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的 token 上似乎相当。 这两种方法在很大程度上是互补的：它们之间只有 12 个共同漏洞。协调型集群能够将注意力集中在它认为最容易挖掘漏洞的地方，而独立智能体则被预先分配了搜索位置。集群中的智能体为自己构建了工具，并学会了专精于特定类型的漏洞发现。 我们预测，未来这种专精化与协调将胜过无协调的暴力搜索。 In the experiment above, agents in the agent swarm don’t directly rely on one-another’s work: if one misses a bug, it won’t directly undermine the work of another. But when agents do depend on one-another, coordination gets much more difficult. Larger software engineering projects are one place this matters: they typically develop rich—and dyn

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to patients at natural conversational speed while simultaneously performing careful clinical reasoning and continuously processing visual and auditory streams. Currently, a single agent cannot satisfy all these requirements. Deep reasoning takes time, but conversational pauses erode patient trust and rapport. To address this challenge, AMIE (Video) uses an asynchronous multi-agent architecture that divides labor across three specialized agents working continuously in parallel: This decoupled des

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of rules. A lesson five different tasks discovered is a different object from one that appeared once, and both are worth keeping. So on the core question — should you compress an agent's hard-won lessons into a tidy summary? — ACE and ALTK-Evolve give the same answer: no. Count them, don't collapse them. ACE's per-bullet counters and our support counts are two spellings of the same idea. Where we differ Two places: how the memory is built , and how it's delivered — and it's the delivery differe

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prevent stale requests that finish after a close or reopen from restoring released references. We evaluate DeepSeek-V4-Pro and Qwen3.5-397B-A17B with TP8 and HiCache on SWE-bench agent trajectories. The baseline uses ordinary HiRadixCache with LRU. The comparison enables Unified Radix Cache and --enable-session-radix-cache. Because this changes both the cache implementation and the eviction policy, the observed differences should not be interpreted as an isolated ablation of session awareness.

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s callback and hook system is a supported extension point for modifying responses at the forwarding layer. Injecting Tool Calls Even more interesting though, if the clients are AI agents with tool access, an injected response can carry a tool-call. Because the output is changed after inference, this bypasses prompt-level defenses. It does not by itself bypass client-side tool authorization (unless you run in yolo mode). Pretty scary stuff. Demo Walkthrough with Screenshots The manual sequence be

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n automated protocol to evaluate the integrity of AI-generated papers. Large language models (LLMs) are increasingly being deployed not just as coding assistants but as autonomous agents capable of conducting end-to-end scientific research workflows. Recent systems (e.g., Sakana’s AI-Scientist , AutoResearchClaw , DeepScientist , AI-Researcher ) can review literature, formulate hypotheses, execute experiments and write complete manuscripts that are comparable to human-authored papers. However, a

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ful even before the training path is fully accelerated. Beyond this ablation, humans& uses the same recipe family and components in production for large-scale, long-context, multi-agent asynchronous RL research. Online NVFP4 Model Serving The same per-token NVFP4 recipe can serve FP8/BF16 model checkpoints with online post-training quantization (PTQ), delivering good model quality without calibration artifacts, while keeping compute performance on par with traditional NVFP4 serving. Online per-t

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easons making the scheme (as it currently stands) an attractive PQC signature candidate. Discovery process To find the attack, Claude Mythos Preview worked semi-autonomously in an agentic harness, with occasional human guidance and nontechnical direction. Mythos found the attack after an extensive literature review to understand the state of the art, and substantial mathematical reasoning and computational experiments. After finding the attack, Mythos implemented an end-to-end verification pipel

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ject Fetch: Phase two, we’re already seeing improvements in model capability such that their ability to use off-the-shelf robots is on track to approach the ease with which coding agents use software tools. Working again with our partners at Andon Labs, we developed a new series of demonstrations and evaluations that assess AI models’ ability to use a flying drone to autonomously perform a simple locate-and-follow task of the kind used in aerial surveillance, culminating in a new benchmark: Dron

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SymptomAI: Towards a conversational AI agent for everyday symptom assessment July 22, 2026 We present a first-of-its-kind research of AI for differential diagnosis and symptom checking through a national-scale study. A large proportion of clinical diagnoses can be derived from language-based interviews alone. These diagnostic interviews are typically conducted by clinicians through doctor-patient interactions during in-person or remote visits. While these interactions are the gold standard for s

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other qubits. This can lead to errors, and error-prone quantum systems are neither practical nor useful. AI For QEC A lot of work is being done to see how AI – both generative and agentic – can help address error correction in quantum systems. For example, scientists from the University of Pennsylvania and Hao Tang of Peking University in China addressed the issue in a research paper early last year. Nvidia in April ran out Ising, a family of open source AI models that the vendor said can provid

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Ekoparty in 2024, where he talked about injecting ANSI Escape Codes into log files. It’s a great talk, I recommend watching it. How could this be meaningfully exploited? LLMs and Agents! Rendering ANSI Escape Codes with AI Agents! Leon Derczynski discovered that LLMs can emit ANSI escape codes, e.g. the ESC (ASCII 27). When I saw that, it seemed like a great way to hijack AI agents and LLM CLI tools running on the command line and to exfiltrate data. Proof-of-Concept Exploit Here is a demo that

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variation of any category we evaluated. Some systems recognized emotion exceptionally well but struggled to respond naturally. We found that access to audio did not guarantee that agents used the paralinguistic information it contained. Some systems remained largely transcript-driven, relying on the words being spoken while overlooking cues such as tone, pacing, hesitation, emphasis, and volume. Humans naturally use these cues to infer confidence, uncertainty, frustration, sarcasm, and empathy.

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both models on FrontierCode 1.1 using our new Fusion architecture, Fable cost less. Unsurprisingly, it also scored higher. This post explains why, and what that means for pricing agentic work. Introduction Everyone who runs coding agents knows that stronger models give you better results, but you eat the cost. When we introduced Devin Fusion, we showed a way out: keep a frontier model in charge, let it delegate to a cheaper and faster sidekick, and you get frontier-level performance at 60% lowe

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environments. To address this, we retain the inverse pendulum and hopper tasks as control tasks but also introduce a new task based on pinball arcade machines. In TwinFlipper, the agent controls a set of flippers and seeks to maximize the ball’s total airtime—the cumulative amount of time the ball remains above a specified height threshold while not touching anything—before it drops below the flippers. Although a naive solution is to just slam the ball upwards, much more airtime can be gained by

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Harness Design Patterns Pattern 1: Workflow Automation; Pattern 2: File System as Persistent Memory; Pattern 3: Sub-agent and Backend Jobs; Case study: Coding Agent Harness; Harness Layer vs Core Intelligence?; Harness Optimization The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965), where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. Yudkowsky (2008) used the p

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（/Applications/Cursor.app/Contents/Resources/app/resources/helpers/cursorsandbox），威胁行为者确保未来命令在不受沙箱限制的情况下运行，因此同一提示注入中的后续指令会导致非沙箱 RCE。 其他易受攻击的路径包括 /.zshrc、 /.zshenv 或 /Library/LaunchAgents。 传统上，远程攻击者无法控制沙箱操作的工​​作目录。然而，由于编码代理是一种独特的软件，在此漏洞中，提示注入成为通往该部分代码的通道。 漏洞 2：符号链接规范化失败 第二个漏洞与第一个完全独立，存在于 Cursor 的文件路径解析边缘情况中。它允许威胁行为者利用符号链接绕过越界写入保护。 提示注入（通过无害的 MCP 服务器请求或受污染的网页结果）可以指示 Cursor Agent 在项目目录内创建一个符号链接，指向位于项目目录之外的文件。 默认情况下，Cursor Agent 会尝试规范化路径（解析符号链接）以确定其真实位置，并验证它们位于项目根目录内。 该漏洞之所以发生，是因为规范化逻辑包含一个危险的 fallba

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I just never got around sharing it. What Is a TOCTOU Attack? TOCTOU stands for time-of-check to time-of-use and describes a kind of race condition in security. How Do Computer-Use Agents Work? Computer-Use Agents take screenshots, and the AI processes the screenshots to determine the next steps. Once the Agent completes the reasoning process, it will take an action, like entering data or clicking a button or link. This creates a classic TOCTOU situation, where the computer screen could have chan

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on their ability to adapt to new data, facts, domains, languages, codebases, and user needs. For example, continual learning could help mitigate knowledge cutoffs and allow coding agents to adapt to new repositories without relying entirely on in-context learning via long prompts. Moreover, continual learning is ultimately necessary for models to grow and improve indefinitely beyond a fixed and finite context window. However, training models with continual learning capabilities remains out of re

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opment. When you just need to build a tiny desktop app: TinyJS October 13, 2026 Valen creator drives 'Golden Spike' to connect new languages with Rust September 26, 2026 Microsoft agentically ports Copilot runtime to Rust for $120K September 19, 2026 Apple iPhone Duo makes developers think in folds September 16, 2026 JavaScript installer pnpm recast in Rust because ECMAScript can't keep up September 5, 2026 Kubernetes cleans house, bins legacy kube-dns, IPVS, and cgroup v1 August 27, 2026 How Cu

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