Xujie Si
Assistant Professor
Studies neuro-symbolic learning and reasoning, program synthesis and verification.
Xujie Si's lab at Toronto works on reasoning & neuro-symbolic AI, compilers & programming languages and reasoning & agents. 31 papers since 2023, 16 at top venues, mostly NeurIPS and ICLR. 5 PhD students and 5 master's students. 10 alumni. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 52%
- PhD students
- 5
- MS students
- 5
- alumni listed
- 10
Papers by yearfrom the lab homepage, 2023–2026
- 13 papers2026 · T1 7 · collecting
- 7 papers2025 · T1 4
- 7 papers2024 · T1 4
- 4 papers2023 · T1 1
T1 venuesOtherVenues: NeurIPS, ICLR, ICML, ACL, CVPR
Research focuspaper titles that use each topic's words
- Reasoning & neuro-symbolic AI5 papers
- Compilers & programming languages4 papers
- Reasoning & agents4 papers
- neuro-symbolic learning0 papers
- program synthesis0 papers
Paper list
From the lab homepage.
202613 papers
- The Canadian Conference on Artificial IntelligenceAre You the A-hole? A Fair, Multi-Perspective Ethical Reasoning Framework
- ICLR (T1)Divide and Abstract: Autoformalization via Decomposition and Abstraction Learning
- COLMDreamProver: Evolving Transferable Lemma Libraries via a Wake-Sleep Theorem-Proving Agent
- The Art, Science, and Engineering of ProgrammingEvaluating LLMs in the Context of a Functional Programming Course: A Comprehensive Study
- NeurIPS (T1)Ground False: Uncovering Errors in Formal Mathematics Benchmarks
- EACL OralIs This LLM Library Learning? Evaluation Must Account For Compute and Behaviour
- ICLR (T1)LogicXGNN: Grounded Logical Rules for Explaining Graph Neural Networks
- NeurIPS (T1)Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generative Modeling
- ICML SpotlightPosition: Theory-Level Autoformalization, From Isolated Statements to Unified Formal Knowledge Bases
- ICML (T1)Scam2Prompt: A Scalable Framework for Auditing Malicious Scam Endpoints in Production LLMs
- NeurIPS (T1)Theory-Scale Auto-Formalization of Logics for Computer Science
- NeurIPS (T1)VerifyThisBench: Joint Evaluation of Code, Specifications, and Proof
20257 papers
- RLCA Large-Scale Study of Behavioral Metric Learning on Distracting Reinforcement Learning Environments
- ACL (T1)APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts
- CVPR (T1)Decoupling Training-Free Guided Diffusion by ADMM
- NeuS : International Conference on Neuro-symbolic Systems OralLearning Minimal Neural Specifications
- ICLR (T1)Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning
- CAVPyEuclid: A Versatile Formal Plane Geometry System in Python
- ICML (T1)TypyBench: Evaluating LLM Type Inference for Untyped Python Repositories
20247 papers
- COLMA Survey on Deep Learning for Theorem Proving
- ICML (T1)Autoformalizing Euclidean Geometry
- SAIVChronosymbolic: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning
- NeurIPS (T1)Code Repair with LLMs gives an Exploration-Exploitation Tradeoff
- TMLR (T1)G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks
- NeurIPS (T1)LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulation
- FMCADModernizing SMT-Based Type Error Localization
20234 papers
- SIGCSEIdentifying Different Student Clusters in Functional Programming Assignments: From Quick Learners to Struggling Students
- NeurIPS (T1)Learning Reliable Logical Rules with SATNet
- APLASTorchProbe: Fuzzing Dynamic Deep Learning Compilers
- ICML OralTowards Reliable Neural Specifications
Where alumni went10 listed
Apply to TorontoFall 2027
Computer Science MSc/PhD
- Round
- Single round (Dec 1)
- Opens
- —
- GRE
- optional
- English
- TOEFL/IELTS if applicable
- Fee
- CAD 135
Confirmed on the program page.
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