Agent

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Runable Inc., a platform that uses artificial intelligence to help businesses build, run and grow, announced Wednesday that it raised $21 million in early-stage funding to scale its operations and reach more enterprise outfits. Susquehanna Venture Capital and Nexus Venture Partners co-led the Series A funding round, alongside continued support from existing investors Together Fund […] The post Runable raises $21M to realize small businesses’ growth vision using AI agents appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Wagtail’s just-released 8.0 release notes are very unusual. Zero admin UI improvements in the highlights, even though UX is one of Wagtail’s biggest strengths. We made a strategic choice to focus on a shiny new API inst…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：- YouTube AboutPressCopyrightContact usCreatorsAdvertiseDevelopersTermsPrivacyPolicy & SafetyHow YouTube worksTest new features

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Take that, OpenAI! Anthropic! Chinese AI models have surpassed their U.S. counterparts in token consumption on OpenRouter. You might think U.S. AI companies dictate the AI economy. You’d be wrong. According to dat…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：On Nvidia's earnings call Wednesday, CEO Jensen Huang casually announced the company had "achieved AGI," one of the tech industry's ultimate goals some of its biggest players have spent years chasing. Almost immediately, Huang dismissed the coveted milestone as "senseless." He's right. For the supposed finish line of the AI race, there is no consensus on what artificial general intelligence means, let alone how we'll know when we've actually got there, which makes achieving it equally arbitrary. Asked about OpenAI's pursuit of AGI, Huang said that when it comes to Nvidia, "for many tasks, we could say that we've already achieved AGI." … Read the full story at The Verge.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Self-hosted speech AI carries an observability trade-off: the numbers that drive capacity planning and cost management stay locked inside the vendor container. Deepgram closes that gap on Amazon SageMaker AI with two capabilities that land billing, usage, and per-GPU metrics directly in your own Amazon CloudWatch account.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Low-skill hacktivists are now 'enabled with the same tooling and sophistication as a state-sponsored group,' according to cybersecurity consultant Unit 42.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：AI coding company Replit is throwing its weight behind the model-routing trend by making its “intelligent model routing” system the The post Replit’s new default: Auto mode picks the best model for each task appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The report, and a separate report by AI researchers, accentuates the seriousness of cybersecurity breaches involving AI agents.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：In this tutorial, we analyze Anthropic’s 1,440 AI-designed protein binder dataset to benchmark 10 leading structure predictors. Discover how target identity, expression titers, and consensus scoring impact experimental success and learn best practices for rigorous cross-validation in protein design workflows The post From In-Silico to Wet-Lab: Evaluating AI Protein Design Performance appeared first on MarkTechPost.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Sai, a computer agent built by Simular, has achieved a 73% success rate on OSWorld 2.0, in a benchmark update The post “Posterity will find it ludicrous”: Sai agent hits 73% on OSWorld 2.0 performing routine (but necessary) work appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：REC · YOUR AGENT IS EDITING FFmpeg as a service, built for agents. Typed operations your agent calls over MCP or REST. Trim, resize, compress, convert. A validated request in, a finished file out. No shell, ever. Start…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Your company, taking part Know what’s going on with your company, at any moment. The questions that normally cost you a morning are already answered. Ask it out loud in a meeting, read it over coffee, or let Atlas tell…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：We are headed to VLDB 2026 to share multiple innovations that power the Databricks platform...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Backprompter Build, test, and ship AI agents — no backend code. A complete workspace to create AI agents from system prompts, chat with and evaluate them, then deploy a polished chat UI for your users — with API access,…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Software delivery platform provider Harness Inc. today announced the launch of Agent-Ready Harness Code Repository and AI Code Review, aimed at developer teams adopting artificial intelligence coding agents at an ever-increasing pace. Now that AI agents produce code faster than a team can write, review, test and deploy it, that work is shifting to where […] The post Harness tackles influx of agent-delivered code with Code Repository and AI Code Review appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The MotivationMore and more enterprises are now asking agents to work with their...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Notifications You must be signed in to change notification settings Fork 3 Star 78 BranchesTags Open more actions menu Latest commit History 135 Commits 135 Commits Folders and files NameName Last commit message Last co…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Presented by Gravitee Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it. That’s because enterprises don't deploy a single agent and watch it run, they deploy fleets, each one calling APIs, calling other agents, reaching into applications that were never built with a machine decision-maker in mind. That's the failure mode that should keep you up at night: a windy, complicated system nobody can see clearly enough to govern. But why do things get so opaque so quickly? Add a second agent to a system, and you've added one connection. Add a tenth, and you haven't added ten connections, you've potentially added dozens, because now any agent might call any other, and each of those calls can trigger a call somewhere else. Complexity doesn't creep up with agent headcount. It compounds with the number of paths between agents, and nobody's job is to draw that graph. A support ticket that used to touch one system might now pass through four agents before a human ever lays eyes on it, and every one of those handoffs is a decision point nobody approved. Most enterprise AI programs stall when the humans responsible for their agents lose the thread. Ask a security team a simple question: which agents can reach which systems, and watch the silence. Ask which agent triggered which downstream action three hops ago. More silence. The instinct is to treat this like a checklist. Approve the agent. Log the agent. Move on. I'd argue this is the wrong instinct. A checklist checks a single point in time. Complexity runs across a chain, and you can't govern a chain with a stack of one-time approvals any more than you can call a diet successful because you had a vegetable once. So where does it actually break down? Permissions creep first. Somebody builds an agent to summarize support tickets, grants it broad API access because scoping it properly would've taken another sprint, and forgets about it. Six months later, that same agent has a path into the payments system. Nobody remembers signing off on that. Nobody did. And ownership thins out the further the chain runs. Five agents touch one workflow, something breaks at step four, and now you're asking who's responsible for a link nobody was ever assigned to own, because the org chart stopped at "deploy the agent" and never got to "name the human who answers for it." This is a story about governance infrastructure that hasn't caught up with how agents actually behave: interconnected, cascading, multiplying faster than the processes built to track them. Fixing the cluster starts with identity. Every agent needs to exist as its own entity, not a shadow permission borrowed from whoever deployed it. Its own name in the register. Its own scoped authority. A named human sponsor who answers for what it does. That part is necessary. But it is nowhere near sufficient. The harder piece is the oversight that holds across the entire chain, not just at each individual link in it. You need to see what an agent did, what it set off downstream, and where that trail ends in real time, not in a report someone pulls together once a quarter. Get agent-level identity right and stop there, and you end up with a filing cabinet full of perfectly documented agents operating inside a system nobody can actually explain. And oversight by itself only tells you what already happened. Watching a chain isn't the same as controlling it. Enforcement is the piece most programs skip: the ability to stop an out-of-policy call before it executes, not just log it for someone to find in a review three weeks later. A dashboard that shows you an agent breached its scope five minutes ago is a monitoring tool. A system that stops the breach from happening in the first place is governance. Enterprises serious about agent accountability need both, and most have only built the first. We're all running at blazing speed to ensure we're not the ones left behind in the race we've found ourselves in, and we're all too aware that there's a cost to slowing down. Every enterprise serious about agentic AI hits the complexity wall eventually. The ones that get past it are the ones who built enough visibility and accountability, so their fleet can keep growing without anyone losing the ability to answer one question: what is this system doing right now, and who's responsible for it. But don't miss the point. Complexity isn't a reason to pump the brakes. The enterprises getting this right aren't slowing down. They're building toward Human-Agent Harmony, where scale and accountability grow together instead of trading off against each other. The real risk was never a single agent doing exactly what it was built to do. It's a hundred of them doing exactly that, all at once, interacting in combinations nobody designed for. That kind of multiplication is what keeps enterprise AI stuck running pilots forever instead of running production. Solve for complexity and autonomy stops being the villain. It starts being the whole point. Rory Blundell is CEO at Gravitee. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact [email protected].

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Hey, Google Engineers: What prompt do you personally refuse to work without, and why?

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：As SaaS giant gets a boost from Claudeforce, users might want to know how their AI use will be monetized

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Hardware companies have realized that note-taking is one of the easiest AI use cases to build for, and consequently have been busy shoving mics into everything from pendants and rings to credit-card-sized pucks and wris…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：I didn't plan to let an AI manage my to-do list Eddie (my AI agent) now manages my to-do list.1 I hadn’t planned that. It kind of happened on its own. As I wrote in one of my previous posts, I like interacting with him…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Plaud has introduced a new AI wearable that's designed to record, transcribe, and summarize your conversations, only this time it looks like earbuds instead of a pin. The Plaud One Explorer Edition can be worn like traditional earbuds or used through its standalone charging case, and the case includes built-in 4G to upload and process conversations without relying on your phone or Wi-Fi to stay connected. Each earbud features 16MB of local storage (for a total of 32MB) and three microphones that can record at a distance of up to two meters. They can record for up to six hours according to Plaud, which is also the maximum estimated battery l … Read the full story at The Verge.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：In the first blog of this series, we looked at how Lakebase Postgres is rewriting...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Can these new wearables deliver on the long-anticipated promises of AI earbuds?

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Plaud Inc., the maker of artificial intelligence-enabled note-taking devices, today introduced the Plaud One Explorer Edition, a pair of earbuds and a charging box that connect people to AI agents for work and everyday digital tasks. Both the Plaud One earbuds and the case can act as listening devices to record nearby conversations, and the […] The post Plaud unveils wearable earbuds with built-in agentic AI interface appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：OpenAI's agentic ChatGPT Work can sign in to your online accounts without any interaction on your part. Is that a privacy risk?

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Press Release New Workday Research: Fragmented AI Is Creating a "Faster but Not Better" Reality for Employees in Hong Kong and Taiwan Download PDF Around a quarter Hong Kong and Taiwan workers spend a significant amount…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：For the complete documentation index, see llms.txt. This page is also available as Markdown. Qwen3.8-Flash-Next is a new open-weight, 125B parameter MoE multimodal model from Qwen. Built on the new Qwen4 architecture, i…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Presented by EDB As enterprises give AI agents more autonomy — the ability to plan, decide, and act across systems without a human approving each step — a hard question moves to the center of every architecture review: When an agent tries to complete an action that it was never authorized to do, what actually stops it? These are your agents, running on your models, touching your data in your infrastructure — and the responsibility for what they do sits with you. That responsibility can’t be met in hindsight or with a set of abstract policies that live on paper but not in practice. Agents need rules in the context of the moment, because they don’t exercise overriding judgment of their own actions. Consider a simple rule: Never open the car door. Followed literally, an agent could never get in or out of the car at all. But if you change the context (the car has just crashed, there’s a fire, someone is hurt and needs to get out), then the rule you actually want is the opposite. Context in the moment is everything. We are asking agents to do intelligent things; that requires intelligent rules. The instinct is to add guardrails around the agent: instructions, policies, and monitoring layered above the model. Those mechanisms matter, but they share a structural limit: The car-door rule is plausible right up until the moment you actually have to decide whether to open the door. Controls at the agent layer are only as reliable as the agent’s output is predictable, and autonomy is precisely the property that makes that output hard to predict. Governance that depends on reviewing an action before it happens cannot keep pace with a system that acts in milliseconds, across many systems at once. Governance has to become executable, and enforced where agents actually do their work: at the operational data layer, in the context, and exactly at the moment it is happening. The data layer is the enforcement point Agents create value by touching data. They query it, retrieve it, transform it, and increasingly act on it. A policy that says an agent should not reach a certain class of data is meaningful only if the system can deny that access at the moment the agent requests it. Additionally, a principle that says AI must be auditable is meaningful only if the organization can reconstruct what the agent did, what data it touched, which user it acted for, and what resulted. When governance lives at the data layer, it holds regardless of how the agent was built or how it behaves, because the control is a property of the database itself, not a promise made by the agent. Agent behavior may be probabilistic. Governance cannot be The enterprise should not rely on a model choosing to follow policy. The policy has to be enforced by the system. That is the difference between hoping an actor stays in bounds and constructing bounds it cannot cross to begin with. The controls that make this real are ones many enterprises already run at the data layer: role- and attribute-based access, row- and column-level security, classification and masking, policy as code, and complete audit trails. What agents change is not the mechanism, but who the mechanism has to recognize. Identity management has to treat the agent as a principal in its own right, with its own identity and a purpose declared when the session opens. Once purpose is bound to identity, the policy engine can evaluate it the same way it evaluates role or department today, and the record of what happened can capture not just who acted and what they touched, but what they declared they were there to do. In practice, this resolves into nine controls, grouped under three imperatives: Enforce it Role- and attribute-based access control enforced at query time, for agents as well as users Dynamic column masking driven by the same policy path Agent identity as a first-class principal, with declared purpose bound at session start and the acting user preserved See it and prove it Classification and tagging that drives policy Session-level audit logging that records which agent acted, for which user, and under what declared purpose Lineage across pipelines, so a result can be traced back to the request that produced it Unify and harden Centralized, portable policy management Encryption at rest and in transit Consistent enforcement across on-prem, cloud, and sovereign or air-gapped environments “Declared purpose is what makes the difference. It becomes an attribute the access layer already understands, evaluated in the same policy path as role and row-level security. The enforcement mechanism does not change. What changes is that the agent's purpose is part of what it evaluates, and part of what the record proves afterward,” says Priyanka Jain, VP, product management, data & AI governance, EDB. Wherever you are in your AI adoption journey, enforcement at the data layer is what lets you move faster rather than slower. The controls are already in the database. The difference is that agents now have to pass through them. A digital leash, not a locked door The goal is not to stop agents from doing useful work. It is to define how far an agent can go, what it can touch, what it can change, what requires escalation, and how the organization can reconstruct events if something goes wrong. Governed this way, agents are identified, scoped, monitored, and auditable. The enterprise can adopt them faster, because security, risk, and leadership teams trust the operating model underneath. Open, sovereign, and enforceable at the source Built on open source Postgres, this open foundation keeps enterprises in control of where their data lives, who can reach it, and under what policy, without ceding governance to a layer they don’t own or can’t inspect. For regulated industries, that combination of data sovereignty and source-level enforcement isn’t a nice-to-have; it’s the precondition for putting agents into production at all. Agentic systems will keep getting more capable and more autonomous. That is a reason to be deliberate about where control lives, not a reason to slow down. The enterprises that enforce governance at the data layer can move aggressively on AI, because the thing protecting their data is more than just wishful thinking. EDB Postgres AI is an open, enterprise-grade sovereign data and AI platform that unifies transactional, analytical, and AI workloads — with governance enforced where the data lives. For the full framework, see EDB’s white paper Governing Agentic AI at Enterprise Speed. Max Romanenko is Chief Technology Officer at EDB. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact [email protected].

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Hugging Bay | Find And Download Open AI Hugging Bay WebPage https://huggingbay.xyz/ https://huggingbay.xyz/.well-known/agent-discovery.json https://huggingbay.xyz/openapi.json https://huggingbay.xyz/api/mcp Open-source…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The Independent AI Coding Community AI Tools Search & browse all AI tools AI Jobs International roles · opportunities Creative Studio Image · Video · Training AI Models Curated models, explained AI Skills Handy prompts,…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Notifications You must be signed in to change notification settings Fork 0 Star 0 BranchesTags Open more actions menu Latest commit History 4 Commits 4 Commits Folders and files NameName Last commit message Last commit…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Notifications You must be signed in to change notification settings Fork 3 Star 4 BranchesTags Open more actions menu Latest commit History 205 Commits 205 Commits Folders and files NameName Last commit message Last com…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：For a decade, identity and access management meant one thing: governing the humans who log in. Employee joins, gets provisioned, gets a manager, gets a departure date, gets offboarded. That loop is well understood. What changed is that the fastest-growing population inside enterprise environments is no longer human, and the governance playbook written for people […]

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：GLM 5.3 Flash | Model APIs | RunInfra RunInfraby RightNow © 2026 RunInfra. All rights reserved. Join the communitySystem status Backed by Combinator AICPA Type II SOC 2 Ask AI about RunInfra Part of RightNow RunInfraby…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Senior Product Engineer ($220k-$300k + Equity) - NYC at Conveo | Y Combinator Conveo Confident decisions in days with AI-led interviews. Senior Product Engineer ($220k-$300k + Equity) - NYC $220K - $300K•New York Job ty…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：AI factories must support increasingly large models and more complex reasoning workloads. To keep up with the insatiable compute demands of AI workloads, hyperscalers and AI-native companies are developing custom AI acc…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：All-new TaskShell 2.0 as a first-class MCP platform Agent-first task management No more app-switching to keep up with your todos. You and your agents now completely in sync with your work, exactly where you work. Sync t…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Agents that interact with a traditional OLTP database often create bottlenecks at the storage layer. New deployments...

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AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Discussion | Link

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Discussion | Link

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25142v1 Announce Type: new Abstract: Autonomous driving has made remarkable progress through imitation learning with massive human demonstration data. However, a trained planner often degrades severely when applied to a new environment zero-shot, because of domain shifts in traffic regulations, road layout and driving behaviors. Therefore, adapting a trajectory planner to a new city typically requires resource-demanding local data collection with a vehicle sensor suite. In this work, we show that driving behavior can be learned from a scalable and efficient alternative. We introduce \emph{SkyDrive}, a framework that utilizes drone-based traffic monitoring to provide efficient supervision for autonomous driving agents in a new environment. While vehicle-based data collection logs the ego and its surroundings, an aerial platform naturally observes many road users simultaneously over an extended field of view. As a result, every vehicle can be a data source with grounded driving behavior, effectively scaling up the amount of supervision. Based on 137 hours of aerial traffic monitoring footage, we extract 650K driving samples and construct a benchmark for trajectory planners and motion predictors. Zero-shot experiments with multiple models reveal significant cross-city domain gaps, but many of them can be alleviated by limited supervision from the sky, e.g., 30 minutes of monitoring per location. Our findings show that aerial traffic monitoring is an efficient and scalable data source for adapting autonomous driving systems in new cities. Data and code will be made publicly available.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.25176v1 Announce Type: new Abstract: Structural health monitoring (SHM) is essential in modern engineering, providing data for condition-based maintenance, lifecycle assessment, and predictive decision-making. Traditionally, SHM relied on visual inspection to detect defects such as cracks and deformations. Early computer vision (CV) methods, including thresholding, edge detection, and handcrafted features, aimed to automate this process but were highly sensitive to noise, imaging variations, and multiscale defects, limiting their reliability. Recent advances in machine learning, particularly convolutional neural networks (CNNs) and You Only Look Once (YOLO), have improved defect detection accuracy and enabled real-time analysis. However, adoption in SHM remains limited due to technical barriers such as data labeling, model training, and deployment, which typically require programming expertise. To address this gap, we introduce YOLOEZ, an open-source, GUI-based tool for end-to-end YOLO model application. YOLOEZ integrates data labeling, training, and inference into a single interface, enabling high-performance model development without code while supporting reproducible workflows. Evaluation against existing software and classical image processing demonstrates that YOLOEZ not only outperforms traditional methods across most detection metrics, but also lowers adoption barriers present in other modern CV tools. By combining accuracy with accessibility, YOLOEZ facilitates wider use of AI-driven monitoring for predictive maintenance, digital twins, and intelligent structural systems.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24934v1 Announce Type: new Abstract: Accurate field plant disease diagnosis requires reliable fusion of uncertain and conflicting perceptual evidence. We present the Hybrid Hierarchical Multi-Agent Framework (H$^{2}$MAF), combining decision-level fusion of EfficientNet-B3 and ConvNeXt-Tiny with semantic arbitration by open-weight multimodal large language models (MLLMs), Gemma 4 E4B and Qwen3.5 4B, using structured JSON evidence to generate explainable diagnoses, risk levels, treatment urgency, and financial exposure. (H$^{2}$MAF) is evaluated on 14,364 images (1,370 test images) across PlantDoc (2,922 images, 27 classes) and two non-public, continuously captured Cornell robot-acquired field datasets: Stage 2 (20 GB; 4,215 images) and Stage 4 (40 GB; 7,227 images), covering Early Blight, Late Blight, and Septoria Leaf Spot under uncontrolled field conditions. On PlantDoc, Gemma improves accuracy from 63.9% to 68.5%, achieving +7.6 points on the 41.7% CNN-conflict subset. Cornell accuracies reach 99.3% and 98.9%, with only 1.7-4.1% disagreement, demonstrating conflict-dependent MLLM utility. The critical-risk error of gemma is 0.14-0.5 points, whereas Qwen overflags by 3.5-14.4 points. These results establish MLLM arbitration as a promising, yet calibration-dependent, approach for explainable agricultural AI and robotic field decision support. Github Link: https://github.com/Applied-AI-Research-Lab/Explainable-AI-Plant-Disease-Detection

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：arXiv:2608.24946v1 Announce Type: new Abstract: Macros constitute a large part of the core area in modern very large-scale integration (VLSI) designs. Moreover, macro positions have a significant impact on the final quality of result (QoR), and macro legalization is typically the final step in determining the macro positions. However, existing approaches related to macro legalization either lack robustness or incur substantial computational costs or neglect the regularity between macros. To address these limitations, we introduce MacroAgent. The novel framework is a four-stage approach: clustering, contour generation, template matching, and inter-cluster refinement. We propose leveraging Large Language Models (LLMs) to discover multiple, effective heuristic regularity-aware contour algorithms. This framework successfully generates robust and effective algorithmic solutions for macro legalization. Compared with state-of-the-art macro legalization works, experimental results on TILOS and Chipyard benchmarks demonstrate a 2 to 8 fold improvement in layout regularity, a 3% to 5% reduction in routed wirelength with comparable congestion after global routing, and significantly better robustness with an acceptable runtime. Furthermore, end-to-end evaluation through Cadence Innovus place-and-route confirms that the regularity improvements translate into tangible PPA gains, including 2.9% lower routed wirelength and 68.3% TNS improvement over the DREAMPlace macro legalization baseline; it also achieves 1.8% lower routed wirelength when integrated into the Innovus macro placement flow.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Earlier this year, Meta created a “plan” to reduce some of its teams by as much as 60 percent to make the company “AI native,” Reuters reported today, citing two people familiar with Meta’s internal affairs. Reuters’ re…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：--> [Submitted on 25 Aug 2026] Title:Reading Is Not Using: Retrieval, Judgment, and the Design of AI Financial Research Workflows View a PDF of the paper titled Reading Is Not Using: Retrieval, Judgment, and the Design…