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A recent arXiv study audited 17 popular shadow APIs used in 187 papers, finding up to a 47.21% performance gap versus official models—e.g., Gemini‑2.5‑flash’s accuracy drops from 83.82% to about 37% on MedQA—highlighting serious reliability and safety risks of unofficial LLM services.
Energy Distance is a statistical metric that quantifies how far two multivariate probability distributions diverge by comparing cross‑distribution and within‑distribution Euclidean distances, and it can be combined with permutation testing to assess the significance of observed shifts.
A recent arXiv paper audits 17 widely used shadow APIs, showing that their outputs can deviate from official large language model APIs by as much as 47.21%, with accuracy on the MedQA benchmark dropping from 83.82% to around 37%, raising serious reliability concerns.
The article walks through a simple two‑box, four‑ball example to illustrate basic probability, conditional probability, joint probability, and then reverses the conditioning to derive Bayes’ theorem, showing each step with concrete calculations and visual partitions of the sample space.
The article traces five eras of KV cache management for LLM inference—from its absence before Transformers to the emerging unified hybrid memory architecture—comparing vLLM, SGLang, and TensorRT‑LLM and offering a decision framework for selecting the right solution in various deployment scenarios.
PhysioDSP is an open‑source Python library that unifies fragmented wearable signal‑processing pipelines by providing type‑safe, reproducible algorithms for activity analysis, ECG peak detection, and HRV scoring, with a modular architecture and ready‑to‑use data models.
This article walks through the fundamentals of variational autoencoders, explaining why they are needed, detailing their three core components, loss formulation, PyTorch implementation, training loop, and multiple inference modes such as anomaly detection, data generation, conditional generation, latent space manipulation, and data imputation.
Yann LeCun argues that the probability‑driven, token‑by‑token design of current large language models may never reach human‑level intelligence, and explains how Energy‑Based Models replace probability distributions with an energy function, offering more flexible training, inference, and multi‑modal capabilities.
