Search “Reasoning”

Elorian Releases Visual Reasoning Model Glimpse

Elorian has released its first visual reasoning model, Glimpse, which is used to compare scene changes at different points in a video and to review the original clip when changes are subtle or rapid. Elorian calls this frame-based reasoning visual thinking. During reasoning, Glimpse can return to the source video, crop regions, extract specified frames, or run code to measure and compare what it sees; this mechanism is called visual scratch space. Co-founder Andrew M. Dai says it achieves frontier performance competitive with Astra on key public visual reasoning benchmarks.

Jeff Dean praised Elorian for releasing the visual reasoning model Glimpse

Andrew M. Dai announced the launch of Glimpse, calling it Elorian's first visual thinking model, which achieves frontier performance comparable to Astra on key public visual reasoning benchmarks and solves the problem of models only looking at a single static image and failing when encountering motion or viewpoint changes. Jeff Dean reposted and praised the work of AndrewDai and the Elorian team.

Elorian Releases Its First Model Glimpse, Using a Visual Draft Space for Image Reasoning

Elorian has released its first model, Glimpse, opening the API as a research preview, with a waitlist for the chat interface. During reasoning, the model can crop image regions, extract video frames, or run code to measure and compare what it sees, which Elorian calls a visual draft space. Glimpse is priced at $0.60 per million input tokens and $3 per million output tokens, and according to Elorian's evaluations it surpasses GPT-6 Astra on scientific chart reasoning and short video description. The company says the model was trained on several hundred GPUs, a scale notably smaller than the frontier systems it benchmarks against, and it initially targets enterprises with large amounts of video, such as robotics, research laboratories, and media and creative firms.

Aleph Alpha Releases Kolibri-1: A 78B-Parameter Open-Weight Model for German-Language Reasoning

Aleph Alpha has released the open-weight model Kolibri-1, with 78B total parameters. It uses a MoE architecture with 384 experts and 6 routed experts, activating only 3.46B parameters during inference, with a 256K Token context that can be extrapolated to about 1 million Tokens. The model focuses on German and English, and its tokenizer segments German words more token-efficiently; for example, the German Bundestag glossary requires only 1137 Tokens, whereas the compared DeepSeek-V4.1-Flash requires 1910 Tokens. Pretraining used 20 trillion Tokens, with data current as of June 18, 2026. The author's hands-on test found its answers correct and its German-language reasoning process evident, and stated that the Merlin-Arthur protocol makes the model hallucinate less in benchmarks, but its actual performance still needs to be verified in real-world use.

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StepFun Step 5 Preview demonstrates strong inference performance on NVIDIA GB300

StepFun states that Step 5 Preview demonstrates strong inference performance on NVIDIA GB300 and looks forward to developers building on it. NVIDIA AI, in a quoted tweet, calls this StepFun's largest open-weight release, with a 600B MoE model capable of handling agent tasks up to 24 hours long, reaching nearly 8K tokens/sec/GPU on NVIDIA GB300 with NVFP4, and providing an NVFP4 checkpoint as well as deployment and post-training paths for SGLang, vLLM, NeMo Automodel, and NVIDIA NIM.

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Leash Bio's Ian Quigley on DEL data, model cheating, and an agentic chemistry reasoning sandbox

In a Latent Space podcast interview, Leash Bio co-founder Ian Quigley discusses how the company tamed DNA-encoded libraries (DEL) and extracted clean, reproducible protein-small molecule interaction data from them. The interview mentions that in its Kaggle competition, none of the roughly 2,000 teams surpassed random performance on an unseen chemical library, and that a model can guess a chemist's identity from molecular structure, while Hermes, using only its own data, matched Boltz on public benchmarks. Ian also previewed that the team is turning years of accumulated data into tens of thousands of lab-in-the-loop simulations for agents to perform verifiable scientific reasoning; that work later developed into Minos, with a preprint, blog post, and open dataset released.

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Google DeepMind launches general physical reasoning model Gemini 4 Argon

Google DeepMind officially launched the general physical reasoning model Gemini 4 Argon and released a demo video of its collaboration with the Apptronik Apollo humanoid robot. The robot only watched a video demonstrating beanbag tossing, practiced repeatedly, and ultimately made 7 consecutive successful throws. The model possesses visual semantic reasoning, physical understanding, and code generation capabilities, and can be used for robot control, simulation environment construction, physical data analysis, and motion optimization. In pure reasoning mode, it scored 87% on the ERQA benchmark. The demo also covers generating MuJoCo simulation environments from robot-perspective videos, human-perspective videos, and hand-drawn sketches, as well as using simulation data to raise the real robot's grasping success rate from 56% to 75%.

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