# AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation arXiv: https://arxiv.org/abs/2404.16333 ยท HTML: https://mlsanity.com/paper/2404.16333/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. - Builds on 19 of its 53 references that are in the map. - First posted on arXiv 25 Apr 2024. ## Placed in - LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/) ## Builds on (most cited first, 19 of 19) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021) - [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020) - [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015) - [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023) - [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020) - [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021) - [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023) - [WizardCoder: Empowering Code Large Language Models with Evol-Instruct](https://mlsanity.com/paper/2306.08568/) (2023) - [TinyLlama: An Open-Source Small Language Model](https://mlsanity.com/paper/2401.02385/) (2024) - [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023) - [CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://mlsanity.com/paper/2305.07922/) (2023) - [SantaCoder: don't reach for the stars!](https://mlsanity.com/paper/2301.03988/) (2023) - [Robustness, Security, Privacy, Explainability, Efficiency, and Usability of Large Language Models for Code](https://mlsanity.com/paper/2403.07506/) (2024) - [Probing Pretrained Models of Source Code](https://mlsanity.com/paper/2202.08975/) (2022) - [Program Translation via Code Distillation](https://mlsanity.com/paper/2310.11476/) (2023) - [Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities](https://mlsanity.com/paper/2212.10017/) (2022) - [CodeBPE: Investigating Subtokenization Options for Large Language Model Pretraining on Source Code](https://mlsanity.com/paper/2308.00683/) (2023) ## Built on by (most cited first, 10 of 10) - [Seeing is Coding: On the Effectiveness of Vision Language Models in Code Understanding](https://mlsanity.com/paper/2602.01785/) (2026) - [The Hidden Cost of Readability: How Code Formatting Silently Consumes Your LLM Budget](https://mlsanity.com/paper/2508.13666/) (2025) - [Marking Code Without Breaking It: Code Watermarking for Detecting LLM-Generated Code](https://mlsanity.com/paper/2502.18851/) (2025) - [ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generation](https://mlsanity.com/paper/2601.09703/) (2026) - [Repo0: Design-Driven Zero-to-All Code Generation](https://mlsanity.com/paper/2608.19854/) (2026) - [Interactive LLM-assisted Curriculum Learning for Multi-Task Evolutionary Policy Search](https://mlsanity.com/paper/2602.10891/) (2026) - [Accessible Smart Contracts Verification: Synthesizing Formal Models with Tamed LLMs](https://mlsanity.com/paper/2501.12972/) (2025) - [From User Interface to Agent Interface: Efficiency Optimization of UI Representations for LLM Agents](https://mlsanity.com/paper/2512.13438/) (2025) - [What Was Once Learned May Need to Be Unlearned: Machine Unlearning for Deprecated API Knowledge in Large Language Models](https://mlsanity.com/paper/2609.25786/) (2026) - [Mind the Gaps: From Failure Attribution to Closed-Form Repair of Code Language Models](https://mlsanity.com/paper/2610.05277/) (2026) ## Abstract (arXiv) Artificial Intelligence (AI) models have emerged as another important audience for programming languages alongside humans and machines, as we enter the era of large language models (LLMs). LLMs can now perform well in coding competitions and even write programs like developers to solve various tasks, including mathematical problems. However, the grammar and layout of current programs are designed to cater the needs of human developers -- with many grammar tokens and formatting tokens being used to make the code easier for humans to read. While this is helpful, such a design adds unnecessary computational work for LLMs, as each token they either use or produce consumes computational resources. To improve inference efficiency and reduce computational costs, we propose the concept of AI-oriented grammar. This aims to represent code in a way that better suits the working mechanism of AI models. Code written with AI-oriented grammar discards formats and uses a minimum number of tokens to convey code semantics effectively. To demonstrate the feasibility of this concept, we explore and implement the first AI-oriented grammar for Python, named SimPy. SimPy is crafted by revising the original Python grammar through a series of heuristic rules. Programs written in SimPy maintain identical AST structures to those in standard Python. This allows for not only execution via a modified AST parser, but also seamless transformation between programs written in Python and SimPy, enabling human developers and LLMs to use Python and SimPy, respectively, when they need to collaborate. In the experiments, compared with Python, SimPy enables a reduction in token usage by 13.5% and 10.4% for CodeLlama and GPT-4, respectively, when completing the same set of code-related tasks. Additionally, these models can maintain or even improve their performance when using SimPy instead of Python for these tasks.