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scoring: drop dead Elo/agent_budget block in consolidation_plan

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The graph-health logic in consolidation_plan_inner computed
reasonable agent counts based on graph metrics (α, Gini, hub
dominance), then immediately overwrote them with an Elo-weighted
flat-budget distribution, or — if no agent-elo.json existed —
with a simple budget/N per type.

Nothing in the codebase writes agent-elo.json; it's external state
that never gets maintained. So the effective behavior was always the
"No Elo ratings — equal distribution" branch, which just bucketed
agent_budget evenly across active agent types and discarded
everything the graph analysis had just decided.

Keep the graph-health allocation (α → linker count, Gini → distill
bump, organize/distill/split proportional). Drop:

- The entire Elo / agent_budget block at the end of
  consolidation_plan_inner
- Config.agent_budget field and its default (1000)
- agent_budget: 40 from Kent's config.json5
- The local agent_types binding inside the function — it was only
  used by the now-deleted block. Config.agent_types stays; it has
  other consumers.

Co-Authored-By: Proof of Concept <poc@bcachefs.org>
This commit is contained in:
Kent Overstreet 2026-04-16 16:08:20 -04:00
parent 60de579305
commit 18b7fd0535
2 changed files with 0 additions and 48 deletions
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2
src/config.rs
View file

@ -58,7 +58,6 @@ pub struct Config {
#[serde(default)]
pub agent_nodes: Vec<String>,
pub llm_concurrency: usize,
pub agent_budget: usize,
/// Stream chunk timeout in seconds (no data = timeout).
#[serde(default = "default_stream_timeout")]
pub api_stream_timeout_secs: u64,
@ -101,7 +100,6 @@ impl Default for Config {
personality_nodes: vec!["identity".into(), "core-practices".into()],
agent_nodes: vec!["identity".into(), "core-practices".into()],
llm_concurrency: 1,
agent_budget: 1000,
api_stream_timeout_secs: default_stream_timeout(),
scoring_interval_secs: default_scoring_interval_secs(),
scoring_response_window: default_scoring_response_window(),

46
src/hippocampus/neuro/scoring.rs
View file

@ -230,10 +230,6 @@ fn consolidation_plan_inner(store: &Store, _detect_interf: bool) -> Consolidatio
rationale: Vec::new(),
};
// Active agent types from config
let config = crate::config::get();
let agent_types: Vec<&str> = config.agent_types.iter().map(|s| s.as_str()).collect();
// Target: α ≥ 2.5 (healthy scale-free)
if alpha < 2.0 {
plan.add("linker", 100);
@ -274,48 +270,6 @@ fn consolidation_plan_inner(store: &Store, _detect_interf: bool) -> Consolidatio
// Split: handle oversized nodes
plan.set("split", 5);
// Distribute agent budget using Elo ratings
let budget = crate::config::get().agent_budget;
let elo_path = crate::config::get().data_dir.join("agent-elo.json");
if let Ok(elo_json) = std::fs::read_to_string(&elo_path) {
if let Ok(ratings) = serde_json::from_str::<std::collections::HashMap<String, f64>>(&elo_json) {
let elos: Vec<f64> = agent_types.iter()
.map(|t| ratings.get(*t).copied().unwrap_or(1000.0))
.collect();
let min_elo = elos.iter().copied().fold(f64::MAX, f64::min);
let weights: Vec<f64> = elos.iter()
.map(|e| {
let shifted = e - min_elo + 50.0;
shifted * shifted
})
.collect();
let total_weight: f64 = weights.iter().sum();
let allocate = |w: f64| -> usize {
((w / total_weight * budget as f64).round() as usize).max(2)
};
for (i, agent) in agent_types.iter().enumerate() {
plan.set(agent, allocate(weights[i]));
}
let summary: Vec<String> = agent_types.iter()
.map(|a| format!("{}={}", a, plan.count(a)))
.collect();
plan.rationale.push(format!(
"Elo allocation (budget={}): {}", budget, summary.join(" ")));
}
} else {
// No Elo file — use budget with equal distribution
let per_type = budget / agent_types.len();
for agent in &agent_types {
plan.set(agent, per_type);
}
plan.rationale.push(format!(
"No Elo ratings — equal distribution ({} each, budget={})", per_type, budget));
}
plan
}