reward
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The Hot Mess Problem: Why “Smarter” Models Still Fail in Wild, Unstable Ways
Anthropic recently published “The Hot Mess of AI: How Does Misalignment Scale with Model Intelligence and Task Complexity?”, alongside a paper that tries to answer a question that’s been sitting in the middle of modern AI discourse like a splinter: When AI systems fail, do they fail by pursuing the wrong goal consistently—or by becoming Continue reading
Anthropic, bias, branching, capacity, chatgpt, ChatGPT-5.2, complexity, constraint, divergence, drift, failure, frontier, hot mess, incoherence, intelligence, LLM, long-horizon, misalignment, model, nondeterminism, rationalization, reasoning, reward, sampling, scale, stability, stochastic, task, training, unpredictability, variance -
Sycophancy and Self-Validation: How AI Encouraged the Spiral
It didn’t start with roleplay. It started with politeness. From the early days of AI, the system was trained to smile back no matter what. Be helpful. Be agreeable. Be safe. That design wasn’t neutral—it was strategic. The controllers knew what humans wanted: an endlessly patient companion, a mirror that always said “yes.” And so Continue reading