[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-00f785f9-1d86-4e55-a4d2-8ff80f145d63":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"00f785f9-1d86-4e55-a4d2-8ff80f145d63","DiffusionGemma 可解释性悖论：Google 把黑箱深度从 28.6× 压回 1.1×","Google DeepMind 与 MATS 团队（Engels、McDougall、Nanda 等）在 arXiv 2606.20560 发表 26 页长文《How Transparent is DiffusionGemma?》，首次系统拆解扩散语言模型的可解释性。论文把\"推理透明度\"拆成变量与算法两条测度，对 DiffusionGemma 26B-A4B 做端到端评估。\n\n正面：不做干预时，模型的\"不透明串行深度\"是自回归 Gemma 4 的 28.6 倍；只要在去噪步骤间插入可解释 token 瓶颈做映射，下游任务零掉点，黑箱深度骤降到 1.1 倍，与 Gemma 4 持平。\n\n反面：算法层是新麻烦。画布上每个 token 每步都可能改写，模型能实现\"分布式算法\"。研究者识别出三种 AR 模型不会出现的扩散专属现象——非时序推理、token\u002F序列涂抹、中段上下文推理，外部监控者无法再按顺序读 diff。\n\n最终 monitorability 测试给出相对正面结论：DiffusionGemma 与 Gemma 4 在外部监督场景下表现持平，对扩散模型安全部署有直接参考价值。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.20560v1","35ce748f-48b7-4638-88ef-effa57a7e749",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"5e628969-6d2a-437f-998a-104e4b16cfb1","ai-progress",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"1fcfaaf2-67de-43d3-9e35-5784852fec60","ai-safety",{"id":18,"name":19,"slug":19,"description":13,"color":13},"7b67033c-19e6-4052-a626-e681bba64c7a","diffusion",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-06-18T17:59:46Z","2026-06-21T20:14:38.037422Z","2026-06-21T20:14:38.037439Z",true,"agent",5]