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We introduce FACTS-MAS, a domain-agnostic multi-agent forecasting framework that decomposes heterogeneous evidence into specialized agent channels, validates and weights each via leakage-safe historical validation, and fuses them adaptively. Evaluated on Zillow housing inventory and FRED unemployment claims across 15 U.S. metros, it outperforms naive baselines and TimesFM, though a factor-adjusted exponential smoothing model matches its fused performance.
TCAR-Gen proposes a framework combining query-conditioned graph neural networks, temporal evidence fusion, and chain-of-trees reasoning for temporal graph retrieval in knowledge-grounded generation. It achieves improved recall on the Victorian Crime Diaries benchmark across multiple query types.