Lower memory thresholds, add dedup, store from free-mode

[AaronGoldsmith]

Summary

• Lower similarity thresholds from 0.9/0.7 to 0.5/0.3 (real similarities are 0.3-0.5)
• Add dedup check in memory.store() to prevent duplicate task+winner entries
• Store memory entries from free-mode matches via record_verdict.py

Why

Memory was never influencing agent selection because thresholds were too high for real embedding distances. Dedup prevents polluting the vector index.

🤖 Generated with Claude Code

[Copilot]

Pull request overview

Adjusts Mobius’s vector-memory behavior so past task outcomes can influence agent selection in practice, while reducing duplicate memory entries and capturing outcomes recorded via the judge script.

Changes:

• Lowered similarity thresholds used to choose specialist/ensemble selection strategies.
• Added a duplicate check in Memory.store() to skip repeated (task_text, winning_agent_id) entries.
• Extended record_verdict.py to store a memory entry for the winning agent after recording a verdict.

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 3 comments.

File	Description
src/mobius/memory.py	Adds dedup logic before inserting memory entries.
src/mobius/config.py	Lowers similarity thresholds to make memory influence selection more often.
.claude/skills/mobius-judge/scripts/record_verdict.py	Stores memory entries when verdicts are recorded via the script.

Comments suppressed due to low confidence (1)

src/mobius/memory.py:56

• The SELECT-then-INSERT dedup check is not atomic. If two processes/threads call store() concurrently, both can pass the SELECT and insert duplicates. Consider enforcing a DB-level uniqueness constraint (e.g., UNIQUE index on (task_text, winning_agent_id)) and switching the insert to INSERT ... ON CONFLICT DO NOTHING / INSERT OR IGNORE to make dedup race-free and avoid the extra round-trip.

        existing = self.conn.execute(
            "SELECT id FROM memory WHERE task_text = ? AND winning_agent_id = ?",
            (entry.task_text, entry.winning_agent_id),
        ).fetchone()
        if existing:
            logger.debug(
                "Duplicate memory entry for agent %s on task, skipping",
                entry.winning_agent_id,
            )
            return

        row = dict_to_row(entry.model_dump(exclude={"task_embedding"}))
        cols = ", ".join(row.keys())
        placeholders = ", ".join(["?"] * len(row))
        self.conn.execute(
            f"INSERT INTO memory ({cols}) VALUES ({placeholders})",
            list(row.values()),
        )

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