Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning

Chenran Zhang; Geng Chen; Na Su; Ruiqi Wu; Tao Zhou; Tengfei Ma; Wen Fan; Yi Zhou; Yuang Yao

REVIEW 4 major objections 3 minor 42 references

Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning

T0 review · 4 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper claims that OphthaReason, trained on the new MM-Retinal-Reason dataset with an uncertainty-aware dynamic thinking mechanism, outperforms general-purpose and medical multimodal LLMs by wide margins on both basic and complex retinal

desk verdict The packet is broken: the full text is a compilers paper (LPO), so the ophthalmic AI claims are unverified and the submission should not go to review as-is. read the letter →

arxiv 2508.16129 v3 pith:OX7OOLV3 submitted 2025-08-22 cs.AI

Ruiqi Wu , Yuang Yao , Tengfei Ma , Chenran Zhang , Na Su , Tao Zhou , Geng Chen , Wen Fan

show 1 more author

Yi Zhou

This is my paper · ORCID

classification cs.AI

keywords ophthalmicAImultimodalreasoningretinalimagingmedicallargelanguagemodelsuncertainty-awaredynamicthinkingtracescomplexclinicaldataset

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that current medical multimodal large language models mostly stop at shallow visual feature matching, which falls short of real clinical diagnosis. To close that gap, it introduces MM-Retinal-Reason, described as the first ophthalmic multimodal dataset spanning both basic perception tasks and complex reasoning tasks that weave together chief complaints, medical history, and retinal images. It also introduces OphthaReason, described as the first ophthalmology-specific multimodal reasoning model that produces step-by-step reasoning traces, powered by a new Uncertainty-Aware Dynamic Thinking (UADT) mechanism that gauges per-sample uncertainty via entropy and adjusts how deep the model thinks using a shaped advantage signal. The paper reports that OphthaReason outperforms general-purpose, medical, RL-based medical, and ophthalmic multimodal LLMs by at least 24.92%, 15.00%, 21.20%, and 17.66%, respectively. If the underlying dataset is clinically valid and free of leakage, the work would provide both a benchmark and a model for realistic clinical reasoning on retinal images.

What carries the argument

The central object is the Uncertainty-Aware Dynamic Thinking (UADT) mechanism: it computes per-sample uncertainty from output entropy and uses a shaped advantage function to modulate how many reasoning steps the model performs before answering. It does the work of letting a single model behave like a fast perceiver on easy cases and a deliberate reasoner on hard clinical cases. The other load-bearing object is the MM-Retinal-Reason dataset itself, which supplies the paired images, clinical context, questions, and step-by-step reasoning traces that make both training and evaluation of complex reasoning possible.

What would settle it

Sampling complex-reasoning cases from MM-Retinal-Reason, having retinal specialists re-annotate the gold answers blind, and measuring inter-annotator agreement; checking for near-duplicate images between training and test splits. Low agreement or visible leakage would collapse the claimed margins.

Watch

Extended reading notes

Core claim

The central claim is that an ophthalmology-specific multimodal reasoning model, OphthaReason, trained and benchmarked on a new dataset, MM-Retinal-Reason, beats existing general-purpose and medical multimodal LLMs by wide margins on both basic and complex reasoning tasks. The dataset is claimed to be the first of its kind in ophthalmology, covering the full spectrum from perception-style basic reasoning to complex reasoning that combines clinical context with retinal imaging. The model's defining mechanism, UADT, estimates sample-level uncertainty from output entropy and uses a shaped advantage mechanism to control exploration depth, so the same model can answer simple questions quickly and

Load-bearing premise

The complex-reasoning labels in MM-Retinal-Reason are clinically valid and correctly annotated, and the training and test splits are free of leakage, so the reported margins reflect genuine reasoning rather than annotation artifacts.

Editorial extensions

If this is right

Ophthalmic AI could move from single-label image classification to multi-turn clinical reasoning with visible step-by-step justifications.
One dynamic-thinking model could replace the current all-or-nothing strategy of either answering immediately or always reasoning at full length, saving compute on easy cases.
The reported margins imply existing medical multimodal LLMs are comparatively weak on complex reasoning, making this dataset a stress test for them.
The recipe—combine a reasoning dataset with uncertainty-controlled thinking depth—generalizes in principle to other imaging specialties that must merge patient history with scans.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The appended full text in the record is an unrelated compiler-optimization paper, so the claims above rest on the abstract and reader notes for MM-Retinal-Reason, not on that text.
The abstract omits annotation protocol, clinician involvement, inter-annotator agreement, dataset size, and train/test separation; the reported margins stand or fall on the clinical validity and leakage-freeness of the complex-reasoning labels.
A direct test of UADT would compare it against fixed reasoning budgets on the same dataset, to see whether entropy-based dynamic depth—not simply more data or a stronger base model—causes the gains.
If released, the dataset would allow the community to check whether frontier general-purpose models eventually close the gap with scale alone, or whether an ophthalmology-specific dataset remains necessary.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The first thing you need to know: the full text attached to arXiv:2508.16129 is not the ophthalmic paper described in the abstract. It is a completely different paper about discovering LLVM peephole optimizations (LPO, arXiv:2508.16125v2). That mismatch is the dominant fact here. Per the review rules, I am treating the full text as part of the manuscript, and the manuscript is internally inconsistent: title and abstract describe one work, body describes another. That alone is grounds for a desk reject, no matter how plausible either component might be.

To be fair, the abstract for the ophthalmic work is coherent and the direction is sensible. A retinal multimodal dataset that splits basic and complex reasoning, an ophthalmology-specific MLLM with step-by-step traces, and a dynamic-thinking mechanism that adapts depth by entropy are all reasonable ideas. If the actual paper delivers what the abstract promises, it could be a meaningful contribution to the medical MLLM subfield. The claimed margins over four baseline families are large, but large margins are not surprising in a self-built benchmark where the model is custom-fit.

The problem is that nothing in this packet supports the claims. No dataset size, annotation protocol, clinician involvement, inter-annotator agreement, train/test separation, baseline magnitudes, error bars, or statistical tests are reported. The percentages are exact but unverifiable. There is also a circularity risk: UADT's entropy thresholds and advantage coefficients, plus RL reward weights, may be tuned on the same MM-Retinal-Reason benchmark used for evaluation. Those details would need to be checked in the real paper.

For the record, the LPO paper that was actually supplied looks like solid systems work—formally verified optimizations, reproducible benchmark, real LLVM engagement. But it is not this paper, and it does not rescue the submission.

Bottom line: if a corrected full text appears, the ophthalmic paper deserves a serious look. As submitted, it is unverifiable and internally inconsistent. I would desk reject, but keep an eye out for the fixed version.

Referee Report

4 major / 3 minor

Summary. The abstract announces two contributions: the MM-Retinal-Reason dataset, described as the first ophthalmic multimodal dataset covering basic and complex reasoning, and OphthaReason, an ophthalmology-specific multimodal reasoning model trained with a novel Uncertainty-Aware Dynamic Thinking (UADT) mechanism. The abstract claims state-of-the-art margins of at least 24.92%, 15.00%, 21.20%, and 17.66% over four classes of baselines. However, the full-text document supplied for review is a different paper (LPO, arXiv:2508.16125v2, cs.PL) about discovering missed compiler peephole optimizations. Consequently, the review packet contains no description of MM-Retinal-Reason, no specification of UADT, and no experimental details for the claimed margins. The central claims are therefore unverified.

Significance. If the abstract's claims are correct, the work would be a notable step: the first ophthalmology-specific multimodal reasoning dataset spanning both perceptual and clinical reasoning, the first ophthalmic reasoning model with step-by-step traces, and a new uncertainty-adaptive thinking mechanism. The stated margins over four baseline families would be substantial. However, because the correct full text is absent, none of these contributions can be assessed; no machine-checked proofs, reproducible code, or evaluation details are available in the supplied packet, so the contribution cannot be validated from the evidence presented.

major comments (4)

[Full text (mismatched manuscript)] The supplied full text is 'LPO: Discovering Missed Peephole Optimizations with Large Language Models', not the MM-Retinal-Reason/OphthaReason paper described in the abstract. There is no dataset construction, no annotation protocol, no model architecture, no training details, no baseline configurations, and no evaluation metrics in the packet. This makes it impossible to verify the central SOTA claim. This is a load-bearing defect of the submission as received.
[Abstract (reported margins)] The abstract reports precise percentage margins without defining the metric (accuracy, F1, or another score) or whether the improvements are relative or absolute. No baseline magnitudes, confidence intervals, error bars, or statistical tests are reported. For example, a 24.92% relative gain over a low-performing baseline is not comparable to a 17.66% absolute gain on a strong baseline. The claim as stated is not interpretable.
[Abstract (benchmark validity)] The dataset is the foundation for the claimed advances, yet the abstract gives no size, modality composition, clinical task inventory, labeling protocol, clinician involvement, inter-annotator agreement, or train/test split. The complex reasoning labels are the ground truth for the entire evaluation; without evidence of their clinical validity and leakage control, every reported margin is only as good as the benchmark.
[Abstract (UADT)] UADT is described only conceptually: entropy-based uncertainty estimation with a shaped-advantage mechanism that adjusts exploration depth. No equations, definitions, or algorithm pseudocode are provided. Consequently, the reader cannot tell whether the entropy threshold and shaped-advantage coefficients were tuned on MM-Retinal-Reason itself, which would undermine the SOTA comparison. The formal specification and sensitivity analysis are needed.

minor comments (3)

[Abstract] Please specify whether the 'at least X%' improvements are relative or absolute, and include a table with baseline scores and effect sizes.
[Abstract] The phrase 'full spectrum of perception and reasoning' and the term 'dynamic multimodal reasoning' should be defined or mapped to concrete task categories and metrics.
[Full text] The GitHub link in the abstract points to OphthaReason, while the supplied full text's artifact URL points to the LPO repository; the submission materials are inconsistent.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity can be assessed/exhibited: the supplied full text is a different paper (LPO, arXiv:2508.16125v2), so the ophthalmic paper's derivation chain is absent from the packet.

full rationale

The requested paper is arXiv:2508.16129 (MM-Retinal-Reason / OphthaReason), but the provided full text is arXiv:2508.16125v2, 'LPO: Discovering Missed Peephole Optimizations with Large Language Models', a compiler optimization paper. The only in-scope text from the ophthalmic paper is the abstract. The abstract claims state-of-the-art margins and describes the Uncertainty-Aware Dynamic Thinking (UADT) mechanism, but it provides no equations, no fitting procedure, no dataset construction or annotation protocol, no baseline configurations, and no citation chain. There is therefore no derivation chain that can be reduced to its inputs, and no quoted equation or fitted parameter that could be exhibited as circular. The reader's provisional concern—that UADT entropy thresholds or RL reward weights might have been tuned on the same MM-Retinal-Reason benchmark used for evaluation—is a plausible data-quality risk, but it is not supported by any quoted text in the packet. Per the hard rules, speculation about tuning without an exhibited reduction is not circularity. No self-citations are load-bearing because none appear in the available abstract. Accordingly, the honest finding is 0: no circularity can be demonstrated from the supplied evidence, and the SOTA claim remains unverified rather than circular.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

Abstract-only ledger: the central claim depends on the training pipeline for OphthaReason, the UADT mechanism, and the dataset labels. Two groups of tuned hyperparameters are inferable (UADT entropy/advantage settings, RL training settings) but no values are reported in the abstract. No invented entities are introduced. The unexamined domain assumptions are the clinical validity and leakage-freedom of MM-Retinal-Reason labels and the premise that entropy-measured uncertainty tracks the reasoning depth a sample needs.

free parameters (2)

UADT uncertainty threshold and exploration-depth modulation coefficients = not reported (abstract only)
UADT estimates sample-level uncertainty via entropy and shapes exploration depth with an advantage mechanism; such coefficients are normally tuned on validation data and directly affect reported margins. Abstract gives no values.
OphthaReason training hyperparameters (reward weights, RL settings) = not reported (abstract only)
The model is trained with a reinforcement-learning paradigm on the new dataset; loss and reward weighting determine benchmark outcomes and are absent from the abstract.

assumptions (3)

domain assumption Standard MLLM fine-tuning and RL training procedures behave as assumed
The abstract describes training in one sentence; the pipeline is treated as conventional reinforcement-learning fine-tuning.
domain assumption MM-Retinal-Reason labels are clinically valid, correctly annotated, and free of leakage
All evaluation rests on this benchmark; no annotation protocol, clinician involvement, or train/test separation is given in the abstract.
domain assumption Entropy-based uncertainty correlates with sample-level reasoning depth needs
Load-bearing premise of UADT's dynamic thinking mechanism; asserted without evidence in the abstract.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning." pith.science (2026). https://pith.science/paper/OX7OOLV3

@misc{pith2026250816129,
  author       = {Pith},
  title        = {Pith review of: Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OX7OOLV3}},
  note         = {Machine review of arXiv:2508.16129}
}

read the original abstract

Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities with reinforcement learning paradigm. Although several multimodal reasoning models have been explored in the medical domain, most of them focus exclusively on basic reasoning, which refers to shallow inference based on visual feature matching. However, real-world clinical diagnosis extends beyond basic reasoning, demanding reasoning processes that integrate heterogeneous clinical information (such as chief complaints and medical history) with multimodal medical imaging data. To bridge this gap, we introduce MM-Retinal-Reason, the first ophthalmic multimodal dataset with the full spectrum of perception and reasoning. It encompasses both basic reasoning tasks and complex reasoning tasks, aiming to enhance visual-centric fundamental reasoning capabilities and emulate realistic clinical thinking patterns. Building upon MM-Retinal-Reason, we propose OphthaReason, the first ophthalmology-specific multimodal reasoning model with step-by-step reasoning traces. To enable flexible adaptation to both basic and complex reasoning tasks, we specifically design a novel method called Uncertainty-Aware Dynamic Thinking (UADT), which estimates sample-level uncertainty via entropy and dynamically modulates the model's exploration depth using a shaped advantage mechanism. Comprehensive experiments demonstrate that our model achieves state-of-the-art performance on both basic and complex reasoning tasks, outperforming general-purpose MLLMs, medical MLLMs, RL-based medical MLLMs, and ophthalmic MLLMs by at least 24.92\%, 15.00\%, 21.20\%, and 17.66\%. Project Page: \href{https://github.com/lxirich/OphthaReason}{link}.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 35 canonical work pages

[1]

[n. d.]. InstCombine contributor guide — LLVM 22.0.0git documenta- tion — llvm.org.https://llvm.org/docs/InstCombineContributorGuide. html. Accessed 01-08-2025

work page 2025
[2]

[n. d.]. LLVM InstCombine Pull Requests on GitHub. https://github.com/llvm/llvm-project/pulls?q=is%3Aopen+is% 3Apr+label%3Allvm%3Ainstcombine. Accessed 01-08-2025

work page 2025
[3]

[n. d.]. LLVM Language Reference Manual — LLVM 22.0.0git docu- mentation — llvm.org.https://llvm.org/docs/LangRef.html. Accessed 01-08-2025

work page 2025
[4]

[n. d.]. Overview - CPU 2017 — spec.org.https://www.spec.org/ cpu2017/Docs/overview.html. Accessed 12-01-2026

work page 2017
[5]

[n. d.]. The LLVM Compiler Infrastructure Project — llvm.org.https: //llvm.org. Accessed 01-08-2025

work page 2025
[6]

Aho, Monica S

Alfred V. Aho, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman. 2006. Compilers: Principles, Techniques, and Tools (2nd Edition). Addison Wesley

work page 2006
[7]

Sorav Bansal and Alex Aiken. 2006. Automatic generation of peephole superoptimizers. InProceedings of the 12th international conference on Architectural support for programming languages and operating systems. ACM, San Jose California USA, 394–403. doi:10.1145/1168857.1168906

work page arXiv 2006
[8]

Sorav Bansal and Alex Aiken. 2008. Binary Translation Using Peephole Superoptimizers. In8th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2008, December 8-10, 2008, San Diego, California, USA, Proceedings, Richard Draves and Robbert van Renesse (Eds.). USENIX Association, 177–192.http://www.usenix.org/events/ osdi08/tech/full_paper...

work page 2008

Show all 42 references

[9]

Gergö Barany. 2018. Finding missed compiler optimizations by dif- ferential testing. InProceedings of the 27th international conference on compiler construction. 82–92

2018
[10]

2015.Optgen: A Generator for Local Optimizations

Sebastian Buchwald. 2015.Optgen: A Generator for Local Optimizations. Lecture Notes in Computer Science, Vol. 9031. Springer Berlin Heidel- berg, Berlin, Heidelberg, 171–189. doi:10.1007/978-3-662-46663-6_9 LPO: Discovering Missed Peephole Optimizations with Large Language Mod...

2015 doi
[11]

Chris Cummins, Volker Seeker, Dejan Grubisic, Mostafa Elhoushi, Youwei Liang, Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Kim Hazelwood, Gabriel Synnaeve, and Hugh Leather. 2023. Large Lan- guage Models for Compiler Optimization. arXiv:2309.07062 (Sept. 2023). doi:10.485...

2023 doi arXiv
[12]

Davidson and Christopher W

Jack W. Davidson and Christopher W. Fraser. 1984. Automatic gener- ation of peephole optimizations. InProceedings of the 1984 SIGPLAN symposium on Compiler construction - SIGPLAN ’84. ACM Press, Mon- treal, Canada, 111–116. doi:10.1145/502874.502885

1984
[13]

1991.Crafting a Compiler with C

Charles N Fischer and Richard J LeBlanc Jr. 1991.Crafting a Compiler with C. Benjamin-Cummings Publishing Co., Inc

1991
[14]

Shuzheng Gao, Cuiyun Gao, Wenchao Gu, and Michael Lyu
[15]

Spandan Garg, Roshanak Zilouchian Moghaddam, and Neel Sundare- san. 2023. Rapgen: An approach for fixing code inefficiencies in zero- shot.arXiv preprint arXiv:2306.17077(2023)

2023 arXiv
[16]

Dejan Grubisic, Chris Cummins, Volker Seeker, and Hugh Leather
[17]

Davide Italiano and Chris Cummins. 2024. Finding Missed Code Size Optimizations in Compilers using LLMs.arXiv preprint arXiv:2501.00655(2024)

2024 arXiv
[18]

Compiler generated feedback for large language models.arXiv preprint arXiv:2403.14714(2024)

2024 arXiv
[19]

Chris Lattner and Vikram Adve. 2004. LLVM: A compilation frame- work for lifelong program analysis & transformation. InInternational symposium on code generation and optimization, 2004. CGO 2004.IEEE, 75–86

2004
[20]

Rajeev Joshi, Greg Nelson, and Keith Randall. 2002. Denali: a goal- directed superoptimizer.ACM SIGPLAN Notices37, 5 (May 2002), 304–314. doi:10.1145/543552.512566

2002
[21]

Zhengyang Liu, Stefan Mada, and John Regehr. 2024. Minotaur: A SIMD-oriented synthesizing superoptimizer.Proceedings of the ACM on Programming Languages8, OOPSLA2 (2024), 1561–1585

2024
[22]

2002.LLVM: An infrastructure for multi-stage optimization

Chris Arthur Lattner. 2002.LLVM: An infrastructure for multi-stage optimization. Ph. D. Dissertation. University of Illinois at Urbana- Champaign

2002
[23]

Nuno P Lopes, Juneyoung Lee, Chung-Kil Hur, Zhengyang Liu, and John Regehr. 2021. Alive2: bounded translation validation for LLVM. InProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation. 65–79

2021
[24]

Zhibo Liu, Dongwei Xiao, Zongjie Li, Shuai Wang, and Wei Meng
[25]

José Antonio Hernández López, Boqi Chen, Mootez Saad, Tushar Sharma, and Dániel Varró. 2025. On Inter-Dataset Code Duplication and Data Leakage in Large Language Models.IEEE Trans. Software Eng.51, 1 (2025), 192–205. doi:10.1109/TSE.2024.3504286

2025
[26]

Henry Massalin. 1987. Superoptimizer: a look at the smallest program. ACM SIGARCH Computer Architecture News15, 5 (Nov. 1987), 122–126. doi:10.1145/36177.36194

1987
[27]

Nuno P Lopes, David Menendez, Santosh Nagarakatte, and John Regehr
[28]

William M McKeeman. 1998. Differential testing for software.Digital Technical Journal10, 1 (1998), 100–107

1998
[29]

David Menendez and Santosh Nagarakatte. 2016. Termination- checking for LLVM peephole optimizations. InProceedings of the 38th International Conference on Software Engineering. 191–202

2016
[30]

Manasij Mukherjee and John Regehr. 2024. Hydra: Generalizing Peephole Optimizations with Program Synthesis.Proceedings of the ACM on Programming Languages8, OOPSLA1 (April 2024), 725–753. doi:10.1145/3649837

2024 doi
[31]

W. M. McKeeman. 1965. Peephole optimization.Commun. ACM8, 7 (1965), 443–444. doi:10.1145/364995.365000

1965
[32]

Amir Pnueli, Michael Siegel, and Eli Singerman. 1998. Translation validation. InInternational Conference on Tools and Algorithms for the Construction and Analysis of Systems. Springer, 151–166

1998
[33]

Nikita Popov. 2025. llvm-compile-time-tracker: LLVM compile-time performance tracking infrastructure.https://github.com/nikic/llvm- compile-time-tracker

2025
[34]

Raimondas Sasnauskas, Yang Chen, Peter Collingbourne, Jeroen Ketema, Gratian Lup, Jubi Taneja, and John Regehr. 2017. Souper: A synthesizing superoptimizer.arXiv preprint arXiv:1711.04422(2017)

2017 arXiv
[35]

George C Necula. 2000. Translation validation for an optimizing compiler. InProceedings of the ACM SIGPLAN 2000 conference on Pro- gramming language design and implementation. 83–94

2000
[36]

Theodoros Theodoridis, Manuel Rigger, and Zhendong Su. 2022. Find- ing missed optimizations through the lens of dead code elimination. In Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems. 697–709

2022
[37]

Yingwei Zheng. 2023. LLVM Opt Benchmark.https://github.com/ dtcxzyw/llvm-opt-benchmark. Accessed: 30-12-2025

2023
[38]

Xin Zhou, Martin Weyssow, Ratnadira Widyasari, Ting Zhang, Junda He, Yunbo Lyu, Jianming Chang, Beiqi Zhang, Dan Huang, and David Lo. 2025. LessLeak-Bench: A First Investigation of Data Leak- age in LLMs Across 83 Software Engineering Benchmarks.CoRR abs/2502.06215 (2025). arX...

2025 doi arXiv
[39]

Eric Schkufza, Rahul Sharma, and Alex Aiken. 2013. Stochastic super- optimization.ACM SIGARCH Computer Architecture News41, 1 (2013), 305–316

2013
[2015]

InProceed- ings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation

Provably correct peephole optimizations with alive. InProceed- ings of the 36th ACM SIGPLAN Conference on Programming Language Design and Implementation. 22–32
[2023]

In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis

Exploring missed optimizations in webassembly optimizers. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. 436–448
[2024]

Search-based llms for code optimization.arXiv preprint arXiv:2408.12159(2024)

2024 arXiv

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

[n. d.]. InstCombine contributor guide — LLVM 22.0.0git documenta- tion — llvm.org.https://llvm.org/docs/InstCombineContributorGuide. html. Accessed 01-08-2025

work page 2025

[2] [2]

[n. d.]. LLVM InstCombine Pull Requests on GitHub. https://github.com/llvm/llvm-project/pulls?q=is%3Aopen+is% 3Apr+label%3Allvm%3Ainstcombine. Accessed 01-08-2025

work page 2025

[3] [3]

[n. d.]. LLVM Language Reference Manual — LLVM 22.0.0git docu- mentation — llvm.org.https://llvm.org/docs/LangRef.html. Accessed 01-08-2025

work page 2025

[4] [4]

[n. d.]. Overview - CPU 2017 — spec.org.https://www.spec.org/ cpu2017/Docs/overview.html. Accessed 12-01-2026

work page 2017

[5] [5]

[n. d.]. The LLVM Compiler Infrastructure Project — llvm.org.https: //llvm.org. Accessed 01-08-2025

work page 2025

[6] [6]

Aho, Monica S

Alfred V. Aho, Monica S. Lam, Ravi Sethi, and Jeffrey D. Ullman. 2006. Compilers: Principles, Techniques, and Tools (2nd Edition). Addison Wesley

work page 2006

[7] [7]

Sorav Bansal and Alex Aiken. 2006. Automatic generation of peephole superoptimizers. InProceedings of the 12th international conference on Architectural support for programming languages and operating systems. ACM, San Jose California USA, 394–403. doi:10.1145/1168857.1168906

work page arXiv 2006

[8] [8]

Sorav Bansal and Alex Aiken. 2008. Binary Translation Using Peephole Superoptimizers. In8th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2008, December 8-10, 2008, San Diego, California, USA, Proceedings, Richard Draves and Robbert van Renesse (Eds.). USENIX Association, 177–192.http://www.usenix.org/events/ osdi08/tech/full_paper...

work page 2008