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Memory write-injection looks like a cheap way to add knowledge to a deployed model, but these results show the added facts cannot be chained into multi-hop reasoning by the model itself — orchestration must live in the application layer. Builders wiring RAG-style memory into agents should chain written atoms explicitly in prompts or system logic, and benchmark vendors should adopt coverage, synergy and distortion reporting. The tested beds include Chinese frontier models Qwen3 and DeepSeek, making the findings broadly relevant across model families.

The takeaway is that attention architecture choice has become a strategic fork: American spinouts standardise on SWA while Chinese labs treat attention design as proprietary IP. Engineers evaluating open-weight models should look past parameter counts and check which attention variant a model uses, since it shapes cost and context behaviour. The comparison with Mistral's stack gives a concrete reference point for judging the trade-offs.

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The benchmark shows that outcome-only evaluation masks invalid execution, since models can hit the final goal while violating constraints mid-run — a gap that matters most in regulated domains like payments where intermediate errors carry financial or compliance consequences. Agent builders can use the 50-task suite and deterministic checks to measure constraint compliance and tool-call overhead, not just task success. Teams choosing models for workflow automation get a direct comparison of frontier and open models under identical conditions.

FYAN introduces evidence-grounded semantic auditing that ensures formal statements preserve informal specifications through explicit proof-transfer obligations. Researchers working on formal verification and theorem proving can use this approach to improve accuracy in mathematical formalization, while developers can leverage ODENumLib, the 9,355-line Lean library for numerical analysis of ordinary differential equations that FYAN built.

This research addresses a critical gap in VLM distillation where students can match teacher outputs without relying on the same visual evidence. The CW-OPD method provides a corrective signal that makes visual evidence reliance explicit rather than implicit. Developers working with smaller models can leverage this approach to achieve performance comparable to much larger models while maintaining better visual grounding.