Bridging the Knowledge Void: Inference-time Acquisition of Unfamiliar Programming Languages for Coding Tasks

• URL: http://arxiv.org/abs/2602.06976v1
• Date: Fri, 16 Jan 2026 09:06:47 GMT
• Title: Bridging the Knowledge Void: Inference-time Acquisition of Unfamiliar Programming Languages for Coding Tasks
• Authors: Chen Shen, Wei Cheng, Jingyue Yang, Huan Zhang, Yuhan Wu, Wei Hu,
• Abstract summary: Large Language Models (LLMs) in coding tasks are often a reflection of their extensive pre-training corpora.<n>We propose ILA-agent, a general ILA framework that equips LLMs with a set of behavioral primitives.<n>We instantiate ILA-agent for Cangjie and evaluate its performance across code generation, translation, and program repair tasks.
• Score: 22.908904483320953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The proficiency of Large Language Models (LLMs) in coding tasks is often a reflection of their extensive pre-training corpora, which typically collapses when confronted with previously unfamiliar programming languages. Departing from data-intensive finetuning, we investigate the paradigm of Inference-time Language Acquisition (ILA), where an LLM masters an unfamiliar language through dynamic interaction with limited external resources. In this paper, we propose ILA-agent, a general ILA framework that equips LLMs with a set of behavioral primitives. By modeling essential human-like behaviors as a suite of tools, ILA-agent enables LLMs to incrementally explore, apply, and verify language knowledge through structured interactions with the official documentation and execution environment. To provide a rigorous evaluation in a low-resource setting, we construct Cangjie-bench, a multi-task benchmark based on the novel statically-typed language Cangjie. We instantiate ILA-agent for Cangjie and evaluate its performance across code generation, translation, and program repair tasks. Results using diverse LLMs demonstrate that ILA-agent significantly outperforms retrieval-augmented baselines. Further analysis of agent trajectories characterizes the emergent behavior patterns while highlighting persisting performance gaps.

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