TLDR AI

TLDR AI 2026-05-19

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01GENERAL

02HEADLINES & LAUNCHES

QWEN3.7 PREVIEW LANDS ON ARENA

Qwen3.7 Preview is now on Arena for Text and Vision. Qwen3.7 Max Preview ranks 13th overall in Text Arena, while Qwen3.7 Plus Preview ranks 16th overall in Vision Arena.

threadreaderapp.com1 min read

ANTHROPIC ACQUIRES SDK STARTUP STAINLESS

Anthropic acquired developer tools startup Stainless, whose SDK automation platform was widely used by AI companies, including OpenAI, Google, and Cloudflare.

anthropic.com4 min read

CURSOR RELEASED COMPOSER 2.5

Cursor introduced Composer 2.5, an updated coding agent trained with targeted reinforcement learning, synthetic data, and new distributed training techniques.

cursor.com7 min read

03DEEP DIVES & ANALYSIS

WHAT POLITICAL CENSORSHIP LOOKS LIKE INSIDE AN LLM'S WEIGHTS

Qwen3.5-9B's political censorship is a small circuit that can be read and turned off. The factual knowledge is already in pretraining. The censorship behavior is layered on top of the facts. The model never loses the knowledge, it just learns to route around it.

vas-blog.pages.dev109 min read

AGENT EVALUATION: A DETAILED GUIDE

LLM evaluation has shifted from static benchmarks to more dynamic, real-world agent systems. Effective evaluation now requires realistic harnesses to test agents over long time horizons in complex environments. This is crucial as agents increasingly adopt high-stakes roles, such as coding and medicine, necessitating rigorous performance measurement and outcome-oriented evaluation.

cameronrwolfe.substack.com53 min read

04ENGINEERING & RESEARCH

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zenity.ioSponsor

GENERALIZATION DYNAMICS OF LM PRE-TRAINING

Language models (LMs) undergo unpredictable switches between parroting patterns and exhibiting adaptive intelligence during pre-training, a phenomenon termed "mode-hopping." This behavior cannot be corrected by standard optimization techniques and presents as a competition for model capacity, influenced by data from each training window. Researchers propose leveraging these dynamics to better select pre-training checkpoints, curate data for stable generalization, and evaluate metrics predicting LM behavior.

jiaxin-wen.github.io17 min read

FINE-TUNING NVIDIA COSMOS PREDICT 2.5 WITH LORA/DORA FOR ROBOT VIDEO GENERATION

NVIDIA Cosmos Predict 2.5 generates videos from text, adapting for specific tasks like robot manipulation using LoRA/DoRA to inject trainable adapters, minimizing memory use. These methods offer efficient fine-tuning on a single GPU, preventing catastrophic forgetting while generating synthetic trajectories quickly. Fine-tuning with LoRA and DoRA significantly improves video quality, with LoRA more suited for tight memory conditions and DoRA preferred for addressing training instability.

huggingface.co9 min read

HRM-TEXT

HRM-Text is a 1B text generation model based on the HRM architecture. It can be trained with 130-600x less compute and 150-900x less data than foundation models, making foundation model pretraining accessible. The 0.6B parameter version of the model can be trained on 8 H100s on a single node in about 50 hours for around $800. The 1B parameter model can be trained on 16 H100s on two nodes in about 46 hours for around $1,472.

github.comGitHub repository

05MISCELLANEOUS

VERA ARRIVES: NVIDIA'S FIRST CPU BUILT FOR AGENTS LANDS AT TOP AI LABS

The first Nvidia Vera CPUs recently arrived at Anthropic, OpenAI, SpaceXAI, and Oracle. They were hand-delivered by Nvidia Vice President of Hyperscale and High-Performance Computing, Ian Buck. Vera features 88 custom Nvidia-designed Olympus cores, 1.2 TB/s of memory bandwidth, and 50% faster per-core performance. It is the host processor for Vera Rubin NVL72, which pairs via second-generation Nvidia NVLink-C2C to a pair of Rubin GPUs.

blogs.nvidia.com4 min read