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ARC-SORT 提出一种免训练、可实时运行的跟踪检测式多目标跟踪器，利用交互式多模型(IMM)卡尔曼滤波的预测协方差自适应调整搜索区域，并通过外观证据纠正交叉时的身份交换，在 SportsMOT 上达到 81.82 HOTA 且身份切换最少。
PrivMeSA introduces a privacy-aware self-evolving multi-agent system where a local clinical LLM agent selectively delegates to remote specialists, using reinforcement learning to balance accuracy against re-identification risk and distilling consultations into reusable local lessons, reducing patient disclosure from 98.0% to 0.2% while improving accuracy by up to 15.8 percentage points on an MIMIC-IV-ED benchmark.
This paper demonstrates that large language models can perform fully automated, large-scale deanonymization of pseudonymous online accounts by matching unstructured text across platforms, achieving up to 68% recall at 90% precision and rendering previous practical obscurity obsolete.
AURA is an LLM-powered anonymization framework that balances privacy protection against agentic web-search re-identification while preserving contextual utility through adaptive privacy scopes and mask-reconstruct methods.