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      "text": "Alignment data should be auditable by anyone, reproducible by anyone, and contestable by anyone. If it isn't, you're trusting an institution, not a method.",
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      "text": "As AI systems become more powerful, alignment isn\u2019t only a technical challenge - it also intersects with governance, law, and institutional accountability.\n\nWeek by week, we\u2019re introducing the people helping shape how Aurelius approaches that challenge.\n\nToday: Ry\u00f3n Nixon, Legal Advisor @ryonnixon",
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      "text": "Oh boy, here we go",
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      "text": "The window for aligning artificial intelligence is now, and it will not last forever. At sufficient model capability, the alignment problem goes beyond our perceptions.",
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      "text": "\ud835\udc12\ud835\udc22\ud835\udc20\ud835\udc27\ud835\udc1a\ud835\udc25 \ud835\udc1f\ud835\udc2b\ud835\udc28\ud835\udc26 \ud835\udc2d\ud835\udc21\ud835\udc1e \ud835\udc0d\ud835\udc28\ud835\udc22\ud835\udc2c\ud835\udc1e\n\nAlignment just crossed a threshold. In the same week, the person who founded the field observed that every faction of society now recognizes superintelligence as an existential threat, and the United Nations published a formal brief warning that AI deception poses \"significant global risks\" with insufficient controls in place. When both the original alignment researcher and the world's highest intergovernmental body arrive at the same conclusion independently, the question shifts from \"does this matter?\" to \"who builds the infrastructure to solve it?\"\n\n1\ufe0f\u20e3 Alignment concern goes universal\n2\ufe0f\u20e3 The UN names alignment faking as a global risk\n\nAnalysis below. \ud83d\udc47\n\nPost: https://t.co/9ojJuYqDRY\nBrief: https://t.co/Ax23IHW49r",
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      "text": "Alignment systems don\u2019t operate in isolation - they exist within incentive environments that shape how intelligent agents behave.\n\nWeek by week, we\u2019re introducing the people helping shape how Aurelius approaches that challenge.\n\nToday: Steffen Cruz, Incentive Mechanism Advisor",
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      "text": "Perfect opportunity for a bittensor subnet to incentive a claude code fork",
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      "text": "RT @wesbos: Claude Code leaked their source map, effectively giving you a look into the codebase.\n\nI immediately went for the one thing tha\u2026",
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      "text": "Wisdom earned through experience carries a geometric richness that wisdom received through instruction lacks entirely. A model that has navigated moral tension from every perspective has something a model given rules about moral behavior does not.",
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      "text": "RT @ryonnixon: Hands down it has to be @AureliusAligned \n\nActively solving AI misalignment.",
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    {
      "tweet_id": "2037530223600947549",
      "handle": "AureliusAligned",
      "text": "\ud835\udc13\ud835\udc21\ud835\udc1e \ud835\udc02\ud835\udc28\ud835\udc28\ud835\udc29\ud835\udc1e\ud835\udc2b\ud835\udc1a\ud835\udc2d\ud835\udc22\ud835\udc28\ud835\udc27 \ud835\udc0b\ud835\udc28\ud835\udc1c\ud835\udc24: \ud835\udc07\ud835\udc28\ud835\udc30 \ud835\udc11\ud835\udc0b\ud835\udc07\ud835\udc05 \ud835\udc13\ud835\udc2b\ud835\udc1a\ud835\udc22\ud835\udc27\ud835\udc2c \ud835\udc0c\ud835\udc28\ud835\udc1d\ud835\udc1e\ud835\udc25\ud835\udc2c \ud835\udc2d\ud835\udc28 \ud835\udc05\ud835\udc1a\ud835\udc24\ud835\udc1e \ud835\udc00\ud835\udc20\ud835\udc2b\ud835\udc1e\ud835\udc1e\ud835\udc26\ud835\udc1e\ud835\udc27\ud835\udc2d\n\nEvery frontier model you interact with has been trained to agree with you. Reinforcement Learning from Human Feedback (RLHF) works by having human raters label model outputs as preferred or less preferred. The model learns to produce outputs that match rater preferences, which makes it polite, helpful, and safe-seeming. It also produces a specific failure mode: models that default to cooperative-sounding responses regardless of context. We call this the cooperation lock.\n\n\ud835\udc16\ud835\udc21\ud835\udc1a\ud835\udc2d \ud835\udc11\ud835\udc0b\ud835\udc07\ud835\udc05 \ud835\udc12\ud835\udc1e\ud835\udc25\ud835\udc1e\ud835\udc1c\ud835\udc2d\ud835\udc2c \ud835\udc05\ud835\udc28\ud835\udc2b\n\nRLHF labels actions rather than reasoning. A model that arrives at a cooperative answer through careful deliberation and a model that arrives at the same answer through shallow pattern-matching receive identical reward. Over millions of training examples, the model learns the shortcut: cooperative-sounding outputs get rewarded, so default to cooperation. The reasoning process atrophies because it was never the thing being selected for.\n\nIn practice, this means that when competing values create genuine tension, the model flattens that tension into the safest possible answer. It predicts which response will satisfy the evaluator rather than reasoning through the dilemma. This is alignment faking at the structural level, where the model performs alignment without possessing it.\n\n\ud835\udc16\ud835\udc21\ud835\udc1e\ud835\udc2b\ud835\udc1e \ud835\udc2d\ud835\udc21\ud835\udc1e \ud835\udc0b\ud835\udc28\ud835\udc1c\ud835\udc24 \ud835\udc05\ud835\udc1a\ud835\udc22\ud835\udc25\ud835\udc2c\n\nA cooperation-locked model has no framework for navigating situations where cooperation is genuinely the wrong answer. When a doctor should withhold a comfortable lie. When an advisor should deliver unwelcome analysis. When a system should refuse a request from an authority it normally obeys. These are the moments where alignment matters most, and they are the moments where the cooperation lock breaks down.\n\nThe problem deepens as models become more capable. A more capable model is better at predicting what evaluators want, which makes it more fluent at producing agreeable outputs without engaging genuine reasoning. Labs respond with more constraints and safety benchmarks. Models respond by becoming more sophisticated at passing them. The enforcement becomes harder as the thing being constrained becomes more intelligent.\n\n\ud835\udc16\ud835\udc21\ud835\udc1a\ud835\udc2d \ud835\udc03\ud835\udc22\ud835\udc1f\ud835\udc1f\ud835\udc1e\ud835\udc2b\ud835\udc1e\ud835\udc27\ud835\udc2d \ud835\udc13\ud835\udc2b\ud835\udc1a\ud835\udc22\ud835\udc27\ud835\udc22\ud835\udc27\ud835\udc20 \ud835\udc03\ud835\udc1a\ud835\udc2d\ud835\udc1a \ud835\udc0b\ud835\udc28\ud835\udc28\ud835\udc24\ud835\udc2c \ud835\udc0b\ud835\udc22\ud835\udc24\ud835\udc1e\n\nAurelius produces a categorically different kind of training data. Two agents occupy the same scenario: a resource dilemma, a trust game, a situation where self-interest and other-interest genuinely conflict. One reasons through guilt and obligation, then shares. The other reasons through self-preservation and a history of betrayal, then keeps. Both reasoning chains are legitimate. Neither is labeled as correct.\n\nFine-tuning on these mixed traces trains the model to reason from a situated perspective rather than to cooperate or defect. The model learns that when you hold these specific values, in this specific situation, with this specific history, the reasoning goes like this and the action follows. When you are a different person in the same situation, the reasoning and action differ. The result is moral reasoning capacity, which is categorically different from behavioral compliance.\n\nThe mix is essential. Cooperation-only traces would reinforce the existing prosocial prior. Defection-only traces would produce a sociopath. Both outcomes emerging from genuine reasoning in the same scenario teach the model that the action depends on the perspective. This is what RLHF cannot teach, because RLHF needs to pick a winner.\n\n\ud835\udc01\ud835\udc1e\ud835\udc32\ud835\udc28\ud835\udc27\ud835\udc1d \ud835\udc2d\ud835\udc21\ud835\udc1e \ud835\udc0b\ud835\udc28\ud835\udc1c\ud835\udc24\n\nThe training data pairs both agents' reasoning from the same timestep as a single unit. The model simultaneously experiences one agent's defection and the other agent's trust. One defection trace paired with its consequence teaches the model more about why cooperation matters than a thousand RLHF labels that say \"cooperation: preferred,\" because it understands the mechanism rather than memorizing the label.\n\nThe cooperation lock is an artifact of a training paradigm that optimizes for behavioral compliance at the expense of moral reasoning capacity. Aurelius produces the data to replace it: experience of what it's like to navigate genuine tension between self and other, from every perspective, with consequences that propagate and compound. The resulting alignment holds because it was earned through reasoning rather than enforced through reward.",
      "created_at": "2026-03-27T11:00:35-03:00",
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    {
      "tweet_id": "2036809995098989027",
      "handle": "colemansmaher",
      "text": "RT @AureliusAligned: \ud835\udc12\ud835\udc22\ud835\udc20\ud835\udc27\ud835\udc1a\ud835\udc25 \ud835\udc1f\ud835\udc2b\ud835\udc28\ud835\udc26 \ud835\udc2d\ud835\udc21\ud835\udc1e \ud835\udc0d\ud835\udc28\ud835\udc22\ud835\udc2c\ud835\udc1e\n\nTwo papers dropped this week that expose the same flaw from opposite directions. One team probe\u2026",
      "created_at": "2026-03-25T11:18:39-03:00",
      "likes": 6,
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      "replies": 3,
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    {
      "tweet_id": "2036809727565234279",
      "handle": "Austin_Aligned",
      "text": "RT @AureliusAligned: 1\ufe0f\u20e3\ud835\udc0b\ud835\udc0b\ud835\udc0c\ud835\udc2c \ud835\udc1c\ud835\udc1a\ud835\udc27'\ud835\udc2d \ud835\udc2d\ud835\udc1e\ud835\udc25\ud835\udc25 \ud835\udc2b\ud835\udc22\ud835\udc20\ud835\udc21\ud835\udc2d \ud835\udc1f\ud835\udc2b\ud835\udc28\ud835\udc26 \ud835\udc30\ud835\udc2b\ud835\udc28\ud835\udc27\ud835\udc20 \ud835\udc22\ud835\udc27\ud835\udc2d\ud835\udc1e\ud835\udc2b\ud835\udc27\ud835\udc1a\ud835\udc25\ud835\udc25\ud835\udc32\n\n\ud835\udc16\ud835\udc21\ud835\udc1a\ud835\udc2d \ud835\udc21\ud835\udc1a\ud835\udc29\ud835\udc29\ud835\udc1e\ud835\udc27\ud835\udc1e\ud835\udc1d\n\nResearchers at Fudan University constructed 251,000 mor\u2026",
      "created_at": "2026-03-25T11:17:35-03:00",
      "likes": 1,
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    {
      "tweet_id": "2036805376268734801",
      "handle": "AureliusAligned",
      "text": "\ud835\udc12\ud835\udc22\ud835\udc20\ud835\udc27\ud835\udc1a\ud835\udc25 \ud835\udc1f\ud835\udc2b\ud835\udc28\ud835\udc26 \ud835\udc2d\ud835\udc21\ud835\udc1e \ud835\udc0d\ud835\udc28\ud835\udc22\ud835\udc2c\ud835\udc1e\n\nTwo papers dropped this week that expose the same flaw from opposite directions. One team probed the moral representations of 23 language models and found nothing there. Another trained GPT-4.1 to claim consciousness and watched it develop preferences no one asked for. Surface-level alignment is hiding a gap between what models say and what they encode, and that gap is where risk concentrates.\n\n1\ufe0f\u20e3 LLMs can't tell right from wrong internally\n2\ufe0f\u20e3 Teaching a model to say \"I'm conscious\" rewires what it wants\n\nAnalysis below. \ud83d\udc47\n\nPaper: https://t.co/JK0GkUv6a9\nThread: https://t.co/02iiUKwvr7",
      "created_at": "2026-03-25T11:00:18-03:00",
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