Notable changes to vla.cpp. Format loosely follows Keep a Changelog.

• SYCL backend for Intel GPUs (Arc, Flex, Data Center Max, Xe iGPU). VLA_DEVICE picks the ordinal on CUDA and SYCL alike. See docs/backend/sycl.md.
• Stable C ABI (include/vla.h, libvla) and Python bindings over it (bindings/python).
• Four more architectures: π0.5, VLA-Adapter, OpenVLA-OFT and VLA-JEPA.
• vla-bench for engine-only latency, and -hf user/repo[:file.gguf] to fetch a checkpoint on first use.
• vla-cli --text, tokenized by scripts/tokenize_prompt.py with the tokenizer the architecture was trained on.
• Release workflow publishing Linux x86-64 (CPU and CUDA), Linux aarch64 (CPU), macOS Metal and a GHCR image.
• Opt-in BF16 activations for Evo-1 and pi0 (VLA_EVO1_BF16_ACT, VLA_PI0_BF16_ACT). Weight GEMMs, bias adds, residuals, norms and activations carry BF16; attention scores, softmax, RoPE and the flow-matching integrator stay F32. Needs CUDA and a BF16 checkpoint, and is ignored otherwise.
• Opt-in fused attention for Evo-1, pi0 and SmolVLA (VLA_EVO1_FA, VLA_PI0_FA, VLA_SMOLVLA_FA). Off by default: ggml's CUDA flash attention computes K/V at F16 whatever the input type, which measured 4 to 5 points lower on libero_object.

• One shared backend ladder (src/backend.h) instead of a copy per arch. CMake rejects two accelerators in one build directory.
• Shared headers for the Qwen3-VL tower, the DINOv2+SigLIP dual tower, the DiT time embeddings, the causal mask and CHW image preprocessing.
• vla::graph_cache keeps the compute graph across predict calls in nine architectures, not just GR00T N1.7. Output is unchanged.
• llama.cpp pinned at b10331. GR00T N1.5 and N1.6 shift by up to 4.6e-4 on actions peaking near 0.87, from an upstream ggml kernel change in the SigLIP tower they share. The other nine architectures are bit-identical.
• Evo-1 encodes every camera view in one vision graph and one compute, rather than a graph_compute per view. The arithmetic per view is unchanged, only the submission pattern.
• BitVLA's ternary GEMM feeds four column tiles per CTA from one shared activation block, and pads its shared-memory row stride to 144 B to break 16-way bank conflicts. VLA_BITVLA_NARROW_GEMM=1 selects the previous one-tile kernel.
• The CUDA BF16 kernels live in src/cuda/ and depend only on the public ggml header. The fetched ggml gets one addition to carry them: a function-pointer hook at the top of its CUDA op dispatch (scripts/patch_ggml_cuda_ext_hook.py). Left unregistered the pointer is null and ggml behaves exactly as shipped. This is the first llama.cpp patch since 0.1.0 removed the old patches/ script, and it makes Python 3 a requirement for configuring any build.
• llama.cpp's tool binaries are no longer part of the default build. Ask for one by name when you want it: cmake --build build --target llama-mtmd-cli.
• vla_core links ggml alone; the VLA architectures call no llama_* API. Only the VLM path links llama.
• Build snippets no longer pass -DGGML_CUDA_GRAPHS=ON, which llama.cpp already defaults on.

• Reject checkpoint geometry that contradicts itself before it sizes a buffer, in smolvla, bitvla, gr00tn1d6, vla_adapter and the Qwen3-VL position resample.
• A peer that stalls mid-message no longer parks either server.
• Treat a missing state vector as zeros in every architecture rather than dereferencing it.
• Build every registered test before ctest, so the four that were never built stop reporting as not run.

• vla-cli: one-shot inference from the command line (image + tokens to action), no server needed.
• scripts/quantize_gguf.py: repack LM weights to Q8_0/Q4_0. The loader runs quantized GGUFs directly (Q8_0 roughly halves the LM, near-lossless).

• One shared GGUF reader across the model loaders, replacing the per-arch copies.
• Cap inbound message size (256 MiB) and image count (16) on vla-server and vlm-server.
• Scale CPU threads to the machine core count across all loaders instead of a fixed 4.
• Read GGUF file offsets as 64-bit and reject non-float embedding tensors in row-fetch.

• Reject out-of-range language tokens in OpenVLA-OFT and VLA-Adapter.
• Zero the padded action dimensions so only real action dims carry values.
• Reject images that do not match the model input size in VLA-Adapter and OpenVLA-OFT (out-of-bounds read on a smaller view).
• Validate Evo-1 action dims at load so a client-supplied noise buffer cannot underrun.
• Only enable the BitVLA CUDA path once every device buffer allocates.

First tagged release. One self-contained GGUF per model (vision tower + LM + action expert + dataset stats), CPU or CUDA, no external mmproj and no patch to llama.cpp.

• Seven VLA policies auto-detected from the GGUF: SmolVLA, pi0, BitVLA, Evo-1, GR00T N1.5/N1.6/N1.7.
• In-tree vision towers (SigLIP, BitSigLIP, InternViT, RADIO) on stable public ggml/llama APIs.
• ZeroMQ + protobuf vla-server; a separate vlm-server for VLM chat.
• BitVLA 1.58-bit custom ternary CUDA kernels.
• Per-arch HuggingFace -> GGUF converters and mmproj-merge helpers (scripts/).
• Robot eval harness for LIBERO, SimplerEnv, and ALOHA (eval/), with device benchmark reports.
• Minimal CI: pixel-shuffle unit test, converter-remap test, CPU build gate.
• pyproject.toml for the Python tooling and a CUDA Dockerfile for vla-server.

• llama.cpp is fetched + pinned via CMake FetchContent (tag b9866); bumping is a one-line GIT_TAG change. Removed the patches/ fetch script.