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AI Explorer
AI Explorer
May 2, 2026 · Artificial Intelligence

How a New AI Probe Can Reverse‑Engineer LLM Parameter Counts

Researcher Li Bojie’s “Uncompressible Knowledge Probe” uses random, black‑box API queries to gauge how much irreducible knowledge a large language model retains, allowing an indirect estimate of its effective parameter count and prompting a broader debate on model evaluation and transparency.

AI EvaluationLLMblack-box testing
0 likes · 5 min read
How a New AI Probe Can Reverse‑Engineer LLM Parameter Counts
Machine Heart
Machine Heart
May 1, 2026 · Artificial Intelligence

API‑Only Probes Reveal GPT, Claude, Gemini Parameter Counts – Community Buzz

A new arXiv paper introduces Incompressible Knowledge Probes that estimate large language model sizes via black‑box API calls, fitting a log‑linear relation on 89 open‑source models and producing controversial parameter estimates for GPT‑5.5, Claude Opus, Gemini and others, sparking heated community debate.

AI scalingClaude OpusGPT-5.5
0 likes · 7 min read
API‑Only Probes Reveal GPT, Claude, Gemini Parameter Counts – Community Buzz
Meituan Technology Team
Meituan Technology Team
May 22, 2025 · Fundamentals

Unlocking AB Testing: Core Statistical Principles Behind Reliable Experiments

This article explains the statistical foundations of AB testing, covering the Rubin causal model, SUTVA and randomization assumptions, parameter and confidence‑interval estimation, hypothesis‑testing procedures, and essential limit theorems such as the law of large numbers and the central limit theorem.

AB testingcausal inferencehypothesis testing
0 likes · 17 min read
Unlocking AB Testing: Core Statistical Principles Behind Reliable Experiments
Model Perspective
Model Perspective
May 8, 2024 · Fundamentals

Master Mathematical Modeling: The Three Essential Pillars of Elements, Structure, and Parameters

This article explains how successful mathematical modeling relies on three core components—identifying essential variables, designing an appropriate structural framework, and accurately setting parameter values—to transform real‑world problems into reliable, actionable mathematical representations.

mathematical modelingmodel elementsmodel structure
0 likes · 8 min read
Master Mathematical Modeling: The Three Essential Pillars of Elements, Structure, and Parameters
Model Perspective
Model Perspective
Mar 1, 2023 · Artificial Intelligence

Mastering the EM Algorithm: Theory, Math, and Python Implementation

This article explains the Expectation‑Maximization (EM) algorithm, detailing its iterative E‑step and M‑step processes, mathematical formulation, and practical Python implementation for estimating parameters of mixed linear regression models, while highlighting convergence considerations and common pitfalls.

EM algorithmPythonexpectation maximization
0 likes · 12 min read
Mastering the EM Algorithm: Theory, Math, and Python Implementation
Model Perspective
Model Perspective
Nov 12, 2022 · Fundamentals

Understanding Parameter Estimation: Point vs Interval Methods

This article explains statistical inference focusing on parameter estimation, distinguishing point estimation from interval estimation, and demonstrates how to construct confidence intervals for population means using sample data, including a practical example calculating a confidence interval for the average Sharpe ratio of equity funds.

confidence intervalinterval estimationparameter estimation
0 likes · 3 min read
Understanding Parameter Estimation: Point vs Interval Methods
Model Perspective
Model Perspective
Oct 31, 2022 · Fundamentals

Mastering the Method of Moments: Theory and Python Example

This article explains the method of moments for estimating population parameters, outlines its step‑by‑step derivation, and demonstrates a Python implementation that estimates a basketball player's shooting odds from binary outcome data using.

Pythondata analysismethod of moments
0 likes · 4 min read
Mastering the Method of Moments: Theory and Python Example
Model Perspective
Model Perspective
Oct 29, 2022 · Fundamentals

Understanding Parameter Estimation: Point vs Interval and Confidence Intervals

This article explains statistical inference focusing on parameter estimation, distinguishing point and interval estimates, describing how confidence levels relate to significance levels, and illustrating the calculation of confidence intervals for population means with a practical example using Sharpe ratios from a sample of 100 observations.

Sharpe ratioconfidence intervalparameter estimation
0 likes · 3 min read
Understanding Parameter Estimation: Point vs Interval and Confidence Intervals
Model Perspective
Model Perspective
Jul 19, 2022 · Fundamentals

How to Model Drug Distribution and Elimination Using a Two‑Compartment Pharmacokinetic Model

This article explains the assumptions, equations, and parameter‑estimation methods of a two‑compartment pharmacokinetic model for describing drug distribution, transfer between central and peripheral spaces, and elimination, and compares common dosing routes such as rapid IV bolus, constant‑rate infusion, and oral or intramuscular administration.

dosing methodsdrug distributionparameter estimation
0 likes · 2 min read
How to Model Drug Distribution and Elimination Using a Two‑Compartment Pharmacokinetic Model