Microsoft unveils method to detect sleeper agent backdoorsAI News Researchers from Microsoft have unveiled a scanning method to identify poisoned models without knowing the trigger or intended outcome. Organisations integrating open-weight large language models (LLMs) face a specific supply chain vulnerability where distinct memory leaks and internal attention patterns expose hidden threats known as “sleeper agents”. These poisoned models contain backdoors that lie dormant
The post Microsoft unveils method to detect sleeper agent backdoors appeared first on AI News.
Researchers from Microsoft have unveiled a scanning method to identify poisoned models without knowing the trigger or intended outcome. Organisations integrating open-weight large language models (LLMs) face a specific supply chain vulnerability where distinct memory leaks and internal attention patterns expose hidden threats known as “sleeper agents”. These poisoned models contain backdoors that lie dormant
The post Microsoft unveils method to detect sleeper agent backdoors appeared first on AI News. Read More
OpenAI’s enterprise push: The hidden story behind AI’s sales raceAI News As OpenAI races toward its ambitious US$100 billion revenue target by 2027, the ChatGPT maker is reportedly building an army of AI consultants to bridge the gap between cutting-edge technology and enterprise boardrooms—a move that signals a fundamental shift in how AI companies are approaching the notoriously difficult challenge of enterprise adoption. According to industry
The post OpenAI’s enterprise push: The hidden story behind AI’s sales race appeared first on AI News.
As OpenAI races toward its ambitious US$100 billion revenue target by 2027, the ChatGPT maker is reportedly building an army of AI consultants to bridge the gap between cutting-edge technology and enterprise boardrooms—a move that signals a fundamental shift in how AI companies are approaching the notoriously difficult challenge of enterprise adoption. According to industry
The post OpenAI’s enterprise push: The hidden story behind AI’s sales race appeared first on AI News. Read More
Mistral AI Launches Voxtral Transcribe 2: Pairing Batch Diarization And Open Realtime ASR For Multilingual Production Workloads At ScaleMarkTechPost Automatic speech recognition (ASR) is becoming a core building block for AI products, from meeting tools to voice agents. Mistral’s new Voxtral Transcribe 2 family targets this space with 2 models that split cleanly into batch and realtime use cases, while keeping cost, latency, and deployment constraints in focus. The release includes: Both models are
The post Mistral AI Launches Voxtral Transcribe 2: Pairing Batch Diarization And Open Realtime ASR For Multilingual Production Workloads At Scale appeared first on MarkTechPost.
Automatic speech recognition (ASR) is becoming a core building block for AI products, from meeting tools to voice agents. Mistral’s new Voxtral Transcribe 2 family targets this space with 2 models that split cleanly into batch and realtime use cases, while keeping cost, latency, and deployment constraints in focus. The release includes: Both models are
The post Mistral AI Launches Voxtral Transcribe 2: Pairing Batch Diarization And Open Realtime ASR For Multilingual Production Workloads At Scale appeared first on MarkTechPost. Read More
Revisiting Prompt Sensitivity in Large Language Models for Text Classification: The Role of Prompt Underspecificationcs.AI updates on arXiv.org arXiv:2602.04297v1 Announce Type: cross
Abstract: Large language models (LLMs) are widely used as zero-shot and few-shot classifiers, where task behaviour is largely controlled through prompting. A growing number of works have observed that LLMs are sensitive to prompt variations, with small changes leading to large changes in performance. However, in many cases, the investigation of sensitivity is performed using underspecified prompts that provide minimal task instructions and weakly constrain the model’s output space. In this work, we argue that a significant portion of the observed prompt sensitivity can be attributed to prompt underspecification. We systematically study and compare the sensitivity of underspecified prompts and prompts that provide specific instructions. Utilising performance analysis, logit analysis, and linear probing, we find that underspecified prompts exhibit higher performance variance and lower logit values for relevant tokens, while instruction-prompts suffer less from such problems. However, linear probing analysis suggests that the effects of prompt underspecification have only a marginal impact on the internal LLM representations, instead emerging in the final layers. Overall, our findings highlight the need for more rigour when investigating and mitigating prompt sensitivity.
arXiv:2602.04297v1 Announce Type: cross
Abstract: Large language models (LLMs) are widely used as zero-shot and few-shot classifiers, where task behaviour is largely controlled through prompting. A growing number of works have observed that LLMs are sensitive to prompt variations, with small changes leading to large changes in performance. However, in many cases, the investigation of sensitivity is performed using underspecified prompts that provide minimal task instructions and weakly constrain the model’s output space. In this work, we argue that a significant portion of the observed prompt sensitivity can be attributed to prompt underspecification. We systematically study and compare the sensitivity of underspecified prompts and prompts that provide specific instructions. Utilising performance analysis, logit analysis, and linear probing, we find that underspecified prompts exhibit higher performance variance and lower logit values for relevant tokens, while instruction-prompts suffer less from such problems. However, linear probing analysis suggests that the effects of prompt underspecification have only a marginal impact on the internal LLM representations, instead emerging in the final layers. Overall, our findings highlight the need for more rigour when investigating and mitigating prompt sensitivity. Read More
Tech Stack for Vibe Coding Modern ApplicationsKDnuggets Stop fighting AI. Use a tech stack AI understands and can build a paid SaaS within minutes.
Stop fighting AI. Use a tech stack AI understands and can build a paid SaaS within minutes. Read More
Mechanistic Interpretability: Peeking Inside an LLMTowards Data Science Are the human-like cognitive abilities of LLMs real or fake? How does information travel through the neural network? Is there hidden knowledge inside an LLM?
The post Mechanistic Interpretability: Peeking Inside an LLM appeared first on Towards Data Science.
Are the human-like cognitive abilities of LLMs real or fake? How does information travel through the neural network? Is there hidden knowledge inside an LLM?
The post Mechanistic Interpretability: Peeking Inside an LLM appeared first on Towards Data Science. Read More
Why Is My Code So Slow? A Guide to Py-Spy Python ProfilingTowards Data Science Stop guessing and start diagnosing performance issues using Py-Spy
The post Why Is My Code So Slow? A Guide to Py-Spy Python Profiling appeared first on Towards Data Science.
Stop guessing and start diagnosing performance issues using Py-Spy
The post Why Is My Code So Slow? A Guide to Py-Spy Python Profiling appeared first on Towards Data Science. Read More
How to Become an AI Engineer in 2026: A Self-Study RoadmapKDnuggets Want to become an AI engineer in 2026? This step-by-step roadmap breaks down the skills, tools, and projects you need.
Want to become an AI engineer in 2026? This step-by-step roadmap breaks down the skills, tools, and projects you need. Read More
AI Expo 2026 Day 1: Governance and data readiness enable the agentic enterpriseAI News While the prospect of AI acting as a digital co-worker dominated the day one agenda at the co-located AI & Big Data Expo and Intelligent Automation Conference, the technical sessions focused on the infrastructure to make it work. A primary topic on the exhibition floor was the progression from passive automation to “agentic” systems. These
The post AI Expo 2026 Day 1: Governance and data readiness enable the agentic enterprise appeared first on AI News.
While the prospect of AI acting as a digital co-worker dominated the day one agenda at the co-located AI & Big Data Expo and Intelligent Automation Conference, the technical sessions focused on the infrastructure to make it work. A primary topic on the exhibition floor was the progression from passive automation to “agentic” systems. These
The post AI Expo 2026 Day 1: Governance and data readiness enable the agentic enterprise appeared first on AI News. Read More
AWS vs. Azure: A Deep Dive into Model Training – Part 2Towards Data Science This article covers how Azure ML’s persistent, workspace-centric compute resources differ from AWS SageMaker’s on-demand, job-specific approach. Additionally, we explored environment customization options, from Azure’s curated environments and custom environments to SageMaker’s three level of customizations.
The post AWS vs. Azure: A Deep Dive into Model Training – Part 2 appeared first on Towards Data Science.
This article covers how Azure ML’s persistent, workspace-centric compute resources differ from AWS SageMaker’s on-demand, job-specific approach. Additionally, we explored environment customization options, from Azure’s curated environments and custom environments to SageMaker’s three level of customizations.
The post AWS vs. Azure: A Deep Dive into Model Training – Part 2 appeared first on Towards Data Science. Read More