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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Deep Dive into speedup_by_fusion in PyTorch Inductor
Published:
A benchmark-driven analysis of PyTorch Inductor’s speedup_by_fusion config, its runtime logs, and why register spilling can reject fusions that still help.
PyTorch Inductor 中 speedup_by_fusion 深度解析
Published:
详解 PyTorch Inductor 的 speedup_by_fusion 配置：开启方式、工作原理、benchmark 日志示例，以及 register spilling 带来的融合决策争议。
Deep Dive into fuse_nodes in PyTorch Inductor
Published:
A structured walkthrough of how PyTorch Inductor enumerates, scores, and greedily applies graph fusion candidates in fuse_nodes.
PyTorch Inductor 中 fuse_nodes 融合流程深度解析
Published:
系统梳理 PyTorch Inductor 如何在 fuse_nodes 中枚举候选对、执行打分排序，并以贪心策略推进图融合。
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Syntactic Prediction through Reinforcement Learning
Published in ACL ARR 2026 March Submission (under review), 2026
Under review at ACL ARR 2026 March Submission. This is my first paper and it studies reinforcement learning for hierarchical syntactic prediction in large language models.
Fusion-R1: Empower PyTorch Graph Fusion via LLM with Reinforcement Learning
Published in NeurIPS 2026 Conference Submission (under review), 2026
Under review at NeurIPS 2026. Fusion-R1 is an LLM-based fusion proposal layer for TorchInductor trained with GRPO, improving speedup over PyTorch eager mode from 1.74× to 1.83× and reducing average kernel count from 99.10 to 77.18.
IntentEval: Evaluating Whether Large Language Models Answer What Users Actually Mean
Published in ACL ARR 2026 May Submission (under review), 2026
Under review at ACL ARR 2026 May Submission. IntentEval is a benchmark for evaluating whether LLMs answer the user’s intended question, using human-derived probability distributions over interpretations and an intent-aware judge. It achieves 0.90 correlation with LMArena rankings.
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.