Composable Effect Handling for Programming LLM-integrated Scripts
July 29, 2025 Β· Declared Dead Β· π Proceedings of the 1st ACM SIGPLAN International Workshop on Language Models and Programming Languages
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Di Wang
arXiv ID
2507.22048
Category
cs.PL: Programming Languages
Citations
0
Venue
Proceedings of the 1st ACM SIGPLAN International Workshop on Language Models and Programming Languages
Last Checked
4 months ago
Abstract
Implementing LLM-integrated scripts introduces challenges in modularity and performance, as scripts are often coupled to specific LLM implementations and fail to exploit parallelization opportunities. This paper proposes using composable effect handling to separate workflow logic from effectful operations, such as LLM calls, I/O, and concurrency, enabling modularity without sacrificing the opportunity for performance optimization. By treating these operations as abstract interfaces and discharging them via effect handlers, this paper shows that scripts can achieve significant speedups (e.g., 10$\times$ in a Tree-of-Thoughts case study) without compromising modularity. This paper aims to promote composable effect handling as a programming style for LLM scripting.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted