- More details on Fable 5’s cyber safeguards and our jailbreak framework
anthropic-news· 02-jul
Jul 2, 2026Announcements
- Agent4cs: A Multi-agent System for Code Summarization in Large Hierarchical Codebases
arxiv-ai· 03-jul
arXiv:2607.01425v1 Announce Type: new Abstract: Understanding large, complex codebases, especially those with obfuscated structures and incomplete documentation, remains a significant challenge. Existing code summarization solutions often rely on a single language model or coding assistant like Claude Code, and treat source code as flat text, underutilizing the rich interdependencies and hierarchical information within a repository. To address these shortcomings, we propose Agent4cs - a multi-agent framework that summarizes large codebases in a bottom-up fashion, where a summarization agent focuses on producing robust summaries; a keyword-extraction agent proactively identifies critical information from subfolders; and a quality-assurance agent iteratively refines the outputs for readabil…
- Discrete Diffusion Language Models for Interactive Radiology Report Drafting
arxiv-ai· 03-jul
arXiv:2607.01436v1 Announce Type: new Abstract: Diffusion language models, which generate text by denoising a token canvas bidirectionally instead of emitting tokens left to right, have become competitive with autoregressive (AR) generation. Medical foundation models, however, remain almost entirely autoregressive. We adapt a mixture-of-experts diffusion language model, DiffusionGemma-26B, and benchmark it against its same-size AR sibling Gemma-4-26B under an identical LoRA recipe on medical visual question answering datasets, scored by a verbosity-robust LLM judge. Diffusion matches or exceeds AR on all of them, and the finetuned model (3.8B active) is competitive with frontier vision-language models; its decoding is also 3.5-4.4x faster. Beyond this parity, the diffusion model offers a …
- Beyond Next-Token Prediction: An RLVR Proof of Concept for Tool-Use Agents on Atlassian Workflows
arxiv-ai· 03-jul
arXiv:2607.01465v1 Announce Type: new Abstract: Large language models are trained to predict the next token, not to act inside a specific API. In niche enterprise SaaS workflows -- where success means hitting the right endpoint with the right nested arguments in the right order -- this objective mismatch shows up as silent failures: dropped required fields, hallucinated tools, or early stops after a single read. We ask whether Reinforcement Learning with Verifiable Rewards (RLVR), applied directly in the target environment, closes the gap. As a proof of concept we build a suite of five synthetic environments emulating the Jira REST v3 and Confluence v2 APIs at schema fidelity; rewards are computed entirely from the tool-call trace, with no live API, no learned judge, and no human label in…
- World Feedback for Clinical Agents: Diagnosing RL in FHIR Environments
arxiv-ai· 03-jul
arXiv:2607.01470v1 Announce Type: new Abstract: Clinical protocol-execution tasks -- checking a lab value, applying a threshold, placing a correctly structured FHIR order -- are natural candidates for RL from world feedback: once clinical SMEs encode decision logic into a verifier, that verifier grades unlimited rollouts without per-episode annotation. But applying RL requires a sound feedback channel and sufficient base capability. We audit MedAgentBench v1/v2, find a 41.7\% silent-finish ceiling that makes inaction the RL dominant strategy, and construct \textbf{MedAgentBench-v3 (MAB-v3)} (508 tasks, 8.9\% ceiling). Training Qwen3-8B exposes two structural barriers: a \emph{capability ceiling} (10/20 task types have 0\% base performance, zero gradient) and a \emph{format-knowledge barri…
- Procedural Memory Distillation: Online Reflection for Self-Improving Language Models
arxiv-ai· 03-jul
arXiv:2607.01480v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR), along with recent selfdistillation variants such as SDPO, evaluates each rollout against a verifier and updates the policy from that episode-level signal. However, the richer procedural information in the rollout is rarely retained or reused. Across episodes and epochs, the model repeatedly encounters related problems under a changing policy, producing cross-episode signals that episode-local updates cannot capture: which strategies consistently pass verification, which failure modes persist, which patterns recur. We propose Procedural Memory Distillation (PMD), which converts these crossepisode signals into reusable procedural memory and distills it into the policy's weights during trai…
- Auto-FL-Research: Agentic Search for Federated Learning Algorithms
arxiv-ai· 03-jul
arXiv:2607.01366v1 Announce Type: new Abstract: Federated learning (FL) research often depends on many small but consequential algorithmic choices: optimizer variants, server aggregation rules, local training schedules, normalization, regularization, and model architecture. These choices are expensive to explore manually and difficult to compare fairly when candidate changes can also alter the FL training or evaluation path. In this work, we present Auto-FL-Research (AFR), a constrained coding-agent workflow for FL algorithmic recipe search. Agents may propose and implement candidate training algorithms, including server aggregation rules, client update schedules, local objectives, and registered model variants, while task profiles fix the mutation surface, compute budget, communication c…
- PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations
arxiv-ai· 03-jul
arXiv:2607.01306v1 Announce Type: new Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a model's decision. Although many existing methods successfully generate prediction-changing alternatives, they often produce unrealistic or infeasible recommendations due to a lack of explicit mechanisms for incorporating domain knowledge and intervention constraints. Neuro-symbolic AI offers a promising direction by combining data-driven predictive models with symbolic reasoning capable of representing human-understandable rules and feasible actions. This paper presents PACE, a modular neuro-symbolic framework for generating feasibility-aware counterfactual explanations. The framework separates prediction and reasoning int…
- The Wiola Architecture for Efficient Small Language Models
arxiv-ai· 03-jul
arXiv:2607.01394v1 Announce Type: new Abstract: We present Wiola, a fully original Small Language Model (SLM) architecture built from first principles, sharing no structural lineage with any existing model family including GPT, LLaMA, Mistral, or Falcon. Wiola introduces five independently novel components: (i) Spiral Rotary Positional Encoding (SRPE), which embeds token positions on a three-dimensional helical manifold combining absolute, relative, and hierarchical positional signals; (ii) Gated Cross-Layer Attention (GCLA), providing each decoder layer with soft cross-attention access to compressed summaries of two preceding layers for inter-layer coherence; (iii) Adaptive Token Merging (ATM), which dynamically merges se mantically redundant adjacent tokens in middle network layers to r…
- When Should Service Agents Reconsider? Difficulty-Routed Control in Customer-Service Operations
arxiv-ai· 03-jul
arXiv:2607.01426v1 Announce Type: new Abstract: Autonomous customer-service agents are shifting from conversational interfaces toward operational execution roles: they retrieve firm records, apply service policies, and execute backend writes such as refunds, cancellations, exchanges, order modifications, and reservation changes. This shift creates a service-control problem: firms must keep routine service fast and low-friction while preventing operational errors on requests where customer instructions, policy constraints, firm records, and backend writes interact. We propose a difficulty-routed service-control architecture that asks when service agents should reconsider before acting. A lightweight router keeps routine sessions on a low-cost baseline path and routes operationally coupled …
- CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse
arxiv-ai· 03-jul
arXiv:2607.01433v1 Announce Type: new Abstract: Divergent thinking is a crucial aspect of creativity, yet large language models (LLMs) tend to consistently generate similar responses to open-ended questions, in what has been termed the artificial hivemind effect. Here, we introduce CreativityNeuro, a data-free method for enhancing divergent thinking in LLMs via contrastive weight steering. We evaluate our method across multiple creativity assessments and report several main findings. On the Divergent Association Task (DAT), a vocabulary-space creativity test, CreativityNeuro improves performance by up to 14 human percentile points. Next, in a large-scale human evaluation (N=720) on the Alternative Uses Test (AUT) and the Task Task, CreativityNeuro achieves significant improvements in orig…
- Multi-Region Architecture: Going Global Without Going Broke
bytebytego· 02-jul
When an application grows geographically, it is logical to start serving it from a second region to improve latency and availability.15 hrs ago • ByteByteGo754
- How AI Agents Manage Memory and Avoid Forgetfulness
bytebytego· 29-jun
In this article, we will try to understand how that architecture gets built, from the constraint that forces it to exist all the way to the tradeoffs…Jun 29 • ByteByteGo301517
- How OpenAI Delivers Low-Latency Voice AI for 900M Users
bytebytego· 01-jul
In this article, we will look at the entire journey in detail and challenges the OpenAI engineering team faced.Jul 1 • ByteByteGo21125
- Inside Thinking Machines’ Interaction Models
bytebytego· 30-jun
In this article, we will look at what the research preview covers and the concept of an interaction model proposed by Thinking Machines.Jun 30 • ByteByteGo22137
- EP220: RAG vs Graph RAG vs Agentic RAG
bytebytego· 27-jun
RAG connects LLMs to your data and there are three different ways to do it.Jun 27 • ByteByteGo273413
- In defense of AI mandates (xpost)
charity-majors· 02-jul
When you need to execute a coordinated change on a tight timeline, a mandate might be the best and most honest way to fund it. Crossposted from here. I’ve been writing a series of pieces on lessons learned from our AI journey at Honeycomb. I’ve written about the tension between enthusiasts and skeptics, the need for engineering rigor, […]
- Claude Platform
claude-changelog
Release notesCopy pageUpdates to the Claude Platform, including the Claude API, client SDKs, and the Claude Console.Copy pageFor release notes on Claude Apps, see the Release notes for Claude Apps in the Claude Help Center.For updates to Claude Code, see the complete CHANGELOG.md in the claude-code repository. July 1, 2026 We've restored access to Claude Fable 5 and Claude Mythos 5. See our statement for more information. June 30, 2026 We've launched Claude Sonnet 5 (claude-sonnet-5), the next generation of our Sonnet model family, at introductory pricing of $2 / $10 per MTok through August 31, 2026 (standard $3 / $15 thereafter). Claude Sonnet 5 supports a 1M token context window, 128k max output tokens, and the same set of tools and platform features as Claude Sonnet 4.6, except Pr…
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claude-changelog
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- Unmasking the crawls with Attribution Business Insights
cloudflare· 01-jul
Cloudflare’s new Attribution Business Insights dashboard helps website owners understand crawler behavior, appetite, and potential value, fueling business-level conversations around crawl compensation.
- Content Independence Day, one year on: building the business model for the agentic Internet
cloudflare· 01-jul
One year after declaring Content Independence Day, a dynamic market for monetized content has officially emerged. In this report, we examine how the rise of autonomous AI agents is upending traditional search referrals and detail the new infrastructure required to support a sustainable web economy.
- Your site, your rules: new AI traffic options for all customers
cloudflare· 01-jul
For our second Content Independence Day, we’re giving website owners finer options to manage AI traffic. Instead of a one-size-fits-all block, all customers can now easily distinguish and manage Search, Agent, and Training bots, alongside the new ability to protect ad-monetized pages.
- Making AI search smarter
cloudflare· 01-jul
Search is how we find nearly everything on the web — creators, merchants, answers. AI is rewriting the rules, leaving creators caught between staying discoverable in an agentic era and getting paid for their work. Today we're launching two initiatives to help.
- Announcing the Monetization Gateway: charge for any resource behind Cloudflare via x402
cloudflare· 01-jul
We're opening the waitlist for our Monetization Gateway, which will allow you to charge for any web page, dataset, API, or MCP tool behind Cloudflare. The charges will settle in stablecoins over the x402 open protocol, with no payments stack of your own to build.
- affaan-m/ECC
github-trending
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deutsch | Español Warning Official sources only. Install ECC only from verified channels: the GitHub repository github.com/affaan-m/ECC, the npm packages ecc-universal and ecc-agentshield, the GitHub App, the plugin slug ecc@ecc, and the project website ecc.tools. Third-party re-uploads and unofficial mirrors are not maintained or reviewed by the project and may contain malware. 211.9K+ stars | 32.5K+ forks | 230+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / 语言 / 語言 / Dil / Язык /…
- JuliusBrussee/caveman
github-trending
🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman caveman why use many token when few do trick Before/After • Install • What You Get • Benchmarks • Full install guide A Claude Code skill/plugin (also Codex, Gemini, Cursor, Windsurf, Cline, Copilot, 30+ more) that makes agent talk like caveman — cuts ~75% of output tokens, keeps full technical accuracy. Brain still big. Mouth small. Before / After 🗣️ Normal Claude (69 tokens) "The reason your React component is re-rendering is likely because you're creating a new object reference on each render cycle. When you pass an inline object as a prop, React's shallow comparison sees it as a different object every time, which triggers a re-render. I'd recommend using useMemo to memoize…
- santifer/career-ops
github-trending
AI-powered job search system built on Claude Code. 14 skill modes, Go dashboard, PDF generation, batch processing. Career-Ops English | Deutsch | Español | Français | Português (Brasil) | 한국어 | 日本語 | 简体中文 | 繁體中文 | Українська | Русский | Polski | Dansk | العربية I spent months applying to jobs the hard way. So I engineered the system I wish I had. Companies use AI to filter candidates. I just gave candidates AI to choose companies. Now it's open source. FEATURED IN 740+ job listings evaluated · 100+ personalized CVs · 1 dream role landed Also runs on any agent-skill-standard CLI. See Supported CLIs. What Is This Career-Ops (career-ops.org, also known as careerops) turns any AI coding CLI into a full job search command center. Instead of manually tracking applications in a spreadsheet, you …
- ChromeDevTools/chrome-devtools-mcp
github-trending
Chrome DevTools for coding agents Chrome DevTools for agents Chrome DevTools for agents (chrome-devtools-mcp) lets your coding agent (such as Antigravity, Claude, Cursor or Copilot) control and inspect a live Chrome browser. It acts as a Model-Context-Protocol (MCP) server, giving your AI coding assistant access to the full power of Chrome DevTools for reliable automation, in-depth debugging, and performance analysis. A CLI is also provided for use without MCP. Tool reference | Changelog | Contributing | Troubleshooting | Design Principles Key features Get performance insights: Uses Chrome DevTools to record traces and extract actionable performance insights. Advanced browser debugging: Analyze network requests, take screenshots and check browser console messages (with source-mapped stack…
- actions/checkout
github-trending
Action for checking out a repo Checkout v7 What's new Safer fork pull request handling: checkout now refuses to check out fork pull request code by default when the workflow is triggered by pull_request_target or workflow_run. These triggers run with the base repository's GITHUB_TOKEN, secrets, and runner access, where executing a fork's code commonly leads to "pwn request" vulnerabilities. To opt in after reviewing the risks, set the new allow-unsafe-pr-checkout: true input. Migrated actions/checkout to ESM to support new versions of the @actions/* packages. Updated direct and transitive dependencies, including security fixes for known vulnerabilities. Checkout v6 What's new Improved credential security: persist-credentials now stores credentials in a separate file under $RUNNER_TEMP ins…
- Vercel's Andrew Qu on why agents are a new kind of software
latentspace· 03-jul
The Vercel Chief of Software explains how its agent framework, eve, was created — and why skills, sandboxes and agent-readable websites now matter.
- Skill engineering and the case against one-shot AI design
latentspace· 02-jul
Paul Bakaus talks to us about Impeccable, human judgment in a 'loopmaxxing' era, and why agents still need people to steer them.
- Warp CEO Zach Lloyd on why software factories are the next phase of coding
latentspace· 01-jul
Warp's founder thinks every major software project will soon run on an automated factory. He discusses why and how engineers should prepare for this shift.
- 🔬 The Coolest Diffusion Research Isn't in LLMs — Evan Feinberg & Sergey Edunov, Genesis Molecular AI
latentspace· 01-jul
Why the Llama lead left Meta for drug discovery, PEARL's zero-shot OpenBind win, and what becomes possible when co-folding finally crosses the accuracy threshold.
- The website of the future may assemble itself for every visitor
latentspace· 02-jul
Adobe is experimenting with “agentic sites” that generate pages around an individual user’s intent. At AIEWF, we talked to Carlos Sanchez about the Web's future.
- AIEWF Daily Dispatch: Autoresearch and the tension between AI and human agency
latentspace· 02-jul
The software factory vision met resistance today from speakers defending human understanding and control.
- AIEWF Daily Dispatch: The great loops debate and the state of AI engineering
latentspace· 03-jul
The AI Engineer World’s Fair ended with a debate about loops, a report on the state of AI engineering, and closing keynotes focused on what to build next.
- Autoresearch: The feedback loop behind self-improving agents
latentspace· 01-jul
Introspection co-founder Roland Gavrilescu explains autoresearch, agent “recipes,” self-improving loops, and why humans remain central to the software factory.
- How Cursor deploys AI inside the enterprise
latentspace· 01-jul
Cursor's Pauline Brunet explains how her team of Forward Deployed Engineers help organizations implement agents — essentially setting up software factories.
- GenPage: Towards End-to-End Generative Homepage Construction at Netflix
netflix-tech· 29-jun
- Understand to participate
simonw· 02-jul
<p>I saw Geoffrey Litt speak at <a href="https://www.ai.engineer/worldsfair/2026">AIE</a> yesterday, and one framing he used particularly resonated with me:</p> <p><strong>Understand to participate</strong></p> <p>Geoffrey was talking about the challenge of collaborating with coding agents as they construct increasingly large and sophisticated changes, and the need to avoid taking on <a href="https://simonwillison.net/tags/cognitive-debt/">cognitive debt</a> as your understanding drifts from how the code actually works.</p> <p>His argument is that you need to understand the code to a depth that enables you to participate further with the model:</p> <blockquote> <p>You can learn what the agent is doing to make sure you can be an active participant in the creative process. [...]</p> <p>You …
- llm-coding-agent 0.1a0
simonw· 02-jul
<p><strong>Release:</strong> <a href="https://github.com/simonw/llm-coding-agent/releases/tag/0.1a0">llm-coding-agent 0.1a0</a></p> <p>Another Fable 5 experiment. Now that my <a href="https://llm.datasette.io/">LLM library</a> has evolved into more of an agent framework it's time to see what a simple coding agent would look like built on it.</p> <p>I started a <a href="https://github.com/simonw/llm-coding-agent/tree/2466fa03ba8e5122c3bfa93d52167d33bce40ac6">new Python library</a> using my <a href="https://github.com/simonw/python-lib-template-repository">python-lib-template-repository</a> GitHub template repository, then ran these two prompts (here's the <a href="https://claude.ai/code/session_01TEUBvBbMipbFSoqjMiJ7ha">Claude Code for web transcript</a>):</p> <blockquote> <p><code>Write a…
- Using DSPy to evaluate and improve Datasette Agent's SQL system prompts
simonw· 02-jul
<p><strong>Research:</strong> <a href="https://github.com/simonw/research/tree/main/dspy-datasette-agent-prompts#readme">Using DSPy to evaluate and improve Datasette Agent's SQL system prompts</a></p> <p>One of this morning's AIE keynotes covered <a href="https://github.com/stanfordnlp/dspy">dspy</a>, which reminded me I've been meaning to see if it could help me improve the system prompt used by <a href="https://agent.datasette.io">Datasette Agent</a> - so I fired off an asynchronous research task in Claude Code for web using Claude Fable 5:</p> <blockquote> <p><code>Pip install the latest Datasette alpha and datasette-agent and dspy - then figure out how to use dspy to evaluate and improve the main system prompts used by Datasette Agent for the feature where it can execute read onl…