feat(SNNBot): recency-weighted exemplars for fast re-acquisition

Exponential decay (0.95^age) makes newest exemplars dominate the
weighted mean. Old stale exemplars fade naturally, so re-learning
after target movement is always fast regardless of buffer history.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

SNNBot_garage/src/reservoir.nim

@@ -26,6 +26,7 @@ proc initBinaryAimer*(): BinaryAimer =
 
 proc forward*(aimer: var BinaryAimer, input: BitVec80): float =
   ## Kernel regression: weighted circular mean over active exemplars.
+  ## Weights by similarity^2 and exponential recency decay (0.95^age).
   ## Returns angle in [0,360) degrees, or -1.0 sentinel when cold (no weight).
   var sinSum = 0.0
   var cosSum = 0.0
@@ -37,7 +38,9 @@ proc forward*(aimer: var BinaryAimer, input: BitVec80): float =
       sim += popcount(input[w] and aimer.exemplars[i].pattern[w]).int
     let excess = sim - MIN_SIM
     if excess <= 0: continue
-    let weight = float(excess * excess)
+    let age = (aimer.nextSlot - 1 - i + MAX_K) mod MAX_K
+    let recency = pow(0.95, age.float)
+    let weight = float(excess * excess) * recency
     let rad = degToRad(aimer.exemplars[i].angle)
     sinSum += weight * sin(rad)
     cosSum += weight * cos(rad)