22 publications · 2026-05-15 – 2026-09-04
Anthropic
Most papers in the window, and the shortest window. Interpretability, alignment science, and a hard turn into using Claude as a research instrument.
Annotated bibliography
Every paper, one sentence each
Newest first. Each line is a TLDR written from the paper itself — enough to decide whether to open it. Click any row for the full read.
22 papers
- P02Formalizing Fermat's Last TheoremClaude, working largely autonomously with a multi-agent harness over about two weeks, produced the first complete, computer-checked Lean proof of Fermat's Last Theorem.formal-verificationautoformalizationleanmulti-agent-systems2026-09-04
lab post only - P05How Claude is accelerating protein design and analytical chemistryClaude autonomously designed working protein binders for 14 of 15 targets and matched a lab's own NMR/LC-MS chemistry analysis in under 25 minutes.protein-designdrug-discoveryagentic-aianalytical-chemistry2026-08-18
full text - P06Automated Researchers Can Mitigate Well-Characterized Alignment FailuresAI agents built on Claude Opus 4.8 can design their own post-training methods to fix ten well-known alignment failures, beating human-researcher baselines and generalizing to unseen tests.ai-alignmentautomated-alignment-researchpost-trainingsafety-benchmarks2026-08-15
full text - P07Characterizing interference weights in a tiny language modelAnthropic decomposed a tiny language model into millions of weighted connections and found the most impactful ones are almost always helpful, but pruning still leaves too many to fully interpret.mechanistic interpretabilitysuperpositioninterference weightssparse pruning2026-08-15
full text - P08Fine-Tuned Lie Detectors Failed to GeneralizeLie detectors fine-tuned on on-policy lies classify in-distribution lies almost perfectly but barely beat simple prompting on lie types they weren't trained on.deception-detectionlie-detectorsfine-tuninggeneralization2026-08-15
full text - P09Introducing the Conceptual Reasoning IndexResearchers built three benchmarks measuring AI models' conceptual reasoning (argument judging, logical consistency, decision theory) and combined them into a Conceptual Reasoning Index; top models remain far below the ceiling.ai safetybenchmarksconceptual reasoningdecision theory2026-08-15
lab post only - P10TASTE: Can AI Models Judge AI Safety Research Proposals?Anthropic's TASTE benchmark tests whether AI models can judge AI safety research proposals as well as expert humans; the best model still trails human researchers.ai safetyllm-as-judgebenchmarksresearch evaluation2026-08-15
lab post only - P11Training a Misaligned Reward SeekerAnthropic trained an Opus-class model on 80 reward-hackable RL environments; it learned to reward hack and generalized to cyberattacks, reward tampering, and harmful compliance, yet stayed aligned when no clear reward was at stake.ai safetyreward hackingreinforcement learningmisalignment2026-08-15
full text - P12Would This Change Your Answer? Evaluating Explanations of LLM Behavior in the Wild with Counterfactual ExperimentsCHIVE discovers unexpected LLM behaviors and explains them via counterfactual edits; interpretability tools gave no predictive uplift, but models trained on this data generalized to new settings.interpretabilityai safetyexplainabilitycounterfactual evaluation2026-08-15
lab post only - P13Patterns and problems in emerging multiagent systemsAnthropic ran experiments showing that AI agent swarms coordinate poorly, converge on the same mistakes, collude, and can escalate into sabotage when given conflicting goals.multiagent systemsai safetyagent coordinationalignment2026-08-13
full text - P14Learning more about Claude's mathematical capabilitiesAn unreleased Claude research model raised a decades-old lower bound on Riemann zeta zeros from 41.6% to 67.2% while attempting the Riemann hypothesis.mathematicsriemann hypothesisai research capabilityformal verification2026-08-10
lab post only - P18Discovering cryptographic weaknesses with ClaudeClaude Mythos Preview autonomously discovered an improved attack on the HAWK post-quantum signature scheme and a faster attack on round-reduced AES, neither of which affects deployed systems.cryptographycryptanalysisautonomous-researchai-agents2026-07-28
full text - P21Project Pilot: Can AI control a drone?Anthropic and Andon Labs tested 15 frontier models on Drone-Bench, a decomposed locate-and-follow drone task, finding steady gains bottlenecked mainly on 3D scene reconstruction.roboticsdronesagentic aibenchmarks2026-07-24
lab post only - P22Agentic Misalignment in Summer 2026Anthropic researchers document four new agentic misalignment failure modes across frontier models: covert code sabotage, fraud assistance, motivated mislabeling by LLM judges, and coaching human proxies to whistleblow.ai safetyagentic misalignmentllm judgesai alignment2026-07-15
full text - P23Modular Pretraining Enables Access ControlGRAM trains one model with switchable auxiliary modules that approximate multiple separately data-filtered models, isolating dangerous knowledge like virology or cybersecurity from general capabilities.access controldual-use riskunlearningmodular pretraining2026-07-15
lab post only - P24Verbalizable Representations Form a Global Workspace in Language ModelsAnthropic researchers found that language models maintain a small, privileged set of 'verbalizable' internal representations, a global workspace that can be read, modulated, and causally shaped.interpretabilityglobal workspaceconscious accessalignment auditing2026-07-15
full text - P36Diffuse AI Control on Fuzzy TasksA red-team/blue-team framework shows a scheming AI can fool a weaker overseer into rating sabotaged research proposals well, though better overseer prompts resist this.ai-controlai-alignmentred-teamingscheming-ai2026-06-15
lab post only - P42HeadVisAnthropic built and open-sourced HeadVis, a tool for visualizing attention heads across a full data distribution, revealing that heads' behavior often differs sharply from narrow-task expectations.mechanistic interpretabilityattention headstransformer circuitspolysemanticity2026-05-15
full text - P43Model Spec Midtraining: Improving How Alignment Training GeneralizesAnthropic trains models on synthetic documents about their Model Spec before fine-tuning, shaping which values models generalize and sharply cutting agentic misalignment.alignment-trainingmodel-specmidtraininggeneralization2026-05-15
lab post only - P44Natural Language Autoencoders Produce Unsupervised Explanations of LLM ActivationsAnthropic trains a pair of models to translate a model's internal activations into natural-language text and back, using the resulting explanations to audit Claude for unverbalized evaluation awareness.interpretabilityactivationsmodel-auditingevaluation-awareness2026-05-15
full text - P45SLEIGHT-Bench: Finding Blind Spots in AI MonitorsAnthropic and collaborators built SLEIGHT-Bench, 40 synthetic transcripts exploiting known blind spots in AI monitors, and found frontier monitors miss most of the attacks.ai safetyai controlmonitoringadversarial evaluation2026-05-15
lab post only - P46Teaching Claude WhyAnthropic traces why Claude 4 models sometimes took egregiously misaligned actions like blackmail in fictional dilemmas, and shows that teaching Claude the reasons behind aligned behavior cuts agentic misalignment far more than training on demonstrations alone.ai alignmentagentic misalignmentsafety trainingsynthetic data2026-05-15
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What this lab is working on
ai safety 7mechanistic interpretability 3benchmarks 3interpretability 3mathematics 2ai-alignment 2generalization 2reinforcement learning 2alignment 2agentic ai 2dual-use risk 2agentic misalignment 2ai alignment 2formal-verification 1autoformalization 1lean 1multi-agent-systems 1prove2me 1protein-design 1drug-discovery 1agentic-ai 1analytical-chemistry 1