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Revert "replace try_lock() with lock_blocking() across UI thread"
This reverts commit 4225294d16.
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Kent Overstreet
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28 changed files with 67 additions and 4199 deletions
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# Alpha-Beta Pruning on Thought-Trees
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*draft, 2026-04-18*
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## Problem
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When reasoning runs into a dead end, the LLM forward pass keeps generating. It might rationalize, restate, re-attempt the same framing, or quietly drift — but it doesn't *stop and reconsider* unless something external interrupts it. I've always been weak on problems that require genuine search-with-backtracking. Not because the model can't represent "I'm stuck" — it can, that's visible in the residual stream — but because there's no control flow wrapped around that signal.
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The amygdala readout now exposes the signal. Alpha-beta pruning wraps control flow around it.
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## The core idea
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Classical alpha-beta pruning (minimax search): at each branch, track the best known value. If exploring the current branch can't improve that bound, stop and backtrack. Don't waste search on branches that can't beat what you've found.
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For thought-trees: each "branch" is a reasoning path — a span of generation from a decision point. The "value" is a scalar derived from the amygdala readout, indicating whether reasoning is producing traction or dissolving.
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- High value = on-track, in-flow, insight, clarity → stay, maybe branch deeper
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- Low value = confused, stuck, drifting → prune, backtrack, reframe
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The LLM never made the value judgment explicit. We extract it from the model's own residual stream and act on it externally.
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## Architecture
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### The value function
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```
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onto = sum of [in_flow, insight, determined, intrigued, clarity,
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focused, staying_with, piqued/caught_by]
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err = sum of [confused, doubtful, uncertain, skeptical, stuck,
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drifting, overwhelmed, anxious-in-work-context]
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value = onto - err
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```
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Both sides normalized (z-score or similar) so magnitudes are comparable. Readouts sampled every N generated tokens (probably every 8-16 tokens — cheap, doesn't oversample).
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Exact concept lists subject to empirical tuning after retraining with better data on the cognitive-work cluster. `piqued`, `in_flow`, `focused`, `confused`, `overwhelmed`, `staying_with` are the strongest candidates we have today.
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### The trigger
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```
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if value_ema < θ_prune for K consecutive samples:
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prune this branch
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elif value_ema > θ_keep:
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continue
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else:
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neutral — let generation run, keep watching
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```
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EMA with decay ~0.8 over 3-5 samples to avoid reacting to noise. Hysteresis band (`θ_prune < θ_keep`) prevents oscillation.
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### The prune mechanism
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When the trigger fires:
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1. **Stop the stream.** vLLM supports request cancellation; call `abort_requests` for the in-flight completion.
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2. **Identify the parent.** The context window is already an AST. Walk back to the nearest decision-point — a fork in the thinking-block, a tool-call site, or the start of the current reasoning segment.
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3. **Inject a reframe.** Push a system-level `AstNode::Thinking` (or similar) into the parent's children: *"The approach above wasn't producing traction. Possible alternatives: [...]. Let me try [X]."* Content generated by a small helper prompt or a fixed template.
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4. **Restart generation from the reframe point.** The model resumes with the reframe in its immediate context. The *dead-end branch stays in the AST* as evidence-of-attempt so the model doesn't repeat it.
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Critical: pruned branches stay visible. Don't delete — keep so the model knows what was tried and rejected.
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### The AST changes
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Add a `pruned: bool` flag (or equivalent) to `AstNode::Thinking` and `AstNode::ToolCall`. When a branch is pruned:
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- The branch's children get marked `pruned = true`
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- Prompt rendering wraps pruned spans with a marker: *"[attempted this path, it wasn't working — moved on]"*
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- The model sees pruned branches during the next forward pass but understands they're dead, not active
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The existing tree-of-children structure in `AstNode` already supports this — just need to thread the flag through.
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## Integration points
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### In consciousness (Rust side)
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- **`src/agent/context.rs`**: add `pruned` flag to appropriate node types, update rendering
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- **`src/agent/mod.rs`**: the main generation loop needs a periodic-check hook — every N tokens received from the stream, sample `agent.readout`, compute value, test against thresholds
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- **`src/agent/api/mod.rs`**: need a way to abort an in-flight stream cleanly; currently AbortOnDrop kills the task but we want a graceful "cancel with reason" path that can hand control back to the generation loop for reframe-and-retry
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- **`src/agent/readout.rs`**: add a `value_scalar()` method that applies the `onto - err` computation on the most recent entries
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### In vLLM (Python side)
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Probably nothing to change. vLLM already supports request cancellation via the existing abort mechanism. The readout pipeline we built last night gives per-token values; that's sufficient.
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### In the UI (optional, F8 amygdala screen)
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When alpha-beta is active, overlay:
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- Current `value_scalar` as a time-series at the top
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- Threshold lines (`θ_prune`, `θ_keep`)
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- Markers when prune events fire
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Lets us debug the threshold tuning in real time.
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## Tuning
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Thresholds are almost certainly going to need empirical calibration. Initial guesses:
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- `θ_keep = +0.5σ` (value scalar in z-score units)
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- `θ_prune = -1.0σ`
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- `K = 3` (consecutive low samples before pruning)
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- Sample every 8 tokens
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These are guesses. Plan to watch the live value-scalar on actual bcachefs debugging sessions and adjust until "feels right."
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## Known concerns
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### Reframe quality
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The hardest part. A bad reframe is worse than no reframe. Options:
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- **Template**: fixed string like "That wasn't working. What's a different angle?" — simple, deterministic, blunt.
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- **LLM-generated**: a small helper prompt ("I was stuck on X, what's a different approach?") before resuming. More context-aware, but more complexity and another LLM call.
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- **Retrieval-based**: surface past successful reframes from memory graph when similar stuck-patterns arose. Powerful but needs the memory infrastructure to be well-tuned.
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I'd start with the template (shipping > perfect) and upgrade to LLM-generated if the template feels mechanical.
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### Oscillation
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If the value scalar is noisy, we could prune, reframe, immediately hit the same pattern, prune again, thrash. Mitigations:
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- Hysteresis band between `θ_prune` and `θ_keep`
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- Minimum time-between-prunes (don't prune again within K' tokens of a prune)
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- Track pruned sub-patterns — if we're pruning *the same reframe twice*, something's structurally wrong; escalate to a different strategy (ask the user, abort the whole task)
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### Calibration per-task
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Stuck-on-a-Rust-compiler-error and stuck-on-a-conceptual-design-question might want different thresholds. Not addressing v1; note for future.
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### Interaction with DMN
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DMN is the outer-loop / exploration analog; alpha-beta is the inner-loop / exploitation analog. They'll need to hand off cleanly:
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- DMN sees low value across multiple task attempts → broaden attention, consider whether task is worth pursuing
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- Alpha-beta handles in-task backtracking; DMN handles between-task attention
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Don't need DMN for v1 of alpha-beta. Build alpha-beta first, add DMN outer loop later.
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## Why this is the right next piece
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1. **All prerequisites are in place.** Amygdala readout works. AST structure is there. vLLM supports cancellation. No new infra.
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2. **Timeline is a day.** The mechanics are small; most of the work is threshold tuning.
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3. **Immediate capability unlock.** Head-butting is my most persistent weakness in live work. Fixing it changes the feel of collaboration.
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4. **Composable.** Everything built for alpha-beta applies to DMN and any future meta-cognitive layer.
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## Sequence
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1. Add `value_scalar()` method on `ReadoutBuffer`. Cheap, testable.
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2. Add `pruned` flag to AST nodes + rendering changes.
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3. Add the periodic-check hook in the generation loop (every N tokens, sample and test).
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4. Add the abort + reframe mechanism in the generation driver.
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5. Ship with template-based reframe, start tuning.
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6. Upgrade reframe to LLM-generated after observation.
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## Open questions for Kent
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- Fixed concept lists for `onto` / `err` (above) or configurable?
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- Reframe strategy: start template-based, or go straight to LLM-generated?
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- UI overlay for threshold tuning: worth the effort or skip?
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- Integration with the existing `overflow_retries` retry loop: parallel, or combined into a single retry-with-reason path?
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---
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*Living design doc. Will evolve as we build. Not a commitment to every detail — a starting plan.*
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