v0.5.0
parruda/swarmv0.5.0Oct 20, 2026by github-actions[bot]
Upgrading?
AI Summary
Aligns Laya-MLX with upstream v0.4.12, introducing batch and long-text inference, structured decisions, and framework integrations while passing rigorous numerical stability audits.
Key Highlights
- Alignment with upstream Laya v0.4.12 APIs
- Support for batch and long-text inference
- Framework and tool integrations (Router, HTTP/MCP)
- Performance improvements with MLX (18 P50 comparisons won)
- Passed 341-entry numerical stability audit
Breaking Changes
- Upstream Router defaults changed
- Validation and output semantics updated
New Features
- Batch inference
- Long-text inference
- Structured decisions
- Hooks
- Calibration
- Evaluation
- Router support
- HTTP/MCP support
Full Release Notes
# 0.5.0 — upstream v0.4.12 migration This release aligns applicable host APIs with NandhaKishorM/laya `ffc142012af63d015fa19f05f29f9c5ff329a2cc`: batch and long-text inference, structured decisions, hooks, calibration, evaluation, Router, HTTP/MCP, and framework/tool integrations. Native execution and explicit unsupported backend boundaries are preserved. Compatibility changes include upstream Router defaults, validation and output semantics. The LlamaIndex adapter supports the actual current BaseSelector and SingleSelection contracts. See MIGRATION_0_4.md for behavior and dependency details. The 341-entry API audit and three-checkpoint FP32/FP16 numerical/stability campaign passed. The qualification report records unchanged applicable upstream assertions, real SDK HTTP checks, real optional-framework checks and clean-wheel inference. Package 0.5.0 changes version metadata relative to the recorded 0.4.0 development qualification builds; release gates verify the final distribution separately. Performance comparisons use upstream PyTorch 2.14, matched FP32 and fixed inputs. MLX wins all 18 P50 comparisons in the final matrix. Core ML's alternating-order confirmation wins all 18 P50/P95/mean-throughput comparisons; the initial four sequential regressions are retained in the report. Core ML adaptive graph and shape caches trade more resident memory for lower warm-call latency. Cold calls, other hardware and arbitrary workloads are outside these finite guarantees. See QUALIFICATION_0_4_12.md and UPSTREAM_0412_PROGRESS.json for raw evidence and limitations. Original historical benchmark and package receipts remain unchanged. MLX preserves native precision, compilation, row batch limits and bounded question-prefix caching. Local-only model resolution is supported by Agent and Router; ordinary inference needs no PyTorch or Transformers. Publication verified on 2026-10-20: GitHub Release and PyPI wheel/sdist hashes match, public wheel source matches the tag, and a public-index reinstall passes all three offline real-model checks. See `benchmarks/results/release-v050.json`. Post-release CI runs exposed intermittent failures in the unchanged upstream idle-window timing test. Running host contracts before native model tests in a separate process passed one complete CI, but a subsequent run reproduced the timing failure; isolation has not eliminated it. No runtime, assertion or timeout was changed. Failure diagnostics also run the original upstream idle-window tests for comparison. [CI evidence](https://github.com/mizorewww/laya-mlx/actions/runs/39354961828). The original pinned upstream suite reproduced the same idle-window failure on the same CI runner; all 254 native/integration tests passed. This timing instability remains documented in [CI evidence](https://github.com/mizorewww/laya-mlx/blob/main/docs/CI_TIMING.md).
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