LLMs fail continuous memory math 65% of the time. And standard unit tests don’t catch it.
If you are building zero-copy LangGraph swarms or using shared prefix caching to reduce API costs across multiple agents, your LLM-generated memory handoffs are likely corrupting physical block boundaries.
We benchmarked Gemini 2.5 Pro on a 2D Asymmetric Ring Buffer. The LLM repeatedly collapsed the 2D problem into a 1D solution, forgot the modulo wrap, and failed silently 65% of the time (21/32 runs). Worse, the LLM’s self-generated unit tests are structurally blind to the edge case.
We just open-sourced ImpactArbiter: a deterministic PyTorch autograd trap that verifies the physical tensor logic of your serving code against SymPy AST oracles. When it catches a bug, it feeds the gradient divergence back to the model to auto-heal the continuous math.
We manually distilled the foundational Radix and PagedAttention oracles. Before we lock the ASTs via our Root Validation Protocol, I need help finding the false positives. Can you write a valid, optimized 2D prefix routing implementation that our SymPy oracle incorrectly hard-blocks?
Full breakdown, live demo, and agent reasoning trace here: https://maniksundar.substack.com/p/the-physics-illusion-why-llms-still
Repo to test the trap: https://github.com/msunda17/impactarbiter-cli
HN Thread (would love your thoughts here!): https://news.ycombinator.com/item?id=48181654