May 2026 monthly summary focused on modular prefill testing architecture, enhanced test discovery for model loaders, and restoring robust backward pass in training. Deliverables span two repos with a consistent emphasis on business value: extensibility for experimentation, reliability in gradient flow, and reduced maintenance overhead through shared resources and clearer separation of concerns.

April 2026 focused on strengthening testing, expanding platform coverage, and stabilizing numerical backends to deliver measurable business value and robust performance. Key work included expanding prefill testing across mesh and multichip configurations in tt-xla with memory-management improvements to reduce OOM risk and increase CI reliability; enabling prefill testing on wh-galaxy and qb2-blackhole architectures to broaden CI coverage; fixing Metal backend embedding stability in tt-mlir by reshaping 1D index tensors to 2D to align with metal logic; and ensuring correctness of exponentiation by switching to pow_tensor for negative exponents. These results improved feedback loops, cross-platform reliability, and numerical correctness in production workloads.

March 2026 (tt-mlir): Delivered a critical bug fix to sharding attribute resolution in collective operations, improving local shape correctness and stability for sharded MoE models. Focused on out_sharding handling in getOperandShardingAttr with a robust fallback to compute accurate local shapes, preventing slice-index errors during UpdateGlobalToLocalShapes.

February 2026 monthly summary: Delivered scalable Llama deployment enhancements and expanded prefill/testing workflows in tt-forge-models, strengthened testing infrastructure for longer sequences and larger batch sizes in tt-xla, and introduced a tensor replication pass to improve sharding reliability in tt-mlir. These efforts increase deployment performance, model evaluation coverage, and the reliability of distributed compute paths, enabling more scalable benchmarks and faster business insights.

Nov 2025 monthly summary for tenstorrent/tt-mlir: Delivered foundational tensor operations and MLIR/StableHLO integration work enabling more capable neural network pipelines. Implemented MaxPool2dWithIndices (returns values and indices) to support unpooling and gradient computation, and extended MLIR/StableHLO by decomposing stablehlo.select_and_scatter into ttir.max_pool2d_with_indices and ttir.scatter_in_dim for greater flexibility. Updated verifiers, introduced a separate FlatBuffers schema entry for the new op, and expanded test coverage to validate end-to-end behavior across TTIR/TTNN and StableHLO. These changes establish groundwork for advanced pooling-based layers and improve cross-dialect interoperability, delivering tangible business and technical value.

September 2025: Delivered core backend improvements for Tenstorrent MLIR/TTIR integration and JAX compatibility. Implemented StableHLO to TTIR conversion for tenstorrent.uniform to ttir.rand with operand/attribute extraction and test refactor; fixed MLIR lowering shape handling for jax.random.uniform by enforcing int32 shape before lowering, improving stability and correctness on the Tenstorrent backend.

January 2025 monthly summary focused on delivering core platform capabilities and strengthening validation for numeric computations across TTIR and TT-XLA. Key initiatives drove broader applicability, reliability, and business value in ML workloads.