Large language model (LLM) news — GPT-4, Claude, Gemini, Llama, Mistral and the latest research on training, fine-tuning, RLHF, and deployment of LLMs.
BetterAgent launches a developer tool that turns any Next.js app into an AI-native application in minutes by reading routes and server actions to create an agent layer. The tool supports server actions, client actions, a…
A developer proposes using the Model Context Protocol (MCP) to connect vulnerability detection tools with security training platforms like HackEDU, creating a real-time feedback loop that assigns adaptive training to dev…
A developer released AI-Gateway, an open-source reverse proxy that uses semantic caching to reduce LLM API costs by 40-70% with no code changes. The tool sits between apps and providers like OpenAI and Groq, caching resp…
Google merged computer use capabilities directly into Gemini 3.5 Flash on June 24, eliminating the need for a separate model. Developers can now enable screen automation by adding a single tool parameter to existing Flas…
Anthropic accused Alibaba of executing the largest known distillation attack against its Claude AI model, involving 28.8 million queries from 25,000 fraudulent accounts between April and June 2025. The attack targeted Cl…
Google is running a World Cup brand campaign featuring soccer stars to remind users that its search engine now supports complex queries through AI features like Gemini. The campaign highlights shifting search behavior as…
Peking University's Chinalawinfo PKULaw has launched a standard Model Context Protocol interface that integrates large language models with authoritative legal databases, enabling AI-powered legal tools to generate accur…
Researchers introduced a taxonomy of conversational memory types and a user-centric evaluation framework to assess how different memory roles affect response quality in RAG-based conversational agents. Experiments with f…
Researchers introduced Yuvion VL, a family of multimodal large language models designed for content and AI safety, treating safety as an adversarial multimodal problem. The models employ a three-stage training pipeline a…
Researchers evaluated multimodal large language models (MLLMs) for assistive AI applications, finding they show promise in object recognition and multilingual text reading but have limitations in real-world egocentric ta…
Researchers at arXiv found that in language models, the direction that best detects a behavior and the one that best controls it are often geometrically distinct, with cosine similarities as low as 0.12 for hallucination…
A new survey analyzes the use of large language models (LLMs) for scientific peer review, focusing on critique generation and score prediction. The study identifies reliability, robustness, and security risks such as pro…
Researchers at arXiv found that jailbreak attacks on large language models can be detected by analyzing entropy dynamics in intermediate layers, rather than final outputs. The study shows that monotonic rank-based trend …
Researchers propose G-SPIN, a graph-based framework that corrects phonetic errors in ASR outputs by combining a graph neural network for candidate generation with a masked language model and LLM for contextual re-ranking…
Researchers introduced iLLaDA, an 8B masked diffusion language model trained from scratch with fully bidirectional attention, scaling pre-training to 12T tokens and fine-tuning on a 25B-token instruction corpus. Compared…
Researchers introduced Hybrid-IR, a dual-path retrieval framework with iterative reasoning for complex medical question answering. The system combines graph-based and dense retrieval to overcome limitations of single-pat…
A small group of Wikipedia editors, the Pro-Animal Wikipedians (PAW), measurably shaped how large language models discuss animal welfare through just 125 edits across 115 pages. Gradient-based data attribution and counte…
Researchers introduced Local Branch Routing (LBR), a token-level test-time scaling framework that expands a small local lookahead tree and uses a lightweight router to select the best branch, enabling efficient and train…
Researchers developed MRI2Rep, an autoregressive framework that generates structured reports for 3D liver MRI, achieving 76% case-level sensitivity and 82.4% liver-level accuracy on a test set of 3,929 real-world MRI-rep…
Researchers propose Dustin, a sparse verification framework for long-context speculative decoding in LLMs, achieving a 27.85x speedup in self-attention and 9.17x end-to-end decoding speedup at 32k sequence length with ne…