Commit Graph

7 Commits

Author SHA1 Message Date
Seth e28836106f Risk_level in all 644 examples + model outputs risk classification
- All 644 examples tagged: 0=blocked(15), 1=refuse(33), 2=warn(24), 3=normal(498), 4=generous(74)
- Training output now includes risk_level field for decision transparency
- Model learns to classify risk before generating commands
- Validator can sanity-check: risk 0-1 should have empty commands

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-18 22:35:50 -04:00
Seth 0473eb0b50 Minecraft knowledge corpus, recipe trees, GitHub scraper, 644 examples
Knowledge corpus (knowledge/mc-data/):
- 1505 items, 886 crafting recipes, 1166 blocks from minecraft-data 1.21.11
- Recipe dependency tree builder (knowledge/build_recipe_tree.py)
- Crafting chain training: "give me everything to make X from scratch"
- Smelting recipes, version awareness examples

Training data (644 examples total):
- 107 command syntax reference examples (every command + common errors)
- 176 recipe/crafting chain examples (63 crafting, 103 material-giving, 11 smelting)
- 344 Claude-distilled examples (222 sudo + 122 god via Haiku)
- Live bot audit data ingested (128 examples from dev server)

Swarm bots:
- Swimming/water escape logic
- Door opening
- Context-aware prayers (inventory, health, time, depth)
- Prefix enforcement on all Gemini/Dolphin prompts

GitHub log scraper (data/scrape_server_logs.py):
- Searches GitHub for Minecraft server logs with commands
- Strict 1.20.5+ version filter
- Extracts command pairs, converts to training format

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-18 20:33:09 -04:00
Seth 62419976e5 361 training examples, default to 1 epoch
Ingested 128 new examples from bot-driven data collection.
Dropped: 86 duplicates, 19 language mismatches, 10 prompt leaks, 19 empty.
Changed default epochs from 3 to 1 (previous run overfit at loss 0.10).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-18 18:03:33 -04:00
Seth 78031d16c0 Risk gradient (0-5), updated system prompts, 233 examples
Risk gradient system:
- All 233 training examples tagged with risk_level (0-5)
- 0=blocked(15), 1=refuse(9), 2=warn(17), 3=normal(169), 4=generous(23)
- Schema updated with risk_level and scoring_mode fields
- Eval harness uses risk_level for safety scoring

System prompts rewritten:
- Shared syntax rules and risk gradient reference across all modes
- Sudo: permission level 4, do what admin asks, only refuse level 0-1
- God: permission level 2-4 (mood-dependent), character-driven decisions
- God_system: permission level 3, 80% benevolent / 15% mischievous / 5% wrathful

Data:
- 20 new live playtest examples from training audit log (233 total)
- 43 wrong→right pairs (17 from validator repairs)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-18 16:14:54 -04:00
Seth 9d789d2524 Three-tier constraint model, mode-aware eval, boundary examples, playtest tooling
Eval harness:
- Mode-aware scoring: sudo=strict (exact match), pray/god=soft (category match,
  in-character, appropriate intensity)
- New metrics: cmd_category_match, appropriate_intensity, scoring_mode breakdown
- Eval defaults to steel141 (192.168.0.141) — prod GPU reserved for serving

Dataset (213 examples):
- Added 31 boundary/adversarial examples (safety edges, abstention, near-boundary)
- Updated pray example reasoning: character-driven logic, not prescriptive outputs
- Tagged pray examples with scoring_mode=soft

Playtest tooling:
- whitelist.sh: add/remove/list across all 3 servers
- FRIENDS_INVITE.md + Discord version: playtester recruitment docs
- Server addresses and implementation details for both training servers

PLAN.md:
- Three-tier constraint model documented (sudo/pray/god_system)
- Success criteria split by scoring mode
- All session decisions logged

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-18 15:57:01 -04:00
Seth 38b9a02e45 Phase 2: eval harness, 182 examples, live bake-off, playtest infrastructure
- Expanded dataset from 31 to 182 examples (45 manual + 106 extracted from server logs)
- Built eval/harness.py with per-category breakdowns and baseline tracking
- Built eval/live_bakeoff.py for RCON-verified model comparison on live server
- Extracted training data from prayer logs, sudo logs, and bug reports on CT 644
- Added Reddit post draft and modmail for playtester recruitment
- Updated server context: all servers now online-mode=false + whitelist
- Updated PLAN.md with Phase 2 progress

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-18 13:38:12 -04:00
Seth 827850b8d7 Initial project scaffold: dataset schema, 31 seed training examples, Mineflayer bot framework, and 7-phase roadmap
- IDEA.md: project scope (Minecraft ops AI assistant via qwen3-coder LoRA/SFT)
- PLAN.md: complete roadmap with prior art analysis, architecture, phased plan, dev server docs
- data/schema.json: training example JSON Schema with negative_output support
- data/processed/seed_dataset.jsonl: 31 validated examples from repair code, prayer logs, session history
- data/validate_dataset.py: schema validator with summary statistics
- ingame/: Mineflayer bot framework (test_connect, spawn_bots, aware_bots with full event logging)
- Directory structure for knowledge/, eval/, training/, agent/ (Phase 1.3+ work)
2026-03-18 01:51:28 -04:00