session-persistence

name	session-persistence
description	Manage autoresearch.jsonl logging, session initialization, segment tracking, and session recovery. Use when starting, resuming, or recording experiments.
user-invocable	false

Session Persistence

All experiment data is persisted to autoresearch.jsonl in JSONL format (one JSON object per line). This file survives across discards, crashes, and context resets — it is the canonical record of everything tried.

JSONL Record Types

Config Header

Written once at session init (and again on re-initialization when the optimization target changes):

{"type":"config","name":"<session name>","metricName":"<primary metric>","metricUnit":"<unit>","bestDirection":"<lower|higher>"}

Field	Type	Description
type	"config"	Distinguishes from experiment records
name	string	Human-readable session name
metricName	string	Primary metric name (must match METRIC output)
metricUnit	string	Display unit (inferred or explicit)
bestDirection	"lower" or "higher"	Optimization direction

Experiment Record

Written after every experiment (keep, discard, crash, or checks_failed):

{"run":5,"commit":"a1b2c3d","metric":14600,"metrics":{"compile_µs":4100},"status":"keep","description":"Inline hot loop","timestamp":1699564800000,"segment":0,"confidence":2.3,"asi":{"hypothesis":"inlining reduces call overhead"}}

Field	Type	Description
run	number	Sequential experiment number (1-indexed)
commit	string	Git short hash (7 chars) for keep; empty string for others
metric	number	Primary metric value (0 for crashes)
metrics	object	Secondary metric name→value pairs
status	string	"keep", "discard", "crash", or "checks_failed"
description	string	What was tried this run
timestamp	number	Milliseconds since epoch (date +%s000 in bash)
segment	number	Current segment index (0-indexed)
confidence	number or null	Confidence score (null if < 3 runs)
asi	object or null	Agent-supplied intelligence (free-form key-value)

Segments

A segment groups experiments under a single baseline and config. Segments increment when init_experiment is called again (e.g., when the optimization target changes mid-session).

• Segment 0: experiments from initial setup
• Segment 1+: experiments after re-initialization

Confidence scoring and baseline comparisons only consider experiments within the current segment.

Session Recovery

When resuming (context reset, crash, or explicit resume):

1. Read autoresearch.jsonl line by line.
2. Parse config headers: Each "type":"config" line starts a new segment. Extract metricName, metricUnit, bestDirection.
3. Parse experiment records: Reconstruct the results list with all fields. Track which segment each belongs to.
4. Rebuild secondary metrics: Collect all unique keys from metrics objects across the current segment.
5. Find baseline: First experiment record in the current segment.
6. Find best kept: Best keep-status metric in the current segment (respecting direction).
7. Compute run count: Total experiment records (to set the next run number).
8. Resume looping from where the session left off.

Also read autoresearch.md and autoresearch.ideas.md for context on what was tried and what ideas remain.

Writing Records

When logging an experiment:

1. Compute all fields (including confidence via the confidence-scoring skill).
2. Serialize as a single JSON line (no pretty-printing, no trailing newline between records).
3. Append to autoresearch.jsonl — never overwrite the file.
4. The JSONL file is the only record that survives discarded/crashed experiments. Every run must be logged before any git revert happens.

Reporting

After logging, print a one-line summary:

Run #5: keep | total_µs: 14,600 (-3.8%) | confidence: 2.3× | "Inline hot loop"

Components:

• Run number and status
• Primary metric with delta % vs baseline
• Confidence score (if available)
• Description (truncated if long)

After every 5 runs or on shutdown, print an expanded summary showing all runs in the current segment with their status, metrics, and descriptions.