All notable changes to TensorOS are documented in this file. The focus here is code and measured behavior, not release-note marketing.

2.8× performance improvement. Decode speed improved from 454 ms/tok to 162 ms/tok through SMP parallel GEMV, JIT-compiled forward kernels, and critical bug fixes in both the JIT loop counters and the SMP trampoline.

Page tables relocated from 0x1000 to 0x10000 (18 pages). The original address collided with the BIOS Data Area, causing AP bootstrap failures. All 4 CPUs now come online reliably.

All JIT forward kernels (vadd, dot, vmul, rmsnorm) emitted vecs/2 as the loop count instead of vecs. This halved the computation, producing incorrect results for every JIT-accelerated operation.

Changed AP entry from jmp rax to call rax to ensure 16-byte stack alignment required by the System V ABI. Misalignment caused SSE2 movaps faults on APs.

Six native x86_64 kernels lazy-compiled on first LLM inference:

• vadd (dim=3072) — residual connections
• dot (head_dim=96) — attention score computation
• axpy (head_dim=96) — attention value accumulation
• fused_silu_mul (ff_dim=8192) — FFN gate ⊙ up projection
• rope (head_dim=96) — rotary position encoding
• rmsnorm (dim=3072) — RMS normalization

Emitted into a 2 MB W^X code pool (max 64 concurrent buffers).

• smp_dispatch() partitions GEMV rows across all online CPUs
• Supports Q4_0 and Q8_0 fused AVX2 GEMV paths
• Dispatches when ncpu > 1 && out_dim >= 64
• BSP + APs synchronized via smp_wait_all()

7 new AVX2 integer SIMD instruction emitters added to the JIT engine. Integer Q4×Q8 GEMV compiler implemented (disabled pending correctness verification).

First coherent LLM inference achieved. Phi-3.5 Mini Instruct (3.8B params, Q4_0) now generates correct English text on bare-metal x86_64, running under QEMU WHPX at ~800 ms/tok (454 ms/tok decode, 5.5s prefill for 12 tokens).

Prompt: "What is an operating system?" Output: "An operating system (OS) is a complex piece of software that man..."

This release fixes critical numerical bugs in quantized inference that produced garbage output since the initial LLM integration.

All Q4_0 (4-bit quantized) dequantization code used an interleaved nibble layout instead of the GGML standard layout. For each 32-element block packed into 16 bytes:

• Wrong (interleaved): out[2*j] = lo_nibble, out[2*j+1] = hi_nibble
• Correct (GGML standard): out[j] = lo_nibble, out[j+16] = hi_nibble

This caused element-order corruption at every F32 boundary (RMSNorm multiply, residual connections), compounding through all 32 transformer layers. Aggregate statistics (min/max/sum) were identical since values were just permuted within blocks, making the bug invisible to earlier verification.

Files fixed:

• runtime/nn/llm.c — llm_embed() Q4_0 case
• runtime/nn/llm.c — q4_0_dot32() (SSE2 + aarch64 paths)
• runtime/nn/llm.c — q4_1_dot32() (SSE2 + aarch64 paths)
• runtime/nn/llm.c — q4_0_dot32_avx2() (AVX2 8-wide path)
• runtime/nn/llm.c — llm_gemv_q4_fused_avx2() (4-row batched GEMV)
• runtime/nn/llm.c — llm_gemv_q4_fused_range_avx2() (parallel worker GEMV)
• runtime/nn/llm.c — AVX2 helper replaced: q4_unpack_v8f → q4_unpack_lo_v8f + q4_unpack_hi_v8f

Hardcoded 1e-6f replaced with model-specific epsilon loaded from GGUF metadata (general.rms_norm_eps). Phi-3.5 uses 1e-5.

Complete rewrites of sinf, cosf, expf, logf, sqrtf — custom bare-metal implementations had catastrophic precision errors affecting RoPE frequency computation and softmax normalization.

• Loaded rope_factors_short and rope_factors_long tensors from GGUF
• Applied frequency scaling in llm_rope_precompute() based on position vs original context length (4096 for Phi-3.5)
• Enables correct positional encoding for extended-context models

• runtime/nn/flash_attn.c — Flash Attention kernel interface
• runtime/nn/paged_attn.c — PagedAttention (vLLM-style) interface
• runtime/nn/safetensors.c — Safetensors format loader
• runtime/nn/onnx.c — ONNX Runtime integration interface
• runtime/compute/vulkan_compute.c — Vulkan/WebGPU compute backend interface
• runtime/pseudocode/pseudo_stdlib.c — Pseudocode standard library
• kernel/drivers/dma/pcie_dma.c — PCIe DMA engine
• kernel/net/distributed.c — Distributed inference networking
• boot/uefi_stub.c — UEFI boot support

All values now match a Python/NumPy reference implementation exactly:

Previously, Q[0] was -0.014042 (30× too small) and L0 output range was [-0.37, 0.37] instead of [-3.60, 2.51] — a 10× dynamic range loss.

• Multiboot1 bootloader with x86_64 long mode + SSE2
• SMP bootstrap (INIT-SIPI-SIPI)
• Tensor-aware memory manager (heap + arena + slab)
• GGUF model format parser
• Complete transformer forward pass
• Q4_0, Q4_1, Q6_K, Q8_0 quantization support
• AVX2+FMA SIMD acceleration with 4-row batched GEMV
• x86_64 JIT code generator for Q8_0 GEMV kernels
• SentencePiece and BPE tokenizers
• Temperature sampling with top-k and nucleus filtering
• Virtio-blk and virtio-net drivers
• ARP/IPv4/UDP/ICMP network stack
• AI shell with 20+ commands
• Pseudocode JIT compiler (lexer, parser, IR, 4-tier optimization)
• Model package manager
• ARM64 / Raspberry Pi 4 boot stub