Xuanyu Peng, Dominic Kennedy, Yuyou Fan, Ben Greenman, John Regehr, Loris D’Antoni

• Read: 10 Dec 2025
• Published: 06 Dec 2025

POPL 2026

https://www.arxiv.org/abs/2512.06442

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Q&A (link)

What are the motivations for this work?

• See Abstract, Introduction.
• When writing compiler optimizations, performing static analysis on the results is necessary. Each static analysis is based on a collection of abstract transformers that provide abstract semantics for the concrete instructions that make up a program.
• The current static analysis coverage for LLVM backend is limited.
  1. Abstract transformers are usually started in a imprecise form and get optimized later. This process is very expensive.
  2. Across 17 different LLVM backends, only four have any abstract transformers at all for LLVM instructions representing target-specific (e.g. x86-64, RISC-V, AArch64) intrinsics. In this case, developers who substitute intrinsics for portable code while chasing performance can see degraded dataflow-driven optimizations in nearby code because the portable code could be analyzed but the intrinsics cannot.
• Operations that lack abstract transformers must be analyzed conservatively: they return top (concept from abstract representation, of course), the unknown value.
• The work synthesizes abstract transformer for formally specified (using an MLIR dialect) contrect instruction semantics, it addresses the following research questions in the context of finite, Galois-connection-based abstract domains:
  • Is it practical to automatically generate abstract transformer for multiple domains that are used in real-world compilers using existing formally specified concrete instruction semantics?
  • How precise can it be while remaining sound?
  • Can the synthesis procedure navigate the search space without meaningful help from users?

What is the proposed solution?

• See Introduction.
• Treat synthesis as an optimization problem. Use stochastic search techniques inspired by Stoke, where candidate transformers evolve through a sequence of random modifications guided by the cost function induced by the objective.

What is the work’s evaluation of the proposed solution?

• See Introduction, Section 2.
• Validated their prototype by synthesizing transformers for three non-relational, compiler-friendly abstract domains (KnownBits, signed and unsigned ConstantRange) for 39 instructions that are present both in LLVM and in MLIR’s Arith dialect.
• Evaluated by synthesizing abstract transformers for the abstract domains and operations used in the LLVM IR. The results show that NiceToMeetYou complements the precision of LLVM transformers (when measured on 8-bit and 64-bit integers) of 7/47 transformers in the KnownBits domain and 19/47 in ConstantRange. With the addition of a handwritten reduced-product operator for combining synthesized transformers across different abstract domains, the synthesized transformers exceed LLVM’s precision on 22/47 operators.

What is your analysis of the identified problem, idea and evaluation?

What are the contributions?

• See Introduction.
• Propose a framework for synthesizing abstract transformers that leverages existing formal semantics for instructions, and is not limited to specific abstract domains and does not require program templates
• Design an algorithm that incrementally synthesizes the meet of multiple abstract transformers, which enables an MCMC-based search procedure that can discover individual smaller transformers that can be added to the meet.
• Implement the algorithm in NiceToMeetYou, a tool that effectively balances MCMC-based exploration with SMT-based verification. Apply NiceToMeetYou on the bread-and-butter abstract domains from LLVM, namely KnownBits and ConstantRange
• Conduct an evaluation showing how NiceToMeetYou can synthesize abstract transformers for real LLVM operators.

What are future directions for this research?

• See Section 6, 9.
• According to the authors, their furture work is gonna be related to automatic production of additional elements of a compiler bug report.
• In my point of view, the algorithm of C-Reduction is clearly not optimal in terms of efficiency. It is basically:
  1. An outer while loop checking if fixpoint is reached
  2. An inner for loop iterating through all the pre-defined transformations.
• I believe that the algorithm can be better designed to eliminate certain transformations during the process of search, which should improve the performance.

What questions are you left with?

NONE

What is your take-away message from this paper?

NONE