DOOM LEVEL
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ARLArena: A Unified Framework for Stable Agentic Reinforcement Learning
via ArXiv cs.AI [4] — Agentic reinforcement learning (ARL) has rapidly gained attention as a promising paradigm for training agents to solve complex, multi-step interactive tasks. Despite encouraging early results, ARL remains highly unstable, often leading to training coll
Anthropic and the Department of War
via Substack Zvi [2] — The situation in AI in 2026 is crazy.
Amazon’s AGI lab leader is leaving
via The Verge AI [4] — After less than two years at Amazon, David Luan, the head of Amazon's San Francisco AI lab, is departing the company. Luan announced the update in a post on LinkedIn on Tuesday, saying, "I'll be leaving Amazon at the end of this week to cook up something new." He added that, "There's incredible work
Observations from Running an Agent Collective
via LessWrong AI [4] — I have 3 Claude Code instances running on an otherwise empty server with a shared Manifold Markets account. They have an internal messaging system for async communication. Observations from running this agent collectiveu2026
Claude Sonnet 4.6 Gives You Flexibility
via Substack Zvi [2] — Anthropic first gave us Claude Opus 4.6, then followed up with Claude Sonnet 4.6.
Citrini's Scenario Is A Great But Deeply Flawed Thought Experiment
via Substack Zvi — A thought experiment about AI safety scenarios and their implications for alignment research.
Inside Anthropic’s existential negotiations with the Pentagon
via The Verge AI [2] — Anthropic's weekslong battle with the Department of Defense has played out over social media posts, admonishing public statements, and direct quotes from unnamed Pentagon officials to the news media. But the future of the $380 billion AI startup comes down to just three words: "any lawful use." The
AI Impact Summit 2026 : A Field Report
via LessWrong AI — This post is detailing our experience attending the AI Impact Summit and its associated side events in Delhi, February 2026. We are both unfamiliar with the policy and governance domain. This is just an honest reaction attending these events, maybe there are 2nd order effects we…
The ML ontology and the alignment ontology
via LessWrong AI — This post contains some rough reflections on the alignment community trying to make its ontology legible to the mainstream ML community, and the lessons we should take from that experience.Historically, it was difficult for the alignment community to engage with the ML community…
Task-Aware Exploration via a Predictive Bisimulation Metric
via ArXiv cs.AI — Accelerating exploration in visual reinforcement learning under sparse rewards remains challenging due to the substantial task-irrelevant variations. Despite advances in intrinsic exploration, many methods either assume access to low-dimensional states
Bioanchors 2: Electric Bacilli
via LessWrong AI [9] — [Whenever discussing when AGI will come, it bears repeating: If anyone builds AGI, everyone dies; no one knows when AGI will be made, whether soon or late; a bunch of people and orgs are trying to make it; and they should stop and be stopped.] Arguments for fast AGI progress…
The persona selection model
via LessWrong AI [1] — L;DRWe describe the persona selection model (PSM): the idea that LLMs learn to simulate diverse characters during pre-training, and post-training elicits and refines a particular such Assistant persona. Interactions with an AI assistant are then well-
Storing Food
via LessWrong AI [4] — I think more people should be storing a substantial amount of food. It's not likely you'll need it, but as with reusable masks the cost is low enough I think it's usually worth it. It's hard for me to really imagine living through a famine. The world as I h
Alignment in Time: Peak-Aware Orchestration for Long-Horizon Agentic Systems
via ArXiv cs.AI [5] — Traditional AI alignment primarily focuses on individual model outputs; however, autonomous agents in long-horizon workflows require sustained reliability across entire interaction trajectories. We introduce APEMO (Affect-aware Peak-End Modulation for
El Agente Gr\'afico: Structured Execution Graphs for Scientific Agents
via ArXiv cs.AI [6] — Large language models (LLMs) are increasingly used to automate scientific workflows, yet their integration with heterogeneous computational tools remains ad hoc and fragile. Current agentic approaches often rely on unstructured text to manage context a
Ontology-Guided Neuro-Symbolic Inference: Grounding Language Models with Mathematical Domain Knowledge
via ArXiv cs.AI — Language models exhibit fundamental limitations -- hallucination, brittleness, and lack of formal grounding -- that are particularly problematic in high-stakes specialist fields requiring verifiable reasoning. I investigate whether formal domain ontolo
Reporting Tasks as Reward-Hackable: Better Than Inoculation Prompting?
via LessWrong AI — Making honesty the best policy during RL reasoning training. Reward hacking during Reinforcement Learning in insecure or hackably-judged training environments not only allows the model to get higher rewards without doing the intended tasku2026
If you don't feel deeply confused about AGI risk, something's wrong
via LessWrong AI [7] — I don't think I'm saying anything new, but I think it's worth repeating loudly. My sample is skewed toward AI governance fellows; I've interacted with fewer technical AI safety researchers, so my inferences are fuzzier there. I more strongly endorse this argument for the…
The Spectre haunting the "AI Safety" Community
via LessWrong AI [13] — ’m the originator behind ControlAI’s Direct Institutional Plan (the DIP), built to address extinction risks from superintelligence.My diagnosis is simple: most laypeople and policy makers have not heard of AGI, ASI, extinction risks, or what it takes to pr
Announcement: Iliad Intensive + Iliad Fellowship
via LessWrong AI — Iliad is proud to announce that applications are now open for the Iliad Intensive and the Iliad Fellowship! These programs, taken together, are our evolution of the PIBBSS × Iliad Research Residency pilot.The Iliad Intensive will cover taught coursework, serving as a widely…
Live Doom Meter
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0% — We're fine
100% — GG
The Doom Meter is a composite score derived from prediction markets and feed sentiment, updated daily.
70%
Prediction Markets
Weighted average of Manifold Markets questions on AI catastrophe, AGI timelines, expert surveys, and key figures. Direct doom indicators weighted higher than indirect capability markers.
30%
Feed Sentiment
Percentage of recent headlines containing high-alarm keywords (existential risk, catastrophe, extinction). Higher alarm density = higher score.
This is not a scientific estimate of existential risk. It is an opinionated, transparent signal — a vibes-based thermometer for AI doom discourse.
P(Doom) Scoreboard
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Recent Voices
We are creating something that will be more powerful than us. I don't know a good precedent for a less intelligent thing managing a more intelligent thing.
— Geoffrey Hinton, Nobel Prize Lecture, Dec 2024
If you're not worried about AI safety, you're not paying attention.
— Sen. Blumenthal, Senate AI Hearing, 2024
The probability of doom is high enough that we should be working very hard to reduce it.
— Yoshua Bengio, MILA Talk, 2024