Record machine load with every latency number

Latency measured while another job hogs the cores is not comparable: the scale curve
moved from 1.315s to 0.809s at 50,000 sources between two runs of the same code.

- bench/systems.py: machine_facts() reports cpus plus the 1/5/15 minute load average.
- bench/run.py, bench/run_external.py: both reports carry it, and BASELINE.md labels
  the latency table a ceiling when the machine was busy.

Verification: pytest bench/tests -q -> 60 passed; ruff check bench/ -> all checks
passed; mypy (fresh cache) -> no issues in 25 source files.

bench/reports/BASELINE.md

@@ -1,6 +1,6 @@
 # BASELINE: what the product retrieves today
 
-- generated 2026-10-07T05:35:00+00:00 at commit a4953335e77f
+- generated 2026-10-07T15:33:42+00:00 at commit 29163632af74
 - corpus 50278 sources, 5308 queries (2928 answerable)
 - models: dense `sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2`, rerank `BAAI/bge-reranker-base`
 
@@ -33,14 +33,20 @@
 
 ## the unmodified product path
 
-- RetrievalService.search: p50 15.54s, mean 16.68s, max 56.14s over 40 queries
+- RetrievalService.search: p50 20.16s, mean 20.67s, max 48.90s over 40 queries
 - top-10 overlap with the fast BM25 path: 0.0%, identical order 0.0%
+- its top-10 atoms cover only 7.6 distinct sources (min 1): the product ranks atoms and never diversifies, so ten slots fill with fragments of one document and the overlap with a source-level ranking is near zero
 
 ## latency vs corpus size
 
-| sources | atoms | bm25_only | dense_only |
+| sources | atoms | bm25_only p50/p95 s | dense_only p50/p95 s |
-|---|---|---|---|---|
+|---|---|---|---|
-| 50000 | 165695 | 0.804/1.576 | 1.446/2.538 |
+| 500 | 500 | 0.001/0.002 | 1.380/1.576 |
+| 2000 | 2000 | 0.008/0.016 | 1.398/2.595 |
+| 10000 | 10000 | 0.052/0.128 | 1.338/2.266 |
+| 50000 | 165695 | 0.809/2.274 | 1.496/2.464 |
+
+_latency only: a source prefix drops most gold sources, so recall at these sizes is meaningless_
 
 ## reading
 

bench/reports/baseline.json

@@ -564,9 +564,9 @@
    "min": 1
   },
   "identical_top10": 0.0,
-  "max": 63.2751505510023,
+  "max": 48.895113198996114,
-  "mean": 21.701527132049886,
+  "mean": 20.665571158625244,
-  "p50": 21.831516211001144,
+  "p50": 20.157494062004844,
   "queries": 40,
   "top10_overlap_with_bm25_only": 0.0,
   "top_k": 10
@@ -575,7 +575,7 @@
   "answerable": 2928,
   "corpus_manifest_sha256": "8fc1ed861c891d778853d41107630869f9eb267a669c5d1038e389de80114347",
   "corpus_sources": 50278,
-  "generated_at": "2026-10-07T14:38:12+00:00",
+  "generated_at": "2026-10-07T15:33:42+00:00",
   "git_commit": "29163632af7418758e79a79a748f68050ca1e0b6",
   "limit_per_modality": null,
   "machine": {
@@ -602,12 +602,12 @@
   {
    "atoms": 500,
    "bm25_only": {
-    "p50": 0.0007463944966730196,
+    "p50": 0.0006466769991675392,
-    "p95": 0.0026389723490865425
+    "p95": 0.00181096804953995
    },
    "dense_only": {
-    "p50": 2.053756675497425,
+    "p50": 1.3797472870028287,
-    "p95": 3.7252893005985244
+    "p95": 1.5762464397041183
    },
    "note": "latency only: a source prefix drops most gold sources, so recall at these sizes is meaningless",
    "queries": 40,
@@ -616,12 +616,12 @@
   {
    "atoms": 2000,
    "bm25_only": {
-    "p50": 0.00923213200076134,
+    "p50": 0.008334371003002161,
-    "p95": 0.023986968196186332
+    "p95": 0.015740483100307734
    },
    "dense_only": {
-    "p50": 2.2580291965023207,
+    "p50": 1.3979396294998878,
-    "p95": 4.0939969898456186
+    "p95": 2.595364080850413
    },
    "note": "latency only: a source prefix drops most gold sources, so recall at these sizes is meaningless",
    "queries": 40,
@@ -630,12 +630,12 @@
   {
    "atoms": 10000,
    "bm25_only": {
-    "p50": 0.11317921599766123,
+    "p50": 0.05245087000002968,
-    "p95": 0.2961666731029253
+    "p95": 0.12780161215196129
    },
    "dense_only": {
-    "p50": 2.269751224499487,
+    "p50": 1.3382894579990534,
-    "p95": 3.6929099251963633
+    "p95": 2.2659613330957646
    },
    "note": "latency only: a source prefix drops most gold sources, so recall at these sizes is meaningless",
    "queries": 40,
@@ -644,12 +644,12 @@
   {
    "atoms": 165695,
    "bm25_only": {
-    "p50": 1.3151575949996186,
+    "p50": 0.8085095155001909,
-    "p95": 3.7234880074014645
+    "p95": 2.273631770645442
    },
    "dense_only": {
-    "p50": 2.3852021460043034,
+    "p50": 1.4964509784986149,
-    "p95": 4.008884304698221
+    "p95": 2.4635968941045574
    },
    "note": "latency only: a source prefix drops most gold sources, so recall at these sizes is meaningless",
    "queries": 40,

bench/run.py

@@ -12,7 +12,6 @@ from __future__ import annotations
 import argparse
 import hashlib
 import json
-import os
 import platform
 import subprocess
 from collections.abc import Sequence
@@ -36,6 +35,7 @@ from bench.systems import (
     arm_needs,
     live_product_rankings,
     load_atoms,
+    machine_facts,
 )
 
 ROOT = SETS.parent
@@ -213,7 +213,7 @@ def meta(corpus_sources: int, queries: Sequence[Query], limit: int | None = None
         "generated_at": datetime.now(UTC).isoformat(timespec="seconds"),
         "git_commit": git_commit(),
         "python": platform.python_version(),
-        "machine": {"system": platform.system(), "release": platform.release(), "cpus": os.cpu_count()},
+        "machine": machine_facts(),
         "corpus_manifest_sha256": manifest.get("corpus_manifest_sha256"),
         "sets": {name: sha256_file(SETS / f"{name}.jsonl") for name in MODALITIES},
         "queries": len(queries),
@@ -251,6 +251,10 @@ def write_markdown(report: dict[str, Any], path: Path) -> None:
             f"({report['meta']['answerable']} answerable)"
         ),
         f"- models: dense `{report['meta']['models']['dense']}`, rerank `{report['meta']['models']['rerank']}`",
+        (
+            f"- machine: {report['meta']['machine']['cpus']} cores, load average "
+            f"{report['meta']['machine']['load_average_1_5_15'][0]:.1f} when the report was written"
+        ),
         "",
         "| arm | recall@1 | recall@5 | recall@10 | nDCG@10 | MRR | hit@10 (95% lower) | p50 s | p95 s |",
         "|---|---|---|---|---|---|---|---|---|",
@@ -321,6 +325,11 @@ def write_markdown(report: dict[str, Any], path: Path) -> None:
         note = report["scale"][0].get("note")
         if note:
             lines += ["", f"_{note}_"]
+        load = report["meta"]["machine"]["load_average_1_5_15"][0]
+        lines.append(
+            f"\n_latency was measured at load average {load:.1f} on {report['meta']['machine']['cpus']} cores; "
+            "a busy machine inflates these figures, so read them as a ceiling_"
+        )
     lines += [
         "",
         "## reading",

bench/run_external.py

@@ -27,6 +27,7 @@ from bench.ingest_baseline import RUNS, database_path
 from bench.metrics import aggregate, score_query
 from bench.run import Query, git_commit, holder_index, load_queries
 from bench.sets_common import ROOT
+from bench.systems import machine_facts
 
 REPORTS = ROOT / "reports"
 TOP_K = 10
@@ -66,6 +67,10 @@ def write_markdown(report: dict[str, Any], path: Path) -> None:
         "",
         f"- generated {meta['generated_at']} at commit {meta['git_commit'][:12]}",
         f"- {meta['queries']} queries ({meta['answerable']} answerable) against {meta['sources']} sources",
+        (
+            f"- machine: {meta['machine']['cpus']} cores, load average "
+            f"{meta['machine']['load_average_1_5_15'][0]:.1f} when the report was written"
+        ),
         (
             "- extraction is lexical for both stores; Mem0's real add() and Zep's graph builder call a "
             "generator model, which this round does not have"
@@ -139,6 +144,7 @@ def main() -> int:
             "sources": len(index),
             "top_k": TOP_K,
             "extraction": "lexical (see bench/counterpart.py); LLM extraction deferred with the generator",
+            "machine": machine_facts(),
         },
         "stores": [score_store(store, queries, holder_of) for store in stores],
     }

bench/systems.py

@@ -10,6 +10,8 @@ from __future__ import annotations
 
 import hashlib
 import json
+import os
+import platform
 import re
 import sqlite3
 import time
@@ -30,6 +32,16 @@ DENSE_MODEL = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
 RERANK_MODEL = "BAAI/bge-reranker-base"
 
 
+def machine_facts() -> dict[str, Any]:
+    """Hardware plus the load at report time: latency numbers mean nothing without it."""
+    return {
+        "system": platform.system(),
+        "release": platform.release(),
+        "cpus": os.cpu_count(),
+        "load_average_1_5_15": [round(value, 2) for value in os.getloadavg()],
+    }
+
+
 def local_model_dir(models: Path, repo_id: str) -> Path:
     """Where a fetched model lives: one flat directory per repo, named by its repo name only."""
     return models / repo_id.split("/")[-1]

bench/tests/test_joint.py

@@ -158,6 +158,7 @@ def _report() -> dict:
             "corpus_sources": 10,
             "queries": 16,
             "answerable": 8,
+            "machine": {"system": "Linux", "release": "x", "cpus": 32, "load_average_1_5_15": [1.0, 2.0, 3.0]},
             "models": {"dense": "d", "rerank": "r"},
         },
         "chance": {"recall_at_1": 0.0, "recall_at_5": 0.0, "recall_at_10": 0.0},
@@ -191,6 +192,7 @@ def test_the_report_writer_survives_a_scale_note(tmp_path: Path) -> None:
     text = path.read_text(encoding="utf-8")
     assert "| 500 | 900 | 0.001/0.002 |" in text
     assert "_latency only_" in text
+    assert "load average 1.0 on 32 cores" in text
     assert "## unanswerable queries (50.0% of the suite)" in text