Safeguard generative AI applications with Amazon Bedrock GuardrailsArtificial Intelligence In this post, we demonstrate how you can address these challenges by adding centralized safeguards to a custom multi-provider generative AI gateway using Amazon Bedrock Guardrails.
In this post, we demonstrate how you can address these challenges by adding centralized safeguards to a custom multi-provider generative AI gateway using Amazon Bedrock Guardrails. Read More
Scale creative asset discovery with Amazon Nova Multimodal Embeddings unified vector searchArtificial Intelligence In this post, we describe how you can use Amazon Nova Multimodal Embeddings to retrieve specific video segments. We also review a real-world use case in which Nova Multimodal Embeddings achieved a recall success rate of 96.7% and a high-precision recall of 73.3% (returning the target content in the top two results) when tested against a library of 170 gaming creative assets. The model also demonstrates strong cross-language capabilities with minimal performance degradation across multiple languages.
In this post, we describe how you can use Amazon Nova Multimodal Embeddings to retrieve specific video segments. We also review a real-world use case in which Nova Multimodal Embeddings achieved a recall success rate of 96.7% and a high-precision recall of 73.3% (returning the target content in the top two results) when tested against a library of 170 gaming creative assets. The model also demonstrates strong cross-language capabilities with minimal performance degradation across multiple languages. Read More
Google Antigravity: AI-First Development with This New IDEKDnuggets Google Antigravity marks the beginning of the “agent-first” era, It isn’t just a Copilot, it’s a platform where you stop being the typist and start being the architect.
Google Antigravity marks the beginning of the “agent-first” era, It isn’t just a Copilot, it’s a platform where you stop being the typist and start being the architect. Read More
How to Run Coding Agents in ParallelTowards Data Science Get the most out of Claude Code
The post How to Run Coding Agents in Parallel appeared first on Towards Data Science.
Get the most out of Claude Code
The post How to Run Coding Agents in Parallel appeared first on Towards Data Science. Read More
PRISMA: Reinforcement Learning Guided Two-Stage Policy Optimization in Multi-Agent Architecture for Open-Domain Multi-Hop Question Answeringcs.AI updates on arXiv.org arXiv:2601.05465v1 Announce Type: new
Abstract: Answering real-world open-domain multi-hop questions over massive corpora is a critical challenge in Retrieval-Augmented Generation (RAG) systems. Recent research employs reinforcement learning (RL) to end-to-end optimize the retrieval-augmented reasoning process, directly enhancing its capacity to resolve complex queries. However, reliable deployment is hindered by two obstacles. 1) Retrieval Collapse: iterative retrieval over large corpora fails to locate intermediate evidence containing bridge answers without reasoning-guided planning, causing downstream reasoning to collapse. 2) Learning Instability: end-to-end trajectory training suffers from weak credit assignment across reasoning chains and poor error localization across modules, causing overfitting to benchmark-specific heuristics that limit transferability and stability. To address these problems, we propose PRISMA, a decoupled RL-guided framework featuring a Plan-Retrieve-Inspect-Solve-Memoize architecture. PRISMA’s strength lies in reasoning-guided collaboration: the Inspector provides reasoning-based feedback to refine the Planner’s decomposition and fine-grained retrieval, while enforcing evidence-grounded reasoning in the Solver. We optimize individual agent capabilities via Two-Stage Group Relative Policy Optimization (GRPO). Stage I calibrates the Planner and Solver as specialized experts in planning and reasoning, while Stage II utilizes Observation-Aware Residual Policy Optimization (OARPO) to enhance the Inspector’s ability to verify context and trigger targeted recovery. Experiments show that PRISMA achieves state-of-the-art performance on ten benchmarks and can be deployed efficiently in real-world scenarios.
arXiv:2601.05465v1 Announce Type: new
Abstract: Answering real-world open-domain multi-hop questions over massive corpora is a critical challenge in Retrieval-Augmented Generation (RAG) systems. Recent research employs reinforcement learning (RL) to end-to-end optimize the retrieval-augmented reasoning process, directly enhancing its capacity to resolve complex queries. However, reliable deployment is hindered by two obstacles. 1) Retrieval Collapse: iterative retrieval over large corpora fails to locate intermediate evidence containing bridge answers without reasoning-guided planning, causing downstream reasoning to collapse. 2) Learning Instability: end-to-end trajectory training suffers from weak credit assignment across reasoning chains and poor error localization across modules, causing overfitting to benchmark-specific heuristics that limit transferability and stability. To address these problems, we propose PRISMA, a decoupled RL-guided framework featuring a Plan-Retrieve-Inspect-Solve-Memoize architecture. PRISMA’s strength lies in reasoning-guided collaboration: the Inspector provides reasoning-based feedback to refine the Planner’s decomposition and fine-grained retrieval, while enforcing evidence-grounded reasoning in the Solver. We optimize individual agent capabilities via Two-Stage Group Relative Policy Optimization (GRPO). Stage I calibrates the Planner and Solver as specialized experts in planning and reasoning, while Stage II utilizes Observation-Aware Residual Policy Optimization (OARPO) to enhance the Inspector’s ability to verify context and trigger targeted recovery. Experiments show that PRISMA achieves state-of-the-art performance on ten benchmarks and can be deployed efficiently in real-world scenarios. Read More
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