AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.13470v1 Announce Type: new Abstract: Although large language models (LLMs) have demonstrated remarkable capabilities, their reliance on cloud-scale infrastructure poses fundamental challenges for deployment in agentic pipelines, including latency, privacy, connectivity, and substantial computational cost. Small language models (SLMs) offer a compelling alternative: recent studies suggest that many repetitive and narrowly scoped subtasks in agentic workloads may be better served by specialized SLMs than by monolithic LLMs. However, the limited capacity and context windows of SLMs can constrain long-horizon reasoning and interaction-heavy orchestration strategies such as iterative verification and debate. This motivates a complementary, non-interactive…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.13466v1 Announce Type: new Abstract: Enterprise AI adoption has reached 78% of organizations globally, yet the infrastructure to govern that adoption has not kept pace. This paper identifies and characterizes the attestation deficit, a structural condition in which organizations maintain governance policies but cannot produce auditable, tamper-evident evidence of enforcement within regulatory timelines. Drawing on empirical data from the Stanford 2026 AI Index Report (362 documented incidents), the IBM/Ponemon 2026 Cost of a Data Breach study (USD 4.99M average cost, 92% lacking access controls), and the EY/AIUC-1 Consortium survey (38% end-to-end monitoring, 17% agent-to-agent coverage), this paper demonstrates that the governance failure is organiz…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.13463v1 Announce Type: new Abstract: The increasing deployment of AI agents in long-horizon tasks yields massive execution logs. Diagnosing failures within these records is crucial for reliability, as it transforms outcome-level signals into actionable interventions. The sheer scale of the data renders human review impractical, driving the need for automated root-cause attribution (RCA). However, automated RCA methods using LLMs suffer from low diagnostic accuracy, especially as execution traces grow larger. They struggle because relevant information is often sparse, distributed across distant actions, and disconnected from the visible failure, reducing root-cause attribution to a massive search problem. Existing RCA methods typically rely on one-sho…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.13457v1 Announce Type: new Abstract: Timeseries multimodal large language models (TS-MLLMs) have recently begun leveraging the reasoning capabilities of large language models (LLMs) for question-answering tasks. However, these models often fail to capture dynamic temporal patterns, providing only implicit reasoning that lacks the underlying explanations critical for high-stakes applications like healthcare. While reinforcement learning (RL)-based timeseries language models aim to address this, they often fall short because they are trained on narrow, in-distribution data and struggle with out-of-distribution compositional questions. To address these challenges, we present TimeThink, a synthetic framework for eliciting compositional timeseries reasoni…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.13437v1 Announce Type: new Abstract: Scientific research is a continuous process that emphasizes inheritance. Methods developed by predecessors are often expanded upon by new researchers to explore more novel and in-depth scientific questions. However, the change of lab staff, such as student graduation, leads to a lack of personnel capable of replicating methods. Methods that have been developed with significant effort and resources cannot be continued. To address these limitations, we propose LabAgent, a reproduce and discovery harness tailored for a lab's continuous work. LabAgent employs two mechanisms to guarantee that all skills can be executed and verified and to record the corrective methods and experiences, allowing for direct correction or…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.13436v1 Announce Type: new Abstract: Large Language Model (LLM) agents offer a promising path toward autonomously managing long-term physical tasks without human intervention. However, physical tasks require agents to continuously observe the environment, make consequential actions, and remain effective as the environment changes. Existing approaches either require substantial data and retraining, or primarily focus on agents operating in the virtual world. In this work, we explore the feasibility of building a self-adaptive physical AI agent that manages long-term physical tasks in a zero-shot manner and adapts to environmental changes without human intervention. We design a multi-agent framework that integrates planning, tool calling, observation,…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.13422v1 Announce Type: new Abstract: LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We study this problem through Vibe Patenting, an end-to-end patent-drafting testbed for AI-agent evaluation. A separately-invoked LLM judge evaluates generated patent drafts and provides structured feedback for iterative revision. Across multiple inventions and drafting-agent configurations, judge-guided revision consistently improves judge-assessed quality, while unguided revision tends to saturate. Notably, iterative judge feedback enables a low-reasoning agent to approach the performance of a substantially more expensive high-reasoning agent. Stronger models and inc…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.13406v1 Announce Type: new Abstract: When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect? Towards autonomous and evolving intelligence, RSI is being claimed at many scales, while no single framework that formally describes these emerging instances exists. Its counterpart in the classical realm, iterative policy improvement, is characterized by generalized policy iteration (GPI), a framework of broad applicability with well-understood theoretical properties, but only where the update principle and the evaluation base lie outside the agent. In this paper, we propose Generalized Agent Iteration (GAI), a formal framework that describes iterative policy improvement and RSI as two cases of a single…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is to adequately approximate the Pareto front; that is, to obtain a high-quality solution set with 1) good convergence (closeness to the Pareto front) and 2) good diversity (spread across the Pareto front). Existing MOBO methods typically aim to accomplish these two tasks simultaneously, i.e., driving the search towards the Pareto front while maintaining a diverse set of nondominated solutions, such that the solutions, ideally, can gradually approach the entire front. When sufficient search budgets are available, this approach is effective. However, co…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2609.13356v1 Announce Type: new Abstract: In this work, we present ZGCM-1, a fully open 7B dense foundation model trained from scratch with extreme data, system, and algorithmic efficiency. ZGCM-1 is founded on a core premise: compact models cannot passively memorize the open web, but can overcome parametric capacity limits by coupling deliberate internal thinking with active external tool use. To support this paradigm across a 256K context, we develop an end-to-end, high-efficiency open training recipe: Architecture & System Co-design: interleaved gated sliding-window and full attention, and a stable FP8 Muon optimizer; Progressive Curriculum & MDP Mid-Training: context scaling across 16K, 64K, and 256K, and the reformulation of interaction traces into M…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Discussion | Link

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Discussion | Link

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Discussion | Link

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Most marketing teams aspire to be data-driven. In practice, getting a trusted answer,...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Every Postgres vendor calls itself "managed." Few of them agree on what that word...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：When OpenAI CEO Sam Altman, Anthropic CEO Dario Amodei, Google DeepMind cofounder Demis Hassabis, and SpaceX head Elon Musk loosely agreed over the weekend to slow down AI development, skeptics spotted an ulterior motive immediately. The AI titans had declared that their aim was to "pace the frontier," signing on at least partially to a proposal for embedding third-party auditors, regulating domestic labs, and reaching a global slowdown agreement. Their critics, however, argued they simply wanted to stop would-be competitors, kneecap the open-source movement, and avoid real legal safeguards - some dubbed it an outright "cartel." The truth … Read the full story at The Verge.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：A practitioner's map of the 3 layers in a modern agent stack, with verified sources and an overlap analysis. The post Agent Harness vs Agent Framework vs MCP: Which Layer Owns the Loop, State, Tools, Permissions, and Recovery appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Discussion | Link

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Learn how Abnormal AI deployed Amazon Bedrock AgentCore Code Interpreter as an ephemeral compute scratch pad for the agents behind its real-time email threat detection at billion-message scale, plus the sandbox design decisions and practical lessons for builders deploying Code Interpreter in production.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Dario Amodei kicked off a flood of statements over the past few days about AI safety by publishing a long essay titled "We Must Pace the Frontier" detailing why AI development should be slowed down. Other AI leaders and politicians are speaking out in favor of or opposing his points, and we've compiled some of them here. Anthropic CEO Dario Amodei Amodei's Saturday morning essay outlined three steps for pacing AI development: embedded third-party evaluators that can verify if a company is adhering to safety practices and commitments and report incidents, coordination between frontier AI companies in democratic countries on standards and li … Read the full story at The Verge.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯： The contagion of fear Bryan Cantrill responds to the tweet by former Anthropic employee Jacob Coxon confirming that many Anthropic researchers believe AI "could kill us all by the end of the decade". Bryan shares a story of his own youthful mistakes causing unjustified panic among less technical peers, and warns against doing the same: These ghoulish claims strike brazenly at the hearth, and given the obvious importance of AI, it is unsurprising that they have leapt into the mainstream, with people asking the natural question: how would that happen? The answers always rely on hand-wavy extrapolation into the future; for example, Jacob Coxon cites "hacking critical infrastructure" and "extinction-level bioweapons" without further elaboration. But Coxon is not a…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Reward AI has released OM-1 (Omnibody Model 1), a general-purpose manipulation policy trained entirely on human demonstrations captured with a 7-DoF wearable glove, with no teleoperation or on-robot data. The policy runs on industrial arms and humanoids at human speed, learns a new task from under 30 minutes of data, and pairs electromagnetic hand tracking (60% lower overshoot than visual-inertial at 67 cm/s) with an RL-trained control layer that runs on its own clock. No weights, code, or API are public yet. The post Reward AI Releases OM-1: A Robot Policy Trained on Human Demonstrations Only, With No Teleoperation or On-Robot Data appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Anyone running stateful Apache Spark™ Structured Streaming queries in production...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Sakana AI researchers Jeffrey Seely and Julian Gould introduce Augmented Lagrangian Predictive Coding (PC-ALM), a local-learning alternative to backpropagation. By attaching a Lagrange multiplier to each layer constraint, PC-ALM keeps predictive coding's layer-local updates while recovering exact backprop gradients in linear networks. It matches BP across widths and depths from 8 to 128 at an inference budget of T = 2L, lifts gradient cosine to BP from 0.604 to 0.909 in the reference cell, and trains 1000-layer residual MLPs within about 2 points of backprop on MNIST. MIT-licensed JAX code is available. The post Sakana AI Researchers Introduce PC-ALM, a Layer-Local Alternative to Backpropagation That Trains 1000-Layer Networks appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Amazon Bedrock AgentCore Identity now offers a Consent portal, a managed web experience and session binding endpoint for AgentCore Gateway. This post walks through provisioning a portal, configuring GitHub and Slack 3LO targets, and the end-user consent flow, and shows how to review activity in AWS CloudTrail.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯： My comment on What blog posts influenced your thinking the most? — Lobste.rs. An early Joel Spolsky one for me was The Law of Leaky Abstractions. I read that near the start of my career and it's encouraged me to always be looking for improved understanding of the layers under where I'm working, just in case one of those abstractions leaks. A more recent one, from 2018, is Migrations: the sole scalable fix to tech debt by Will Larson. I absolutely love his idea that migrations (e.g. replacing one service with a new one, or switching database engines, or whatever) are part and parcel of software engineering and are a skill that you should invest in and get good at, not avoid or treat as special one-offs. The Engineer/Manager Pendulum by Charity Majors was…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：NVIDIA CEO Jensen Huang speaks during the G20 Innovation Ministerial in Chapel Hill, North Carolina, on September 2, 2026. (Photo by Matt RAMEY / AFP via Getty Images) | AFP via Getty Images Nvidia CEO Jensen Huang took a call from President Trump on Monday while onstage at the All-In Podcast's All-In Summit. It's not the first time Huang has taken a call from the president during work, but this time he put Trump on speakerphone before a big crowd. During the call, the president launched into his take on recent fears about AI development, which he called a "hoax," and told the crowd that "the robots will not be taking over." It's already a frothy week for AI news. The call with the president followed Anthropic CEO Dario Amodei's long essay published this weeken…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Discussion | Link

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Beijing hits back after Anthropic CEO calls for US to impede China’s progress in AI China has dismissed what it said were “fearmongering” calls for the US to block Beijing’s AI industry, even as the top Chinese spy chief warned the evolving technology could threaten Communist party rule. The US tech entrepreneur and Anthropic chief executive, Dario Amodei, wrote an essay published over the weekend that called for a global slowdown in AI capabilities, but also said Washington should actively impede Beijing’s advancement in order to keep a technological edge. Continue reading...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Christina Koch sits down with James Manyika, Google’s Senior Vice President of Research, Labs, Technology & Society.