Minecraft World Inference Engine

Overview

Minecraft World Inference Engine is a research-oriented system for analyzing Minecraft worlds through probabilistic inference, not packet exploits or illegal client behavior.

Instead of "reading the answer directly", the engine tries to infer:

• seed candidates, when feasible
• likely base locations
• important structures

using only chunk data that a legitimate client can already observe.

Core Idea

If a human can infer a base from patterns, the process can be modeled as a search + scoring problem.

The project treats world analysis as an inference pipeline:

• State: observed world data and extracted chunk features
• Transition: new evidence added as the player moves and loads more chunks
• Score: probability that a candidate chunk, path, structure, or seed is meaningful

Scoring Model

Score(x) = w1 * heuristic(x) + w2 * ML(x)

Where:

• heuristic(x) captures interpretable signals such as entropy, block density, light level, and unnatural placement patterns
• ML(x) is a lightweight classifier, such as logistic regression, that predicts whether a chunk is valid, active, or meaningful

System Architecture

[Minecraft Mod (Java)]
        |
        v
   [Chunk Data]
        |
        v
 [IPC / Local API]
        |
        v
 [Core Engine (C)]
        |
        v
[Scoring + Inference + DB]
        |
        v
[Result -> Mod Overlay]

Components

1. Minecraft Mod (Java)

Responsible for client-side observation and visualization:

• scans already-loaded chunks
• extracts lightweight features from visible world state
• forwards structured signals to the core engine
• renders overlays such as heatmaps and highlights

Example feature inputs:

• block placement patterns
• container presence such as chests or furnaces
• light and activity signals
• bedrock pattern information

2. Core Engine (C)

The core engine is the main inference layer, responsible for:

• base detection
• optional seed inference
• structure prediction
• probabilistic ranking and search
• local state management and caching

Possible techniques:

• scoring systems
• beam search
• candidate ranking
• incremental evidence fusion

3. Local Database

The database stores compact signals, not raw world dumps.

Example schema:

• chunk_x
• chunk_z
• activity_score
• block_density
• container_count
• bedrock_thickness
• confidence

Key Features

1. Base Detection

The system does not rely on direct remote chest scanning. Instead, it scores chunks using signals that often correlate with player activity.

Score(chunk) = sum(wi * featurei)

Candidate features:

• player-placed blocks
• torch or light density
• container presence
• unnatural local patterns

2. Trail Tracking

The engine can detect and follow traversal evidence such as:

• roads
• nether highways
• portal activity

These trails can be used to rank nearby chunks as possible base targets.

3. Chunk Heatmap

Visual output may include:

• high-probability base zones
• medium-confidence activity areas
• regions worth rescanning after new evidence arrives

4. Structure Prediction

Once enough observations accumulate, the engine can attempt to predict likely locations of:

• strongholds
• fortresses
• bastions

5. Seed Inference

Advanced mode:

• input: partial world observations
• output: top-K seed candidates

Possible methods:

• probabilistic scoring
• beam search
• candidate pruning

Performance Strategy

The system is designed to stay lightweight on the client side:

• only scans chunks that are already loaded
• keeps feature extraction cheap
• moves heavier computation into the C core
• uses caching and state deduplication
• updates the database incrementally

Non-Goals

This project explicitly does not aim to:

• x-ray or read hidden containers remotely
• exploit packets
• bypass anti-cheat
• brute-force the entire world blindly

The design goal is inference from legitimate observations, not unauthorized access.

Design Philosophy

The world is treated as a search-and-scoring problem rather than a direct extraction problem.

This leads to a hybrid approach:

• interpretable heuristics for fast reasoning
• lightweight ML for ranking and validation
• incremental search as more evidence becomes available

Conceptually, it is closer to an inference engine than a cheat client.

Development Plan

Phase 1

• build the CLI core engine in C
• implement basic scoring
• support first-pass base detection

Phase 2

• build the Minecraft mod in Java
• add chunk scanning
• add overlay rendering

Phase 3

• connect mod and core via IPC or local API
• add local database and history tracking
• support incremental session analysis

Phase 4

• add ML-based scoring
• add seed inference
• add adaptive beam search

Final Goal

Build a system that can reason effectively about Minecraft worlds without hacks or exploits by turning world interpretation into an inference problem.

In short:

• not hacking
• not packet exploitation
• not raw world dumping
• yes to search, scoring, ranking, and probabilistic reasoning

Current Implementation

This repository now includes a working Phase 1 foundation:

• a C CLI core engine
• CSV-based chunk signal ingestion
• stdin/live batch ingestion for external clients
• heuristic scoring for base, trail, activity, and structure ranking
• append-only local snapshot persistence
• a Java bridge scaffold for future Minecraft client integration
• a Fabric client mod skeleton that can trigger scans in-game
• sample data for smoke testing

Current repository layout:

core/
  include/infercraft/
  src/
data/
java-bridge/
fabric-mod/
samples/
build.ps1

Input Schema

The Phase 1 CLI reads chunk observations from CSV.

Required columns:

dimension,chunk_x,chunk_z,player_placed_ratio,light_density,container_count,
unnatural_pattern_score,trail_score,bedrock_signal,activity_noise

Example:

overworld,14,-8,0.87,0.71,5,0.81,0.18,0.05,0.08
nether,2,40,0.22,0.36,0,0.48,0.90,0.62,0.09

CLI Usage

Build:

./build.ps1

If PowerShell blocks script execution on Windows:

powershell -ExecutionPolicy Bypass -File .\build.ps1

Analyze sample data:

./infercraft.exe --input samples/base_signals.csv --top 5

Analyze via stdin:

cmd /c "type samples\base_signals.csv | infercraft.exe --input - --no-persist"

Rank by another signal:

./infercraft.exe --input samples/base_signals.csv --rank-by trail --top 3

Disable persistence:

./infercraft.exe --input samples/base_signals.csv --no-persist

Available CLI options:

• --input <path>: source CSV of chunk observations
• --db <path>: append-only local snapshot file
• --top <count>: number of top-ranked chunks to print
• --rank-by <base|activity|trail|structure>: ranking mode
• --no-persist: skip writing snapshot output

Build requirements:

• clang, gcc, or cl available on PATH
• build.ps1 also checks common msys64 compiler locations on Windows

Output

The CLI produces:

• best base candidate
• best trail candidate
• best structure candidate
• top-N ranked chunks for the selected metric
• optional snapshot rows appended to data/chunk_signals.csv

Java Bridge

The java-bridge/ directory contains a plain Java transport layer for feeding chunk signals into the core engine over stdin.

This gives the project a clean integration seam:

• Minecraft client or mod collects chunk features
• Java bridge serializes them to the core CSV schema
• the C engine ranks candidates and returns text output

Because the current machine has a Java runtime but no javac on PATH, the bridge source was scaffolded but not compiled here.

Fabric Mod Skeleton

The fabric-mod/ directory contains a separate Fabric client project that reuses the Java bridge source and is designed to call infercraft.exe from inside Minecraft.

Current skeleton features:

• Fabric client entrypoint
• I keybind for manual scans
• lightweight HUD status overlay
• simple local config bootstrap
• chunk signal collection around the player
• process bridge into the C core

Runtime flow:

1. build infercraft.exe in the repository root
2. launch the Fabric client project
3. press I in-game
4. inspect the returned best-candidate summary in the HUD

Environment note:

• the Fabric project targets Java 17
• this workspace currently does not have Gradle or a Java 17 JDK, so the Fabric scaffold was created but not compiled here

Notes

Phase 1 intentionally uses a simple CSV snapshot store instead of SQLite so the project can stay portable and dependency-light while the inference model is still evolving.

The next natural step is to add:

• richer feature extraction
• chunk clustering across nearby coordinates
• IPC between the C engine and a Java mod
• stronger structure and seed inference pipelines