May 20, 2026 · Artificial Intelligence

Can Tabular Anomaly Detection Move Beyond One‑for‑One? OFA‑TAD Introduces a One‑for‑All Paradigm

Tabular anomaly detection traditionally requires training a separate model for each dataset (one‑for‑one), but the new OFA‑TAD framework trains once on multiple source tables and directly transfers to unseen target tables without fine‑tuning, leveraging multi‑view distance encoding, MoE fusion, and synthetic pseudo‑anomalies to achieve state‑of‑the‑art performance across 34 datasets in 14 domains.

Mixture of ExpertsOFA-TADmulti-view distance

0 likes · 10 min read

Can Tabular Anomaly Detection Move Beyond One‑for‑One? OFA‑TAD Introduces a One‑for‑All Paradigm

PaperAgent

May 13, 2026 · Artificial Intelligence

One-for-All Multi-Agent Collaboration: Adaptive Cross-Task Topology Design

The paper introduces OFA-MAS, a one‑for‑all multi‑agent system that learns a universal topology designer using task‑aware graph encoding and a Mixture‑of‑Experts generator, achieving superior performance, OOD generalization, robustness, and efficiency across six major benchmarks.

LLMMixture of ExpertsTask-Aware Graph Encoder

0 likes · 14 min read

One-for-All Multi-Agent Collaboration: Adaptive Cross-Task Topology Design