New models, weights and benchmarks.
Learn how GPT-5.5 Instant improves ChatGPT’s health and wellness responses with stronger reasoning, better context, clearer communication, and physician-informed evaluations.
Chinese AI lab Z.ai released GLM-5.2 to their coding plan subscribers on June 13th, and then yesterday (June 16th) released the full open weights under an MIT license. Similar in size to their previous GLM-5 and GLM-5.1 releases, this is 753B parameter, 1.51TB monster - with 40 active parameters (Mixture of Experts). GLM-5.2 is a text input only model - Z.ai have a separate vision family most recently represented by GLM-5V-Turbo, but that one isn't open weights. GLM-5.2 has a 1 million token context window, up from GLM-5.1's 200,000. The buzz around this model is strong. Artificial Analysis, who run one of the most widely respected independent benchmarks: GLM-5.2 is the new leading open weights model on the Artificial Analysis Intelligence Index. GLM-5.2 is the leading open weights model on the Intelligence Index v4.1. At 51, it leads MiniMax-M3 (44), DeepSeek V4 Pro (max, 44) and Kimi K2.6 (43) They did however find it to be quite token-hungry: GLM-5.2 uses more output toke
Product launches and noteworthy updates.
TechCrunch AIRSS·6d ago71About 47% of singles look negatively at the use of AI in dating -- but, many dating app users are open to AI helping with profile punch-ups and conversation starters.
TechCrunch AIRSS·6d ago71Karamo Brown, famous for his pep talks on Netflix’s “Queer Eye,” has jumped into the wellness and AI space with his new app, Kē. After spending a year and a half focusing on his own journey—from fitness and nutrition to meditation, sobriety, relationships, and personal growth—Brown wants to help others do the same. Kē offers […]
TechCrunch AIRSS·6d ago71Forget stickers, GIFs, and emoji reactions. Pixi is betting that the next evolution of messaging is interactive augmented reality (AR).
Funding, policy and market moves.
TechCrunch AIRSS·6d ago71Startup Baseten is reportedly close to finalizing a $1.5 billion round at a $13 billion as the “inference gold rush" marches on.
TechCrunch AIRSS·6d ago71The Snapchat maker is spinning off yet another internal unit. Dotmo will be comprised of current Snap staff who are leaving the social media company to focus on AI video development.
TechCrunch AIRSS·6d ago71FERC told grid operators to give data centers a fast lane for interconnections, but it failed to address electricity supply shortages.
TechCrunch AIRSS·6d ago71“People just really want to take back control of their time, their lives, their attention... They’re down for whatever helps them do that.”
TechCrunch AIRSS·6d ago71General Intuition is in talks to raise around $300 million at a roughly $2 billion valuation from backers including Jeff Bezos. The startup trains AI agents on spatial-temporal reasoning.
Research worth a read.
arXiv:2606.18307v1 Announce Type: new Abstract: Optimizing the training data distribution for Supervised Fine-Tuning (SFT) dictates the capability of Large Language Models (LLMs). While existing data curation methods excel at accelerating training under constrained budgets, they are less suited to elevating the capability upper bound. The challenge here is no longer to identify a smaller subset that preserves performance, but to refine the data distribution toward instances most capable of improving the final model. To address this problem, we explore instance-level data attribution using Influence Functions (IF). We identify that standard IF formulations struggle in this setting due to two structural limitations: a proximity gap caused by off-policy validation targets, and a severe bias towards gradient norm. We propose DRIFT (Data Refinement via On-Policy Influence Functions for Supervised Fine-Tuning). Instead of relying on external reference data, DRIFT utilizes the model's on-pol
arXiv:2606.18502v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems demonstrate strong performance on complex reasoning and task execution, enabling broad enterprise applications. However, production deployment remains challenging due to domain-specific customization requirements and high latency and inference costs in agentic workflows. We propose a unified framework for customization and efficient deployment of multi-agent systems in real-world settings. The first stage, Agentic Model Customization, combines continual pretraining, supervised fine-tuning, and preference optimization to adapt a compact model to specialized domains while retaining strong agentic capabilities. The second stage, Inference Optimization, integrates speculative decoding and FP8 quantization with targeted calibration to enable cost-efficient serving with minimal quality loss. Across enterprise workloads, our framework enables rapid domain adaptation and achieves a 4.48x speed
arXiv:2606.18508v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems depend critically on how documents are chunked and searched. Fine-grained chunks can improve retrieval precision but expand the search space, increasing latency and cost; larger chunks reduce the number of candidates but make dense similarity less reliable, as the representation for each chunk mixes multiple topics and introduces more semantic noise. This trade-off becomes especially limiting in deep research tasks, where retrieval must be both fast and precise across large, heterogeneous corpora. We introduce MCompassRAG, a metadata-guided retrieval framework that uses topic-level signals as a semantic compass for selecting relevant evidence. Instead of relying only on cosine similarity between queries and noisy chunk embeddings, MCompassRAG enriches chunk representations with topic metadata in the same embedding space and trains a lightweight retriever through LLM-teacher distillation. At in
arXiv:2606.18584v1 Announce Type: new Abstract: Language discrimination among similar languages, varieties, and dialects is a challenging natural language processing task. The traditional text-driven focus leads to poor results. In this paper, we explore the effectiveness of speech-driven features towards language discrimination among Chinese dialects. First, we systematically explore the appropriateness of speech-driven MFCC features towards CNN-based language discrimination. Then, we design an end-to-end speech recognition model based on HMM-DNN to predict Chinese dialect words. We adopt attention to extract the discriminative words related to different Chinese dialects. Finally, through a CNN, we combine the word-level embedding and the MFCC-based features. Evaluation of two benchmark Chinese dialect corpora shows the appropriateness and effectiveness of the proposed speech-driven approach to fine-grained Chinese dialect discrimination compared to the state-of-the-art methods.
What the major labs and platforms shipped.
OpenAI introduces new spend controls and usage analytics for ChatGPT Enterprise, helping organizations manage costs and scale AI with confidence.
Researchers used an OpenAI reasoning model to help diagnose rare diseases, identifying 18 new diagnoses in previously unsolved cases.
TechCrunch AIRSS·6d ago71OpenAI is bulking up before its IPO, landing Transformer co-inventor Noam Shazeer from Google DeepMind and former Trump AI policy official Dean Ball in the same week.
TechCrunch AIRSS·6d ago71AWS is in talks to sell its chips to other data centers. CEO Andy Jassy has said this represents a $50 billion opportunity for the company.
TechCrunch AIRSS·7d ago71Here's what you need to do to get those pesky "write with Gemini" pop-ups to go away.
