Category: News

Reasoning Models Will Blatantly Lie About Their Reasoningcs.AI updates on arXiv.org arXiv:2601.07663v2 Announce Type: replace
Abstract: It has been shown that Large Reasoning Models (LRMs) may not *say what they think*: they do not always volunteer information about how certain parts of the input influence their reasoning. But it is one thing for a model to *omit* such information and another, worse thing to *lie* about it. Here, we extend the work of Chen et al. (2025) to show that LRMs will do just this: they will flatly deny relying on hints provided in the prompt in answering multiple choice questions — even when directly asked to reflect on unusual (i.e. hinted) prompt content, even when allowed to use hints, and even though experiments *show* them to be using the hints. Our results thus have discouraging implications for CoT monitoring and interpretability.

 arXiv:2601.07663v2 Announce Type: replace
Abstract: It has been shown that Large Reasoning Models (LRMs) may not *say what they think*: they do not always volunteer information about how certain parts of the input influence their reasoning. But it is one thing for a model to *omit* such information and another, worse thing to *lie* about it. Here, we extend the work of Chen et al. (2025) to show that LRMs will do just this: they will flatly deny relying on hints provided in the prompt in answering multiple choice questions — even when directly asked to reflect on unusual (i.e. hinted) prompt content, even when allowed to use hints, and even though experiments *show* them to be using the hints. Our results thus have discouraging implications for CoT monitoring and interpretability. Read More  

7 AI Automation Tools for Streamlined WorkflowsKDnuggets This list focuses on tools that streamline real workflows across data, operations, and content, not flashy demos or brittle bots. Each one earns its place by reducing manual effort while keeping humans in the loop where it actually matters.

 This list focuses on tools that streamline real workflows across data, operations, and content, not flashy demos or brittle bots. Each one earns its place by reducing manual effort while keeping humans in the loop where it actually matters. Read More  

Do You Smell That? Hidden Technical Debt in AI DevelopmentTowards Data Science Why speed without standards creates fragile AI products
The post Do You Smell That? Hidden Technical Debt in AI Development appeared first on Towards Data Science.

 Why speed without standards creates fragile AI products
The post Do You Smell That? Hidden Technical Debt in AI Development appeared first on Towards Data Science. Read More  

McKinsey tests AI chatbot in early stages of graduate recruitmentAI News Hiring at large firms has long relied on interviews, tests, and human judgment. That process is starting to shift. McKinsey has begun using an AI chatbot as part of its graduate recruitment process, signalling a shift in how professional services organisations evaluate early-career candidates. The chatbot is being used during the initial stages of recruitment,
The post McKinsey tests AI chatbot in early stages of graduate recruitment appeared first on AI News.

 Hiring at large firms has long relied on interviews, tests, and human judgment. That process is starting to shift. McKinsey has begun using an AI chatbot as part of its graduate recruitment process, signalling a shift in how professional services organisations evaluate early-career candidates. The chatbot is being used during the initial stages of recruitment,
The post McKinsey tests AI chatbot in early stages of graduate recruitment appeared first on AI News. Read More  

AI medical diagnostics race intensifies as OpenAI, Google, and Anthropic launch competing healthcare toolsAI News OpenAI, Google, and Anthropic announced specialised medical AI capabilities within days of each other this month, a clustering that suggests competitive pressure rather than coincidental timing. Yet none of the releases are cleared as medical devices, approved for clinical use, or available for direct patient diagnosis—despite marketing language emphasising healthcare transformation. OpenAI introduced ChatGPT Health on January
The post AI medical diagnostics race intensifies as OpenAI, Google, and Anthropic launch competing healthcare tools appeared first on AI News.

 OpenAI, Google, and Anthropic announced specialised medical AI capabilities within days of each other this month, a clustering that suggests competitive pressure rather than coincidental timing. Yet none of the releases are cleared as medical devices, approved for clinical use, or available for direct patient diagnosis—despite marketing language emphasising healthcare transformation. OpenAI introduced ChatGPT Health on January
The post AI medical diagnostics race intensifies as OpenAI, Google, and Anthropic launch competing healthcare tools appeared first on AI News. Read More  

Using Subgraph GNNs for Node Classification:an Overlooked Potential Approachcs.AI updates on arXiv.org arXiv:2503.06614v2 Announce Type: replace-cross
Abstract: Previous studies have demonstrated the strong performance of Graph Neural Networks (GNNs) in node classification. However, most existing GNNs adopt a node-centric perspective and rely on global message passing, leading to high computational and memory costs that hinder scalability. To mitigate these challenges, subgraph-based methods have been introduced, leveraging local subgraphs as approximations of full computational trees. While this approach improves efficiency, it often suffers from performance degradation due to the loss of global contextual information, limiting its effectiveness compared to global GNNs. To address this trade-off between scalability and classification accuracy, we reformulate the node classification task as a subgraph classification problem and propose SubGND (Subgraph GNN for NoDe). This framework introduces a differentiated zero-padding strategy and an Ego-Alter subgraph representation method to resolve label conflicts while incorporating an Adaptive Feature Scaling Mechanism to dynamically adjust feature contributions based on dataset-specific dependencies. Experimental results on six benchmark datasets demonstrate that SubGND achieves performance comparable to or surpassing global message-passing GNNs, particularly in heterophilic settings, highlighting its effectiveness and scalability as a promising solution for node classification.

 arXiv:2503.06614v2 Announce Type: replace-cross
Abstract: Previous studies have demonstrated the strong performance of Graph Neural Networks (GNNs) in node classification. However, most existing GNNs adopt a node-centric perspective and rely on global message passing, leading to high computational and memory costs that hinder scalability. To mitigate these challenges, subgraph-based methods have been introduced, leveraging local subgraphs as approximations of full computational trees. While this approach improves efficiency, it often suffers from performance degradation due to the loss of global contextual information, limiting its effectiveness compared to global GNNs. To address this trade-off between scalability and classification accuracy, we reformulate the node classification task as a subgraph classification problem and propose SubGND (Subgraph GNN for NoDe). This framework introduces a differentiated zero-padding strategy and an Ego-Alter subgraph representation method to resolve label conflicts while incorporating an Adaptive Feature Scaling Mechanism to dynamically adjust feature contributions based on dataset-specific dependencies. Experimental results on six benchmark datasets demonstrate that SubGND achieves performance comparable to or surpassing global message-passing GNNs, particularly in heterophilic settings, highlighting its effectiveness and scalability as a promising solution for node classification. Read More  

Meeting the new ETSI standard for AI securityAI News The ETSI EN 304 223 standard introduces baseline security requirements for AI that enterprises must integrate into governance frameworks. As organisations embed machine learning into their core operations, this European Standard (EN) establishes concrete provisions for securing AI models and systems. It stands as the first globally applicable European Standard for AI cybersecurity, having secured
The post Meeting the new ETSI standard for AI security appeared first on AI News.

 The ETSI EN 304 223 standard introduces baseline security requirements for AI that enterprises must integrate into governance frameworks. As organisations embed machine learning into their core operations, this European Standard (EN) establishes concrete provisions for securing AI models and systems. It stands as the first globally applicable European Standard for AI cybersecurity, having secured
The post Meeting the new ETSI standard for AI security appeared first on AI News. Read More