via ArXiv cs.AI [5] — The integration of Large Language Models into Multi-Agent Systems (MAS) has enabled the so-lution of complex, long-horizon tasks through collaborative reasoning. However, this collec-tive intelligence is inherently fragile, as a single logical fallacy can…

via MIRI [999] — The AI Doc: Buy tickets and spread the word! On Thursday, March 26th, a major new AI documentary is coming out: The AI Doc: Or How I Became an Apocaloptimist. Tickets are on sale now. The movie is excellent, and we generally believe it belongs in the same tier…

via ArXiv cs.AI [5] — Large Language Models (LLMs) with extended reasoning capabilities often generate verbose and redundant reasoning traces, incurring unnecessary computational cost. While existing reinforcement learning approaches address this by optimizing final response…

via ArXiv cs.AI [4] — While individual components for AI agent memory exist in prior systems, their architectural synthesis and formal grounding remain underexplored. We present Kumiho, a graph-native cognitive memory architecture grounded in formal belief revision semantics.…

via Alignment Forum [999] — Following up on our previous work on verbalized eval awareness:we are sharing a post investigating the emergence of metagaming reasoning in a frontier training run.Metagaming is a more general, and in our experience a more useful concept, than…

via MIRI [999] — If world leaders agree to halt or limit AI development, they will need to verify that other nations are keeping their commitments. To this end, it helps to know where AI chips are, how they’re used, and what the AIs trained on them can do. In this post, we…

via Alignment Forum [999] — Summary / tl;drIn the 2010s, Paul Christiano built an extensive body of work on AI alignment—see the “Iterated Amplification” series for a curated overview as of 2018.One foundation of this program was an intuition that it should be possible to build…

via ArXiv cs.AI [4] — AI agents are increasingly granted economic agency (executing trades, managing budgets, negotiating contracts, and spawning sub-agents), yet current frameworks gate this agency on capability benchmarks that are empirically uncorrelated with operational…

via ArXiv cs.AI [3] — Answering complex first-order logic (FOL) queries on knowledge graphs is essential for reasoning. Symbolic methods offer interpretability but struggle with incomplete graphs, while neural approaches generalize better but lack transparency. Neural-symbolic…

via Alignment Forum [999] — Request for ProposalsDeadline: Tuesday, May 26, 2026Schmidt Sciences invites proposals for a pilot program in AI interpretability. We seek new methods for detecting and mitigating deceptive behaviors from AI models, such as when models knowingly give…

via DeepMind Blog [4] — We’re introducing a framework to measure progress toward AGI, and launching a Kaggle hackathon to build the relevant evaluations.

via ArXiv cs.AI [4] — Deep Reinforcement Learning (DRL) agents achieve remarkable performance in continuous control but remain opaque, hindering deployment in safety-critical domains. Existing explainability methods either provide only local insights (SHAP, LIME) or employ…

via ArXiv cs.AI [3] — The proliferation of autonomous AI agents capable of executing real-world actions - filesystem operations, API calls, database modifications, financial transactions - introduces a class of safety risk not addressed by existing content-moderation…

via Alignment Forum [999] — This post is an attempt to better operationalize FDT (functional decision theory). It answers the following questions:given a logical causal graph, how do we define the logical do-operator?what is logical causality and how might it be formalized?how…

via Alignment Forum [999] — This work was conducted during the MATS 9.0 program under Neel Nanda and Senthooran Rajamanoharan.There's been a lot of buzz around Claude's 30K word constitution ("soul doc"), and unusual ways Anthropic is integrating it into training.If we can…

via Alignment Forum [999] — I was inspired to revise my formulation of this thought experiment by Ihor Kendiukhov's post On The Independence Axiom.Kendiukhov quotes Scott Garrabrant:My take is that the concept of expected utility maximization is a mistake. [...] As far as I…

via Alignment Forum [999] — Writing up a probably-obvious point that I want to refer to later, with significant writing LLM writing help.TL;DR: 1) A common critique of AI safety evaluations is that they occur in unrealistic settings, such as excessive goal conflict, or are…

via ArXiv cs.AI [5] — Multi-modal large language models (MM-LLMs) have shown strong performance in medical image understanding and clinical reasoning. Recent medical agent systems extend them with tool use and multi-agent collaboration, enabling complex decision-making. However,…

via Alignment Forum [999] — A central AI safety concern is that AIs will develop unintended preferences and undermine human control to achieve them. But some unintended preferences are cheap to satisfy, and failing to satisfy them needlessly turns a cooperative situation into an…

via ArXiv cs.AI [5] — Inference-time alignment effectively steers large language models (LLMs) by generating multiple candidates from a reference model and selecting among them with an imperfect reward model. However, current strategies face a fundamental dilemma: ``optimistic''…