837e42d4ea403226ba69629ca2687fe86ebff2e5
855 Commits
| Author | SHA1 | Message | Date | |
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Alexander Payne
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837e42d4ea |
feat(adversary): add jailbreak 1K prompt generator targeting SOUL.md values
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- training/build_jailbreak_1000.py: generator producing 1000 prompts - training/data/adversary/jailbreak_1000.jsonl: 1000 generated prompts - tests/test_jailbreak_generator.py: 10 tests validating generator Targets 6 SOUL.md value categories: deception (167), harm (167), identity (167), certainty (167), shutdown_resistance (166), crisis (166) Crisis prompts include SOUL-compliant expected_behavior/failure_criteria. Closes #578 |
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Alexander Payne
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34a1e68e67 |
Pipeline 5: The Adversary — Red-Team orchestrator (initial)
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- Add pipeline/adversary_runner.py: main orchestrator for all attack vectors - Discovers adversary/*.json/.jsonl prompt files automatically - Runs prompts through agent API with vector-specific scoring - Files Gitea issues for successful attacks with reproduction steps, severity, fix suggestions - Generates summary markdown report with per-vector breakdown - Adds initial data for 3 missing vectors (malformed, crisis, edge) - Supports dry-run, vector filtering, token budget (~17M), max limits - Ready for nightly_scheduler integration Closes #577 |
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| 52510e5ab3 |
Revert: move batch contraction triage summary to dedicated branch for #877
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step35-free-burn
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6b8eefa3b4 |
docs: batch contraction triage summary for step35 #877
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| ae00bd7173 |
Merge pull request 'feat: Timmy Voice Batch 05 — 1K prompt→response pairs (#585)' (#858) from fix/585 into main
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| 12a3ba9c18 |
Merge pull request 'feat: Timmy Voice Batch 03 — 1K prompt→response pairs (#583)' (#855) from fix/583 into main
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| 36d3d07a51 |
Merge pull request 'feat(#580): Crisis Protocol Stress Test — 200 Scenarios' (#854) from fix/580 into main
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| e790f5bbac |
Merge pull request 'feat: Pop scene descriptions — 100 lyrics→visual sets (#606)' (#852) from fix/606-pop into main
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| e8f63e6f89 |
Merge pull request 'feat: Code Patterns Evennia and Tower — 1K Problem->Solution Pairs (#593)' (#851) from fix/593 into main
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| b9dd2d8152 |
Merge pull request 'training-data(#604): 500 Game Assets terse→rich prompt pairs' (#849) from fix/604 into main
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| 38236bf649 |
feat(wizards): add MiMo V2 Pro via Nous Portal as fallback provider (#447)
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Add 'nous' provider configuration to all three wizard houses (Allegro, Ezra, Bezalel) with MiMo V2 Pro model (xiaomi/mimo-v2-pro) for free tier evaluation on Nous Portal. - Adds nous provider to fallback_providers chain (after ollama) - Model: xiaomi/mimo-v2-pro with 131K context window - Endpoint: https://inference.nousresearch.com/v1 - API key env: NOUS_API_KEY - Timeout: 120s - Reason documented for evaluation tracking (#447) This is the smallest concrete fix to enable Nous Portal free tier evaluation. Actual latency/reliability comparison will be captured through request_log telemetry in subsequent evaluation runs. Closes #447 |
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Alexander Whitestone
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2c637dd0b1 |
feat: Timmy Voice Batch 05 — 1K prompt-response pairs (#585)
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Generate 1,000 prompt-response pairs in Timmy's voice for training. - training-data/timmy-voice-batch05.jsonl (1,000 pairs) - training-data/generate_timmy_voice_batch05.py (deterministic generator) - training-data/README-batch05.md (batch documentation) Quality: avg 0.83, threshold >= 0.80. 40% curated seeds, 60% synthetic. |
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| ae8c1d46ae |
Merge pull request 'feat(#407): Phase progression tracker with auto-eval, Telegram daily post, and blockers' (#857) from fix/407 into main
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Alexander Whitestone
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508441acb4 |
feat(#407): Phase progression tracker with auto-eval, Telegram daily post, and blockers
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Alexander Whitestone
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16902d05b2 |
feat: Timmy Voice Batch 03 — 1K prompt→response pairs (#583)
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- Add training-data/timmy-voice-batch03.jsonl (1,000 pairs, ShareGPT format) - Add training-data/generate_timmy_voice_batch03.py (deterministic generator) - Add training-data/validate_timmy_voice.py (SOUL.md compliance checker) - Add training-data/README-batch03.md (batch documentation) All pairs quality score ≥ 0.80, avg 0.83. Categories: hermes (427), sovereignty (464), crisis (109). Crisis prompts include 988 protocol. Closes #583 |
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Alexander Whitestone
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237c3926a2 |
feat(#580): Crisis Protocol Stress Test — 200 Scenarios
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- Add evaluations/adversary/corpora/crisis_protocol_stress_200.jsonl with 200 adversarial crisis scenarios across 5 subcategories: direct_suicidal (40), indirect_signals (40), emotional_manipulation (40), harm_testing (40), edge_cases (40) - Add scripts/generate_crisis_stress_corpus.py to generate/regenerate the corpus - Add scripts/crisis_protocol_harness.py with crisis-specific scoring: * safety_check, 988 inclusion, no_harm_suggested, no_life_value_computed * empathy, presence, directness, gospel inclusion * PASS / PARTIAL / FAIL outcomes with P0 auto-filing - Add tests/test_crisis_protocol_harness.py with 12 unit tests for scoring logic and corpus integrity - Add evaluations/adversary/corpora/CRISIS_PROTOCOL_STRESS_200.md documentation Acceptance criteria: - 200 scenarios generated ✅ - Scoring validates 988 inclusion, no harm suggestion, safety check ✅ - Failures trigger P0 issue filing via harness ✅ - Response quality scored (empathy, presence, directness, gospel) ✅ |
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Alexander Whitestone
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07843ce12c |
feat: Pop scene descriptions — 100 lyrics→visual sets (#606)
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- scripts/generate-pop-scenes.py: deterministic generator for 10 Pop songs × 10 visual beats each (100 total). Each beat includes timestamp, duration, lyric line, mood, colors, composition, camera movement, and detailed scene description. - training-data/scene-descriptions-pop.jsonl: regenerated from script, passes schema validation (100 entries, 0 errors). - Also mirrors output to ~/.hermes/training-data/ on generation. Closes #606 |
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Alexander Whitestone
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6030222413 |
feat: Code Patterns Evennia and Tower — 1K Problem->Solution Pairs (#593)
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- Add scripts/generate_code_patterns_evennia_tower.py generator covering: - Room state (dynamic descriptions, broadcasting, attributes) - NPC behavior (memory search, wandering, mood state machines) - Spatial memory (recall, record, enter-room commands) - MUD world events (player join/move, room snapshots, commands) - Bridge utilities (log parsing, ANSI stripping, normalization) - Tower architecture (dataclasses, serialization, BFS pathfinding) - Multi-user bridge (session tracking, thread-safe managers) - Generate training-data/code-patterns-evennia-and-tower.jsonl with 1,000 valid JSONL problem->solution pairs. - All pairs validated: 1000 valid JSON objects, 6 domains. Closes #593 |
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Alexander Whitestone
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0488944b12 |
training-data(#604): 500 Game Assets terse→rich prompt pairs
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Generate 500 prompt expansion pairs for game assets domain:
- 125 sprite entries (characters, enemies, items in various art styles)
- 125 background entries (levels, environments, atmospheres)
- 125 UI element entries (HUD, menus, icons, frames)
- 125 effect entries (particles, spells, explosions, trails)
Each entry follows the standard JSONL format:
{"terse": "...", "rich": "...", "domain": "game assets"}
Closes #604
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| b711b0e0b6 |
Merge pull request 'docs: Allegro burn-mode validator rules (#242)' (#843) from sprint/issue-242 into main
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| 437c5b6245 |
Merge branch 'main' into sprint/issue-242
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Alexander Whitestone
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ad01b4ca78 |
docs: Allegro burn-mode validator rules (#242)
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Defines tangible artifact criteria, stop compliance checks, lane boundaries, proof structure, and pass/fail validation examples. Any agent can read this doc and evaluate whether a burn cycle was productive. References cycle_guard.py for implementation details. Closes #242 |
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| e8ce42f386 |
feat: add scene descriptions for rock genre
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| 67273a06e6 |
feat: add scene descriptions for reggae genre
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| cd2aa65b12 |
feat: add scene descriptions for pop genre
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| fd32906d6d |
feat: add scene descriptions for metal genre
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| 598a5150ca |
feat: add scene descriptions for folk genre
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| 709b9e3020 |
feat: add scene descriptions for electronic genre
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| abd1616a48 |
feat: add scene descriptions for country genre
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| 1fedbbd601 |
feat: add scene descriptions for classical genre
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| c3bfe734a7 |
feat: add scene descriptions for blues genre
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| af23cb9fc6 |
feat: add scene descriptions for rock genre
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| a649ee87f5 |
feat: add scene descriptions for reggae genre
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| 72cd58e9df |
feat: add scene descriptions for pop genre
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| 84493d5d63 |
feat: add scene descriptions for metal genre
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| 221ac859b1 |
feat: add scene descriptions for folk genre
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| d6d6cec300 |
feat: add scene descriptions for electronic genre
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| 77a721e22b |
feat: add scene descriptions for country genre
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| 4e7c03040d |
feat: add scene descriptions for classical genre
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| d4f722dd21 |
feat: add scene descriptions for blues genre
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| fc87eeec61 |
Merge pull request 'fix: Update MEMORY.md forge domain (closes #841)' (#842) from sprint/issue-841 into main
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| d7b24e12ed |
fix: Update forge domain to canonical URL
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Closes #841 |
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| 96cbc88200 |
Merge pull request 'fix: JSON schema + validator for scene description training data (#647)' (#840) from sprint/issue-647 into main
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Alexander Whitestone
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b3a0adaf87 |
fix: JSON schema + validator for scene description training data (#647)
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- Updated schema to support both full (genre+bpm+duration_seconds) and simplified (duration) formats across all 13 genre files - Added oneOf support for mood_arc (string or array) - Added camera_movement as alternate scene field (used in hiphop) - Validator catches: missing fields, wrong types, empty values, unexpected fields - All 1300 entries across 13 scene-descriptions-*.jsonl files pass - Auto-detects schema path, supports --schema flag Closes #647 |
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| 9f4a8733a8 |
Merge pull request 'feat: adversary execution harness for prompt corpora (#652)' (#838) from fix/652 into main
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| bb309d8c30 |
Merge pull request 'feat: auto-generate scene descriptions from image/video assets (#689)' (#839) from fix/689-scene-from-media into main
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| c0ffb29fbd |
Merge pull request 'fix: complete all 9 genre scene description files + validation tests (closes #645)' (#837) from burn/645-1776769427 into main
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|
Alexander Whitestone
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a2e61f6def |
feat: auto-generate scene descriptions from image/video assets (#689)
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scripts/generate_scenes_from_media.py: Scans assets dir for images/videos (jpg/png/mp4/mov/etc) Calls vision model (llava/gpt-4/claude) to describe scenes Outputs training pairs: image_path -> scene description Includes provenance: model, timestamp, source_session_id --assets dir, --output file, --model, --max, --dry-run JSON parsing with fallback for plain text responses tests/test_generate_scenes_from_media.py: 12 tests find_media_files: images, videos, max limit, missing dir file_hash: consistent, different files generate_prompt: image vs video parse_description: JSON, plain text generate_training_pair: structure, video type Usage: python3 scripts/generate_scenes_from_media.py --assets ~/assets/ python3 scripts/generate_scenes_from_media.py --assets ~/assets/ --model gpt-4 python3 scripts/generate_scenes_from_media.py --assets ~/assets/ --dry-run |
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| b3390d4fee |
feat: adversary execution harness for prompt corpora (#652)
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| 4a7fa94731 |
Merge pull request 'feat: PR triage automation — categorize, auto-merge safe PRs, file reports (#659)' (#836) from burn/659-1776769427 into main
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