Papers by Guofeng Zhang
Beyond Stochastic Exploration: What Makes Training Data Valuable for Agentic Search (2026.findings-acl)
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| Challenge: | Existing RL-based search agents rely on stochastic exploration, leading to inefficient reasoning trajectories and unstable training. |
| Approach: | They propose a framework to enhance the performance and training stability of search agents by transforming raw reasoning trajectories into hierarchical experience knowledge. |
| Outcome: | The proposed framework exhibits strong cross-task and cross-algorithm generalizations on multiple complex agentic search and mathematical reasoning benchmarks. |
RASD: Retrieval-Augmented Speculative Decoding (2025.findings-acl)
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| Challenge: | Existing methods for generating draft tokens rely on lightweight draft models or additional model structures to generate tokens and retrieve context from databases. |
| Approach: | They propose to use a pruning method to enhance model-based speculative decoding by combining the best-fit model with the best retrieval tree. |
| Outcome: | The proposed method achieves state-of-the-art inference acceleration across tasks such as DocQA, Summary, Code, and In-Domain QA. |
Debate-of-Thoughts: Resolving Knowledge Conflicts in LLMs Through Internal Deliberation (2026.acl-long)
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| Challenge: | Existing methods for retrieval augmented generation are based on a simplistic binary choice of relying on external contexts or memory. |
| Approach: | They propose a framework that transforms conflict resolution into an active deliberation process by incorporating contradictions as opportunities for deeper reasoning. |
| Outcome: | Experiments show that DoT outperforms state-of-the-art methods while generating transparent debate transcripts that explain its decisions. |