Guided by the Plan: Enhancing Faithful Autoregressive Text-to-Audio Generation with Guided Decoding

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arXiv:2601.14304v1 Announce Type: new Abstract: Autoregressive (AR) models excel at generating temporally coherent audio by producing tokens sequentially, yet they often falter in faithfully following complex textual prompts, especially those describing complex sound events. We uncover a surprising capability in AR audio generators: their early prefix tokens implicitly encode global semantic attributes of the final output, such as event count and sound-object category, revealing a form of implicit planning. Building on this insight, we propose Plan-Critic, a lightweight […]

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Attribute-Aware Controlled Product Generation with LLMs for E-commerce

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arXiv:2601.04200v1 Announce Type: new Abstract: Product information extraction is crucial for e-commerce services, but obtaining high-quality labeled datasets remains challenging. We present a systematic approach for generating synthetic e-commerce product data using Large Language Models (LLMs), introducing a controlled modification framework with three strategies: attribute-preserving modification, controlled negative example generation, and systematic attribute removal. Using a state-of-the-art LLM with attribute-aware prompts, we enforce store constraints while maintaining product coherence. Human evaluation of 2000 synthetic products demonstrates high effectiveness, […]

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Obtaining Partition Crossover masks using Statistical Linkage Learning for solving noised optimization problems with hidden variable dependency structure

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In optimization problems, some variable subsets may have a joint non-linear or non-monotonical influence on the function value. Therefore, knowledge of variable dependencies may be crucial for effective optimization, and many state-of-the-art optimizers leverage it to improve performance. However, some real-world problem instances may be the subject of noise of various origins. In such a case, variable dependencies relevant to optimization may be hard or impossible to tell using dependency checks sufficient for problems without noise, making highly […]

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OpenAI Agents SDK improves governance with sandbox execution

OpenAI Agents SDK improves governance with sandbox execution

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OpenAI is introducing sandbox execution that allows enterprise governance teams to deploy automated workflows with controlled risk. Teams taking systems from prototype to production have faced difficult architectural compromises regarding where their operations occurred. Using model-agnostic frameworks offered initial flexibility but failed to fully utilise the capabilities of frontier models. Model-provider SDKs remained closer to the underlying model, but often lacked enough visibility into the control harness. To complicate matters further, managed agent APIs simplified the deployment process […]

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World Collapse Expert (Ian Bremmer): The Real Crisis Is What Comes After Trump

World Collapse Expert (Ian Bremmer): The Real Crisis Is What Comes After Trump

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After predicting the world’s biggest risks for over 25 years, Geopolitical Expert Ian Bremmer reveals the top 10 risks for 2026, and why the AI job threat is far bigger than people think! Ian Bremmer is a political scientist and founder of Eurasia Group, a leading political risk research and consulting firm, and GZERO Media, a global affairs media company. He is also a Professor at Columbia University’s School of International and Public Affairs, and the author of […]

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How Palo Alto Networks enhanced device security infra log analysis with Amazon Bedrock

How Palo Alto Networks enhanced device security infra log analysis with Amazon Bedrock

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This post is co-written by Fan Zhang, Sr Principal Engineer / Architect from Palo Alto Networks. Palo Alto Networks’ Device Security team wanted to detect early warning signs of potential production issues to provide more time to SMEs to react to these emerging problems. The primary challenge they faced was that reactively processing over 200 million daily service and application log entries resulted in delayed response times to these critical issues, leaving them at risk for potential service […]

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TabMGP: Martingale posterior with TabPFN

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arXiv:2510.25154v2 Announce Type: replace-cross Abstract: Bayesian inference provides principled uncertainty quantification but is often limited by challenges of prior and likelihood elicitation. The martingale posterior (MGP) (Fong et al., 2023) offers an alternative by replacing these requirements with a predictive rule. Additionally MGP focuses inference on parameters defined through a loss function. This framework is especially resonant in the era of foundation transformers; practitioners increasingly leverage models like TabPFN for their state-of-the-art capabilities, yet often require epistemic uncertainty […]

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Unicode Explorer using binary search over fetch() HTTP range requests

Unicode Explorer using binary search over fetch() HTTP range requests

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Unicode Explorer using binary search over fetch() HTTP range requests Here’s a little prototype I built this morning from my phone as an experiment in HTTP range requests, and a general example of using LLMs to satisfy curiosity. I’ve been collecting HTTP range tricks for a while now, and I decided it would be fun to build something with them myself that used binary search against a large file to do something useful. So I brainstormed with Claude. […]

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Intel Core Ultra 270K and 250K Plus review: Conditionally great CPUs

Intel Core Ultra 270K and 250K Plus review: Conditionally great CPUs

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Many of our graphics card reviews early last year and in the early 2020s focused on the difficulties of reviewing and recommending graphics cards when the manufacturer-suggested price points effectively didn’t exist. Now, reviews of any new PC component have to contend with the much more broadly awful market for consumer PC parts as AI data center-fueled demand for RAM and flash memory chips drives up prices for DDR5 kits, SSDs, and GPUs. In our August 2025 system guide, […]

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Detect and redact personally identifiable information using Amazon Bedrock Data Automation and Guardrails

Detect and redact personally identifiable information using Amazon Bedrock Data Automation and Guardrails

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Organizations handle vast amounts of sensitive customer information through various communication channels. Protecting Personally Identifiable Information (PII), such as social security numbers (SSNs), driver’s license numbers, and phone numbers has become increasingly critical for maintaining compliance with data privacy regulations and building customer trust. However, manually reviewing and redacting PII is time-consuming, error-prone, and scales poorly as data volumes grow. Organizations face challenges when dealing with PII scattered across different content types – from texts to images. Traditional […]

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