Category: News

Microsoft’s Copilot usage analysis exposes the 2am philosophy question trendAI News F. Scott Fitzgerald observed that “in a real dark night of the soul, it is always three o’clock in the morning.” Microsoft’s latest Copilot usage analysis suggests this nocturnal tendency toward existential contemplation persists in the AI age – with religion and philosophy conversations rising through the rankings during early morning hours. The Microsoft AI
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 F. Scott Fitzgerald observed that “in a real dark night of the soul, it is always three o’clock in the morning.” Microsoft’s latest Copilot usage analysis suggests this nocturnal tendency toward existential contemplation persists in the AI age – with religion and philosophy conversations rising through the rankings during early morning hours. The Microsoft AI
The post Microsoft’s Copilot usage analysis exposes the 2am philosophy question trend appeared first on AI News. Read More  

LabelFusion: Learning to Fuse LLMs and Transformer Classifiers for Robust Text Classificationcs.AI updates on arXiv.org arXiv:2512.10793v1 Announce Type: cross
Abstract: LabelFusion is a fusion ensemble for text classification that learns to combine a traditional transformer-based classifier (e.g., RoBERTa) with one or more Large Language Models (LLMs such as OpenAI GPT, Google Gemini, or DeepSeek) to deliver accurate and cost-aware predictions across multi-class and multi-label tasks. The package provides a simple high-level interface (AutoFusionClassifier) that trains the full pipeline end-to-end with minimal configuration, and a flexible API for advanced users. Under the hood, LabelFusion integrates vector signals from both sources by concatenating the ML backbone’s embeddings with the LLM-derived per-class scores — obtained through structured prompt-engineering strategies — and feeds this joint representation into a compact multi-layer perceptron (FusionMLP) that produces the final prediction. This learned fusion approach captures complementary strengths of LLM reasoning and traditional transformer-based classifiers, yielding robust performance across domains — achieving 92.4% accuracy on AG News and 92.3% on 10-class Reuters 21578 topic classification — while enabling practical trade-offs between accuracy, latency, and cost.

 arXiv:2512.10793v1 Announce Type: cross
Abstract: LabelFusion is a fusion ensemble for text classification that learns to combine a traditional transformer-based classifier (e.g., RoBERTa) with one or more Large Language Models (LLMs such as OpenAI GPT, Google Gemini, or DeepSeek) to deliver accurate and cost-aware predictions across multi-class and multi-label tasks. The package provides a simple high-level interface (AutoFusionClassifier) that trains the full pipeline end-to-end with minimal configuration, and a flexible API for advanced users. Under the hood, LabelFusion integrates vector signals from both sources by concatenating the ML backbone’s embeddings with the LLM-derived per-class scores — obtained through structured prompt-engineering strategies — and feeds this joint representation into a compact multi-layer perceptron (FusionMLP) that produces the final prediction. This learned fusion approach captures complementary strengths of LLM reasoning and traditional transformer-based classifiers, yielding robust performance across domains — achieving 92.4% accuracy on AG News and 92.3% on 10-class Reuters 21578 topic classification — while enabling practical trade-offs between accuracy, latency, and cost. Read More  

Beyond Words and Pixels: A Benchmark for Implicit World Knowledge Reasoning in Generative Modelscs.AI updates on arXiv.org arXiv:2511.18271v3 Announce Type: replace-cross
Abstract: Text-to-image (T2I) models today are capable of producing photorealistic, instruction-following images, yet they still frequently fail on prompts that require implicit world knowledge. Existing evaluation protocols either emphasize compositional alignment or rely on single-round VQA-based scoring, leaving critical dimensions such as knowledge grounding, multi-physics interactions, and auditable evidence-substantially undertested. To address these limitations, we introduce PicWorld, the first comprehensive benchmark that assesses the grasp of implicit world knowledge and physical causal reasoning of T2I models. This benchmark consists of 1,100 prompts across three core categories. To facilitate fine-grained evaluation, we propose PW-Agent, an evidence-grounded multi-agent evaluator to hierarchically assess images on their physical realism and logical consistency by decomposing prompts into verifiable visual evidence. We conduct a thorough analysis of 17 mainstream T2I models on PicWorld, illustrating that they universally exhibit a fundamental limitation in their capacity for implicit world knowledge and physical causal reasoning to varying degrees. The findings highlight the need for reasoning-aware, knowledge-integrative architectures in future T2I systems.

 arXiv:2511.18271v3 Announce Type: replace-cross
Abstract: Text-to-image (T2I) models today are capable of producing photorealistic, instruction-following images, yet they still frequently fail on prompts that require implicit world knowledge. Existing evaluation protocols either emphasize compositional alignment or rely on single-round VQA-based scoring, leaving critical dimensions such as knowledge grounding, multi-physics interactions, and auditable evidence-substantially undertested. To address these limitations, we introduce PicWorld, the first comprehensive benchmark that assesses the grasp of implicit world knowledge and physical causal reasoning of T2I models. This benchmark consists of 1,100 prompts across three core categories. To facilitate fine-grained evaluation, we propose PW-Agent, an evidence-grounded multi-agent evaluator to hierarchically assess images on their physical realism and logical consistency by decomposing prompts into verifiable visual evidence. We conduct a thorough analysis of 17 mainstream T2I models on PicWorld, illustrating that they universally exhibit a fundamental limitation in their capacity for implicit world knowledge and physical causal reasoning to varying degrees. The findings highlight the need for reasoning-aware, knowledge-integrative architectures in future T2I systems. Read More  

Advancing Mathematical Research via Human-AI Interactive Theorem Provingcs.AI updates on arXiv.org arXiv:2512.09443v2 Announce Type: replace-cross
Abstract: We investigate how large language models can be used as research tools in scientific computing while preserving mathematical rigor. We propose a human-in-the-loop workflow for interactive theorem proving and discovery with LLMs. Human experts retain control over problem formulation and admissible assumptions, while the model searches for proofs or contradictions, proposes candidate properties and theorems, and helps construct structures and parameters that satisfy explicit constraints, supported by numerical experiments and simple verification checks. Experts treat these outputs as raw material, further refine them, and organize the results into precise statements and rigorous proofs. We instantiate this workflow in a case study on the connection between manifold optimization and Grover’s quantum search algorithm, where the pipeline helps identify invariant subspaces, explore Grover-compatible retractions, and obtain convergence guarantees for the retraction-based gradient method. The framework provides a practical template for integrating large language models into frontier mathematical research, enabling faster exploration of proof space and algorithm design while maintaining transparent reasoning responsibilities. Although illustrated on manifold optimization problems in quantum computing, the principles extend to other core areas of scientific computing.

 arXiv:2512.09443v2 Announce Type: replace-cross
Abstract: We investigate how large language models can be used as research tools in scientific computing while preserving mathematical rigor. We propose a human-in-the-loop workflow for interactive theorem proving and discovery with LLMs. Human experts retain control over problem formulation and admissible assumptions, while the model searches for proofs or contradictions, proposes candidate properties and theorems, and helps construct structures and parameters that satisfy explicit constraints, supported by numerical experiments and simple verification checks. Experts treat these outputs as raw material, further refine them, and organize the results into precise statements and rigorous proofs. We instantiate this workflow in a case study on the connection between manifold optimization and Grover’s quantum search algorithm, where the pipeline helps identify invariant subspaces, explore Grover-compatible retractions, and obtain convergence guarantees for the retraction-based gradient method. The framework provides a practical template for integrating large language models into frontier mathematical research, enabling faster exploration of proof space and algorithm design while maintaining transparent reasoning responsibilities. Although illustrated on manifold optimization problems in quantum computing, the principles extend to other core areas of scientific computing. Read More  

10 GitHub Repositories to Master Machine Learning DeploymentKDnuggets Master the essential skill of deploying machine learning models with courses, projects, examples, resources, and interview questions.

 Master the essential skill of deploying machine learning models with courses, projects, examples, resources, and interview questions. Read More