AI startup funding, launches, and acquisitions
| AI Reality Check | Cal Newport Chapters 0:00 What is Yan LeCun Up To? 14:55 How is it possible that LeCun could be right about LLM’s be...
This article presents over 20 AI project ideas tailored for various skill levels, providing a roadmap for building portfolio-ready projec...
This article reviews the top 10 AI certifications and courses for 2026, highlighting their significance in a rapidly evolving field and t...
![[2602.20528] Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper presents STAR-LDM, a novel language model that integrates latent diffusion planning with autoregressive generation, enhancing n...
The paper presents T1, a CNN-Transformer hybrid model for robust multivariate time-series imputation, achieving state-of-the-art performa...
The paper presents Bikelution, a federated learning approach for predicting demand in shared micro-mobility systems, addressing privacy c...
The paper presents QEDBench, a benchmark for evaluating the alignment of automated systems in assessing university-level mathematical pro...
The CGSTA framework enhances multivariate time-series anomaly detection by utilizing dynamic layered graphs and stability-aware alignment...
This paper presents In-Context Time-series Pre-training (ICTP), a framework that enhances time-series foundation models (TSFMs) with in-c...
This paper investigates safety alignment in large language models (LLMs) and large reasoning models (LRMs), identifying key factors that ...
The paper proposes a scalable oversight framework for AI systems using partitioned human supervision, addressing challenges in obtaining ...
RooseBERT introduces a specialized language model for political discourse, enhancing the analysis of political debates through improved s...
This article presents a novel framework, ChannelTokenFormer, for robust multivariate time series forecasting, addressing challenges of de...
This article evaluates biases in Large Language Models (LLMs) used as judges in communication systems, assessing their reliability and pr...
This article presents a framework for evaluating AI agent behavior through consumer choice experiments, highlighting biases in decision-m...
The paper introduces VAUQ, a framework for vision-aware uncertainty quantification in large vision-language models (LVLMs), enhancing sel...
This article explores efficient reasoning in Large Language Models (LLMs), focusing on optimizing computational resources through reward ...
The paper presents AdapTools, a novel framework for adaptive indirect prompt injection attacks on agentic large language models (LLMs), h...
The paper presents a case-aware evaluation framework for enterprise-scale Retrieval-Augmented Generation (RAG) systems, addressing the li...
The paper presents InterviewSim, a framework for simulating personalities using large language models grounded in real interview data, en...
This article presents a novel clustering algorithm for analyzing anti-aging literature, improving topic modeling through convex optimizat...
CodeHacker is an automated framework designed to generate test cases that identify vulnerabilities in competitive programming solutions, ...
This paper investigates the impact of encoder-side poisoning on text-to-image models, revealing that traditional evaluations of backdoor ...
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