Gaussians only at the selected locations. On Replica it reaches an average PSNR 1.05 dB above MonoGS and uses 89.3% fewer Gaussians than VINGS-Mono, at comparable online processing latency.
China People's Public Security University
Jianpeng Xu（First author）Fanliang Bu（Corresponding author）
Sparse-Mono Sparse Gaussian Splatting Simultaneous Localization and Mapping
arXiv Sep 21, 2026
A Refreshable, Near-Domain-Controlled Audio Benchmark for Telecom Fraud Detection: TeleAntiFraud 2.0
The work presents TeleAntiFraud 2.0, a refreshable Chinese audio benchmark for telecom fraud detection organized as monthly frozen snapshots, in which mixed-tree generation makes fraud and lawful near-domain calls share context and diverge only at label-bearing actions, and it reports that three text classifiers reach perfect Macro-F1 against unrelated or ordinary negatives but drop to 0.65-0.68 with near-domain sibling negatives, while full-set audio and ASR+LLM evaluations expose class-prior shortcuts, prediction collapse, and snapshot sensitivity.
The work presents TeleAntiFraud 2.0, a refreshable Chinese audio benchmark for telecom fraud detection organized as monthly frozen snapshots, in which mixed-tree generation makes fraud and lawful near-domain calls share context and diverge only at label-bearing actions, and it reports that three text classifiers reach perfect Macro-F1 against unrelated or ordinary negatives but drop to 0.65-0.68 with near-domain sibling negatives, while full-set audio and ASR+LLM evaluations expose class-prior shortcuts, prediction collapse, and snapshot sensitivity.
Ant Group (China)Northeastern University China People's Public Security University
Huiyuan Liu（First author）
TeleAntiFraud 2.0 Mixed-Tree Anti-Fraud Generation Pipeline Telecom Fraud Detection
arXiv Sep 21, 2026
Structured Skill Optimization Under Frozen Weights: A New Path for Audio Anti-Fraud Detection
Without changing any parameters of the audio-language model, FRAUDSkill optimizes an external layer of skill programs, routing policies, and decision rules and combines structured output control with validation-guided multi-path inference, raising Macro-F1 on the TeleAntiFraud benchmark from 41.54% for the shared frozen-model baseline to 73.50% while cutting the invalid-output rate from 36.04% to 1.94%.
Without changing any parameters of the audio-language model, FRAUDSkill optimizes an external layer of skill programs, routing policies, and decision rules and combines structured output control with validation-guided multi-path inference, raising Macro-F1 on the TeleAntiFraud benchmark from 41.54% for the shared frozen-model baseline to 73.50% while cutting the invalid-output rate from 36.04% to 1.94%.
Peking University Northeastern University China People's Public Security University
Chengxian Hu（First author）
FRAUDSkill Frozen-Weight Skill Optimization Telecom Fraud Detection Audio-Language Model

You’re all caught up

Monocular Gaussian SLAM stops painting every keyframe: Sparse-Mono reads the existing map first and uses 89.3% fewer Gaussians than VINGS-Mono on Replica

A Refreshable, Near-Domain-Controlled Audio Benchmark for Telecom Fraud Detection: TeleAntiFraud 2.0

Structured Skill Optimization Under Frozen Weights: A New Path for Audio Anti-Fraud Detection