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20242026
most citedCodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis

2 citations · 4 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.PL2025

Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis

Anjiang Wei, Tianran Sun, Tarun Suresh +3

Program verification relies on loop invariants, yet automatically discovering strong invariants remains a long-standing challenge. We investigate whether large language models (LLM…

cs.AI2025★ 1 cited

SATBench: Benchmarking LLMs' Logical Reasoning via Automated Puzzle Generation from SAT Formulas

Anjiang Wei, Yuheng Wu, Yingjia Wan +6

We introduce SATBench, a benchmark for evaluating the logical reasoning capabilities of large language models (LLMs) through logical puzzles derived from Boolean satisfiability (SA…

cs.CL2025

SuperCoder: Assembly Program Superoptimization with Large Language Models

Anjiang Wei, Tarun Suresh, Huanmi Tan +4

Superoptimization is the task of transforming a program into a faster one, and ideally the very fastest possible one, while preserving its input-output behavior. In this work, we i…

cs.AR2025★ 1 cited

VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation

Anjiang Wei, Huanmi Tan, Tarun Suresh +5

Recent advances in Large Language Models (LLMs) have sparked growing interest in applying them to Electronic Design Automation (EDA) tasks, particularly Register Transfer Level (RT…

cs.PL2025★ 2 cited

CodeARC: Benchmarking Reasoning Capabilities of LLM Agents for Inductive Program Synthesis

Anjiang Wei, Tarun Suresh, Jiannan Cao +6

Inductive program synthesis, or programming by example, requires synthesizing functions from input-output examples that generalize to unseen inputs. While large language model agen…