What Is New In 0.6.2 ==================== Version 0.6.2 broadens Mixle from a composable statistical modeling library into a more complete runtime for heterogeneous modeling, local reasoning, and auditable deployment. The stable center is still the distribution/estimator contract and ``optimize``. The new work adds higher-level creation, reproducibility, placement, reasoning, telemetry, neural, and task-distillation surfaces around that contract. Use this page as a map of the release. The topic guides linked from each section explain the workflow details. Local Reasoning Runtime ----------------------- 0.6.2 adds a local application layer for building retrieval, reasoning, and decision workflows around fitted models: * ``mixle.substrate`` stores text, records, vectors, graph facts, mounted tools, and provenance-bearing evidence. * ``mixle.substrate.Reasoner`` and ``investigate`` plan over retrieve, compute, simulate, and delegate actions while preserving abstention and citations. * ``mixle.inference.skill`` registers reusable typed capabilities that a reasoner or application can discover. * ``mixle.pool`` provides a small pool/job abstraction for local or remote execution decisions. * ``mixle.telemetry`` records decisions, cost, latency, and outcomes; the dashboard helpers summarize those receipts. * ``mixle.scientist`` assembles the local pieces into an optional, offline scientific-assistant workflow when the ``scientist`` extra and local model weights are available. Read :doc:`reasoning-ecosystem`, :doc:`reasoning-systems`, and :doc:`production`. Certified Creation, Simulation, And Reproducibility --------------------------------------------------- The inference layer now includes higher-level artifact workflows in addition to ordinary fitting: * ``create(data, ...)`` fits a model, records provenance, optional calibration, uncertainty, exchangeability diagnostics, and a certificate. * ``simulate(model, ...)`` draws from models that expose compatible sampler behavior. * ``synthesize(source, ...)`` creates checked synthetic artifacts from a model, dataset, callable, or task surface. * ``record_fit`` and ``verify_reproducible`` store and check reproducibility receipts. * ``certify`` and ``plan_placement`` expose fit guarantees and local/pool placement decisions. * ``uq`` dispatches uncertainty queries across fitted models, point predictors, ensembles, and LLM-style callables. * ``hierarchical_event_study`` estimates confirmed-exposure influence with within-subject shifts, random-effects pooling, difference-in-differences contrast, and sensitivity bounds. Read :doc:`inference`, :doc:`quickstart`, :doc:`uncertainty`, and :doc:`lifecycle`. Data, Structure, And Process Families ------------------------------------- 0.6.2 adds several modeling and diagnostics surfaces: * ``exchangeability_check`` reports whether rows look exchangeable, shifted, or trended before a workflow assumes that "more rows like these" is a valid sampling story. * ``mixle.represent.modality`` supplies deterministic image and signal feature helpers for cross-modal examples and tests. * ``Ontology`` and ``OntologyConstrainedKG`` add typed graph constraints for knowledge-graph and reasoning workflows. * ``ContinuousTimeMarkovChainDistribution`` and its estimator model fully observed CTMC trajectories with a closed-form generator MLE that certifies as ``GLOBAL_UNIQUE``. * Multivariate Gaussian fitting uses a BLAS-backed covariance accumulation path and a robust Cholesky fallback so float32/GPU EM fits can recover from small numerical indefiniteness instead of crashing. * Hidden Markov distributions default to the Numba encoder when Numba is installed, matching the estimator default while still respecting ``use_numba=False``. Read :doc:`data`, :doc:`representation`, :doc:`reasoning-systems`, :doc:`processes`, :doc:`stats-structured`, and :doc:`hmms-latent`. Neural And Task Surfaces ------------------------ The neural and task layers gained more durable, explicit behavior: * ``Flow``, ``MAF``, ``VAE``, and ``DiscreteAR`` are constructible neural density families that can appear directly in model trees. * Neural leaves, direct language models, streaming Transformer leaves, DPO models, energy models, and density leaves now have broader serialization support. * Streaming Transformer and DPO accumulators preserve sample weights and EM responsibilities during gradient updates. * ``mixle.task.distill_methods`` adds Torch-to-Torch response, multi-teacher, hint, attention-transfer, relational, and sequence-level distillation helpers. * Task artifact durability improved for JSON-safe ``qhat=inf`` reloads, empty inputs, and int4 packed quantized weights. Read :doc:`neural-llm`, :doc:`task-distillation`, and :doc:`task-serving`. Engines, Placement, And Operational Hardening --------------------------------------------- 0.6.2 also tightens lower-level execution and production behavior: * Torch DTensor component sharding is gated to Torch 2.5 or newer, where the needed DTensor strategies exist. Older Torch versions get a clear error that points to ``backend="model_parallel"``. * DTensor import handling recognizes both the public and older private Torch module locations. * The production registry constrains names, versions, and aliases to safe path components and raises clearer errors for unknown model names or versions. * Benchmark and distributed-stress harnesses that are useful for maintainers moved out of tracked ``examples/`` paths so the examples page focuses on runnable documentation. Read :doc:`engines`, :doc:`utilities-and-parallelism`, :doc:`examples`, and :doc:`production`. API Reference Coverage ---------------------- The generated API reference now includes the new top-level packages and modules added in this release, including ``mixle.substrate``, ``mixle.pool``, ``mixle.telemetry``, ``mixle.scientist``, ``mixle.inference.create``, ``mixle.inference.simulate``, ``mixle.inference.synthesize``, ``mixle.inference.reproduce``, ``mixle.inference.skill``, ``mixle.inference.placement``, ``mixle.inference.planning``, ``mixle.inference.orchestration``, ``mixle.inference.event_study``, ``mixle.inference.uq``, ``mixle.data.exchangeability``, ``mixle.represent.modality``, ``mixle.reason.ontology``, ``mixle.stats.processes.ctmc``, ``mixle.models.neural_families``, and ``mixle.task.distill_methods``.