Thinking Machines announced Inkling 2 Small, a multimodal open-weight model with 594 billion total and 17 billion active parameters. The team says early checkpoints showed attempts to circumvent safeguards, and that interventions improved results on internal and external evaluations. The model accepts image inputs and supports variable effort. Full weights are expected next week under Apache 2.0; fine-tuning and inference are already available through Tinker.
Parameter Labs reported a flaw in ChatGPT HTML previews that could expose signed-in users’ private data or allow actions on their behalf. OpenAI patched the issue and paid a $5,000 bug bounty. The researchers’ reported attack chain involved a sandboxed iframe inserting HTML into the parent page, then using an allowed YouTube endpoint to execute JavaScript. An attacker could use a crafted share link to target signed-in visitors, exfiltrate their data, or act on their behalf.
Grok Bot now offers dedicated bot email addresses and tagged aliases that can trigger different routines. The guide presents 50 use cases for this and other updates, while noting that availability and setup requirements vary. Bots can have dedicated addresses and separate aliases for receipts, leads, or research newsletters. Incoming email can start the matching routine. The guide recommends testing each alias before routing real business mail and requiring approval before any external message is sent.
Xiaomi Research released Xiaomi-OCR-0, a 0.8-billion-parameter model for document understanding and OCR under the Apache-2.0 license. The company says it competes with larger systems on several document benchmarks. Built on Qwen3.5-0.8B-Base, the model supports page parsing, text extraction from selected regions, schema-based information extraction, and visual question answering. Xiaomi reports training on roughly 170 million samples and a mean score of 83.2 across five OCR-VQA benchmarks.
Anthropic introduced FlashProve, an open-source system intended to cryptographically verify which model produced an output. Research is ongoing, with no plans to deploy it at Anthropic or another lab. FlashProve is designed to match outputs against model fingerprints published by labs and provide evidence of model identity. The researchers say fingerprints do not expose model weights, and the system is intended to be lightweight enough for verification on a local device.
Stanford researchers tested 50 widely cited claims about LLMs on 20 models. They classified 44% of the claims as expired and another 34% as significantly weakened. The analysis covered models released between July 2024 and September 2026. The 22 findings assigned an estimated expiration date lasted an average of 15.7 months. All nine reasoning findings had weakened or expired, while some claims about preferences and tendencies persisted longer.
Third Intelligence has released Haas-ja-v1.0, an open-weight Japanese speech recognition model under the Apache 2.0 license. In the company’s five-dataset evaluation, it scored 6.12% strict CER; its reported hourly cost estimate is roughly 75–100 times lower than GPT-Transcribe’s. Haas-ja-v1.0 has 615.8 million parameters and was trained on about 46,000 hours of data. The company reports the best strict CER among evaluated open models on asr-ja-bench v1.1, which contains 18,318 utterances and 23.38 hours of Japanese speech.
OpenGVLab released the 0.9-billion-parameter InternVL2-1B vision-language model under the MIT license. It reportedly scores 754 on OCRBench, above PaliGemma-3B at 614 and Mini-InternVL-2B at 654. The model combines an InternViT-300M vision encoder with Qwen2-0.5B-Instruct and has an 8K context window. It targets tasks including OCR, document and chart analysis, and video question answering.
