Papers by Weihai Lu
MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for multimodal stance detection face contextual grounding, cross-modal interpretation ambiguity, and single-pass reasoning fragility. |
| Approach: | They propose a multi-agent framework that integrates Retrieval Augmentation for contextual grounding, specialized Multimodal Analysis agents for nuanced interpretation, Reasoning-Enhanced Debate stage and Self-Reflection for robust adjudication. |
| Outcome: | Extensive experiments on five datasets show that the proposed framework outperforms state-of-the-art methods. |
Decoding the Market’s Pulse: Context-Enriched Agentic Retrieval Augmented Generation for Predicting Post-Earnings Price Shocks (2026.eacl-long)
Copied to clipboard
| Challenge: | Existing methods for forecasting large stock price movements after corporate earnings calls are prone to **narrative bias** Existing approaches lack temporal-causal reasoning and are unable to predict large stock prices. |
| Approach: | They propose a retrieval-augmented framework that deploys a team of cooperative LLM agents . they retrieve structured evidence from a Causal-Temporal Knowledge Graph built from financial statements and earnings calls . |
| Outcome: | The proposed framework outperforms larger LLMs and fine-tuned models in macro-F1, MCC, and Sharpe for the same forecasting horizon. |