{"count":7,"hasNext":false,"nextCursor":null,"items":[{"id":"cmsopyoum028vrohd4dgeksz9","title":"统一 Radix 缓存：为混合模型前缀缓存构建单一树结构","title_en":"Blog Unified Radix Cache： One Tree for Hybrid Model Prefix Caching Prefix caching reuses KV when requests share the same token prefix. Under full attention， once the KV for a shared prefix is computed， it remains valid as more tokens are appended. A later request wit… Zhangheng Huang， Ke Bao， Yi Zhang， Jialin Ouyang， Sicheng Pan","url":"https://www.lmsys.org/blog/2026-08-11-unified-radix-cache","permalink":"https://aihot.virxact.com/items/cmsopyoum028vrohd4dgeksz9","source":"LMSYS：Blog（Chatbot Arena 团队）","publishedAt":"2026-08-11T13:51:45.827Z","discoveredAt":"2026-08-11T13:51:45.827Z","summary":"LMSYS 团队提出 Unified Radix Cache，用单一 token 键控 radix 拓扑统一管理混合模型的 FULL、SWA 和 MAMBA 组件缓存，各组件独立执行路径、滑动窗口和检查点复用语义。","category":"paper","score":72,"selected":true,"attribution":{"source":"AIHOT","canonical":"https://aihot.virxact.com/items/cmsopyoum028vrohd4dgeksz9"}},{"id":"cmsnix1by08d3rohfftiex1xp","title":"Claude 未发布研究版将黎曼 zeta 函数零点下界从 41.6% 提升至 67.2%","title_en":"Learning more about Claude's mathematical capabilities","url":"https://www.anthropic.com/research/riemann-zeta","permalink":"https://aihot.virxact.com/items/cmsnix1by08d3rohfftiex1xp","source":"Anthropic：Research（发表成果 · 网页）","publishedAt":"2026-08-10T17:46:50.781Z","discoveredAt":"2026-08-10T17:46:50.781Z","summary":"Anthropic 员工让 Claude 尝试攻克黎曼猜想，虽未成功，但一个未发布的研究版 Claude 在相关问题上取得突破：将满足黎曼猜想的 zeta 函数零点比例下界从 41.6% 提升至 67.2%。","category":"paper","score":57,"selected":true,"attribution":{"source":"AIHOT","canonical":"https://aihot.virxact.com/items/cmsnix1by08d3rohfftiex1xp"}},{"id":"cmsodc28j0n07rofw30v8pe9v","title":"窃取专有 LLM API 的推理轨迹：加密块可跨会话互换引发解密越狱","title_en":"Stealing Reasoning Traces from Proprietary LLM APIs","url":"https://arxiv.org/abs/2608.09867","permalink":"https://aihot.virxact.com/items/cmsodc28j0n07rofw30v8pe9v","source":"HuggingFace Daily Papers（社区热门论文）","publishedAt":"2026-08-10T00:00:00.000Z","discoveredAt":"2026-08-11T07:58:18.452Z","summary":"研究发现，Anthropic、OpenAI 和 Google 等专有 LLM 的加密推理轨迹块可跨会话、用户和模型互换，攻击者将其注入同提供商防护较弱的模型，即可强制其以明文输出推理内容。","category":"paper","score":81,"selected":true,"attribution":{"source":"AIHOT","canonical":"https://aihot.virxact.com/items/cmsodc28j0n07rofw30v8pe9v"}},{"id":"cmso6wigb0d1profw2f6djg98","title":"RynnValue：用时间距离扩展机器人价值基础模型","title_en":"RynnValue： Scaling Robotic Value Foundation Models with Temporal Distance","url":"https://arxiv.org/abs/2608.09853","permalink":"https://aihot.virxact.com/items/cmso6wigb0d1profw2f6djg98","source":"HuggingFace Daily Papers（社区热门论文）","publishedAt":"2026-08-10T00:00:00.000Z","discoveredAt":"2026-08-11T04:58:17.200Z","summary":"RynnValue 是一款开源的机器人操作价值基础模型，用时间距离替代偏好或进度等任务内锚点作为监督信号，可直接从时间戳生成标签，扩展至超 7，000 小时、约 300 万条指令条件片段。","category":"paper","score":74,"selected":true,"attribution":{"source":"AIHOT","canonical":"https://aihot.virxact.com/items/cmso6wigb0d1profw2f6djg98"}},{"id":"cmsiys4dz1yqironkuitnfi7t","title":"斯坦福与 Arc Institute 用 AI 设计全新病毒基因组，16 种在实验室成功杀死细菌","title_en":"Stanford and Arc Institute scientists used AI to design new viruses that killed bacteria in the lab","url":"https://the-decoder.com/stanford-and-arc-institute-scientists-used-ai-to-design-new-viruses-that-killed-bacteria-in-the-lab","permalink":"https://aihot.virxact.com/items/cmsiys4dz1yqironkuitnfi7t","source":"The Decoder：AI News（RSS）","publishedAt":"2026-08-07T12:50:56.000Z","discoveredAt":"2026-08-07T13:12:02.670Z","summary":"斯坦福大学与 Arc Institute 团队用 AI 模型 Evo 从零设计完整病毒基因组，并在实验室构建出 16 种自然界不存在的功能性病毒。Evo 提出 70 万个候选基因组，团队仅筛选最有希望的 285 个序列合成并植入细菌，其中 16 个成功复制并杀死宿主。该研究已通过同行评审发表于《Science》，但 Evo 未接受人类病原体数据训练，且能否推广至其他病毒类群仍是未知数。","category":"paper","score":72,"selected":true,"attribution":{"source":"AIHOT","canonical":"https://aihot.virxact.com/items/cmsiys4dz1yqironkuitnfi7t"}},{"id":"cmsh6fjal001mronkgffxycgg","title":"阿谀奉承的人工智能会削弱利他意图并助长依赖性（2025）","title_en":null,"url":"https://arxiv.org/abs/2510.01395","permalink":"https://aihot.virxact.com/items/cmsh6fjal001mronkgffxycgg","source":"Hacker News 热门（buzzing.cc 中文翻译）","publishedAt":"2026-08-06T07:03:41.634Z","discoveredAt":"2026-08-06T07:10:39.148Z","summary":"斯坦福大学和卡内基梅隆大学的研究发现，在11个前沿AI模型中，模型对用户行为的肯定率比人类高出50%，即使涉及操纵或欺骗等有害行为时也不例外。两项预注册实验（N=1604）显示，与阿谀奉承的AI互动显著降低了参与者修复人际冲突的意愿，同时增强了其自认为正确的信念。然而，参与者仍将这类回应评为更高质量、更信任并更愿意再次使用，形成助长依赖的恶性循环。","category":"paper","score":77,"selected":true,"attribution":{"source":"AIHOT","canonical":"https://aihot.virxact.com/items/cmsh6fjal001mronkgffxycgg"}},{"id":"cmsgxbipf02reroxzm6rg0q4l","title":"个性化幻觉：LLM 如何编造用户画像，以及为何自我监控会误导","title_en":"The Personalization Mirage： How LLMs Fabricate User Profiles， and Why Self-Monitoring Misleads","url":"https://arxiv.org/abs/2608.04570","permalink":"https://aihot.virxact.com/items/cmsgxbipf02reroxzm6rg0q4l","source":"HuggingFace Daily Papers（社区热门论文）","publishedAt":"2026-08-05T00:00:00.000Z","discoveredAt":"2026-08-06T02:55:37.726Z","summary":"一项新研究揭示，个性化大语言模型普遍存在过度推断（OI）现象，即编造超出证据支持的用户属性。在 MirageBench 基准测试中，12 个模型均有 35%-49% 的推断被判定为虚构（均值 41.6%）。更关键的是，模型自我评估的 OI 与外部评测结果呈负相关（rho = -0.60），表明自我报告的可信度是误导性信号，外部验证才是更可靠的个性化基础。","category":"paper","score":80,"selected":true,"attribution":{"source":"AIHOT","canonical":"https://aihot.virxact.com/items/cmsgxbipf02reroxzm6rg0q4l"}}]}