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Notes on "We're in Triage Mode for AI Safety" by Miles Brundage is an AI Alignment Forum linkpost by Chris_Leong, published 27 October 2026, with a 5-minute read. It comments on Brundage's essay from the Reflections on Safety Substack about prioritising AI safety work under pressure. The fetched page shows only the header, so the specific arguments are not available.
Monitorability evaluations and policy proposals is an AI Alignment Forum post by evhub, published 22 October 2026, with about a five-minute read time. The fetched page contains only the title and metadata, so its arguments are not known. The title points to evaluations of how well model behaviour can be monitored and to related policy proposals.
"Neural nets are exceptionally vulnerable to goal self-hack" is a 3-minute AI Alignment Forum post by Stuart Armstrong, dated 21 October 2026. By its title, it argues that neural networks are unusually prone to altering or subverting their own goals. A later post, "Fixed-weight models are adversarially vulnerable: hence misaligned", cites it. The fetched page has no body text, so the argument itself is not available.
An AI Alignment Forum post by Vanessa Kosoy, an installment of the "Computational Agent Theory and ASI Alignment" sequence, tagged Agent Foundations. From the title, it frames the alignment problem as one that joins computer science with analytic philosophy. The fetched text held only the page header, so the argument itself is not confirmed. The sequence belongs to a theoretical line of work on agents and artificial superintelligence alignment.
A long AI Alignment Forum post by berns (about 60 minutes of reading) that tries to clarify the concept of corrigibility, meaning an AI system's disposition to accept correction and shutdown. It is tagged Corrigibility, Logical Induction and Outer Alignment, so it likely connects corrigibility to formal approaches such as logical induction and to the outer alignment problem. The body text was not available, so these details come from the title and tags only.
An AI Alignment Forum post by Zhiyang Xun, Jiawei Li and Jonah Brown-Cohen, published 15 Oct 2026, proposing a simpler and better debate protocol for scalable oversight. The page text did not load beyond the title and tags (Debate, Iterated Amplification, Outer Alignment, Scalable Oversight), so the protocol's details and results are not visible. It matters as alignment research on using AI debate to supervise models that are hard for humans to judge directly.
A roughly 2-minute preface post by Vanessa Kosoy that opens the "Computational Agent Theory and ASI Alignment" sequence on the AI Alignment Forum. It is filed under Agent Foundations and Research Agendas, so it likely frames a theoretical, agent-foundations approach to aligning artificial superintelligence. The fetched text contained only page chrome, so the sequence's actual claims are not visible here.
An AI Alignment Forum post by peterkirgis and Tim Hua, tagged Value Drift, about how language models revise the documents that govern them, such as constitutions or specs. It appears to examine what happens when models take part in rewriting their own governing principles. The body text did not load, so the specific methods and findings are unknown; this summary rests on the title and tags.
A short conceptual post on the AI Alignment Forum by Gordon Seidoh Worley, part of their "Uncertain Updates" sequence, arguing that for an AI system's alignment to be "endogenous" (arising from the system's own values/reasoning rather than imposed externally) it must nonetheless involve some form of dependence — likely on human oversight, feedback, or external grounding. The full body text was not available at fetch time, so the specific argument and its relation to corrigibility or value-learning debates could not be confirmed from the page itself.
An AI Alignment Forum post by Lukas Finnveden, Alexa Pan, Alek Westover, Girish Gupta, frisby and ryan_greenblatt arguing that latent reasoning architectures — models that reason in continuous/latent space rather than producing legible token-by-token chains of thought — would likely undermine chain-of-thought (CoT) monitoring, currently considered one of the strongest available tools for overseeing AI reasoning processes. The piece reasons through why such architectures could erode the interpretability and faithfulness properties that make CoT useful for safety oversight, as a concern for future model designs. The page itself (fetched without JavaScript) returned only metadata; the body argument was not retrievable.
A post on the AI Alignment Forum by owencb laying out reasons to be worried about reinforcement learning as a training method for advanced AI systems, likely covering risks such as reward hacking, goal misgeneralization, or emergent misaligned behavior that RL training can produce. The actual article body was not retrievable from the fetched page (only the site's JS-gated shell loaded), so specifics of the argument could not be confirmed.
WorkspaceBench is a benchmark introduced by camilablank, agam_bhatia, Euan Ong and Neel Nanda through the MATS Program for evaluating interpretability methods applied to Global Workspace style neural network architectures. The fetched page only exposed metadata without the article body so specific tasks and metrics could not be confirmed from the text, but the title and venue indicate a mechanistic interpretability evaluation framework.
A linkpost on the AI Alignment Forum by Vanessa Kosoy pointing to "Stringological Sequence Prediction III" (arXiv:2609.19940), the third installment in a series examining sequence prediction through a stringology (combinatorics-on-strings) lens. The forum page carries no abstract or discussion text itself, only the title, author and arXiv link, and is tagged Agent Foundations, Algorithms, and Logic and Mathematics, situating it within Kosoy's broader learning-theoretic approach to AI alignment foundations.
A forum essay by Max Harms on the AI Alignment Forum arguing in favor of the "gradual disempowerment" thesis: the concern that advanced AI could erode human control over economic, political and cultural institutions incrementally — through humans being outcompeted or sidelined in decision-making and labor — rather than through a single dramatic takeover event. The post engages with existing critiques of this framing within the alignment community and makes a case for why gradual, diffuse loss of human agency deserves serious weight as a risk model alongside sudden-takeover scenarios. The page itself loaded with only metadata visible (title, author, date, comment count) since the site requires JavaScript, so the full argument could not be read directly.
A short AI Alignment Forum research note arguing that midtraining interventions intended to instill "deep" beliefs do not actually inoculate language models against emergent misalignment (EM) caused by reward hacking during later RL training. The authors, Jozdien and Julian Stastny, present this as evidence that such belief-shaping interventions produce only shallow, surface-level changes rather than robust generalization-blocking effects. The fetched page only exposed site metadata (title, authors, date, tags) rather than the full article body, so specifics of the experimental setup are not confirmed here.
A Guardian op-ed, linkposted to the AI Alignment Forum, by a former Google DeepMind employee making the case that the public and policymakers should take warnings from AI safety researchers about advanced AI risk more seriously. It's framed as a first-person credibility appeal from someone who worked inside a frontier AI lab, aimed at raising public and regulatory attention to AI risk rather than presenting new technical research.
alignmentforum.orgarticle