arXiv Machine Learning

SAIL: Sound Abstract Interpreters with LLMs

arXiv:2511. 13663v2 Announce Type: replace-cross Abstract: How to construct globally sound abstract interpreters to safely approximate program behaviors remains a bottleneck in abstract interpretation.

arXiv AI

Jun 8

Discovering Interpretable Algorithms by Decompiling Transformers to RASP

arXiv:2602. 08857v2 Announce Type: replace-cross Abstract: Recent work has shown that the computations of Transformers can be simulated in the RASP family of programming languages.

By Xinting Huang, Aleksandra Bakalova, Satwik Bhattamishra, William Merrill, Michael Hahn

llms

arXiv AI

Jul 16

Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code

arXiv:2607. 13921v1 Announce Type: cross Abstract: Languages with rich static semantics, such as Rust, provide stronger guarantees for AI-generated code, but their strictness makes generation more difficult.

By Niels M\"undler-Sasahara, Hristo Venev, Dawn Song, Martin Vechev, Jingxuan He

llms

arXiv AI

Jun 16

Large Language Models as Optimizers: A Survey of Direct vs. Tool-Augmented Approaches and Their Performance Frontiers

arXiv:2606. 15577v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly involved in complex mathematical optimization, even if the pragmatic user who triggers them is unaware of it.

By Roko Peran, Luka Hobor, Mihael Kovac, Mario Brcic

llms benchmarks

arXiv Machine Learning

Jul 28

Benchmarking LLMs for Verilog Design Flows

arXiv:2607. 22759v1 Announce Type: cross Abstract: Large language models (LLMs) show promise in code generation, but their capabilities to produce correct, synthesizable hardware description language (HDL) code still remain to be properly benchmarked.

By Angshuman Chakravertty, Rahul Koshti, Buddhi Prakash Sharma, Vinay Chamola

llms benchmarks

arXiv AI

Aug 11

Quokka: Accelerating Program Verification with LLMs via Invariant Synthesis

arXiv:2509. 21629v4 Announce Type: replace-cross Abstract: Program verification relies on loop invariants, yet automatically discovering strong invariants remains a long-standing challenge.

By Anjiang Wei, Tianran Sun, Tarun Suresh, Haoze Wu, Ke Wang, Alex Aiken

llms fine-tuning benchmarks

arXiv AI

Aug 5

IR2Solve: Structured Intermediate Representations for Cost-Efficient Optimization Autoformulation

arXiv:2608. 02641v1 Announce Type: cross Abstract: Large language models (LLMs) can translate natural-language optimization problems into solver-ready formulations, but direct code generation is brittle: schema, indexing, and semantic errors can cause compilation failures, infeasible models, or incorrect objectives, while iterative repair, search, and multi-agent workflows increase inference cost.

By Penglin Zhu, Linhai Zhang, Jungang Xu, Xinchi Wei, Xiuqi Wu

llms agents benchmarks

arXiv AI

Aug 14

On the Expressive Power of Transformers

arXiv:2608. 12671v1 Announce Type: new Abstract: Multi-layer transformers form the critical component of essentially all large language models (LLMs) in use today.

By Phokion Kolaitis, Rik Sengupta

llms

arXiv AI

Aug 18

ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization

arXiv:2602. 15983v3 Announce Type: replace-cross Abstract: Large language models (LLMs) can translate natural language into optimization code, but silent failures pose a critical risk: code that executes and returns solver-feasible solutions may encode semantically incorrect formulations---a feasibility--correctness gap reaching 90 percentage points on compositional problems.

By Junbo Jacob Lian, Yujun Sun, Huiling Chen, Chaoyu Zhang, Hanzhang Qin, Chung-Piaw Teo

llms fine-tuning benchmarks

arXiv Computation and Language

Sep 10

$\Phi$-Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?

arXiv:2609.10226v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in reasoning and code generation, raising the prospect that they could assist in...

By Leilei Ding, Shumin Wang, Yuting Huang, Fanqi Wan, Yinmin Zhang, Qi Han, Yiming Xu, Feiyuan Zhang, Xiaomeng Chu, Guoliang You, Wuyang Zhang, Daxin Jiang, Yanyong Zhang

llms agents benchmarks

arXiv AI

Sep 15

Tool Use Reduces Depth-Induced Collapse in OOD Reasoning

arXiv:2602.21061v2 Announce Type: replace Abstract: Many current paths to more advanced AI depend on the assumption that large language models (LLMs) can generalize learned relationships to solve com...

By David Koplow, Tomer Galanti, Tomaso Poggio

llms agents benchmarks safety

arXiv Machine Learning

Jul 21

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks

arXiv:2607. 17624v1 Announce Type: new Abstract: Transformers are remarkably versatile and their design is largely consistent across a variety of applications.

By Damien Teney, Liangze Jiang, Hemanth Saratchandran, Simon Lucey

llms

arXiv Machine Learning

Jul 7

Equivalence of Context and Parameter Updates in Modern Transformer Blocks

arXiv:2511. 17864v3 Announce Type: replace Abstract: Recent research has established that the impact of context in a vanilla transformer can be represented implicitly by forming a token-dependent, rank-1 patch to its MLP weights.

By Adrian Goldwaser, Michael Munn, Javier Gonzalvo, Benoit Dherin

llms

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