# TIEM: Temporal Integration of Hypergraph Evidence and Skill Memory for Event-Driven Financial Forecasting

arXiv: https://arxiv.org/abs/2608.13024 · HTML: https://mlsanity.com/paper/2608.13024/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2026.

## Built on by (most cited first, 2 of 2)

- [Fully Unleashing the Multimodal Attacker: Meta-Adaptive Jailbreaking of Vision-Language Models](https://mlsanity.com/paper/2608.27531/) (2026)
- [When Charts Deceive Web Agents: A Paired Benchmark for Misleading Visualization-Grounded Tasks](https://mlsanity.com/paper/2610.12823/) (2026)

## Abstract (arXiv)

Event-driven catalyst-outcome forecasting increasingly uses retrieval- and memory-augmented large language model agents for prediction. However, training-data contamination and temporal leakage can create an Evidence Chasm between reported accuracy and true predictive ability. We propose TIEM, a timestamp-gated framework with three coordinated components: an Event-Evidence Hypergraph (EEH) for timestamp-filtered multi-tier retrieval; a Case-based Skill Memory (CSM) for source-tagged temporal skills; and Heterogeneous Evidence-Experience Fusion Reasoning (HEFR) for evidence-experience fusion and prediction. We also introduce FinPURE, a recent-period A-share holdout benchmark, and use a Name-Date Probe to assess per-model name-date sensitivity rather than assuming training cutoffs. Results on five financial forecasting benchmarks show TIEM outperforms current baselines. Our project is available at https://github.com/QwenQKing/Fin_TIEM.