Tech · AI
The self-certifying cache: why LAWS could make on-site robot AI provable
LAWS proposes an inference cache with a deployment-time error bound you can check without ground truth — what it means for on-site robots and BIM AI tools.
Basel's Tail Metric Taught a Neural Net — and Your Studio Has the Same Scarce-Data Problem
A new arXiv paper distils a CVaR risk optimizer into neural students from 104 samples — and the teacher-student trick maps straight onto AEC's data gap.
Generative LLMs eat transistor topology — and your facade is next
TOPCELL fine-tunes LLMs with GRPO to collapse 7nm standard-cell search 85.91×. The pattern transfers: any verifier you own becomes a topology solver.
Structured Progressive Knowledge Activation for LLM-Driven Neural Architecture Search
arXiv:2605.04057v1 Announce Type: new Abstract: This paper focuses on a key challenge in Neural Architecture Search (NAS): integrating established architectural knowledge while exploring new designs under expensive evaluations. Large language models (LLMs) are a promising assist
Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors
arXiv:2605.05520v1 Announce Type: new Abstract: Commercial Microwave Links (CMLs) offer dense spatial coverage for rainfall sensing but produce path-integrated measurements that make accurate ground-level reconstruction challenging. Existing methods typically oversimplify CMLs a
A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers
arXiv:2605.04074v1 Announce Type: new Abstract: AI data centers experience rapid fluctuations in power demand due to the heterogeneity of computational tasks that they have to support. For example, the power profile of inference and training of large language models (LLMs) is qu