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RunInfra

Research from the team.

Open papers from the RunInfra team on attention efficiency, LLM inference, kernel optimization, and the architectures behind production AI infrastructure.

Part of RightNow / Research lab co-designing models and hardware

Compute efficiency

Faster, leaner, more memory-bounded ways to run models on existing hardware.

  1. 01
    arXiv 2606.09682
    cs.LG2026
    • Megakernels
    • CUDA
    • Agents
    • Jaber Jaber
    • Osama Jaber

    AutoMegaKernel: A Statically-Checked Agent Harness for Self-Retargeting Megakernel Synthesis

    Compiles a Llama-family model into one persistent CUDA megakernel with no hand-written CUDA, statically certifies every agent-proposed schedule deadlock-free and race-free before launch, and self-improves the kernel through an unattended agent loop.

    PDFarXivCode
  2. 02
    arXiv 2603.21331
    cs.LG2026
    • GPU
    • Kernel Optimization
    • Agents
    • Jaber Jaber
    • Osama Jaber

    AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

    Autonomous GPU kernel optimization via iterative agent-driven search, using LLM agents to explore the kernel design space and validate candidates on real hardware.

    PDFarXivCode
  3. 03
    arXiv 2605.02568
    cs.LG2026
    • Sparse Attention
    • Streaming Top-k
    • Triton
    • Jaber Jaber
    • Osama Jaber

    StreamIndex: Memory-Bounded Compressed Sparse Attention via Streaming Top-k

    A memory-bounded sparse attention mechanism that selects top-k keys in a single streaming pass over the sequence, fused as a Triton kernel for production inference workloads.

    PDFarXivCode
  4. 04
    arXiv 2603.21365
    cs.LG2026
    • LLM Inference
    • Early Exit
    • Efficiency
    • Jaber Jaber
    • Osama Jaber

    TIDE: Token-Informed Depth Execution for Per-Token Early Exit in LLM Inference

    Per-token early exit in LLM inference, driven by token-informed depth signals that decide when each token has enough computation to commit to a final logit.

    PDFarXivCode

Model architectures

New ways to compose, compute, and adapt model internals at training and inference time.

  1. 01
    arXiv 2604.02051
    cs.LG2026
    • Transformers
    • LoRA
    • Weight Generation
    • Jaber Jaber
    • Osama Jaber

    Ouroboros: Dynamic Weight Generation for Recursive Transformers via Input-Conditioned LoRA Modulation

    A recursive transformer where each layer generates its own weights through input-conditioned LoRA modulation, enabling dynamic capacity allocation without storing additional parameters.

    PDFarXivCode
  2. 02
    arXiv 2603.29090
    cs.LG2026
    • World Models
    • Object-Centric
    • Causal
    • Jaber Jaber
    • Osama Jaber

    HCLSM: Hierarchical Causal Latent State Machines for Object-Centric World Modeling

    Hierarchical causal latent state machines for object-centric world modeling, with explicit slots for entities and the causal relationships between them.

    PDFarXivCode

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