arXiv:2601.07965v1 Announce Type: new Abstract: When a model knows when it does not know, many possibilities emerge. The first question is how to enable a model to recognize that it does not know. A promising approach is to use confidence, computed from the model’s internal signals, to reflect its ignorance. Prior work in specific domains has shown that calibration can provide reliable confidence estimates. In this work, we propose a simple, effective, and universal training-free method that applies […]

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Anthropic’s Claude Mythos Preview (April 2026) introduces a class of AI-accelerated cybersecurity capabilities that intersects directly with Post-Quantum Cryptography (PQC) migration. Drawing on federal guidance from NIST, NSA, OMB, and CISA, and on independent analyses from CETaS (Alan Turing Institute) and the UK AI Security Institute, we present a systems-engineering analysis of how Mythos-class models alter PQC migration timelines, risk surfaces, lifecycle dependencies, and architectural constraints. Mythos performs autonomous reasoning against previously unknown vulnerabilities in production software — […]

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arXiv:2508.06377v2 Announce Type: replace Abstract: We revisit Wald’s celebrated Sequential Probability Ratio Test for sequential tests of two simple hypotheses, under privacy constraints. We propose DP-SPRT, a wrapper that can be calibrated to achieve desired error probabilities and privacy constraints, addressing a significant gap in previous work. DP-SPRT relies on a private mechanism that processes a sequence of queries and stops after privately determining when the query results fall outside a predefined interval. This OutsideInterval mechanism improves upon […]

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The Hidden Tax of Dirty Data on AI Performance — and a Developer’s Shortcut to Fixing It You’ve trained a customer segmentation model, but it’s clustering addresses from different states together. Your recommendation engine is suggesting products to customers in countries you don’t ship to. Your fraud detection system keeps flagging legitimate international transactions. Sound familiar? Here’s the uncomfortable truth: Your model isn’t failing. Your data is. As we discussed in my previous piece on data quality’s role in […]

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Growth hacks work, until they don’t. Most startups begin with quick wins: viral reels, aggressive ads, referral incentives, clever copy. For a while, momentum builds. Metrics go up. Dashboards look healthy. It feels like progress. Then something changes. Your customer acquisition costs start rising. Engagement flattens. What once felt like growth starts to feel like a constant fight. At this point, you usually assume the answer is another tactic. A new channel. A new funnel. A new trick. […]

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Over the last ten years, running has become a huge part of my life. I’m not sure how useful this write-up will be to others, but running preoccupies me enough that I feel the need to document the journey and externalize my thoughts. On 14.02.2026 (Valentine’s day) I ran my longest trail race: Trail du Mont Agel in Monaco. I expected a picturesque and exciting, yet challenging, adventure. I prepared for it by running 20–30 km every week […]

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arXiv:2601.09864v1 Announce Type: new Abstract: We study the input-entropy-constrained Gaussian channel capacity problem in the asymptotic high signal-to-noise ratio (SNR) regime. We show that the capacity-achieving distribution as SNR goes to infinity is given by a discrete Gaussian distribution supported on a scaled integer lattice. Further, we show that the gap between the input entropy and the capacity decreases to zero exponentially in SNR, and characterize this exponent.

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LLMs hallucinate not because they are uncertain — but because they are overconfident. We introduce the Entropy Corridor, a non-invasive inference-time method that constrains layer-wise activation entropy within a bidirectional range. Unlike prior detection-only approaches, our method corrects hallucination in real time by targeting the specific layers where overconfidence originates. On TruthfulQA, the corridor halves hallucination rates while preserving truthfulness — at under 2% latency overhead, with no retraining required. https://x.com/elfatone82/status/2041258848992768289?s=46 submitted by /u/Both_Report_5367 [link] [comments]

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The Clawdbot platform has quickly become the most popular AI chatbot system that businesses use to deliver customer support, execute internal operations, and manage advanced business processes. The bots now serve as critical data protection systems as organizations adopt AI technologies, which require these systems to handle confidential information. Existing protection systems cannot manage AI chatbot security threats because they lack the capabilities to address prompt manipulation and API security breaches. Organizations must comprehend these security dangers because […]

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