AI research

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One Material, Two Transistor Types: The Universal Charge Injector Pointing Toward Stacked AI Chips

A KAIST-led team has reported a single material — degenerately doped tin diselenide — that injects charge efficiently into both n-type and p-type atomically thin semiconductor channels, removing the need for a separate contact metal per polarity. The paper reports a drive current more than a thousand times higher than a nickel electrode in p-type tungsten diselenide, an on/off ratio above a billion in n-type molybdenum disulfide, a subthreshold swing below 70 mV per decade, and a monolayer CMOS inverter with a voltage gain near 340. This article explains the physics, converts the figures into engineering terms, places the result against earlier contact schemes, and sets out the distance between a laboratory inverter and a manufacturable chip.

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AI Model Spots Spoofed Website Addresses With Up to 99% Accuracy

A hybrid deep-learning model published in the International Journal of Electronic Security and Digital Forensics reports 98.9% accuracy on one phishing benchmark and 96.8% on another, reading a web address purely as a string of characters. This article takes the study as published, converts its percentages into the error volumes a security team would actually feel, explains what the convolutional and long short-term memory halves each contribute, maps the spoofing techniques a string classifier can and cannot see, and flags the twenty-six months between the paper’s submission and its publication.

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How AI Models Decide Not to Answer a Question

A model declining a question is not one behaviour but two: a policy judgement about harm and a calibration judgement about uncertainty. We take both apart using OpenAI’s Model Spec, Claude’s constitution, the GPT-5 system card, the safe-completions paper, AbstentionBench, XSTest and OR-Bench – including why over-refusal happens and what a builder can actually change.

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A Blueprint for Keeping Humans in Control of AI: Inside Stanford’s Two Oversight Papers

Stanford GSB researchers William Overman and Mohsen Bayati have published two frameworks for keeping humans in control of AI agents: the Oversight Game, which teaches an agent when to ask and a human when to step in, and Calibrated Collective Oversight, which lets weaker overseers hold a stronger model to a target rate of unsafe actions. We read both papers, set their numbers against the press summary, and turn them into a deployment blueprint.

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AI-Powered VR Helps Planners Design Better Cities

A 14-page paper from the School of Art at East China Jiaotong University became the headline “AI-powered VR helps planners design better cities”. We read its 225-word abstract and the publisher’s 215-word research note. The abstract reports four measured results, including a 0.034-metre median geometric error. The note quotes none of them, and neither text says any planner used the tool.

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Humanoid Robot Learns to Sprint and Perform Spin Kicks Using AI Trained on Human Motion Data

A humanoid robot learned to sprint, throw spin kicks and land aerial cartwheels with one training recipe. We read the BeyondMimic paper from UC Berkeley and Stanford behind the headline, split its 70.8% humanlike score into walking and running, show that 15 of about 150 minutes of motion ran on hardware, explain the motion capture behind the obstacle demos and the licence on its training motion, and set it against the sprint records at the World Humanoid Robot Games.

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Augmentation Without Abdication Names 31 AI Research Tasks. Eleven It Allows Are Generative

Augmentation without abdication is the phrase two American researchers have proposed as the governing norm for artificial intelligence in science, and it arrived on 8 September 2026 with something most position pieces never bother to supply: a list. Charles C Branas of Columbia University and Bruce L Levine of the University of Pennsylvania did not […]

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Long AI Conversations Reveal Misinformation Vulnerabilities Across Seven Leading Chatbots

A University of Arizona team put seven widely used chatbots through 50-turn sequences of sustained misinformation pressure and published the results in Nature’s Scientific Reports. Misinformation affirmation rates ranged from 0.08% to 12.3%, a greater than 150-fold spread across architectures, with GPT-3.5 most vulnerable and Claude 3.5 Sonnet most resistant. The paper also names a new failure mode, conversational reverberation, in which a model oscillates between accepting and rejecting the same false statement across successive turns. This is a close reading of what was tested, what the numbers mean, the correctability dissociation that should change how you pick a model, and the controls that actually target each failure mode in production.

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Can You Teach Yourself to Detect AI Writing? Maybe — Here Is What the Evidence Actually Says

Can you teach yourself to detect AI writing? The honest answer is maybe. There is no single giveaway — only accumulating habits: signature vocabulary such as delve, meticulous and quietly, the “not X, but Y” frame, the rule of three, claim escalation and length that ignores the situation. The evidence is more encouraging than the old consensus: annotators who use these systems daily reached 86.7% to 96.7% true-positive rates individually and 99.3% as a majority vote across 300 articles, beating every automated detector except Pangram, while occasional users managed 56.7% — barely above chance. This breakdown covers the tells that hold up, the human-versus-detector numbers, the Stanford finding that seven detectors falsely flagged 61% of essays by non-native English writers, why the tells keep expiring, and what to do instead if you commission content.

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