Most LLM development still optimizes one model at a time. So this paper from Sakana AI proposes AC/DC (Assessment Coevolving with Diverse Capabilities), where models and tasks evolve together. The main idea is
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New Scientific Article Published on arXiv April 22 2026
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read more: https://alphaxiv.org/abs/2604.14969
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AI Research Challenge: Learn Frontier Techniques and Win
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Don’t miss out on this fun challenge! You’d get to learn current frontier research in-depth and implement them while getting a chance to win prizes. Opportunities like this don’t come often!
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Qwen3.5-Omni: Fully Omnimodal AI Model with Thinker-Talker Architecture
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“Qwen3.5-Omni Technical Report” Qwen3.5-Omni is a fully omnimodal model that understands text, images, video, and audio, then responds with text or real-time speech. The architecture they use is a Thinker-Talker split, where the Thinker performs multimodal reasoning, while the
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AlphaXiv Launches Markdown Papers and Structured Skill Overview Tool
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Yup we have every paper in markdown! Example: https://t.co/oEkZ3sJE8s. We also introduced a SKILL.md that fetches structured, AI-friendly paper overviews from alphaXiv https://t.co/dGzA5Busw4
— alphaXiv (@askalphaxiv) 20 avril 2026Yup we have every paper in markdown! Example: https://
alphaxiv.org/abs/1912.01214
.md
…. We also introduced a SKILL.md that fetches structured, AI-friendly paper overviews from alphaXiv -
1D Ordered Tokens Improve AR Image Model Test-Time Search
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“(1D) Ordered Tokens Enable Efficient Test-Time Search” Most AR image models generate 2D grid tokens patch-by-patch, so partial generations are hard to verify. This paper shows that 1D coarse-to-fine tokens make search much easier, with early tokens encode global semantics,
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Think-Anywhere: Reasoning in Code Generation for Edge Cases
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“Think Anywhere in Code Generation” Most reasoning LLMs think before writing code. But coding often gets hard because the tricky parts only gets revealed mid-implementation when the edge cases or final return logic appear. So this paper introduces Think-Anywhere, where models
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Parcae Scaling Laws: Recurrence Approach for Stable Language Models
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"Parcae Scaling Laws For Stable Looped Language Models" LLMs usually scale with more parameters or more data. But this paper scales a new axis for transformers through recurrence. So instead of making the model bigger, it reuses the same Transformer layers multiple times.
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Prefill-as-Service Enables Cross-Datacenter LLM Serving
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new paper from Kimi! "Prefill-as-a-Service makes long-context LLM serving cross-datacenter" Main idea: smaller KV Cache turns prefill into a scalable remote service. Prefill is compute-bound, decode is memory-bandwidth-bound, but KV cache transfer keeps them trapped in the