# When LLMs Lag Behind: Knowledge Conflicts from Evolving APIs in Code Generation

arXiv: https://arxiv.org/abs/2604.09515 · HTML: https://mlsanity.com/paper/2604.09515/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2026.

## Built on by (most cited first, 3 of 3)

- [When Retrieval Hurts Code Completion: A Diagnostic Study of Stale Repository Context](https://mlsanity.com/paper/2605.14478/) (2026)
- [Toward User-Conditioned Evaluation of Personal LLM Agents under Temporal Interventions](https://mlsanity.com/paper/2607.21635/) (2026)
- [Diagnosing Knowledge Gaps in LLM Tool Use: An Agentic Benchmark for Novel API Acquisition](https://mlsanity.com/paper/2606.03657/) (2026)

## Abstract (arXiv)

The rapid evolution of software libraries creates a significant challenge for Large Language Models (LLMs), whose static parametric knowledge often becomes stale post-training. While retrieval-augmented generation (RAG) is commonly used to provide up-to-date API specifications, "context-memory conflict" arises when external instructions contradict a model's internal parametric knowledge. This paper presents a systematic empirical study of LLM code generation under API evolution (e.g., API deprecation, API modification, and API addition), by constructing a benchmark of 270 real-world updates from eight Python libraries. We evaluate four LLM families of 11 models. Our results show that without comprehensive documentation, LLMs struggle to prioritize external context, averaging only 42.55% of generated code examples are executable in the target environment. While structured documentation and larger model scales improve LLMs' ability to update adoption, they do not fully resolve executability issues with a low 66.36% executable rate. In addition, reasoning-based strategies (e.g., Self-Reflection) significantly boost LLMs' performance with 11% improvement on executable rate. Our findings highlight the persistence of outdated patterns from LLMs, even when API update specifications are provided, and emphasize the need for evolution-aware benchmarks and techniques.