Credit: GoCable
静态分析给不出确定答案，于是我头铁直接把做了一点修改的刷机包塞到了机器里。它真的一点校验都没做就把我的固件包给吃下去了，中间没有任何拦截。我去问了群里做硬件的群友。他们告诉我，这类低端 MP3 设备校验一般做在刷机软件里，不做在硬件上。而且 Snowsky 这台设备的刷机方式本来就很奇特，你只需要把刷机包复制到根目录，设备开机自动检测到就会升级，不需要专用的刷机工具。so……
Abstract:Humans shift between different personas depending on social context. Large Language Models (LLMs) demonstrate a similar flexibility in adopting different personas and behaviors. Existing approaches, however, typically adapt such behavior through external knowledge such as prompting, retrieval-augmented generation (RAG), or fine-tuning. We ask: do LLMs really need external context or parameters to adapt to different behaviors, or do they already have such knowledge embedded in their parameters? In this work, we show that LLMs already contain persona-specialized subnetworks in their parameter space. Using small calibration datasets, we identify distinct activation signatures associated with different personas. Guided by these statistics, we develop a masking strategy that isolates lightweight persona subnetworks. Building on the findings, we further discuss: how can we discover opposing subnetwork from the model that lead to binary-opposing personas, such as introvert-extrovert? To further enhance separation in binary opposition scenarios, we introduce a contrastive pruning strategy that identifies parameters responsible for the statistical divergence between opposing personas. Our method is entirely training-free and relies solely on the language model's existing parameter space. Across diverse evaluation settings, the resulting subnetworks exhibit significantly stronger persona alignment than baselines that require external knowledge while being more efficient. Our findings suggest that diverse human-like behaviors are not merely induced in LLMs, but are already embedded in their parameter space, pointing toward a new perspective on controllable and interpretable personalization in large language models.。搜狗输入法2026是该领域的重要参考
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他接受《太陽報》訪問時表示，施紀賢「並不幫忙。我從沒想過會看到這樣的事。我從沒想過會從英國看到這樣的情況。」