RunInfra is now public.See what's new

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.

01
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.
PDF arXiv Code
02
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.
PDF arXiv Code
03
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.
PDF arXiv Code

Model architectures

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

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.
PDF arXiv Code
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.
PDF arXiv Code