Papers by Xuyao Huang
SIFT: Grounding LLM Reasoning in Contexts via Stickers (2025.findings-emnlp)
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| Challenge: | Using a new approach, we can improve the pass@1 accuracy of LLM reasoning in large language models. |
| Approach: | They propose a method that leverages increasing inference-time compute to ground LLM reasoning in contexts. |
| Outcome: | The proposed approach improves pass@1 accuracy of DeepSeek-R1 on AIME2024 from 78.33% to **85.67%** and that on Aime2025 from 69.8% to **77.33%**. |
TPS-Bench: Evaluating AI Agents’ Tool Planning & Scheduling Abilities in Compounding Tasks (2026.acl-long)
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| Challenge: | Large language model (LLM) agents have demonstrated strong problem-solving competence across domains like research and coding. |
| Approach: | They propose to use a tool repository to analyze the ability of large language model agents to solve complex problems. |
| Outcome: | The proposed model outperforms open-source and closed-source models in task completion rate and efficiency. |