Semantic memory and consolidation

Episodes are events. Semantic memory is what the agent knows about the user after seeing many events: «prefers code examples to formulas», «knows linear algebra», «goal: LoRA fine-tuning by December». It is built through consolidation: periodically the model reads the new episodes and updates a list of facts.

Every fact carries: the source (which episodes), the confidence, the time, the validity (permanent / for now / expires). That makes three things possible:

• Contradictions: a new fact «wants formulas» against an old «prefers code» → the new one wins if it is fresher and supported by several episodes; otherwise ask the user.
• Forgetting: facts without support for a long time fall in confidence and are removed — otherwise the list grows and becomes wrong.
• Decisions: the agent reads the facts before it chooses the depth of explanation, the difficulty, the tone. If the facts do not affect any decision the memory is decoration.

Measure: the decisions with vs without the facts; the share of facts that are correct on human inspection; the share of contradictions that were resolved correctly.

import json, time

CONSOLIDATE = """Here are known FACTS about the user (a JSON list) and NEW EPISODES.
Update the list of facts: add, raise/lower the confidence, mark contradictions. Every fact:
{"text": ..., "confidence": 0-1, "sources": [episode-id], "valid_until": null|date, "type": "preference|knowledge|goal"}
Answer ONLY with the new JSON list."""

def consolidate(llm, facts, episodes):
    out = llm(CONSOLIDATE + "\nFACTS: " + json.dumps(facts, ensure_ascii=False) + "\nNEW EPISODES: " + json.dumps(episodes, ensure_ascii=False), temperature=0, json_mode=True)
    new = json.loads(out)
    return forget(new)

def forget(facts, half_life_d=90, min_conf=0.3):
    now = time.time(); out = []
    for f in facts:
        age = (now - f.get("last_supported", now)) / 86400
        f["confidence"] *= 0.5 ** (age / half_life_d)
        if f["confidence"] >= min_conf and not (f.get("valid_until") and f["valid_until"] < time.strftime("%Y-%m-%d")):
            out.append(f)
    return out

def to_decision(facts):
    pref = [f["text"] for f in facts if f["type"] == "preference" and f["confidence"] > 0.6]
    return {"explanation_depth": "code" if any("code" in p for p in pref) else "intuition"}