/3 DFlash replaces the autoregressive draft head with a block diffusion model. The drafter gets the target model's hidden states injected into the KV cache of every draft layer (not just the first like EAGLE-3). One denoising step. Entire block of 16 tokens produced in a single
LLMS
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DFlash 6x lossless acceleration on Qwen3-8B
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Diffusion LLMs have been losing to autoregressive models for two years. UC San Diego just found the one thing they're actually better at: not generating, drafting. DFlash hits 6x lossless acceleration on Qwen3-8B. 2.5x faster than EAGLE-3. Check out the detailed breakdown
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Abridge uses LangSmith for core evals of AI-generated clinical notes
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LangSmith for Startups Spotlight: @AbridgeHQ
— LangChain (@LangChain) 25 juin 2026
✅ Transforms patient-clinician conversations into contextually aware, clinically useful, and billable AI-generated notes.
✅ Deployed across 250 of the largest health systems in the country.
✅ Uses LangSmith as its core evals… pic.twitter.com/Ko3sSY4v0WLangSmith for Startups Spotlight: @AbridgeHQ Transforms patient-clinician conversations into contextually aware, clinically useful, and billable AI-generated notes. Deployed across 250 of the largest health systems in the country. Uses LangSmith as its core evals
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GLM 5.2 enables trusted open model for automated research tasks
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1/2 This is obviously not a comprehensive benchmark, but it’s clear that we finally have an open model that can be trusted and depended upon on difficult research tasks. You can easily run autoresearch yourself with GLM 5.2 by changing ‘arxiv’ to ‘autoarxiv’ for any arXiv URL:
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Comparison of GLM 5.2 and Opus 4.8 on SDPO paper reproduction
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Here’s a fun comparison between GLM 5.2 and Opus 4.8 on a one-shot reproduction of the SDPO paper This is a hard task: the model must resolve messy verl issues and then run ablations to completion and confirm the paper’s claims. – GLM 5.2 costs $6.21 while Opus 4.8 cost us
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Training step with refined scaffold and reward hacking guards
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Each training step has the model propose a refined scaffold for a task, which it then uses to generate a solution, with reward flowing back to both stages. There are three layers of guard against reward hacking. As DeepReinforce states, the 9B variant achieves a score of 43.1 on
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DeepReinforce releases Ornith-1.0 self-improving open-source coding model
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DeepReinforce has released Ornith-1.0, their new self-improving family of open-source models designed for agentic coding. > Ornith-1.0 learns to write its own task scaffolds during training rather than relying on human-designed harnesses. > The 397B MoE flagship can match
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New tiny LFM2.5-230M model for ultra-low latency robotics and e-commerce
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Here's our new, tiniest model: LFM2.5-230M! 🥳
— Maxime Labonne (@maximelabonne) 25 juin 2026
We went even smaller to power ultra-low latency use cases like e-commerce and robotics.
Here's a demo of LFM2.5-230M running on a Unitree G1, decomposing user prompts into a sequence of tool calls.
Available today on @huggingface! https://t.co/JiuSWnwZCs pic.twitter.com/5PuSamwZLRHere's our new, tiniest model: LFM2.5-230M! We went even smaller to power ultra-low latency use cases like e-commerce and robotics. Here's a demo of LFM2.5-230M running on a Unitree G1, decomposing user prompts into a sequence of tool calls. Available today on @huggingface
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Anthropic’s distillation continues unabated despite update
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This is an interesting update that Anthropic published in their official letter. But in short: they apparently haven't managed to stop the distillation. It's continuing almost seamlessly, just like before.
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Multi-agent collaboration boosts Gemma 4 inference speed 5x
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Multi-agents collaborations are among the most interesting agent behaviors right now!
— Thomas Wolf (@Thom_Wolf) 25 juin 2026
We did an experiment the other day with 100+ agents (an open-collaborations for a week) collaborating to improve the inference speed of Gemma 4 in vLLM. Got a 5x final improvement in speed but… pic.twitter.com/4PFS8L2mqeMulti-agents collaborations are among the most interesting agent behaviors right now! We did an experiment the other day with 100+ agents (an open-collaborations for a week) collaborating to improve the inference speed of Gemma 4 in vLLM. Got a 5x final improvement in speed but