fix(memory): run extended-memory predictor concurrently with rerank

[jkyberneees]

Problem

Every turn, extended-memory recall runs under a hard 5-second budget (FormatExtendedContext). Inside that window, queryAtomsWithPrediction executed two LLM calls sequentially:

1. the paid rerank of literal-query results
2. predictor.Predict for follow-up intent generation

When the memory LLM is slow (by default it reuses the main reasoning model), the rerank consumed most of the 5s window, so Predict inherited a nearly-expired context and failed before its LLM call could complete:

extended memory: predictor LLM failed: context deadline exceeded
extended memory: predicted-intent generation failed: predictor: context deadline exceeded

Fix

Run the predictor concurrently with the literal query + rerank, so both LLM calls share the full window in parallel instead of splitting it sequentially.

• The predictor only calls the LLM (no shared state); the atom store and vector index are mutex-guarded — race-safe (verified with -race).
• The result channel is buffered (cap 1), so the early-return error path cannot leak the goroutine.
• Error handling, stats tracking (trackErr), and the follow-up sink behavior are unchanged.

Testing

• go test -race ./internal/memory/... -count=1 — all pass
• New regression test TestPredictionRunsConcurrentWithRerank: the rerank mock blocks until the predictor starts; under the old sequential behavior this deadlocks and fails the test.

Note

If a single memory LLM call exceeds the 5s per-turn budget on its own, prediction still degrades gracefully to literal-query recall (intended latency cap). Operators hitting that should configure a cheaper dedicated model via memory.extended.llm, as already recommended in docs/EXTENDED_MEMORY.md.

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Status	Name	Latest Commit	Preview URL	Updated (UTC)
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