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Read time: 5 minutes
We’ve all seen those smart-looking step-by-step answers from AI — like it’s walking you through some brilliant Sherlock Holmes logic. But what if I told you… it might all be an illusion? AI pretends to reason?
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AI INSIGHTS
🧠 Is AI Actually Reasoning, Or It Just Fakes it?
Chain-of-Thought prompting is supposed to make AI explain its reasoning step-by-step. But if you’re a critical thinker, you’d ask: “Wait, is the AI actually reasoning through this? Or just making it sound good?”. Harvard researchers just found that these so-called “thinking drafts” might not reflect how the AI actually reasons. In some cases, the model just makes stuff up that sounds smart.
🔍 Framework: Testing AI’s “Reasoning” for Faithfulness
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Intra-Draft Faithfulness: “Continue steps” are straightforward progressions. “Backtrack steps” are when AI reconsiders and corrects itself.
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Draft-to-Answer Faithfulness: Is the final answer actually based on the draft or does it “freestyle” at the end?
📊 Key Findings from Testing 6 Major Models
1. Intra-Draft Faithfulness:
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Backtrack > Continue: AI is more faithful when correcting itself than when following a clean path.
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Later steps > early steps: The final few steps of reasoning are often more reliable.
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Model size ≠ better logic: Larger models tend to be more faithful—but not always. Some still make baffling mistakes.
2. Draft-to-Answer Faithfulness:
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Freestyling at the finish: Many models added new, unrelated reasoning in the answer stage.
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Logical drift: On complex tasks like GPQA, the answers often didn’t align with the draft. Simpler tasks like MMLU fared better, but weren’t immune.
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More complexity, less faithfulness: The harder the problem, the worse the alignment between draft and answer.
Why It Matters: This isn’t a reason to panic but it is a reason to stay skeptical. Just because an AI gives you a step-by-step answer doesn’t mean it truly reasoned its way there. Until models can be held accountable for their own logic, CoT should be seen as a performance, not proof of thought.
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TODAY IN AI
AI HIGHLIGHTS
🚨 Meta is losing more and more top AI minds. Many top researchers just joined French startup Mistral — the same people who helped build Meta’s open-source AI.
🔍 Google is burying the web alive with AI Mode. It wants to control both the narrative and UX. AI Mode is meant to match or beat chatbot-like search tools.
💼 Searches for “AI unemployment” hit an all-time high earlier in 2025. Almost every week (or day), the world takes another step towards super AI. But do you wonder Why AI hasn’t taken your job yet?
🖥️ Google’s viral video model goes live in 71 countries, available directly in Gemini. Veo 3 is breaking the internet with video after video that’s hard to distinguish from the real thing.
👀 An X user posted an annotated breakdown of Claude 4’s system prompt, revealing the secret playbook behind the AI model’s state-of-the-art capabilities. Check it here.
🐬 Scientists studying dolphin communication just won a $100,000 prize for using AI to analyze dolphin whistles. One whistle acts as an alarm signal. And here’s what they found.
💰 AI Daily Fundraising: Oracle is investing $40 billion to buy 400,000 Nvidia chips for OpenAI’s US data center, part of the Stargate project, showing Nvidia’s 70–95% AI chip market dominance.
AI SOURCES FROM AI FIRE
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A backend that makes solo devs feel like a 100-person team
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AI QUICK HITS
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🤖 Google’s world-model bet to build the AI layer before Microsoft owns the UI.
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⚠️ Most AI are still easily tricked into sharing harmful info. Dark LLMs again.
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🔥 Google is now fully “A.G.I.-pilled,” openly chasing human-level AI.
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✍ Microsoft added a new ‘Write’ feature to Notepad using generative AI.
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🖥️ Vercel launches v0-1.0-md for web developers to auto-fix full-stack code.
AI CHART
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🛠️ How ROBIN Works: The Bird Team: Each AI agent plays a specialist role:
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Crow: Scours literature, proposes ideas
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Falcon: Deep-dives into drug candidates
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Finch: Analyzes lab data, generates insights
→ They operate in a scientific feedback loop: hypothesize, test, analyze, repeat.
🧪 Case Study: Drug Discovery for Blindness
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Task: Find treatment for dry age-related macular degeneration. ROBIN zeroed in on RPE cell dysfunction and suggested using a phagocytosis assay
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Surfaced ripasudil, a glaucoma drug, as a top candidate. Lab tests confirmed it outperformed known alternatives
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ROBIN suggested RNA sequencing to explain ripasudil’s effect:
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Boosts ABCA1, a key lipid homeostasis gene
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Affects actin regulation and ribosome biogenesis
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⚖️ Human vs ROBIN → 7.25/10 top drug picks matched expert choices. 88% self-consistency, vs. humans’ 61%. More reliable, less biased.
From idea to insight, ROBIN handles everything like a scientific CI/CD pipeline. With standardized agents, experiments are more replicable — avoiding the “no one can replicate this” drama.
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