Solve, Recover, Reuse: LLM Reasoning Across Workflows with Corrupted Contexts

Large language models (LLMs) increasingly operate in complex workflows and noisy contexts. It's important to have a comprehensive understanding of how model performance varies across workflow and corruption types, and whether both harmful and beneficial transitions arise under these challenges. We introduce an Evolutionary Task Generator (ETG) that generates a series of linked tasks across workflows and corruptions, providing a framework to systematically experiment and analyze: 1) model performance changes across workflows and corruptions, 2) the bidirectional transitions under corrupted contexts, and 3) training effects across workflows and corruptions. Across frontier LLMs, we find that model rankings and performance change substantially across workflows, e.g., DeepSeek-V4-Flash drops from 0.75 in direct solving to 0.20 in downstream reuse. Instance-level analysis shows that both harmful and beneficial transitions arise under corrupted contexts. Training results show that direct-solving gains may not transfer across workflows, and may even degrade model performance under complex workflows with corrupted contexts. Our results highlight the importance of examining LLMs reasoning through the lens of complex workflows and corrupted contexts, and suggest a more constructive direction when such noise is unavoidable in practice: resist the harmful and leverage the beneficial.