Long-context extrapolation has always been a pain point for open-source LLMs: existing methods either rely on expensive continued training, or use a single rescaling factor to extrapolate roughly — aggressive ones collapse on short context, conservative ones fall short on long context. MIT's Song Han team released Jet-Long (arXiv:2607.07740) on July 8 with a fairly clean solution: the core idea is to imagine RoPE's positional encoding as a "bifocal lens" — one group of windows keeps the original RoPE unchanged (preserving short-context fidelity), and the rescaling factor for the other group of windows is dynamically adjusted based on the current sequence length, responsible for long-range extrapolation. The two groups of windows merge attention via inclusion–exclusion, with on-the-fly rotation correction of RoPE at inference. The entire mechanism is purely algorithmic, requiring no fine-tuning. In engineering, the authors fuse it into a single CuTe kernel, achieving 1.39× throughput of FA2 for long-context prefill on H100 (close to the Hopper-only FA4), with single-batch generation overhead ≤4% — previous zero-shot extrapolation methods often halve throughput. In terms of effect, Qwen3-1.7B/4B/8B on 128K context RULER outperforms the strongest baseline by +4.79/+2.18/+2.03 pp, HELMET-RAG ranks first overall, PG-19 perplexity is the lowest. The paper also demonstrates applying Jet-Long directly to Jet-Nemotron's hybrid attention architecture to keep gaining points — showing that the "bifocal" idea is orthogonal to the underlying architecture. The practical significance for the open-source community: any trained Qwen3 checkpoint can gain 128K context capability within 10 minutes, no continued training, no synthetic data, no architecture changes. This drops the threshold of the long-context tax to an unprecedented low.