Four hardcoded constants in the selector and feedback tracker are now
user-tunable via [router.bandit]:
- quality_alpha (EMA smoothing, default 0.3)
- min_observations (samples before observed overrides heuristic, default 3)
- observed_weight (observed/heuristic blend ratio, default 0.7)
- strength_bonus (quality bonus for Strengths-tagged arms, default 0.15)
Each field treats 0 as 'use default', so an empty TOML block is
byte-identical to pre-config behaviour. BanditParams is plumbed via
router.Config{Bandit: ...} and resolveBanditParams() centralises the
fallback so every call site shares the same defaults.
QualityTracker, scoreArm, bestScored, and selectBest signatures now
take the configured values directly rather than reaching for package-
level constants. Tests updated to pass BanditParams{} (defaults) or
explicit overrides where they validate the new tuning paths.
Tracks item #3 from the 'Bandit selector — design decisions deferred'
TODO entry — ships independently of the EMA vs SLM strategic decision.
Implements P-1 through P-6 of the prefer-routing-policy plan.
Adds a config knob that biases routing toward local arms, cloud
arms, or leaves selection unchanged. Default "auto" is
byte-identical to pre-change behavior (the new armTier path with
PreferAuto returns the same value as the old single-arg function).
Mechanism diverged from the plan after empirical testing:
The plan called for a score multiplier applied in bestScored.
Tests revealed the existing cost-floor math (scoreArm divides by
weighted cost which collapses to ~0.001 for free local arms) gives
local arms a ~280x raw-score advantage that a 0.3-0.5 multiplier
can't overcome. A tier-shift in armTier turned out cleaner:
PreferLocal: cloud arms (true API, IsLocal=false && !IsCLIAgent)
get +2 tier shift, landing behind locals.
PreferCloud: IsLocal arms get +2 tier shift, landing behind
cloud. SLM tier-0 arms shift to tier 2 — still
below cloud's tier 3 — so the SLM-protection
semantic (small stuff stays on the small model)
survives PreferCloud. This matches the open
question in the plan, now resolved as: yes, SLMs
keep winning under PreferCloud by design.
The policyMultiplier was kept in bestScored as a within-tier
nudge (mostly cosmetic in practice given the cost-floor dynamics
described above; could matter when costs are calibrated). Worth
revisiting once router-wide cost calibration lands.
Strengths cross-tier promotion is unaffected: the promoted-set
path in selectBest bypasses armTier entirely, so a strongly-tagged
cloud arm still wins SecurityReview tasks under PreferLocal
(validated by TestPreferPolicy_StrengthsBeatsMultiplier).
CLI-agent subprocess arms count as "local" for PreferLocal
purposes — they proxy to cloud but the user-visible behavior is
local. Users who want to exclude them can use --provider X.
Forced arms (--provider X) and incognito take priority over the
policy: forced arm test pins this, incognito-still-wins test pins
the LocalOnly hard filter dominating PreferCloud.
Test coverage (prefer_test.go): ParsePreferPolicy / String round
trips; policyMultiplier table; acceptance scenarios across all
three policies with adjacent-tier arms; SLM-still-wins under
PreferCloud; Strengths beats multiplier; forced-arm bypass;
incognito beats prefer; lone cloud arm wins when no local feasible.
Refs: docs/superpowers/plans/2026-05-23-prefer-routing-policy.md
Apply gofmt -w across the codebase (struct field comment realignment
only — no semantic changes) and silence two errcheck warnings on
fmt.Sscanf / fmt.Fprintf return values in internal/router/discovery
with explicit `_, _ =` discards. Required so `make check` is green
before tagging v0.1.0.
Add EffortLevel (auto/low/medium/high) as a provider-agnostic reasoning
control, replacing the Capabilities.Thinking bool. Each provider maps
the level to its native parameter: Anthropic budget tokens (1K/8K/16K),
OpenAI reasoning_effort (low/medium/high), Google thinking budget
(1K/8K/16K). Task classification auto-infers effort from TaskType and
complexity; filterFeasible excludes arms that lack the required level.
vikingowl