Archived
will.anderson
1264af72a6
Three targeted improvements based on graph health analysis (29K nodes, 104 edges): 1. ENGRAM_WM_THRESHOLD 0.15 → 0.08: sparse graph means BFS paths carry weak signals (0.05-0.12 range). Prior threshold gatekept too aggressively. Grounded in TBRS* cognitive model (θ=0.05); 0.08 is conservative but effective. 2. ENGRAM_INHIBITION_FACTOR 0.1 → 0.2: factor=0.1 (90% suppression) on a sparse graph almost always fully silences targeted nodes. Factor=0.2 (80% suppression) maintains strong inhibition while allowing partially-suppressed nodes to remain faintly active — consistent with partial inhibition in cognitive neuroscience. 3. engram_wm_avg_weight() builtin: computes mean working_memory_weight of all promoted nodes. Returns float bits via el_from_float for EL float_to_str usage. Makes activation quality directly observable in heartbeat ISEs, distinguishing 'many weak activations' (sparse, low avg) from 'few strong' (dense, high avg). Rebuilt engram binary with new runtime.