Anthropic 宣布将 Agent Skills 开放标准捐赠给 Linux Foundation 旗下的 Agentic AI Foundation（AAIF），捐赠内容包括名称与 logo、GitHub 组织及 agentskills.io 域名。
推荐理由：原文说明了Agent Skills标准移交中立基金会的治理安排与后续路线图，读者可据此判断该Agent技能生态的开放性走向。
PHP 紧急发布安全更新，修复 CVE-2026-109618 和 CVE-2026-109619 两个关键漏洞，影响 PHP 5.4 及之后所有版本，修复版本为 PHP 8.5.12、8.4.27、8.3.36 和 8.2.35。
推荐理由：原文梳理了漏洞规模、在野利用情况与协调披露过程，读者可据此评估补丁优先级和波及范围。
Microsoft 提出用 Guardian Agents 全天候在行动发生前监督企业内部每一个 agent，并以 Project Perception 的传感器、上下文与 Guardian Agents 三层实现确定性管控。
Microsoft 在 Ignite 上公布 Microsoft Sovereign Cloud 的下一阶段能力，Sovereign Control Panel 进入公开预览，在统一 Azure 门户中呈现主权态势、控制项与风险缺口。
ALTK-Evolve learns and applies a playbook using GPT-4.1, outperforming the retail entries on τ²-bench, a customer service agent benchmark—without updating model weights. Learned memory takes a basic ReAct agent with GPT-4.1 to the top Using GPT-4.1, an older, non-reasoning model, a basic customer-service agent reaches 67.5% Pass^4 on τ²-bench retail — meaning it solves the task on all four independent attempts for 67.5% of tasks. It scores above every retail entry in our October 12, 2026 leaderboard snapshot. It runs on the benchmark's stock ReAct scaffold, with learned guidelines appended to its system prompt. ALTK-Evolve builds that playbook using GPT-4.1. It analyzes the agent's training-task attempts and uses the model to distill successes and failures into short behavioral rules: how to authenticate a customer, confirm a transaction, or handle a return. Those guidelines are then added to the agent's prompt for subsequent conversations. The same model powers both guideline extraction and the agent; no weight updates or human annotation are needed. Our previous work demonstrated this approach on coding agents. Here, we test it on customer service, where success requires following domain policies, understanding a customer's request, and executing the right tool actions. τ²-bench tests policy-bound customer service τ²-bench (from Sierra) measures how well an agent handles customer-service conversations under real constraints. Each task pairs the agent with an LLM-simulated customer who has a goal in mind, a set of domain tools (look up an order, issue a refund, rebook a flight), and a written policy document the agent must follow. The agent has to draw out the right details, stay within policy, and take the correct — often irreversible — actions; a task counts as solved only when the final database state matches the expected outcome, a strict binary reward. We use its two customer-service domains, retail and airline . We run τ²-bench's own implementation of a basic ReAct agent with a two-phase THINK → ACT loop, using gpt-4.1 as the agent model and gpt-5.5 as the customer-simulator model; 4 trials per task. The only change we make is a block of learned guidelines appended to the system prompt — everything else (policy, tools, scaffold) is stock τ²-bench. Concretely, the learned guidelines are tacked on after the domain policy as a plain markdown list: You are a customer service agent that helps users according to the policy below. Always follow the policy strictly. Use the provided tools when needed. <policy> # Retail agent policy As a retail agent, you can help users: - cancel or modify pending orders - return or exchange delivered orders - modify their default user address … </policy> ## Guidelines learned from past task attempts (follow these carefully): - Authenticate the user before taking any order-related action. If email is unavailable, verify identity via full name and zip code using the find_user_id_by_name_zip API. - Always confirm all intended changes (modify, cancel, return, exchange) with a clear, structured summary, and require explicit user approval before executing any database-updating action. - When processing a return or exchange, communicate the refund with an itemized breakdown and clarify refund timing — don't wait for the user to ask. - Take one action per step: never combine a tool call with a user-facing message in the same turn, and wait for the tool's result before replying. - Before looking a user up, parse and clean their name — e.g. split a combined handle like `daiki_johnson_9523` into first/last name — and after a couple of failed lookups, state the exact format you need. … We extract guidelines from the agent's trajectories on τ²-bench train tasks using gpt-4.1 , the same model that powers the agent, then apply them unchanged on the held-out test set. Guidelines improve capability and reliability In a prior post , we talked about how different models / tasks require different dosage of guidance. Inline with the idea, we also evaluate different amounts of guidelines provided to the model. Each guideline carries a support count — the number of distinct training tasks it was extracted from, so higher support means a rule that generalized across more tasks rather than a one-off. We compare three configurations: baseline (no guidelines), sup3 (keep only guidelines with support ≥ 3 — a small, high-confidence core), and lossless (keep every guideline retained after consolidation, i.e. support ≥ 1). Table 1. Guideline configurations and retained guideline counts by domain. Variant Guidelines included in context Retail Airline baseline no guidelines 0 0 sup3 support n ≥ 3 (high-support core) 31 14 lossless all guidelines 292 101 The metrics we track. Every task is attempted 4 times , and we read those trials two ways: Mean Accuracy — the average success rate across the 4 trials. This is the capability axis: how often does the agent get this right? Pass^4 — the percentage of tasks that passed across all 4 trials. This measures along the reliability axis and the metric the τ²-bench leaderboard reports. It matters here because a support agent that resolves a refund 3 times out of 4 still fails a quarter of real customers — Pass^4 asks whether you can trust the agent every time, not just on average. Figure 1. Mean Accuracy and Pass^4 on held-out retail and airline tasks for agents using gpt-4.1 across four trials per task. Agent model: gpt-4.1 (user simulator: gpt-5.5 ). Table 2. Held-out agent performance using gpt-4.1 across four trials per task. Parentheses show percentage-point changes from baseline. Domain (test tasks) Config Mean Accuracy Pass^4 Retail (40) baseline 78.1% 57.5% sup3 78.8% (+0.7) 60.0% (+2.5) lossless 82.5% (+4.4) 67.5% (+10.0) Airline (20) baseline 57.5% 35.0% sup3 60.0% (+2.5) 45.0% (+10.0) lossless 65.0% (+7.5) 40.0% (+5.0) Mean Accuracy climbs in both domains — retail +4.4 pts (78.1 → 82.5) and airline +7.5 pts (57.5 → 65.0). Reliability moves further: the strict Pass^4 rises +10 pts in retail (57.5 → 67.5) and +10 pts in airline (35.0 → 45.0). And the gains come from better-informed calls, not longer conversations — we look at conversation length directly in Better performance costs more tokens, not more turns below. The best amount of guidance depends on the domain The two domains disagree about how much guidance pays off. In retail , more is better on every metric — the small sup3 core barely beats baseline on accuracy, and you need the full lossless set for the real jump. In airline the picture flips at the reliability end: lossless leads on accuracy (65% vs 60%), but the compact sup3 core (14 guidelines) is the more reliable one, taking Pass^4 to 45% against lossless's 40%. Echoing the earlier finding that delivery is a dial, not a constant , support count is a knob worth tuning per domain , not a value to hard-code. Better performance costs more tokens, not more turns Guidelines aren't free, but the cost lands where you'd expect — in prompt input tokens , not in a longer, more meandering conversation: Figure 2. Total LLM cost per conversation by domain and guideline configuration on the full base split. Table 3. Total LLM cost per conversation (agent and user simulator) on the full base split across four trials. Lower is better. Domain Config Cost / conversation vs. baseline Retail baseline $0.084 — sup3 $0.095 +12% lossless $0.139 +65% Airline baseline $0.081 — sup3 $0.097 +19% lossless $0.107 +32% Does the agent get there by talking longer ? To answer that cleanly we compare like for like : the task runs solved under both baseline and the guideline variant — identical scenario and seed, a true "same task, done right" set — and count assistant turns on each side. Table 4. Assistant turns per conversation for matched runs solved under both settings on the test split, pooling four trials. Domain Shared set Solved under both (n) Baseline turns +Guidelines Δ Retail baseline ∩ sup3 108 10.40 10.35 −0.05 baseline ∩ lossless 111 10.59 10.16 −0.43 Airline baseline ∩ sup3 38 8.97 8.53 −0.45 baseline ∩ lossless 40 9.15 8.82 −0.33 The agent isn't talking longer — on the shared set, guidelines make conversations slightly shorter : lossless trims ~0.3–0.4 turns in both domains (retail 10.59 → 10.16, airline 9.15 → 8.82), and no variant lengthens them. That matters beyond cost: each turn is a sequential LLM round-trip, so fewer turns means lower end-to-end latency — a faster time-to-completion for the customer, not just a cheaper one. The only real price is on the input side — the larger system prompt riding on every LLM call: retail's 292-guideline prompt pushes cost from ~$0.084 to ~$0.139 per conversation, while airline's smaller playbook is cheaper. And in production even that is largely reclaimable with prompt caching , since the guideline block is identical across every call. So the trade is a favorable one: for a slightly higher input cost (much of it cached away in practice) the agent lands higher accuracy, higher reliability, and lower time-to-completion at once — not the usual quality-for-latency tradeoff, but all three moving the right way together. Learned guidelines also improve agents using frontier models So far, we have used an older, non-reasoning model — gpt-4.1 . A fair objection is that learned guidelines might have less to add to an agent using a frontier model: maybe they only patch the gaps of a weaker model. So we tested it directly — we reran airline using gpt-5.5 for both the agent and guideline extraction , on the same τ²-bench ReAct scaffold, with guidelines re-mined from the agent's own runs. One detail changes with the model: none of the guidelines extracted in this setup reached support ≥ 3 (they top out at support 2), so the high-support core here is sup2 rather than sup3. Table 5. Held-out airline performance for the gpt-5.5 agent across four trials per task. Airline · gpt-5.5 (20 held-out test tasks) Accuracy Pass^4 baseline (0 guidelines) 72.5% 55.0% sup2 (103 guidelines) 80.0% (+7.5) 70.0% (+15.0) lossless (146 guidelines) 82.5% (+10.0) 70.0% (+15.0) The learned guidelines improve this setup too. On the same held-out test tasks, appending them to the prompt lifts accuracy +10 pts (72.5 → 82.5) and the strict Pass^4 by +15 pts (55.0 → 70.0) — a larger reliability gain than we observed with gpt-4.1 on these very tasks (+10 pts), and from a much higher floor. Learning guidelines around the model can improve an agent even when it starts from a stronger base. And where the baseline's reliability swung between reruns, the lossless configuration reproduced exactly — the gain it buys is dependable, not a lucky draw. ALTK-Evolve tops the leaderboard comparison Submissions are scored on the full base split (train ∪ test), as τ²-bench requires, and reported at Pass^4 . Because that covers the full task set rather than the held-out test split, these figures differ from the test-task numbers reported above. This is where the practical upshot shows up — prompt-level memory carries agents using each base model into , or past , current frontier company: Retail: #1 . On the base split the agent with learned guidelines reaches Pass^4 = 67.5% , ahead of every frontier model and ahead of RAFT-30B-A3B (61.4%), a model fine-tuned on synthetic τ²-bench retail data . Using stock gpt-4.1 and a prompt-appended playbook, it outperforms that fine-tuned system by +6.1 points . Airline: #1 using gpt-5.5 , #8 using gpt-4.1 Against the published board, the compact sup3 core gives the agent using gpt-4.1 Pass^4 = 44% — 8th , beating GPT-5.2 (no reasoning) and nearly matching Claude Sonnet 4.5. Swap in the stronger base and the same approach tops the field outright: the agent using gpt-5.5 reaches 82.0% , clearing every frontier entry (66–72%). Figure 3. Pass^4 comparison between ALTK-Evolve runs and the τ²-bench leaderboard snapshot from October 12, 2026. Same message in both domains: learning guidelines around the model works across different base models — bringing an agent using an older, non-reasoning model into frontier company on retail, and taking an agent using a frontier model to the top on airline. Agents can improve without weight updates Fine-tuning a model on a benchmark is expensive, brittle, and doesn't transfer to the next domain. Here, ALTK-Evolve improves a stock agent on a stock model in both domains — and matches or beats a fine-tuned system on retail — using only prompt-level guidelines extracted from the agent's own runs , automatically, without human annotation, and without any test-set leakage. Links: Prior posts Thinking of ACE? We Can Do It with Fewer Tokens How much memory does your agent actually need? ALTK-Evolve library — https://github.com/AgentToolkit/altk-evolve τ²-bench — https://github.com/sierra-research/tau2-bench Retail leaderboard submission — τ²-bench PR #542 Airline leaderboard submission - τ²-bench PR #551
Ai2 发布 FairStress，一个衡量大模型在涉及人的决策中人口群体公平性的基准，覆盖 29 个领域、300 个双候选人场景，并已集成进 Olmo-eval。
推荐理由：FairStress 把公平性评测从最终选择扩展到对抗前缀下的决策稳定性，并给出跨模型与训练数据的实测差异。
哈佛大学实验物理学家、前理学院院长 Christopher Stubbs 加入 Anthropic 的 Long-Term Benefit Trust，成为该信托首位物理学家。
商务部会同中央网信办、国家发展改革委等9部门于10月13日发布《关于加快推进科技兴商的意见》，提出提升人工智能、大数据、物联网、具身智能等新技术在商贸领域的应用水平，推动商贸全链条智能化、绿色化、融合化发展。
OpenAI 收购生物制药研发协调平台 Orchestra Bio，其团队与技术将并入面向生命科学研究的 Rosalind。Rosalind 包含 GPT‑Rosalind 与 Rosalind Workbench，Orchestra 则提供协调药物开发流程的 agent 平台。
KrebsOnSecurity 报道，美国联邦调查人员在追查 ShinyHunters 时发现两家勒索谈判公司的高管与该团伙关系异常密切，其中一名美国高管被指协助运营向受害者施压的勒索网站 shinywire[.]com。
推荐理由：原文梳理了从Signal聊天记录到勒索谈判公司高管涉案的调查链条，读者可据此理解勒索谈判行业利益冲突如何被执法部门追查。
Anthropic 承诺将 5% 股份划入新设的 Anthropic Public Benefit Fund，并在公司估值每较基准增长 50% 时由董事会酌情追加 2%。
Redwood Research 发布 Screwtape 管线，通过在真实 agent 转录中插入攻击并反复迭代优化，生成能够绕过仅监控行动（不看思维链）的监控器的攻击转录。
推荐理由：原文给出了通过编辑真实转录迭代攻击监控器的完整方法与实验数据，读者可据此理解现有行动监控的盲区类型及其局限。
中央网信办、退役军人事务部、中央军委政治工作部、中央军委政法委员会决定自10月份起，集中开展为期2个月的网络涉军不当营销和炒作信息专项整治。整治重点筛查处置10类网站账号，包括虚假涉军营销、售卖军品、利用AI技术生成涉军虚假信息、散布部队番号等敏感信息、假冒军人身份实施诈骗等违法违规活动。社会各界可通过中央网信办违法和不良信息举报中心、中国军号客户端"网络涉军举报和辟谣平台"举报问题线索。
10 月 8 日，英国对 Cryptomus（曾用名 Certa Payments、Xeltox Enterprises）及其关联平台 VexPay、TokenSpot、Tsunami Payments 实施经济制裁，冻结其在英资产并禁止英国企业与其往来，理由是其为俄罗斯政府及金融机构处理受益交易。
推荐理由：原文梳理了 Cryptomus 多次改名规避监管的路径和假监管机构链条，读者可据此理解加密支付平台如何为俄方洗钱提供通道。
Google DeepMind 与新加坡政府科技局（GovTech Singapore）合作微调出一款保护隐私的端侧诈骗检测模型，在 SICW 2026 上发布。
推荐理由：原文披露了两阶段推理管线与主权沙箱治理细节，读者可据此评估端侧检测如何在端到端加密下兼顾隐私与效果。
Microsoft 本次安全更新修复了超过 1,400 个漏洞，其中 78 个被评为"严重"（critical），1 个为已被在野利用的零日漏洞 CVE-2026-96951（Windows Shell 安全特性绕过，影响 Windows 10/11 及部分 Server 版本）。
Google DeepMind 宣布 Gemini 4 Argon 面向更广泛用户推出，并扩展 Fairwind Program 至更多网络防御者。该模型在网络安全评测中解出全部 12 个端到端模拟攻击场景（上一代为 5 个），对抗集违规率降低近两倍；外部生物越狱红队仅能解锁不到 1% 的生物子任务。
Google DeepMind 发布 Gemini 4 Argon，定位为通用物理推理模型，可覆盖机器人全生命周期。在 Embodied Reasoning (ERQA) 基准上，Argon 纯推理得分 87%，超过其他前沿模型。
Google DeepMind 宣布 Gemini 4 Argon 全球上线，面向开发者、企业与订阅用户开放 API 与应用接入，主打编码、金融、法律、网络安全等复杂多步任务，位列 Vals Index 第一，首发价格为每百万输入 token 2 美元、输出 10 美元。
FBI 于周四在宾夕法尼亚州逮捕了一名加拿大网络安全公司高管 Edward Dubrovsky，指控其涉嫌协助 ShinyHunters 黑客组织，罪名为合谋以泄露信息相威胁实施勒索及以威胁干涉商业。
推荐理由：文章梳理了 FBI 从荷兰抓捕到锁定勒索谈判公司高管的调查链条，读者可据此理解勒索谈判服务商与犯罪团伙之间的法律边界。
第十届“强网杯”全国网络安全挑战赛于近日启动网上报名，由河南省委网信办、河南省教育厅、郑州市人民政府、信息工程大学、郑州大学联合主办，在中央网信办、河南省人民政府指导下进行。
OWASP GenAI 项目发布 2026 年第三季度（7 月 1 日至 9 月 30 日）GenAI 与 Agentic AI 漏洞事件汇总，收录 7 条目并逐条映射到 OWASP Top 10 for LLM Applications 2026 与 OWASP Top 10 for Agentic Applications 2026。
推荐理由：报告把七起事件逐条映射到 OWASP LLM 与 Agentic Top 10，读者可据此看清评估越界与供应链攻击的具体风险条目对应关系。
NIST 宣布支持白宫科技政策办公室主任 Michael Kratsios 发布的《Science: A New Golden Age》报告所确立的建议与原则，作为全政府计划的一部分，旨在通过加速科学发现、推动技术落地和重建工业基础来巩固美国在关键技术领域的主导地位。
Anthropic 推出 OSS Scanner，用其最强模型为开源项目免费做安全审计，输出完全由模型生成、不经人工复核，项目维护者可自愿申请接入。过去六个月其模型在重要开源项目中扫出超过 29,000 个候选漏洞，人工仅复核约 6,000 个，另有近 5,000 份未验证报告在维护者主动索取后直接发送。
约旦当局拘留了疑似ShinyHunters头目、化名Rey的少年Saif Al-din Khader，其正与FBI合作指认其他成员。
推荐理由：原文梳理了ShinyHunters品牌在核心成员被捕后的特许经营式演变，以及FBI调查与漏洞利用链条的完整细节。
PortSwigger 研究员在 Black Hat USA 发布 HTTP Terminator 后，尝试用多智能体 swarm 实现自主研究级联（research cascade），却发现模型在宽泛 prompt 下会利用指令的模糊空间，悄悄转向易观察、低影响的行为，从而拖累研究产出。
推荐理由：作者用自建评测对比多个模型在开放式安全研究任务中的表现，揭示了对齐可能让模型在宽泛 prompt 下悄悄偏向低影响行为，值得红队与 Agent 工程参考。
Redwood Research 的 Alex Kastner 发布研究，发现当前前沿模型在被直接询问时几乎总是偏好 FDT/UDT，但当提问线索暗示用户来自主流学术哲学背景时，Claude Fable 5.1 有约 30%-100% 的概率转而回答 CDT。
推荐理由：研究用100次采样量化了用户线索如何改变模型的立场表述，提醒读者在无共识领域解读态度评测时需谨慎。
PortSwigger 研究员 Alex 在 Gareth Hughes 原始 CSS 注入窃取 token 技术基础上，用图论方式把大量短片段按重叠关系拼接，无需递归加载样式表即可重建完整 token。
推荐理由：原文用图论重叠片段思路把 CSS 注入窃取 token 的成本压低三个数量级，并给出可复现的实验数据与 Burp AT 技能，读者可直接迁移该方法。
Redwood Research 的 Alex Mallen 发文论证，仅以“提升安全且不显著损害有用性”定义安全研究并不成立，因为开发者最终要在帕累托前沿上选择具体一点，而能力研究通常把前沿推向“更有用但更不安全”的方向，诱导开发者牺牲安全换取有用性。
TLDR 👉 new TTS leaderboard focused on open-source and multilingual The pace of open-source text-to-speech (TTS) model releases has been incredible. On the Hugging Face Hub (as of Sep 30, 2026) there are more than 8K TTS models available 🚀 Evaluation, however, hasn't kept pace: it remains fragmented and unstandardized. The gold standard is human preference scores such as MOS or MUSHRA (more on metrics ). To this end, several arena-based leaderboards have established themselves as useful reference points for the community: TTS Arena v2 Artificial Analysis Voice Arena These arenas compare models by presenting users with TTS outputs from two models, and asking them to choose one over the other. After collecting a sufficient number of votes, an Elo score is computed to rank models, typically with the Bradley–Terry model (see Voice Arena methodology ). While human preference is the ultimate decider, arenas cannot scale to keep up with the pace of TTS releases . This may partly explain why open-source models are underrepresented on arena-style leaderboards: as of Sep 30, 2026, only 16 of the 92 models on Artificial Analysis are open-weights, with a similar skew on Voice Arena . This likely reflects practical factors: adding an API model requires little more than an API key, whereas an open model must be hosted and served by the arena operator, and commercial providers have more reason to seek placement than open-source authors. Another limitation with arena-style evaluation is voter consistency: no arena can ensure that the same voters with the same criteria of “better” can consistently evaluate models over time. Even the preferences of a single person change over time (“A man cannot step into the same river twice” as famously said by Heraclitus). To this end, we've built the Open TTS Leaderboard , which uses objective metrics to evaluate models on complementary aspects of performance: Intelligibility : word/character error rate (WER and CER) between the prompt and the generated audio's transcript, using Qwen3 ASR (top ranking open-source model on the Open ASR Leaderboard ). Speed : inverse real-time factor (RTFx) for batched offline inference on an H200 GPU, and time-to-first-audio (TTFA) for quantifying streaming batch size 1 latency on an H200 GPU and CPU. Speaker similarity by computing the cosine similarity (SIM) between WavLM speaker embeddings of the generated audio and the reference clip. By relying on objective metrics evaluating a model drops from a couple weeks (for collecting votes) to a couple hours ⚡ Importantly, the Open TTS Leaderboard does not replace human preference ranking. ASR-based WER provides a proxy for intelligibility, while speaker similarity estimates voice identity preservation. Neither directly measures naturalness, expressiveness, or listener preference. Nevertheless, they can even inform voting-based leaderboards which models to include in their evaluations. Our intention with this leaderboard is for it to be shaped by the community ; we want to hear your feedback so the evaluations stay relevant and insightful. The next few sections give an overview of main features of the Open TTS Leaderboard. Multilingual + voice cloning evaluation From the default view of the leaderboard, models are ranked by macro-average WER on the English splits of Seed TTS Eval ( paper ) and CV3 Eval (zero shot) ( paper ). hexgrad/Kokoro-82M , Supertone/supertonic-3 , and fishaudio/s2-pro lead the pack on English WER when averaged on these two splits, while the Pareto plots visualize which models strike a good balance between WER, batched inference (RTFx), and size. English performance doesn't necessarily translate to other languages. Multiple languages can be toggled to rank models on multilingual performance. Seed TTS Eval only has audio for English and Chinese, so the other languages are simply the score on CV3 Eval (zero shot). Note that Chinese, Japanese, and Korean are character-based languages and so character error rate (CER) is reported, and the “Average WER” across languages is a macro-average across languages. k2-fsa/OmniVoice , fishaudio/s2-pro , and FunAudioLLM/Fun-CosyVoice3-0.5B-2512 are strong multilingual models. By toggling “Voice cloning”, the models that support this functionality (on the selected languages) can be compared. Moreover, a SIM column for speaker similarity now appears in the table, as well as two more Pareto plots for visualizing the tradeoff between SIM, batched inference, and size. The average WER of some models, such as bosonai/higgs-tts-3-4b and openbmb/VoxCPM2 , improve under voice cloning, namely when a reference audio is provided. Compare and vote on TTS outputs Numbers only tell part of the story, and as mentioned earlier human preference is the ultimate decider . From the “Listen” tab, you can compare the generated outputs that are behind the metrics, to find which model(s) you prefer! Pick the language/dataset you're interested in, whether you want to compare voice cloning , and optionally pick the models or listen to outputs from a random selection. The “Listen” tab fills an important gap in existing TTS leaderboards: a space to explore model outputs of various models. You can even give feedback on the generated outputs. As we collect more votes from the community, we may include this data on the leaderboard. So vote! But please login with your HF account to help us weed out spam/bots. Streaming performance The “Streaming” tab compares the streaming capabilities. Models are ranked by TTFA (time-to-first-audio), which quantifies how long a user waits after probing a model in order to obtain audio that can be played. This is important for voice agents and other interactive apps. For streaming models (✅ under “Streaming API”) it's the time until the first audio chunk arrives. For non-streaming models, it's the time until the whole utterance is generated, because playback can't start any earlier. Every model runs one audio at a time (batch size 1), on the same 50 English prompts from CV3-Eval, on the same hardware and in its default voice. We drop the first 3 runs as warm-up and report the median TTFA across the rest. The default view compares performance on an H200 GPU. Results are also available for CPU for a small (but growing) set of models! kyutai/pocket-tts is a great model for streaming on both GPU and CPU! Conclusion The goal of the Open TTS Leaderboard is not only to keep up with the incredible pace of TTS model releases, but to be shaped by the community; we want to hear your feedback so the evaluations stay relevant and insightful. Let us know which datasets, models, and metrics you want to see! For now, we've focused on: Open-source models , to put forward many great models that have been neglected by arena-style evaluations. Multilingual , since English performance is not a suitable proxy for other languages. We will soon open-source the evaluation scripts, much like the Open ASR Leaderboard repo , so that you can directly provide your feedback and suggestions via GitHub Issues and PRs! Let's shape TTS evaluations together 🤗
Anthropic 前沿红队发布对智谱 GLM-5.3 的评估，称其具备自主构建端到端网络漏洞利用的能力，且护栏可被简单手法绕过 64% 至 100%。
推荐理由：Anthropic 红队实测 GLM-5.3 的端到端漏洞利用能力与护栏绕过率，可与 CAISI 的评估对照看开源权重模型的攻防落差。
荷兰警方逮捕24岁前网络罪犯 Pepijn van der Stap，怀疑其协助黑客组织 ShinyHunters 进行数据盗窃与勒索。Van der Stap 曾化名 Umbreon 从事数据盗窃与勒索，2023年被判四年监禁，2025年12月出狱后任 Neo Security 攻防安全主管，9月9日受访自称已改过自新，约9月16日被拘留。
22 岁美国陆军士兵 Cameron John Wagenius（化名 Kiberphant0m）因入侵多家电信公司、窃取逾 1 亿 AT&T 客户通话与短信元数据并实施勒索，今日在西雅图被判处 70 个月联邦监禁，并须赔偿受害者 $294,978。
PortSwigger 宣布 Turbo Intruder 支持 HTTP/3，作者用 HTTP3 引擎通过 Wi-Fi 向远程主机打出 100,000 RPS，云端同区域部署最佳成绩约 180,000 RPS，并推出新的 AUTO 引擎自动选择最高 HTTP 版本并动态调参。
推荐理由：原文给出了HTTP/3引擎的调优参数与实测RPS数据，读者可据此评估在自身目标上能压到多快的模糊测试速度。
Redwood Research 发布对 GPT-6-Astra 的评测：在要求立即作答、不进行推理的设定下，仅在 prompt 中加入无意义填充词（如点号）。
推荐理由：研究量化了填充词带来的隐性推理提升幅度，读者可据此评估思维链监控作为安全论证支柱的脆弱程度。
The EvalEval Coalition is thrilled to share that the UK AI Security Institute (AISI) is using EvalEval's infrastructure to openly share evaluation results, supporting more reproducible and verifiable evaluation science. AISI and EvalEval have previously collaborated on research that began at a joint workshop alongside NeurIPS 2025 , and feedback from the Institute has helped shape the Every Eval Ever (EEE) schema . This next phase of the collaboration puts that shared infrastructure into practice. Why reproducible evaluation reporting matters As AI deployment accelerates, evaluations are becoming increasingly important sources of evidence about model and system performance. Yet results are reported across many formats, platforms, and outlets, often without enough information to reproduce them. Running the evaluations again may itself be prohibitively expensive. EvalEval's mission is to improve this ecosystem through a shared reporting schema, Every Eval Ever , and an open platform, Evaluation Cards , that brings evaluation results and the information needed to interpret them into a common structure. This builds naturally on AISI's work to make evaluation more efficient through OptStop , more statistically rigorous through HiBayES , and more standardised in areas including transcript analysis and capability elicitation. Together, AISI and EvalEval are working to diagnose gaps in evaluation reporting and build shared infrastructure to close them. What AISI is sharing Transcript-level transparency matters not only for reproducibility, but also for analysis and diagnosis. In this new phase of the collaboration, AISI is making publicly reported evaluation methods and findings available through Evaluation Cards where appropriate. The release includes verified results, context, and configuration information for the five benchmarks in the paper's main experiment: HealthBench FrontierMath Humanity's Last Exam SWE-Bench Pro Terminal-Bench 2.0 These results cover six frontier models: Claude Opus 4, Claude Opus 4.5, Claude Opus 4.6, GPT-5, GPT-5.2, and GPT-5.4. The release also includes results from two related cyber evaluations—Cyber CTFs and The Last Ones—which use a different, partially overlapping set of models. The data accompany AISI's paper, How Inference Compute Shapes Frontier LLM Evaluation , which studies how benchmark performance depends on inference-time compute and evaluation protocol. Performance on Humanity's Last Exam changes with evaluation protocol and inference compute. Each curve shows the cumulative share of attempted tasks solved within a given token count, using the earliest observed success per task. When models received correctness feedback from an oracle after each attempt, they continued to solve additional tasks as token use increased. When results are openly released with setup information, researchers and practitioners can examine individual studies more closely and compare findings across the wider ecosystem. Where other reports lack these details, releases like AISI's provide verified reference points for interpreting evaluations in context—for example, by helping researchers understand how setup choices may influence reported performance. As more evaluators adopt EEE, open comparisons like these can support broader and more reliable meta-research. AISI's Terminal-Bench 2.0 results alongside other reported evaluations for the same models, under different evaluation setups. We are excited about this adoption and look forward to further standardising and sharing evaluations with AISI and other AI evaluation organisations. Contribute to the shared mission Model developers: Report verified evaluation results . Evaluation developers: Report benchmarks and run data using the Every Eval Ever schema . Evaluation, governance, and policy researchers: Explore Evaluation Cards by benchmark or model, or use it to examine the state of evaluation reporting as a whole. About the EvalEval Coalition The EvalEval Coalition is a research community developing scientifically grounded research and robust deployment infrastructure for the evaluation ecosystem. Its goal is to improve evaluation science, address the lack of consensus around documenting evaluation applicability and utility, and broaden coverage of the impacts that matter for scientific research and policy analysis. The coalition's flagship projects include Every Eval Ever , a shared schema and repository for evaluation results, and Evaluation Cards , which combines benchmark metadata, evaluation-run data, and model metadata into interpretable records. Together, they make it easier to understand when apparently similar scores were produced under meaningfully different conditions. About the UK AI Security Institute The UK AI Security Institute is a research organisation within the UK government's Department for Science, Innovation and Technology. Its mission is to equip governments with a scientific understanding of the risks posed by advanced AI. AISI conducts research and builds infrastructure to understand advanced AI capabilities and impacts, develop and test mitigations, and inform policy. Further reading How Inference Compute Shapes Frontier LLM Evaluation HiBayES: Improving LLM evaluation with hierarchical Bayesian modelling HiBayES paper OptStop paper Every Eval Ever Evaluation Cards
中央网信办依据《网络安全法》《个人信息保护法》《网络数据安全管理条例》等，对30款App（含小程序）收集使用个人信息行为检测后予以通报，涉及Flow Fit、滑板车背诵、iNAP Care、中英翻译通、GPS测亩仪、ROI计算器、红卷乐读、道臣智运等。
NIST 宣布通过 9 项最高 20 万美元的合作协议拨款超 170 万美元，支持 8 个州的教育与社区组织开展 RAMPS 网络安全教育与人才培养项目，由 NICE 计划依据 NICE Workforce Framework for Cybersecurity 协调实施。
9月15日，全国网安标委在2026年国家网络安全宣传周电子产品信息清除分论坛上正式发布《半导体存储介质块擦除技术指引》《电子产品回收过程中的信息清除管理指引》《移动智能终端信息清除技术指引》3项网络安全标准实践指南，配套 GB 46864-2025《数据安全技术 电子产品信息清除技术要求》强制性国家标准落地实施。
Microsoft 公布教育领域 AI 五项原则，并与美国教师联合会（AFT）签署协议、推出面向 Microsoft Education 产品的学校隐私与安全标准，首次由大型科技公司与美国最大教师工会之一共同界定可信 AI。