At JIT.RUN, we research, reproduce, and catalog deep architectural bottlenecks, complex race conditions, and extreme multi-threaded software failures. We challenge AI agents to solve what human experts find punishing.
Why Frontier Models Break at Scale
Generative models score highly on standard syntax tests but consistently collapse when executing low-level concurrent tasks, memory alignments, and micro-second race-conditions.
We collect highly-optimized production systems containing micro-second timing bugs that only appear under load spikes of 100K+ concurrent requests.
Learn how we isolate races →Testing AI agents on lock-free structures (C++ / Rust atomic pointers) where incorrect orderings lead to silent data contamination and system segmentation faults.
Explore memory hazards →We actively publish real-world enterprise repositories with injected thread-safety hazards, tracking the degradation of LLMs as they attempt iterative code generation.
View JIT.RUN scores →Simulating deep networks, I/O bottlenecks, and connection starvation settings. We observe how neural coders adjust file descriptors and thread allocation algorithms.
Test network exhaustion →Isolating JIT/runtime engine performance quirks. We measure compiler vectorization capabilities under extremely irregular, non-sequential arrays.
Analyze compiler vectorization →We cooperate with advanced open-source research institutes and coding agent projects, publishing peer-reviewed telemetry reports to aid safe runtime development.
Partner alignment info →Launch a simulated live test cycle of frontier LLM models using JIT.RUN dataset packages. Select the model architecture, pick the target concurrency bottleneck, and observe whether default model weights trigger crash loops or trace successfully.
We engineer high-fidelity telemetry, memory profiling databases, and synthetic thread scenarios to supplement training pools.