Yican Sun

TACAS Conference 2026 Conference Paper

VeriStruct: AI-assisted Automated Verification of Data-Structure Modules in Verus

• Chuyue Sun
• Yican Sun
• Daneshvar Amrollahi
• Ethan Zhang
• Shuvendu Lahiri
• Shan Lu
• David Dill
• Clark Barrett

STOC Conference 2025 Conference Paper

Almost Optimal Time Lower Bound for Approximating Parameterized Clique, CSP, and More, under ETH

• Venkatesan Guruswami
• Bingkai Lin
• Xuandi Ren
• Yican Sun
• Kewen Wu 0001

The Parameterized Inapproximability Hypothesis (PIH), which is an analog of the PCP theorem in parameterized complexity, asserts the following: there is a constant ε> 0 such that for any computable function f :ℕ→ℕ, no f ( k )· n O (1) -time algorithm can, on input a k -variable CSP instance with domain size n , find an assignment satisfying 1−ε fraction of the constraints. A recent work by Guruswami, Lin, Ren, Sun, and Wu (STOC’24) established PIH under the Exponential Time Hypothesis (ETH). In this work, we improve the quantitative aspects of PIH and prove (under ETH) that approximating sparse parameterized CSPs within a constant factor requires n k 1− o (1) time. This immediately implies, for example, that finding a ( k /2)-clique in an n -vertex graph with a k -clique requires n k 1− o (1) time (assuming ETH). We also prove almost optimal time lower bounds for approximating k -ExactCover and Max k -Coverage. Our proof follows the blueprint of the previous work to identify a ”vector-structured” ETH-hard CSP whose satisfiability can be checked via an appropriate form of ”parallel” PCP. Using further ideas in the reduction, we guarantee additional structures for constraints in the CSP. We then leverage this to design a parallel PCP of almost linear size based on Reed-Muller codes and derandomized low degree testing.

STOC Conference 2024 Conference Paper

Parameterized Inapproximability Hypothesis under Exponential Time Hypothesis

• Venkatesan Guruswami
• Bingkai Lin
• Xuandi Ren
• Yican Sun
• Kewen Wu 0001

The Parameterized Inapproximability Hypothesis (PIH) asserts that no fixed parameter tractable (FPT) algorithm can distinguish a satisfiable CSP instance, parameterized by the number of variables, from one where every assignment fails to satisfy an ε fraction of constraints for some absolute constant ε > 0. PIH plays the role of the PCP theorem in parameterized complexity. However, PIH has only been established under Gap-ETH, a very strong assumption with an inherent gap. In this work, we prove PIH under the Exponential Time Hypothesis (ETH). This is the first proof of PIH from a gap-free assumption. Our proof is self-contained and elementary. We identify an ETH-hard CSP whose variables take vector values, and constraints are either linear or of a special parallel structure. Both kinds of constraints can be checked with constant soundness via a “parallel PCP of proximity” based on the Walsh-Hadamard code.

SODA Conference 2023 Conference Paper

Constant Approximating Parameterized k -SETCOVER is W[2]-hard

• Bingkai Lin
• Xuandi Ren
• Yican Sun
• Xiuhan Wang

In this paper, we prove that it is W[2]-hard to approximate k-SETCOVER within any constant ratio. Our proof is built upon the recently developed threshold graph composition technique. We propose a strong notion of threshold graphs and use a new composition method to prove this result. Our technique could also be applied to rule out polynomial time ratio approximation algorithms for the non-parameterized k -SETCOVER problem with k as small as, assuming W[1] ≠ FPT. We highlight that our proof does not depend on the well-known PCP theorem, and only involves simple combinatorial objects.

FOCS Conference 2023 Conference Paper

Improved Hardness of Approximating k-Clique under ETH

• Bingkai Lin
• Xuandi Ren
• Yican Sun
• Xiuhan Wang

In this paper, we prove that assuming the exponential time hypothesis (ETH), there is no $f(k) \cdot n^{k^{o(1 / \log \log k)}}$-time algorithm that can decide whether an n-vertex graph contains a clique of size k or contains no clique of size $k / 2$, and no FPT algorithm can decide whether an input graph has a clique of size k or no clique of size $k / f(k)$, where $f(k)$ is some function in $k^{1-o(1)}$. Our results significantly improve the previous works [1], [2]. The crux of our proof is a framework to construct gap-producing reductions for the k-CLIQUE problem. More precisely, we show that given an error-correcting code $C: \Sigma_{1}^{k} \rightarrow \Sigma_{2}^{k^{\prime}}$ that is locally testable and smooth locally decodable in the parallel setting, one can construct a reduction which on input a graph G outputs a graph $G^{\prime}$ in $\left(k^{\prime}\right)^{O(1)} \cdot n^{O\left(\log \left|\Sigma_{2}\right| / \log \left|\Sigma_{1}\right|\right)}$ time such•if G has a clique of size k, then $G^{\prime}$ has a clique of size K, where $K=\left(k^{\prime}\right)^{O(1)}$. •if G has no clique of size k, then $G^{\prime}$ has no clique of size $(1-\varepsilon) \cdot K$ for some constant $\varepsilon \in(0, 1)$. We then construct such a code with $k^{\prime}=k^{\Theta(\log \log k)}$ and $\left|\Sigma_{2}\right|=\left|\Sigma_{1}\right|^{k^{0. 54}}$, establishing the hardness result above. Our code generalizes the derivative code [3] into the case with a super constant order of derivatives.

AAAI Conference 2020 Conference Paper

TreeGen: A Tree-Based Transformer Architecture for Code Generation

• Zeyu Sun
• Qihao Zhu
• Yingfei Xiong
• Yican Sun
• Lili Mou
• Lu Zhang

A code generation system generates programming language code based on an input natural language description. State-ofthe-art approaches rely on neural networks for code generation. However, these code generators suffer from two problems. One is the long dependency problem, where a code element often depends on another far-away code element. A variable reference, for example, depends on its definition, which may appear quite a few lines before. The other problem is structure modeling, as programs contain rich structural information. In this paper, we propose a novel tree-based neural architecture, TreeGen, for code generation. TreeGen uses the attention mechanism of Transformers to alleviate the longdependency problem, and introduces a novel AST reader (encoder) to incorporate grammar rules and AST structures into the network. We evaluated TreeGen on a Python benchmark, HearthStone, and two semantic parsing benchmarks, ATIS and GEO. TreeGen outperformed the previous state-of-theart approach by 4. 5 percentage points on HearthStone, and achieved the best accuracy among neural network-based approaches on ATIS (89. 1%) and GEO (89. 6%). We also conducted an ablation test to better understand each component of our model.