Highlights

Expanded Cortex-M support across additional CPU variants, model tests,
scratch-buffer planning, CMSIS-NN integration, and CMSIS Pack builds.
Added broad Arm backend coverage for TOSA dialect lowering, dynamic shapes,
FP8/MXFP/BFloat16 paths, VGF profiling, and LLM export workflows.
Added Vulkan support for Linux and Windows desktop GPUs, opt-in wheel builds,
automatic build enablement, and a functional backend availability check.
Expanded Qualcomm QNN support with 2-bit 16a2w quantization, faster VLM/LLM
PTQ flows, multi-batch quantization and evaluation, LPAI tooling, profiling,
and additional operators.
NXP Backend finished MLIR based Neutron Converter flow rollout - all operators using the MLIR flow, Neutron backend now support 48 ATen operators.
Continued Android API migration to Kotlin and added Android model E2E
coverage and Kotlin examples.

Runtime and Core

Removed the legacy _use_edge_ops toggles from the runtime/export path.
Added explicit dim-order legalization for channels-last portable fallbacks.
Hardened tensor-layout validation and segment loading against null metadata.
Improved symbolic input metadata preservation during exported-program pass
replay.
Added direct bundled-program serialization improvements for embedded flows.

Arm and Cortex-M

Added builds for more Cortex-M variants on Corstone-300, including M0+.
Added AoT scratch-buffer planning and broader MLPerf Tiny model coverage.
Expanded TOSA dialect lowering for activation, binary, conversion, data
layout, FFT, reduction, shape, and unary operators.
Added or improved VGF support for profiling, shader segments, BFloat16,
MXFP, grid sampling, and persistent IO mappings.
Ethos-U and VGF dependencies can now be installed with ExecuTorch using optional packages “pip install executorch[ethos_u,vgf]”
Added SmolLM2 and Qwen3-VL export and evaluation coverage.
Added CMSIS Pack infrastructure and an ExecuTorch 1.4 public API manifest.

Qualcomm

Added additional QNN operators, including addmm, select_scatter, variance,
diagonal, fill, cdist, LayerNorm variants, and hyperbolic functions.
Added 2-bit weight quantization support and multi-batch LLM quantization.
Decoupled calibration, quantization, compilation, and evaluation stages for
faster LLM and VLM workflows.
Added LPAI partitioning, tensor dumping, and runtime heap profiling.

NXP

Updated the Neutron software stack to 3.1.3.
Added backend profiling and MLPerf Tiny profiling tests.
Expanded Neutron operator coverage and removed several broadcasting
restrictions.
Fixed partition-output handling, format inference, BatchNorm fusion, and
multiprocessing conversion issues.

CoreML and MLX

Vulkan

XNNPack

Added quantized same-padding convolution support in XNNPACK, including fixes
for even Conv1d kernels and ViT concatenation quantization parameters.
Expanded RISC-V XNNPACK coverage across multiple RVV QEMU configurations.

Added BFloat16 and FP16 activation support to CPU SDPA.
Added BFloat16-output quantized embeddings on CPU.
Improved numerical accuracy by accumulating Half and BFloat16 softmax,
log-softmax, mean, and sum operations in FP32.
Fixed portable anti-aliased bilinear upsampling correctness.

Added grammar and grammar_type fields to GenerationConfig.
Added an unoptimized ExecuTorch runner example for Espressif ESP32.
Migrated Android Module, TrainingModule, SGD, and LLM extension APIs from
Java to Kotlin.
Added Kotlin examples and Android model end-to-end testing.

Fixed channels-last handling for portable fallbacks and Cortex-M DS-CNN.
Fixed a QNN runtime null dereference when op-package options are absent.
Fixed quantized Conv1d same padding for even kernels in XNNPACK.
Fixed several Arm partitioning, dynamic-shape, quantization, and runner
integration issues.
Fixed multiple NXP Neutron partition, format, and conversion issues.