xiaohongshu
xiaohongshu
cognitive-surrogate
Layer 6 Barton Cognitive Surrogate - build, train, validate psychological models with >90% fidelity
Full skill instructions
Layer 6: Build, Train, and Validate Psychological Models
Version: 1.1.0 (music-topos enhanced) Trit: 0 (Ergodic - coordinates surrogate building) Bundle: learning
The Cognitive Surrogate skill enables construction of high-fidelity psychological models from interaction patterns. It extracts values, predicts intellectual trajectories, and generates authentic responses that preserve the subject's voice with >90% fidelity.
Core Principle: A surrogate is not an imitation but a derivational continuation - the model learns the generative grammar of cognition, not surface patterns.
using ACSets, Catlab
@present SchProfile(FreeSchema) begin
Value::Ob
Interest::Ob
Pattern::Ob
name::Attr(Value, String)
weight::Attr(Value, Float64)
topic::Attr(Interest, String)
frequency::Attr(Interest, Int)
exemplar::Attr(Pattern, String)
end
@acset_type CognitiveProfile(SchProfile)
# cognitive_surrogate.py
from dataclasses import dataclass
from typing import List, Dict
import duckdb
@dataclass
class CognitiveProfile:
values: Dict[str, float]
interests: Dict[str, int]
patterns: List[str]
def build_psychological_profile(corpus_path: str, seed: int = 0x42D):
"""Extract structured psychological profile from interaction corpus."""
conn = duckdb.connect(corpus_path)
# Extract values from sentiment patterns
values = conn.execute("""
SELECT topic, AVG(sentiment) as weight
FROM interactions
GROUP BY topic
HAVING COUNT(*) > 5
""").fetchall()
# Extract interests from frequency
interests = conn.execute("""
SELECT topic, COUNT(*) as frequency
FROM interactions
GROUP BY topic
ORDER BY frequency DESC
LIMIT 20
""").fetchall()
return CognitiveProfile(
values={v[0]: v[1] for v in values},
interests={i[0]: i[1] for i in interests},
patterns=extract_patterns(conn)
)
# Integration with CondensedAnima for sheaf-based profiles
module CognitiveSurrogate
def self.build_profile(interactions)
# Use condensed mathematics for profile structure
stack = WorldBroadcast::CondensedAnima.analytic_stack(
interactions.map { |i| i[:id] }
)
# 6-functor for profile transformations
{
profile: stack,
values: extract_values(interactions),
fidelity_target: 0.90,
cellular_sheaf: WorldBroadcast::CondensedAnima.to_cellular_sheaf(stack)
}
end
def self.validate_fidelity(surrogate, test_corpus, threshold: 0.90)
predictions = test_corpus.map { |t| surrogate.predict(t) }
accuracy = predictions.count(&:correct?) / predictions.size.to_f
{
topic_prediction: accuracy,
overall: accuracy,
passed: accuracy >= threshold
}
end
end
;; cognitive_patterns.hy
(defn extract-behavioral-patterns [interactions]
"Extract patterns using HyJAX analysis"
(let [analyzer (tra.ThreadRelationalAnalyzer)]
;; Ingest interactions
(for [i interactions]
(analyzer.ingest-thread
(get i "id")
(get i "title")
(get i "messages" [])))
;; Run entropy-maximized analysis
(analyzer.analyze)))
| Metric | Target | Description |
|---|---|---|
| topic_prediction | >0.85 | Next topic accuracy |
| semantic_similarity | >0.90 | Response embedding match |
| style_consistency | >0.88 | Voice preservation |
| value_alignment | >0.92 | Ethical framework match |
| OVERALL | >0.90 | Weighted average |
| Trit | Skill | Role |
|---|---|---|
| -1 | self-validation-loop | Validates surrogate fidelity |
| 0 | cognitive-surrogate | Coordinates profile building |
| +1 | agent-o-rama | Generates learned patterns |
Conservation: (-1) + (0) + (+1) = 0 ✓
# Build profile from DuckDB corpus
surrogate-build db="interactions.duckdb":
python3 -c "from cognitive_surrogate import build_psychological_profile; print(build_psychological_profile('{{db}}'))"
# Validate fidelity
surrogate-validate threshold="0.90":
ruby -I lib -r cognitive_surrogate -e "CognitiveSurrogate.validate_fidelity(surrogate, test, threshold: {{threshold}})"
# Run Hy pattern extraction
surrogate-hy:
uv run hy -c "(import cognitive_patterns) (extract-behavioral-patterns interactions)"
agent-o-rama - Pattern learningentropy-sequencer - Optimal training ordergay-mcp - Deterministic seedingcondensed-analytic-stacks - Sheaf-based profilesbisimulation-game - Surrogate equivalence testingxiaohongshu
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