Ling and Ring Technical Report 2.6 Efficient and Instantaneous Agentic Intelligence at the Scale of Trillions of Parameters
COMPUTING
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Scaling compute on context to study Moby Dick and Fed livestreams
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Joe you might not want to hear this but as we speak I'm scaling compute on your context. the model is studying Moby Dick, it's developing new theories of orality, it's watching Fed livestreams.
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Claude in a channel instance with sandbox for coding and testing
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Tag Claude in a channel, it launches an instance with its own sandbox. It clones repositories, writes code, tests, compiles everything in this isolated environment and the sandbox is deleted when finished. One instance per thread, its own memory and its
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AI Voice Dictation 2026: Automatic Punctuation, Context and Homophones
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10. Voice dictation that really works. Open any keyboard, tap the microphone and speak. The 2026 AI dictation automatically punctuates, follows context and correctly distinguishes "là", "leurs" and "ils sont". Speak a complete WhatsApp message or an email.
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DFlash boosts inference 15x on NVIDIA Blackwell with low latency
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Increase inference performance by up to 15x without sacrificing responsiveness. DFlash, an open source lightweight block diffusion model designed for speculative decoding, delivers up to 15x higher throughput on NVIDIA Blackwell while maintaining the same user interactivity
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AI in 2040: Nearly Optimal Stack, Massive Current Inefficiency
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AI in 2040 will not be built on the stack we use today. It will be much closer to optimal. The current stack exhibits 3-4 orders of magnitude data inefficiency and 4-5 orders of magnitude compute inefficiency. Nearly optimal AI is what
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ArtiFixer delivers sharp results, outperforming baselines by 1-3 dB PSNR
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The results are incredibly sharp. When benchmarked on challenging datasets with sparse views like Mip-NeRF 360 and DL3DV, ArtiFixer handles highly degraded initial renderings effortlessly. It outperforms existing baselines (like GenFusion and 3DGUT) by a massive 1–3 dB PSNR
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ArtiFixer uses DMD to make bidirectional video models 70x faster
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Bidirectional video models provide great coherence but are computationally heavy. ArtiFixer solves this via Self-Forcing-style Distribution Matching Distillation (DMD). By distilling the bidirectional model into a causal auto-regressive one, ArtiFixer achieves up to a 70x
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OpenRouter to solve GPU shortage in research
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Tired of seeing “out of capacity” while looking for GPUs? Introducing OpenRouter for compute
— alphaXiv (@askalphaxiv) 23 juin 2026
In the past 6 months, overwhelming demand for compute has made pricing and availability huge challenges for the research community
We aggregate compute offerings from across the… pic.twitter.com/Z3IdyPvbkmTired of seeing "capacity exhausted" when searching for GPUs? Introducing OpenRouter for Compute Over the past 6 months, overwhelming demand for compute has made pricing and availability real challenges for the research community We aggregate
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Centralized AI processing limits responsiveness for time-sensitive use cases
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Most AI systems still rely on centralized processing: → Data is sent → Processed → Returned That model works, but for time-sensitive use cases, responsiveness and control become critical.