Papers by Xiaoyang Yuan
RISER: Orchestrating Latent Reasoning Skills for Adaptive Activation Steering (2026.findings-acl)
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| Challenge: | Existing methods for domain-specific reasoning with large language models require updating parameter updates. |
| Approach: | They propose a plug-and-play intervention framework that adaptively steers LLM reasoning in activation space. |
| Outcome: | The proposed framework achieves zero-shot accuracy improvements of 3.4–6.5% over the base model while outperforming chain-of-thought-style reasoning with 2–3 higher token efficiency and robust accuracy gains. |
HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification. |
| Approach: | They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models. |
| Outcome: | The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets. |