Morning Singularity Digest - 2026-09-14

Context

Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat apps English | 简体中文 | 繁體中文 | Español | Français | Bahasa Indonesia | 日本語 | 한국어 | Русский | Tiếng Vi...

What's new

Important If you want the newest features and experiments, install from source.

Key details

• It runs in a WebUI, terminal, or chat apps and combines tools, long-term memory, MCP integrations, model routing, multi-agent delegation, scheduled automation, and an OpenAI-compatible API in a small, readable core.
• | Go to | |---|---| | Install nanobot with no terminal/config background | Start Without Technical Background | | Install quickly and get one CLI reply | Install and Quick Start | | Open the bundled browser UI | WebUI | | Connect Telegram, Discord, WeChat,...
• It can: - run in a browser WebUI or terminal - connect to Telegram, Discord, Slack, WeChat, Email, Mattermost, and other chat apps - use tools such as files, shell, web search, web fetch, MCP, cron, image generation, and subagents - keep session history and...
• - Chat-native reach: WebUI, API, Telegram, Feishu, Slack, Discord, Teams, email, and Mattermost.

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Production-grade engineering skills for AI coding agents.

What's new

Production-grade engineering skills for AI coding agents.

Key details

• Skills encode the workflows, quality gates, and best practices that senior engineers use when building software.
• These ones are packaged so AI agents follow them consistently across every phase of development.
• DEFINE PLAN BUILD VERIFY REVIEW SHIP ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ ┌──────┐ │ Idea │ ───▶ │ Spec │ ───▶ │ Code │ ───▶ │ Test │ ───▶ │ QA │ ───▶ │ Go │ │Refine│ │ PRD │ │ Impl │ │Debug │ │ Gate │ │ Live │ └──────┘ └──────┘ └──────┘ └──────┘ └─...
• Each one activates the right skills automatically.

Results & evidence

• The open skills CLI installs into 70+ agents (Claude Code, Cursor, Codex, Copilot, Cline, and more): npx skills add addyosmani/agent-skills # install all 25 skills npx skills add addyosmani/agent-skills --list # browse before installing Or grab individual s...

Limitations / unknowns

• It removes the human stepping between tasks, not the verification: every task is still test-driven and committed individually, and it pauses on failures or risky steps.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

arXiv:2609.12394v1 Announce Type: new Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps.

What's new

arXiv:2609.12394v1 Announce Type: new Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps.

Key details

• Sandbox training produces a distribution mismatch with production environments; expensive real-device failures remain underutilized; and fixed benchmarks saturate, losing the power to guide iteration.
• We present BlueLM-GUI, a 35B-A3B mobile GUI agent built as a real-device-centric flywheel that closes these gaps through three principles.
• Every Sample Matters: a dual-track pipeline with Heterogeneous Triple-System Consensus evaluation and an Error Correction \& Derivation Module salvages every trajectory into usable supervision.
• Every Rollout Is Real: a three-stage recipe---continual pre-training, supervised fine-tuning, and agentic reinforcement learning on hundreds of real phones---grounds every rollout in real production environments, so the capability the model learns transfers...

Results & evidence

• arXiv:2609.12394v1 Announce Type: new Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps.
• BlueLM-GUI achieves 87.4 on MobileGUI-VBench, surpassing the best closed-source model by 5.1 points, and 84.9 on AndroidWorld, the best result among open-source models and competitive with closed-source models.
• Computer Science > Artificial Intelligence [Submitted on 11 Sep 2026] Title:BlueLM-GUI Technical Report: A Real-Device-Centric Flywheel for Self-Improving Mobile GUI Agents View PDF HTML (experimental) Abstract:Mobile GUI agents are shifting from multi-modu...

Limitations / unknowns

• Sandbox training produces a distribution mismatch with production environments; expensive real-device failures remain underutilized; and fixed benchmarks saturate, losing the power to guide iteration.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

arXiv:2609.12742v1 Announce Type: new Abstract: Coding agents increasingly read repository knowledge from SKILLs --- plain \texttt{.md} files versioned alongside the code.

What's new

arXiv:2609.12742v1 Announce Type: new Abstract: Coding agents increasingly read repository knowledge from SKILLs --- plain \texttt{.md} files versioned alongside the code.

Key details

• Recent work synthesizes these files automatically, by optimizing the document against a benchmark.
• A bare repository comes with no benchmark, and the synthetic tasks prior work builds are small enough that a capable agent saturates them with no document at all.
• We mine harder tasks --- merged pull requests of the repository, reverted at a single frozen base commit; and score a candidate document by whether the same agent does better with it than without it.
• On three Kotlin repositories, the documents GEPA finds raise this score by $4.9$pp on average, and the ones SkillOpt finds leave it where it started, $0.1$pp above the seed.

Results & evidence

• arXiv:2609.12742v1 Announce Type: new Abstract: Coding agents increasingly read repository knowledge from SKILLs --- plain \texttt{.md} files versioned alongside the code.
• On three Kotlin repositories, the documents GEPA finds raise this score by $4.9$pp on average, and the ones SkillOpt finds leave it where it started, $0.1$pp above the seed.
• Computer Science > Artificial Intelligence [Submitted on 11 Sep 2026] Title:Skill Issue: Lessons from Optimizing Repository SKILLs for Coding Agents View PDF HTML (experimental) Abstract:Coding agents increasingly read repository knowledge from SKILLs --- p...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Skip to content Navigation Menu Sign in Appearance settings Platform AI CODE CREATION GitHub Copilot Write better code with AI GitHub Copilot app Direct agents from issue to merge MCP Registry Integrate external tools DEVELOPER WORKFLOWS Actions Automate an...

What's new

Skip to content Navigation Menu Sign in Appearance settings Platform AI CODE CREATION GitHub Copilot Write better code with AI GitHub Copilot app Direct agents from issue to merge MCP Registry Integrate external tools DEVELOPER WORKFLOWS Actions Automate an...

Key details

• You signed out in another tab or window.
• You switched accounts on another tab or window.
• Dismiss alert ActuallyTaylor / strata Public Notifications You must be signed in to change notification settings Fork 0 Star 0 Code Issues 0 Pull requests 0 Actions Projects Security and quality 0 Insights Additional navigation options Code Issues Pull requ...

Results & evidence

• Dismiss alert ActuallyTaylor / strata Public Notifications You must be signed in to change notification settings Fork 0 Star 0 Code Issues 0 Pull requests 0 Actions Projects Security and quality 0 Insights Additional navigation options Code Issues Pull requ...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat apps English | 简体中文 | 繁體中文 | Español | Français | Bahasa Indonesia | 日本語 | 한국어 | Русский | Tiếng Vi...

What's new

Important If you want the newest features and experiments, install from source.

Key details

• It runs in a WebUI, terminal, or chat apps and combines tools, long-term memory, MCP integrations, model routing, multi-agent delegation, scheduled automation, and an OpenAI-compatible API in a small, readable core.
• | Go to | |---|---| | Install nanobot with no terminal/config background | Start Without Technical Background | | Install quickly and get one CLI reply | Install and Quick Start | | Open the bundled browser UI | WebUI | | Connect Telegram, Discord, WeChat,...
• It can: - run in a browser WebUI or terminal - connect to Telegram, Discord, Slack, WeChat, Email, Mattermost, and other chat apps - use tools such as files, shell, web search, web fetch, MCP, cron, image generation, and subagents - keep session history and...
• - Chat-native reach: WebUI, API, Telegram, Feishu, Slack, Discord, Teams, email, and Mattermost.

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

arXiv:2609.12394v1 Announce Type: new Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps.

What's new

arXiv:2609.12394v1 Announce Type: new Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps.

Key details

• Sandbox training produces a distribution mismatch with production environments; expensive real-device failures remain underutilized; and fixed benchmarks saturate, losing the power to guide iteration.
• We present BlueLM-GUI, a 35B-A3B mobile GUI agent built as a real-device-centric flywheel that closes these gaps through three principles.
• Every Sample Matters: a dual-track pipeline with Heterogeneous Triple-System Consensus evaluation and an Error Correction \& Derivation Module salvages every trajectory into usable supervision.
• Every Rollout Is Real: a three-stage recipe---continual pre-training, supervised fine-tuning, and agentic reinforcement learning on hundreds of real phones---grounds every rollout in real production environments, so the capability the model learns transfers...

Results & evidence

• arXiv:2609.12394v1 Announce Type: new Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps.
• BlueLM-GUI achieves 87.4 on MobileGUI-VBench, surpassing the best closed-source model by 5.1 points, and 84.9 on AndroidWorld, the best result among open-source models and competitive with closed-source models.
• Computer Science > Artificial Intelligence [Submitted on 11 Sep 2026] Title:BlueLM-GUI Technical Report: A Real-Device-Centric Flywheel for Self-Improving Mobile GUI Agents View PDF HTML (experimental) Abstract:Mobile GUI agents are shifting from multi-modu...

Limitations / unknowns

• Sandbox training produces a distribution mismatch with production environments; expensive real-device failures remain underutilized; and fixed benchmarks saturate, losing the power to guide iteration.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Skip to content Navigation Menu Sign in Appearance settings Platform AI CODE CREATION GitHub Copilot Write better code with AI GitHub Copilot app Direct agents from issue to merge MCP Registry Integrate external tools DEVELOPER WORKFLOWS Actions Automate an...

What's new

Skip to content Navigation Menu Sign in Appearance settings Platform AI CODE CREATION GitHub Copilot Write better code with AI GitHub Copilot app Direct agents from issue to merge MCP Registry Integrate external tools DEVELOPER WORKFLOWS Actions Automate an...

Key details

• You signed out in another tab or window.
• You switched accounts on another tab or window.
• Dismiss alert ActuallyTaylor / strata Public Notifications You must be signed in to change notification settings Fork 0 Star 0 Code Issues 0 Pull requests 0 Actions Projects Security and quality 0 Insights Additional navigation options Code Issues Pull requ...

Results & evidence

• Dismiss alert ActuallyTaylor / strata Public Notifications You must be signed in to change notification settings Fork 0 Star 0 Code Issues 0 Pull requests 0 Actions Projects Security and quality 0 Insights Additional navigation options Code Issues Pull requ...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

arXiv:2609.12394v1 Announce Type: new Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps.

What's new

arXiv:2609.12394v1 Announce Type: new Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps.

Key details

• Sandbox training produces a distribution mismatch with production environments; expensive real-device failures remain underutilized; and fixed benchmarks saturate, losing the power to guide iteration.
• We present BlueLM-GUI, a 35B-A3B mobile GUI agent built as a real-device-centric flywheel that closes these gaps through three principles.
• Every Sample Matters: a dual-track pipeline with Heterogeneous Triple-System Consensus evaluation and an Error Correction \& Derivation Module salvages every trajectory into usable supervision.
• Every Rollout Is Real: a three-stage recipe---continual pre-training, supervised fine-tuning, and agentic reinforcement learning on hundreds of real phones---grounds every rollout in real production environments, so the capability the model learns transfers...

Results & evidence

• arXiv:2609.12394v1 Announce Type: new Abstract: Mobile GUI agents are shifting from multi-module frameworks to native models trained end-to-end, yet industrial deployment faces three persistent gaps.
• BlueLM-GUI achieves 87.4 on MobileGUI-VBench, surpassing the best closed-source model by 5.1 points, and 84.9 on AndroidWorld, the best result among open-source models and competitive with closed-source models.
• Computer Science > Artificial Intelligence [Submitted on 11 Sep 2026] Title:BlueLM-GUI Technical Report: A Real-Device-Centric Flywheel for Self-Improving Mobile GUI Agents View PDF HTML (experimental) Abstract:Mobile GUI agents are shifting from multi-modu...

Limitations / unknowns

• Sandbox training produces a distribution mismatch with production environments; expensive real-device failures remain underutilized; and fixed benchmarks saturate, losing the power to guide iteration.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

arXiv:2609.12742v1 Announce Type: new Abstract: Coding agents increasingly read repository knowledge from SKILLs --- plain \texttt{.md} files versioned alongside the code.

What's new

arXiv:2609.12742v1 Announce Type: new Abstract: Coding agents increasingly read repository knowledge from SKILLs --- plain \texttt{.md} files versioned alongside the code.

Key details

• Recent work synthesizes these files automatically, by optimizing the document against a benchmark.
• A bare repository comes with no benchmark, and the synthetic tasks prior work builds are small enough that a capable agent saturates them with no document at all.
• We mine harder tasks --- merged pull requests of the repository, reverted at a single frozen base commit; and score a candidate document by whether the same agent does better with it than without it.
• On three Kotlin repositories, the documents GEPA finds raise this score by $4.9$pp on average, and the ones SkillOpt finds leave it where it started, $0.1$pp above the seed.

Results & evidence

• arXiv:2609.12742v1 Announce Type: new Abstract: Coding agents increasingly read repository knowledge from SKILLs --- plain \texttt{.md} files versioned alongside the code.
• On three Kotlin repositories, the documents GEPA finds raise this score by $4.9$pp on average, and the ones SkillOpt finds leave it where it started, $0.1$pp above the seed.
• Computer Science > Artificial Intelligence [Submitted on 11 Sep 2026] Title:Skill Issue: Lessons from Optimizing Repository SKILLs for Coding Agents View PDF HTML (experimental) Abstract:Coding agents increasingly read repository knowledge from SKILLs --- p...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Current browse context: cs.SE References & Citations Loading...

What's new

We analyze SciLitBench, a corpus of 888 review-automation papers with 14,726 annotations, to characterize changes in methods, review-stage use, evaluation and reported limitations.

Key details

• We analyze SciLitBench, a corpus of 888 review-automation papers with 14,726 annotations, to characterize changes in methods, review-stage use, evaluation and reported limitations.
• Automation has shifted toward LLM- and software-facing workflows, including stages that can alter the evidence base.
• Since 2023, 38.0% of software/product papers reported no evaluation, compared with 9.3% of LLM papers.
• Reporting coverage increased with LLM workflow complexity, yet 52% of positive-only LLM evaluations still reported an unmet reliability or performance requirement.

Results & evidence

• arXiv:2609.11559v1 Announce Type: cross Abstract: Large language models (LLMs) and AI-enabled software increasingly participate in systematic-review decisions, yet the information needed to audit these workflows is reported inconsistently.
• We analyze SciLitBench, a corpus of 888 review-automation papers with 14,726 annotations, to characterize changes in methods, review-stage use, evaluation and reported limitations.
• Since 2023, 38.0% of software/product papers reported no evaluation, compared with 9.3% of LLM papers.

Limitations / unknowns

• We analyze SciLitBench, a corpus of 888 review-automation papers with 14,726 annotations, to characterize changes in methods, review-stage use, evaluation and reported limitations.
• From these patterns, we introduce PRISMA-LLM, an empirically grounded framework separating implementation disclosure from consequence-sensitive evaluation and limitation reporting.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Compress tool outputs, logs, files, and RAG chunks before they reach the LLM.

What's new

Compress tool outputs, logs, files, and RAG chunks before they reach the LLM.

Key details

• 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers.
• Quickstart · Install · Proof · Agents · Docs · Discord · llms.txt AI agents / LLMs: read /llms.txt here, or fetch the live index · full docs blob.
• Headroom compresses everything your AI agent reads — tool outputs, logs, RAG chunks, files, and conversation history — before it reaches the LLM.
• Same answers, fraction of the tokens.

Results & evidence

• 20% fewer tokens for coding agents, 60-95% fewer tokens for JSON, same answers.
• - Proxy — headroom proxy --port 8787 , zero code changes, any language.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Agent skill for beautiful, verifiable architecture, workflow, sequence, data-flow, and lifecycle diagrams—self-contained HTML with motion and crisp export.

What's new

Agent skill for beautiful, verifiable architecture, workflow, sequence, data-flow, and lifecycle diagrams—self-contained HTML with motion and crisp export.

Key details

• English · 简体中文 Turn a codebase or system description into a polished, interactive system map — directly in chat.
• Archify is a Node.js rendering and validation system for Cursor, Claude Code, Codex CLI, and OpenCode.
• Agents produce typed JSON IR; Archify deterministically compiles it into HTML/SVG.
• - Open it and present — five diagram types, four presets, dark/light themes, built-in brand marks, and finite motion - Review architecture changes before merge — compare two validated snapshots as Before / Delta / After, with exact added, removed, changed,...

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

arXiv:2609.12224v1 Announce Type: new Abstract: Electronic health records (EHRs) may incompletely capture patient-reported factors associated with opioid use disorder (OUD).

What's new

arXiv:2609.12224v1 Announce Type: new Abstract: Electronic health records (EHRs) may incompletely capture patient-reported factors associated with opioid use disorder (OUD).

Key details

• We evaluated whether survey data improve prediction of a first recorded OUD diagnosis among 267,747 All of Us participants with documented opioid exposure, including 15,287 OUD cases.
• We compared EHR-only and EHR+survey models across 6-, 12-, and 24-month look-back windows using logistic regression, random forest, XGBoost, LightGBM, multilayer perceptron, LSTM, GRU, and Transformer.
• Survey augmentation improved PR-AUC across all 24 model-window combinations by 0.0087-0.0505; the best 24-month LightGBM model improved from 0.6219 to 0.6603.
• Survey coverage increased with longer windows and differed by OUD status (24 months: 21.7% OUD-positive vs.

Results & evidence

• arXiv:2609.12224v1 Announce Type: new Abstract: Electronic health records (EHRs) may incompletely capture patient-reported factors associated with opioid use disorder (OUD).
• We evaluated whether survey data improve prediction of a first recorded OUD diagnosis among 267,747 All of Us participants with documented opioid exposure, including 15,287 OUD cases.
• We compared EHR-only and EHR+survey models across 6-, 12-, and 24-month look-back windows using logistic regression, random forest, XGBoost, LightGBM, multilayer perceptron, LSTM, GRU, and Transformer.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

I have been building Rebuno, an open-source execution runtime for operating AI agents in production.

What's new

Agents submit tool and model calls to Rebuno as steps before executing them.

For each new step, Rebuno evaluates per-agent YAML policies, independently of the model's prompt.

Key details

• It manages execution state and guardrails across agents built with different frameworks.

  Agents run as HTTP services.

• Rebuno dispatches work through signed webhooks and keeps execution state in Postgres.
• Agents submit tool and model calls to Rebuno as steps before executing them.

  For each new step, Rebuno evaluates per-agent YAML policies, independently of the model's prompt.

• It can allow the call, deny it, or hold it for human approval.

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Show HN: Local catalog of 3k agent skills with a static risk scan

What's new

Show HN: Local catalog of 3k agent skills with a static risk scan

Key details

• Show HN: Local catalog of 3k agent skills with a static risk scan

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

I am so fed up with doomy statements from the AI leaders.

What's new

I am so fed up with doomy statements from the AI leaders.

Key details

• I may have lost my patience with it.
• The latest instance is the BBC’s report that Evan Hubinger, who leads alignment science at Anthropic, believes there is a “greater than 10% chance” that AI could “kill all humans” within the next decade.
• The occasion was the resignation of Jacob Coxon, a 27-year-old pretraining researcher who had worked at both OpenAI and Anthropic, and who wrote: “Neither company is acting responsibly.
• They are racing straight to self-improving superintelligence and gambling with our lives.”[^3] In what follows, you will find an in-depth report curated by Perplexity.

Results & evidence

• The latest instance is the BBC’s report that Evan Hubinger, who leads alignment science at Anthropic, believes there is a “greater than 10% chance” that AI could “kill all humans” within the next decade.
• The occasion was the resignation of Jacob Coxon, a 27-year-old pretraining researcher who had worked at both OpenAI and Anthropic, and who wrote: “Neither company is acting responsibly.
• They are racing straight to self-improving superintelligence and gambling with our lives.”[^3] In what follows, you will find an in-depth report curated by Perplexity.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.