OpenAI Reportedly Boosts Simulated GPT-5.6 Sol Performance on ARC-AGI-3 Benchmark

A Chinese tech news report claims OpenAI improved the performance of a hypothetical "GPT-5.6 Sol" model on the interactive ARC-AGI-3 benchmark by 188.42%, raising its score to 38.3%. This improvement was reportedly achieved by implementing inference preservation and context compression within the model's execution harness. Although the report references non-existent models and future dates, suggesting a simulation or speculative scenario, the underlying techniques of maintaining reasoning history and managing context truncation are critical for improving LLM performance in agentic workflows. The optimization addressed two main flaws: the discarding of private reasoning history after each action and the loss of early action history due to rolling context window truncation. By passing the previous response ID via the Responses API and compressing context, the model retained its strategy while reducing output tokens to one-sixth.