v0.4.0

Extropy v0.4.0 Release Notes

Release date: 2026-02-24

This document is the full release narrative for v0.4.0. It is intentionally
more detailed than the high-level changelog entry and is designed for operators,
maintainers, and applied-research users upgrading existing studies.

Executive Overview

v0.4.0 is the first release where the platform behavior is strongly aligned with
the current study architecture:

• scenario-first pipeline ownership,
• explicit quality gates across sampling and networking,
• timeline-safe simulation semantics for evolving scenarios,
• stricter automation contracts for agentic harnesses.

The release includes broad runtime and tooling hardening from spec generation
through final simulation analysis, with emphasis on realism, determinism, and
operational clarity.

Why This Release Matters

In v0.3.0 and earlier, there were still mixed-era command and data contracts.
Those contracts made it easier for automation to drift into stale command patterns
or for long evolving scenarios to end too early due to static convergence heuristics.

v0.4.0 addresses these structural issues by:

1. simplifying the command path to one canonical pipeline,
2. giving scenario stage ownership over scenario semantics (including household focus),
3. making simulation logic explicit about evolving timelines and new-information epochs,
4. improving data quality gates and topological checks before expensive simulation runs.

Major Improvements by Area

1) CLI and Pipeline Architecture

The command flow is now explicitly:

extropy spec -> extropy scenario -> extropy persona -> extropy sample -> extropy network -> extropy simulate -> extropy results

What changed:

• scenario extension generation is integrated in extropy scenario (instead of split command chains),
• output/inspection flow is centered on results and query,
• command docs and operator docs are aligned with implemented flags and behaviors.

Operational impact:

• less command ambiguity,
• fewer stale script paths,
• clearer ownership of population vs scenario semantics.

2) Simulation Runtime Realism and Evolving Timelines

v0.4.0 brings structural changes to evolving scenario handling:

• timeline-aware stopping behavior:
  • convergence/quiescence auto-stop is no longer blindly applied when future timeline events remain,
  • explicit override available via scenario config and runtime flag semantics.
• provenance/epoch-aware re-reasoning:
  • new information can trigger re-reasoning in a principled way,
  • supports committed-agent reconsideration patterns without content keyword heuristics.
• conversation interleaving:
  • conversations can be interleaved during timestep reasoning loops,
  • avoids unrealistic "all talk only after all reasoning" dynamics.

Operational impact:

• improved temporal realism for multi-event scenarios,
• reduced premature stop risk in long-horizon studies,
• better alignment between information arrival and agent updates.

3) Sampling, Household Coherence, and Scenario Ownership

Sampling and household pathways were hardened:

• stronger lifecycle normalization and household coherence behaviors,
• exact count guarantees improved in sampling loops,
• better partner/dependent naming and member consistency handling.

Also, household semantics are now clearly scenario-owned where they affect downstream
simulation behavior. This prevents base-population artifacts from leaking scenario-specific
assumptions.

Operational impact:

• higher-quality sample datasets,
• fewer contradictory household/persona states before network and sim stages,
• clearer study reproducibility.

4) Network Generation and Topology Gating

Network stage received quality and determinism upgrades:

• generated network config defaults are enabled by default for meaningful topology,
• deterministic structural role handling improved,
• strict gate behavior hardened for practical acceptance criteria,
• resource and worker auto-tuning improved for large studies.

Operational impact:

• fewer low-information network outputs,
• better reproducibility across reruns,
• clearer gate outcomes before simulation spend.

5) Provider Runtime Reliability and Long-Call Stability

Provider and runtime call paths were hardened:

• long-response stability improvements (including streaming-oriented fixes),
• async client reuse and event-loop-close handling fixes,
• rate/queue and token accounting reliability improvements.

Operational impact:

• lower failure probability during high-fidelity or long-duration runs,
• better run stability under high throughput settings.

6) THINK/SAY and Reasoning Trace Fidelity

The simulation now better tracks and persists internal/external divergence:

• THINK vs SAY pathways are persisted and classifiable,
• macro context and intent accountability are strengthened in prompt flow,
• conversation turn behavior is more explicit by fidelity tier.

Operational impact:

• stronger micro-realism in agent expression and social signaling,
• better post-run diagnostics for behavior interpretation.

Breaking Changes and Required Migration Work

The following are breaking or operationally significant:

1. extend workflow removed from active user path.
2. global/root --json usage removed from CLI surface; use cli.mode=agent.
3. estimate command is hidden/disabled pending parity work; do not depend on it in release automation.
4. scenario/household semantics ownership shifted to scenario artifacts.
5. scripts using older --study-db assumptions should migrate to study-folder + --study conventions.

Migration Checklist

1. Set automation mode:
   • extropy config set cli.mode agent
2. Update pipeline scripts to canonical flow:
   • spec -> scenario -> persona -> sample -> network -> simulate -> results
3. Verify scenario and persona file path assumptions:
   • scenario/<name>/scenario.vN.yaml
   • scenario/<name>/persona.vN.yaml
4. Remove hard dependencies on estimate in CI paths until re-enabled.
5. Re-baseline long evolving scenario comparisons because stopping/re-reasoning semantics changed.

Known Operational Guidance for v0.4.0

• For expensive studies, run gated dry passes first (sample/network validation) before full sim.
• Use one small simulation smoke pass before large multi-timestep runs.
• Keep run metadata and scenario versions with seed references for comparability.
• Prefer execution through an agentic harness for deterministic triage and replay.

Versioning and Packaging

• Package version: 0.4.0
• Version source: extropy/__init__.py
• Publish workflow: GitHub release-triggered PyPI publish flow remains unchanged.

Suggested GitHub Release Body (Copy/Paste)

Title:

v0.4.0 - Scenario-first pipeline hardening, timeline-safe simulation, and runtime reliability

Body:

v0.4.0 is a major architecture and operations release. It finalizes the scenario-first command path, hardens sampling/network quality gates, and upgrades simulation behavior for evolving timelines with timeline-aware stopping, epoch-based re-reasoning, and improved conversation scheduling.

This release also includes broad command/documentation alignment, provider/runtime stability improvements, and stronger reasoning trace fidelity (THINK vs SAY persistence and accountability context).

Breaking changes include removal of legacy extend workflow paths from active operation, removal of root --json command usage patterns in favor of cli.mode=agent, and temporary hiding/disablement of estimate until parity work is complete.

See CHANGELOG.md for full detail and migration checklist.