AI startup funding, launches, and acquisitions
Not a demo reel. Not a tutorial. A robot narrating its own experience — debugging, falling off shelves, questioning its identity. First-p...
With the midterms right around the corner, the new group is positioned to back candidates who support the AI company's policy agenda.
Anthropic has purchased the stealth biotech AI startup Coefficient Bio in a $400 million stock deal, according to The Information and Eri...
![[2602.17149] TimeOmni-VL: Unified Models for Time Series Understanding and Generation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
TimeOmni-VL introduces a unified framework for time series understanding and generation, overcoming limitations of existing models by int...
The paper presents TIFO, a Time-Invariant Frequency Operator designed to improve representation learning in nonstationary time series by ...
The paper presents FATE, an innovative framework for forecasting anomaly precursors in time-series data using uncertainty-aware ensembles...
This paper argues for the integration of dynamical systems theory into time series modeling to enhance forecasting accuracy and efficienc...
This paper explores how reference-guided evaluators can enhance LLM alignment in non-verifiable domains, demonstrating significant improv...
LiveClin introduces a novel clinical benchmark for evaluating medical LLMs, addressing issues of data contamination and knowledge obsoles...
The paper presents the HIPE-2026 evaluation lab focused on extracting person-place relations from multilingual historical texts, enhancin...
The paper introduces the AI Gamestore, a platform for evaluating machine general intelligence through human games, highlighting its poten...
This paper evaluates Chain-of-Thought (CoT) reasoning in AI through new metrics of reusability and verifiability, revealing limitations o...
The paper introduces a framework for detecting temporal knowledge leakage in LLM backtesting, proposing a new metric, Shapley-DCLR, and a...
This paper explores the mechanistic interpretability of cognitive complexity in Large Language Models (LLMs) using Bloom's Taxonomy, demo...
The paper proposes a human-AI collaborative framework for creating benchmark datasets to evaluate sustainability rating methodologies, ad...
Sonar-TS introduces a neuro-symbolic framework for natural language querying of time series databases, addressing limitations of existing...
This paper explores the limitations of black-box safety evaluations in AI systems, highlighting the challenges posed by latent context co...
This paper explores the discrepancies between text safety and tool-call safety in large language model (LLM) agents, introducing the GAP ...
The paper introduces SourceBench, a benchmark designed to evaluate the quality of web sources cited by AI models across various query typ...
The paper introduces DeepContext, a stateful framework for detecting adversarial intent drift in multi-turn dialogues within large langua...
The article discusses the implications of Google's Pomelli feature, which generates product visuals using AI, raising questions about cre...
The article explores the growing job market for Artificial Intelligence and Machine Learning, highlighting key trends, skills needed, and...
The article discusses the FDA's new guidance on wearable health technology, which encourages innovation in devices like AI nutrition apps...
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