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chore: finalize repo audit hygiene (#257)

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Elliot Chen
2026-06-06 13:59:12 +08:00
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parent ab23e40b28
commit 00f1dfaec5
27 changed files with 459 additions and 199 deletions
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use-cases/openher/integration/context_features.py
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@ -1,8 +1,8 @@
"""
Neural network context features — showing how EverCore expands
Neural network context features — showing how EverOS expands
the persona engine's perception from 8D to 12D.
The 4 additional relationship dimensions from EverCore allow the
The 4 additional relationship dimensions from EverOS allow the
neural network to produce different behavioral signals depending
on the history between user and persona.
@ -13,21 +13,21 @@ Full source: https://github.com/kellyvv/OpenHer/blob/main/engine/genome/genome_e
# 5D Drive System (internal motivation)
# ══════════════════════════════════════════════
DRIVES = ['connection', 'novelty', 'expression', 'safety', 'play']
DRIVES = ["connection", "novelty", "expression", "safety", "play"]
# ══════════════════════════════════════════════
# 8D Behavioral Signals (neural network output)
# ══════════════════════════════════════════════
SIGNALS = [
'directness', # 0=indirect hints → 1=straight talk
'vulnerability', # 0=guarded → 1=emotionally open
'playfulness', # 0=serious → 1=playful/teasing
'initiative', # 0=reactive → 1=proactive leading
'depth', # 0=small talk → 1=deep conversation
'warmth', # 0=cold/distant → 1=warm/caring
'defiance', # 0=compliant → 1=rebellious/stubborn
'curiosity', # 0=indifferent → 1=intensely curious
"directness", # 0=indirect hints → 1=straight talk
"vulnerability", # 0=guarded → 1=emotionally open
"playfulness", # 0=serious → 1=playful/teasing
"initiative", # 0=reactive → 1=proactive leading
"depth", # 0=small talk → 1=deep conversation
"warmth", # 0=cold/distant → 1=warm/caring
"defiance", # 0=compliant → 1=rebellious/stubborn
"curiosity", # 0=indifferent → 1=intensely curious
]
# ══════════════════════════════════════════════
@ -36,31 +36,30 @@ SIGNALS = [
CONTEXT_FEATURES = [
# ── 8D from Critic LLM (per-turn perception) ──
'user_emotion', # -1=negative → 1=positive
'topic_intimacy', # 0=professional → 1=intimate
'time_of_day', # 0=morning → 1=late night
'conversation_depth', # 0=just started → 1=deep conversation
'user_engagement', # 0=dismissive → 1=invested
'conflict_level', # 0=harmonious → 1=conflict
'novelty_level', # 0=routine topic → 1=novel topic
'user_vulnerability', # 0=guarded → 1=open
# ── 4D from EverCore (cross-session relationship) ──
'relationship_depth', # 0=stranger → 1=old friend
'emotional_valence', # -1=negative history → 1=positive history
'trust_level', # 0=no trust → 1=deep trust
'pending_foresight', # 0=nothing pending → 1=unresolved concern
"user_emotion", # -1=negative → 1=positive
"topic_intimacy", # 0=professional → 1=intimate
"time_of_day", # 0=morning → 1=late night
"conversation_depth", # 0=just started → 1=deep conversation
"user_engagement", # 0=dismissive → 1=invested
"conflict_level", # 0=harmonious → 1=conflict
"novelty_level", # 0=routine topic → 1=novel topic
"user_vulnerability", # 0=guarded → 1=open
# ── 4D from EverOS (cross-session relationship) ──
"relationship_depth", # 0=stranger → 1=old friend
"emotional_valence", # -1=negative history → 1=positive history
"trust_level", # 0=no trust → 1=deep trust
"pending_foresight", # 0=nothing pending → 1=unresolved concern
]
# Neural network dimensions
N_DRIVES = len(DRIVES) # 5
N_CONTEXT = len(CONTEXT_FEATURES) # 12 (8 + 4 from EverCore)
N_SIGNALS = len(SIGNALS) # 8
RECURRENT_SIZE = 8 # Internal "mood" state
N_DRIVES = len(DRIVES) # 5
N_CONTEXT = len(CONTEXT_FEATURES) # 12 (8 + 4 from EverOS)
N_SIGNALS = len(SIGNALS) # 8
RECURRENT_SIZE = 8 # Internal "mood" state
INPUT_SIZE = N_DRIVES + N_CONTEXT + RECURRENT_SIZE # 5 + 12 + 8 = 25
HIDDEN_SIZE = 24
# Architecture: 25D input → 24D hidden (tanh) → 8D output (sigmoid)
# The 4 EverCore dimensions mean the same neural network produces
# The 4 EverOS dimensions mean the same neural network produces
# DIFFERENT behavioral signals for strangers vs. old friends,
# even with identical conversation context.