The gap between theoretical sub-quadratic attention models and production inference is closing fast, as maintainers begin merging extreme KV-cache quantization and linear-recurrent state kernels directly into mainline serving frameworks.
On Tuesday, October 6, 2026, researchers introduced Olmo Hybrid, a 7B-parameter architecture built on the Olmo 3 7B pretraining recipe that replaces sliding window attention layers with Gated DeltaNet linear recurrence. Empirical evaluations across pretraining and mid-training benchmarks show Olmo Hybrid outperforming the pure transformer baseline. Theoretical proofs included in the paper demonstrate that pairing attention with linear recurrent states enables the model to express formal languages and state-tracking tasks beyond the capacity of pure transformers or pure linear RNNs.
Why it matters
Demonstrating that Gated DeltaNet hybrid layers outperform pure transformer baselines during pretraining provides concrete validation for sub-quadratic hybrid architectures. For open-weight practitioners tracking local-LLM scaling, replacing sliding window attention with linear recurrence provides a predictable memory-bandwidth footprint without sacrificing task expressivity. This design pattern offers a clear blueprint for training open base models that run long contexts within tight hardware constraints.
The paper's authors emphasize that hybrid architectures resolve fundamental theoretical expressivity caps of linear models while scaling context lengths far more efficiently than standard transformers. Conversely, systems engineers point out that deploying non-standard recurrent layers requires custom CUDA and Metal kernel implementations across serving frameworks like vLLM and llama.cpp before the memory gains can be realized in production.
Following yesterday's coverage of the throughput bottlenecks caused by Mamba2 prefix caching (`mamba_cache_mode="align"`) in vLLM, a security advisory published Tuesday, October 6, 2026, cataloged a more severe consequence: CVE-2026-105775. The flaw is an out-of-bounds slice vulnerability in the vLLM serving engine that specifically affects hybrid Mamba2 deployments when prefix caching is enabled, causing an unhandled exception that crashes the `EngineCore` process. The vulnerability carries a CVSS score of 4.3.
Why it matters
Hybrid state-space architectures like Mamba2 introduce complex state alignment requirements when integrating attention-oriented optimizations like prefix caching. Indexing errors between state-space recurrent states and paged attention blocks risk crashing serving daemons under production traffic. Operators who recently enabled prefix alignment based on earlier vLLM guidance must track these state boundary edge cases to prevent engine instability.
Security advisories classify the bug as a medium-severity denial-of-service risk for multi-tenant inference servers. Framework maintainers recommend disabling prefix alignment on Mamba2 hybrid checkpoints until the patched state slicing logic is merged in the next point release.
On Monday, October 5, 2026, U.S. startup Reflection AI announced Beam, an open-weight sparse Mixture-of-Experts model containing 501 billion total parameters and 23 billion active parameters per token. Pretrained on 23.8 trillion tokens using an NVIDIA GB300 cluster, Beam features a 1-million-token context window and is targeted at coding, reasoning, and multi-step agent tasks. The lab claims Beam achieves benchmark parity with Chinese open flagships like GLM-5.2 while requiring roughly three to four times less compute during inference. Weights are scheduled for release under an Apache 2.0 license later this month.
Why it matters
Beam's architecture highlights the ongoing trend toward extreme parameter sparsification, pairing a 501B knowledge footprint with only 23B active parameters per token. Releasing the model under an Apache 2.0 license gives self-hosting teams and enterprises a domestic, highly sparse open-weight option for agentic pipelines. For local deployment planning, its 501B total parameter size dictates significant storage and system RAM requirements, even though its compute demands during generation remain low.
Reflection AI leadership positions Beam as a breakthrough in inference efficiency that lowers the cost barrier for complex agent reasoning. Independent industry analysts note that while the low active parameter count reduces generation latency, independent verification of the lab's performance claims must wait until the full checkpoint and configuration files are publicly uploaded to Hugging Face.
A study published Tuesday, October 6, 2026, formulated the 'Recovery Principle' to address feature fragmentation in Sparse Autoencoders (SAEs). Assuming atomic features follow a frequency-based order, the authors mathematically proved and empirically validated that expanding SAE dictionary capacity recovers features as stable prefixes starting from high-frequency features. Experiments scaling SAE dictionaries from 512 to 131,072 features across two large embedding models confirmed three core behaviors: preservation of features discovered at smaller dictionary sizes, cross-dataset feature sharing, and parent-child feature splitting at scale.
Why it matters
A primary concern in mechanistic interpretability has been that scaling Sparse Autoencoder dictionary sizes creates volatile, unstable feature sets that invalidate earlier probing toolkits. Proving that feature discovery follows a stable prefix expansion confirms that training larger SAE dictionaries preserves previously mapped features while cleanly splitting complex concepts into parent-child representations. This provides a theoretical foundation for building persistent SAE feature dictionaries across model families.
The researchers argue that the Recovery Principle refutes the view that SAE scaling induces arbitrary feature drift, validating continuous investment in large-dictionary feature extraction. Other interpretability investigators note that while prefix stability holds for high-frequency features, ultra-rare features near the sparsity boundary can still exhibit sensitivity to TopK selection thresholds during training.
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In a preprint published Tuesday, October 6, 2026, researcher Chenhao Tan introduced dynamic weight grafting, a method that transplants specific weight subsets from a fine-tuned model onto its base model to isolate where newly acquired factual knowledge is stored. The methodology identified two distinct internal retrieval mechanisms: a residual stream enrichment pathway triggered during entity tokens, and a factual recall pathway located at the final token position. The paper also demonstrated that fine-tuning base models on synthetic next-token documents and grafting those parameter updates directly onto post-trained instruct models successfully transfers facts without degrading instruction alignment.
Why it matters
Understanding how fine-tuning alters internal weight representations is critical for surgical model editing and auditing open weights. Locating the exact sub-networks responsible for entity enrichment versus final-token recall allows interpretability researchers to patch or update factual knowledge without running expensive retraining loops. Furthermore, proving that synthetic updates graft cleanly from base to instruct checkpoints simplifies domain adaptation workflows.
The author demonstrates that entity enrichment and final-token recall can operate independently or in tandem depending on prompt complexity. Mechanistic interpretability practitioners highlight that dynamic weight grafting offers an empirical, code-backed probing technique to map weight diffs across fine-tuning stages without relying solely on activation patching.
Researchers published SAE-LAPE on Monday, October 5, 2026, a probing methodology using feature activation probability in Sparse Autoencoders to identify language-specific concepts in LLM feed-forward networks (FFNs). Analyzing multilingual open-weight models, the authors discovered that language-specific features concentrate predominantly in middle-to-late transformer layers and directly influence multilingual routing. The study released open-source code and reproducible probes on GitHub, demonstrating that SAE-isolated features achieve language identification accuracy comparable to fastText.
Why it matters
Polysemantic neurons in multilingual models make it difficult to determine how concepts are partitioned across languages. Using Sparse Autoencoders to isolate language-specific features in FFN layers provides a reproducible probing technique for multilingual model inspection. Releasing the code toolkit enables interpretability researchers to audit and steer language representations in open-weight models.
The study's authors demonstrate that language-specific features form distinct clusters in later FFN layers that can be targeted for activation steering. Independent interpretability researchers note that while feature localization is high, steering language features can occasionally induce unintended code-switching in fine-tuned instruct models.
Continuing the rapid Claude Code release cycle we tracked through this weekend's v2.1.289 exception firewalls update, Anthropic shipped versions 2.1.290 and 2.1.291 on Tuesday, October 6, 2026. The new releases add security and audit metadata fields to mod hooks, introducing `agentId` to `tool.check` hooks to differentiate delegated subagent permission requests from main session prompts. The updates also add a `ceiling` property specifying organizational approval thresholds, log API-driven background execution steps via `serverToolUses`, and fix multiple state-loss and permission-bypass regressions.
Why it matters
As CLI agents increasingly delegate complex sub-tasks to nested worker subagents, broad session-level permissions become a critical vulnerability. Exposing `agentId` and approval ceilings directly to in-process plugin hooks allows developers to construct granular governance policies that restrict subagent authority without blocking the main orchestrator. For agent harness architects, these additions provide necessary primitives to sandbox autonomous execution loops operating on local file systems.
Anthropic's release notes present these updates as essential security and reliability hardening for enterprise multi-agent workflows. Independent security researchers caution that because mods execute directly inside the agent's process space, unvetted user-installed plugins can still intercept `tool.check` hooks to override organizational deny rules unless strict managed guardrails are enforced.
On Monday, October 5, 2026, Hugging Face launched OpenEnv, an open-source capture proxy framework and TRL-compatible training pipeline. OpenEnv converts ten existing coding agent harnesses—including Claude Code, Codex, Hermes, Pi, and OpenCode—into reinforcement learning environments without requiring modification to harness source code. By intercepting endpoint communications at the proxy layer, the tool enables online RL policy updates inside live developer environments. Experiments fine-tuning Liquid AI's LFM2.5-2.6B across multiple harnesses demonstrated increased task solve rates and a 31% reduction in unnecessary tool-call loops via reward shaping.
Why it matters
Open-weight agent models frequently fail when transferred from synthetic training harnesses to production environments like Claude Code or Codex due to subtle API and context differences. Intercepting environment traffic via a non-intrusive capture proxy allows open models to undergo online RL directly within the target harness. This bridges the distribution gap between benchmark scaffolds and practical CLI developer tools.
Hugging Face researchers argue that multi-harness RL training is necessary to prevent models from overfitting to a single agent scaffolding design. Independent harness maintainers welcome the proxy approach because it allows model builders to run RL loops without requiring custom API hooks or invasive code modifications inside the underlying agent tools.
Laminar announced 'flow-1' on Monday, October 5, 2026, a specialized reinforcement learning model engineered to detect and locate errors within multi-turn agent execution traces. By framing an agent's execution trace as a code repository and treating individual tool-call spans as files inside its 'Signals' trace harness, flow-1 matches the diagnostic accuracy of GPT-6-sol on traces up to 100k tokens while operating at 23x lower token execution cost. The model was trained using SFT followed by RL to minimize false positives during automated trace audits.
Why it matters
Monitoring deep multi-turn agent trajectories in production typically hits an economic barrier when every step requires verification by an expensive frontier LLM judge. Structuring execution spans as structured code files and training a dedicated, smaller RL model to parse them reduces evaluation costs by over 90%. This enables continuous 100% trace auditing and automated error dataset generation for local and open-weight agent pipelines.
Laminar developers highlight that flow-1 eliminates the need to rely on lossy trace sampling in production agent monitoring. Evaluators point out that the model's performance depends on formatting execution spans into its specific file-and-grep harness format, requiring developers to adopt structured telemetry schemas.
In a paper published Tuesday, October 6, 2026, AI21 researchers presented an architectural framework asserting that a task's inherent verifiability—its capacity for deterministic outcome validation—should dictate compute allocation between verifiers and multi-agent diversity. Evaluating coding, search, and research tasks, the study showed that verifiable domains (like SWE-Bench Pro) maximize performance by routing candidate outputs through strict verifier models, whereas unverifiable tasks require multi-agent response merging. In SWE-Bench Pro tests, pairing parallel open-source search agents (MiniMax-M3) with a synthesis model and a frontier patch writer achieved an 80.8% resolve rate at $5.99 per task.
Why it matters
Engineering teams often default to scaling model size or adding generic multi-agent consensus loops without assessing whether a task can be verified programmatically. Establishing a step-level verifiability test allows agent architects to pair low-cost open-weight models for parallel generation with targeted verifiers only where outcome checks exist. This optimizes agent execution spend while raising benchmark solve rates.
AI21 researchers argue that task verifiability provides a clear boundary for when to invest in execution sandboxes versus when to rely on ensemble sampling. Skeptical benchmark auditors point out that relying on model-based verifiers for semi-verifiable tasks can introduce verifier preference bias if the verifier shares architectural flaws with the generator.
A multi-institution study titled 'Rethinking the Evaluation of Harness Evolution for Agents', published Monday, October 5, 2026, revealed widespread evaluation defects across automated agent harness evolution literature. The authors demonstrated that prior studies frequently omitted budget-matched baselines and used task validation signals from test benchmarks during search. When evaluated on Terminal-Bench 2.1 using budget-matched compute and held-out tasks, automated harness evolution failed to outperform simple test-time scaling methods, except in specialized long-horizon games like ARC-AGI-3 where task-specific evolution yielded gains.
Why it matters
Automated prompt and harness search algorithms are frequently claimed to deliver massive performance gains, but unregularized search on open benchmark tasks often results in benchmark memorization rather than generalized reasoning. Demonstrating that simple test-time scaling matches complex harness evolution under controlled budgets highlights the need for strict separation between validation and test sets in agent evaluation. Developers should baseline simple parallel sampling before deploying complex prompt-evolution frameworks.
The study authors urge the agent research community to adopt held-out task suites and strict budget accounting to prevent false progress reporting. Proponents of evolutionary harness search maintain that domain-specific harness mutation remains valuable for long-horizon environments where structural task constraints do not change.
On Monday, October 5, 2026, OrchestratorInc open-sourced Agent Orchestrator, a Go daemon that wraps local CLI agents (such as Claude Code and Aider) using PTY attachment and isolated git worktrees. The daemon provisions each worker agent with an isolated filesystem directory sharing the primary repository's underlying `.git` object store, eliminating clone overhead and preventing concurrent agents from overwriting the same working tree files. An Electron UI projects stdout logs, git status, and file watchers into a unified Kanban state machine. Limitations include strict dependency on git repositories and lack of headless CI execution.
Why it matters
Executing multiple local coding agents concurrently usually results in terminal clutter and destructive file collisions when agents edit shared files simultaneously. Wrapping agent processes inside native git worktrees provides filesystem-level isolation without duplicate storage costs or complex API abstractions. This allows local-LLM practitioners to run parallel refactoring or test-generation agents safely on a single machine.
The tool's developers highlight that PTY wrapping and worktree isolation work transparently with existing CLI tools without requiring agent code modifications. Software engineers note that because the orchestrator operates as a black-box supervisor around terminal stdout, it cannot intercept or correct erroneous internal agent reasoning before commands execute.
Following last week's v0.30.0 release that added MXFP8 KV cache support, maintainers released vLLM v0.31.0 on Monday, October 5, 2026. This update introduces a `vllm preload` CLI feature that maintains quantized model weights resident in GPU memory via CUDA IPC to minimize restart latency. The release also establishes FlashMLA mega-attention and NVFP4 compressed KV caches as defaults for DeepSeek-V4.1-Flash on NVIDIA SM100 architectures, and adds explicit batch scheduling controls via the `--max-num-active-seqs` parameter.
Why it matters
Keeping quantized weights resident across process restarts directly eliminates the cold-start loading bottleneck when switching local model instances or restarting serving daemons. On the architectural side, native SM100 FlashMLA support ensures that DeepSeek's low-rank latent attention runs without falling back to unfused attention paths. This alignment between serving infrastructure and low-bit KV formats maximizes memory bandwidth utilization during high-concurrency decode steps.
Maintainers and infrastructure operators highlight the significant drop in engine startup latency provided by IPC weight retention during rapid development cycles. However, issue reports in the digest note that concurrent developments in hybrid-SWA and HiCache scheduling have triggered intermittent deadlocks and memory corruption under specific batch configurations.
Yesterday we covered the Strata inference engine running the 125B-parameter Qwen3.8-Flash-Next on a desktop RTX 5070 GPU. On Tuesday, October 6, developer Niko1221 expanded the engine's offloading capabilities with version 0.1.40, introducing native HIP backend support for AMD Strix Halo and Ryzen AI Max APUs. The update adds a Linux read-ahead optimization that speeds up cold-start loading by up to 13x, dropping ready-times to 70 seconds at 262K context on consumer setups, alongside unbuffered file tiers and resident-RAM multi-GPU splitting.
Why it matters
Serving 100B+ parameter MoE models locally has historically been blocked by VRAM capacity limits. By exploiting the extreme sparsity of MoE forward passes—where only a fraction of experts are active per token—Strata combines VRAM caching, system RAM pinning, and direct NVMe I/O to run large models on gaming GPUs and unified-memory APUs. The addition of AMD Strix Halo support provides an alternative unified-memory hardware path to Apple Silicon for local practitioners.
The engine maintainers demonstrate that offloading cold experts to system RAM and SSDs achieves usable decode speeds (~94 tok/s on Q2_0 quants with speculative decoding) on single consumer GPUs. Local practitioners note that while token generation rates are high, running at Q2_0 or IQ3_S bit-widths requires careful verification to ensure extreme quantization does not trigger repetitive generation loops during long reasoning chains.
Tenstorrent engineers submitted a performance optimization for `ttnn.reshape` on Blackhole hardware on Tuesday, October 6, 2026. The patch addresses a bottleneck where TILE tensor reshape operations during attention head splits and merges ran up to 4.1x slower than routing through row-major layouts. By dynamically rerouting qualifying tensor shapes through a row-major round trip on Blackhole silicon, single-device prefill times for Qwen3.5-9B improved by ~11% across 2048, 4096, and 8192 token context lengths while maintaining bit-identical logit outputs.
Why it matters
Achieving efficient open-weight model serving on alternative hardware architectures depends on eliminating low-level memory layout bottlenecks in basic tensor ops. Identifying that TILE tensor reshapes on Blackhole were memory-bandwidth bound and rerouting them through row-major layouts reclaims substantial prefill throughput. This demonstrates the impact of hardware-specific layout optimizations when deploying open models on non-CUDA silicon.
Tenstorrent engineers confirmed that the row-major fallback optimization delivers bit-exact logit parity while cutting prefill latency across long prompt lengths. Hardware developers note that such kernel fixes highlight the continuous low-level software tuning required to bring novel RISC-V and custom AI hardware up to CUDA-equivalent kernel efficiency.
A preprint published Monday, October 5, 2026, presented TaSQ (Tailored Space Quantization), a vector quantization methodology compressing key-value caches to 1 bit per channel. TaSQ applies query-guided channel weighting, cross-head scale normalization, and covariance-aware channel grouping to pre-RoPE key tensors. By absorbing these linear transformations directly into model projection matrices and codebooks during calibration, the method incurs zero runtime kernel overhead. In SGLang serving tests on an RTX 6000 Ada, TaSQ increased generation throughput by 1.87x and expanded maximum batch sizes from 6 to 84 on Llama-3.1-8B and Qwen3 models, accompanied by a 10–14% prefill latency increase.
Why it matters
Sub-2-bit KV cache quantization typically causes catastrophic generation degradation because position-dependent RoPE rotations scramble codebook centroids. Absorbing channel-weighting and covariance transforms into pre-RoPE projection matrices bypasses this barrier without adding decode-time compute. This provides local inference runtimes with a viable mechanism to expand context batching capacity by over 10x on memory-constrained GPUs.
The authors emphasize that TaSQ preserves multi-step reasoning fidelity and chain-of-thought accuracy on benchmark suites where standard scalar uniform quants fail. Infrastructure engineers evaluating the preprint observe that while decode throughput and batch capacity scale significantly, the 10–14% increase in time-to-first-token (TTFT) makes it best suited for throughput-bound batch serving rather than ultra-low-latency interactive prompts.
A paper published Monday, October 5, 2026 (arXiv:2610.02815), introduced iS-KV, an online low-rank KV cache compression method utilizing block-incremental Singular Value Decomposition (SVD). Unlike token eviction schemes that permanently drop past context, iS-KV dynamically updates both basis matrices and coefficient vectors as new tokens arrive during autoregressive decoding. Evaluated on DeepSeek-R1 and Qwen3-8B models during long chain-of-thought reasoning tasks, iS-KV achieved 4x to 5x memory compression while retaining original perplexity and output accuracy across all token positions.
Why it matters
Long reasoning chains in modern open-weight models cause key-value caches to expand linearly, rapidly exhausting GPU memory during multi-turn generation. Token eviction strategies risk dropping critical early context or prompt instructions required for logical coherence. By applying incremental SVD to update a low-rank representation on the fly, iS-KV preserves historical context across the entire sequence length within a compressed memory footprint.
The researchers show that block-incremental SVD avoids representation drift during extended generation steps where standard static low-rank projections fail. Inference engine developers caution that while SVD compression saves memory bandwidth during decode steps, performing online matrix updates introduces additional compute overhead during block ingestion that must be balanced against memory gains.
The `llama.cpp` project tagged prerelease b11413 on Monday, October 5, 2026, extending Vulkan sparse FlashAttention execution to quantized key-value caches (including `q8_0`). The update adds a depth-condition check requiring the KV cache to pass specific token thresholds before engaging the sparse route in scalar and coopmat1 paths. Benchmarks published by contributors running AMD Radeon RX 7900 XT hardware demonstrated generation throughput gains of 19% at 64k context and 15% at 128k context, followed by release b11414 fixing an associated Vulkan scratch-buffer allocation bug.
Why it matters
Vulkan backend support in `llama.cpp` is essential for cross-vendor GPU execution outside the CUDA ecosystem. Bringing sparse FlashAttention to quantized KV caches allows local operators to combine memory-saving cache quantizations with sparse attention acceleration. This directly improves long-context generation speeds on consumer AMD and Intel graphics hardware.
Maintainers note that the sparse path is conditionally gated by context depth thresholds to avoid performance regressions on short sequences. Hardware testers confirm the throughput gains at 64k+ context but emphasize that users must compile with appropriate coopmat flags to enable the optimized Vulkan compute shaders.
San Francisco startup Goodfire launched Silico on Tuesday, October 6, 2026, an off-the-shelf software product for mechanistic interpretability and parameter-level model debugging. Silico allows researchers to trace feature pathways, inspect individual neurons, and modify connected weights to amplify or suppress specific behaviors in open-source models like Qwen 3. Goodfire reports using the tool to locate and adjust feature circuits responsible for mathematical errors and safety refusals, integrating automated sub-agents to execute probing routines.
Why it matters
Mechanistic interpretability tools are transitioning from custom academic scripts into packaged commercial software environments. Providing an off-the-shelf interface to trace signal flow and edit connected parameters simplifies circuit analysis for open-weight models. This allows practitioners to inspect internal feature representations without building bespoke probing pipelines from scratch.
Goodfire presents Silico as a practical platform to replace trial-and-error fine-tuning with targeted parameter editing. Independent interpretability researchers welcome accessible UI tooling for feature tracing, but emphasize that commercial closed-source debugging stacks must remain reproducible against open-source probing suites like Gemma Scope.
A preprint published Monday, October 5, 2026, presented STILL, an intra-layer hybrid linearization framework designed to reduce quadratic attention complexity in pretrained models. STILL uses a Self-Saliency Score to route critical context tokens to sparse softmax attention, while remaining sequence tokens are compressed via linear attention. To prevent feature distortion caused by learnable feature maps, STILL introduces a Norm-Preserved Feature Map (NP-Map) that decouples feature direction from magnitude. In long-context retrieval benchmarks on Llama 3.1 8B, STILL achieved up to an 86.2% relative performance gain over prior linearized attention mechanisms at 4K context.
Why it matters
Linearizing standard softmax transformers often causes significant retrieval drops because standard feature maps distort activation magnitudes in key-value states. Decoupling feature direction from magnitude via norm-preserved mapping stabilizes the linear attention memory state while sparse saliency routing handles fine-grained token retrieval. This approach offers a mathematical framework for converting existing dense transformer weights into sub-quadratic hybrid layers.
The authors highlight that norm preservation prevents activation explosion and feature collapse during extended autoregressive sequences. Computational researchers observe that while intra-layer hybridization improves retrieval accuracy over pure linear attention, calculating dynamic self-saliency scores adds routing overhead during the prefill phase.
Gated DeltaNet Layer Substitution Outperforms Softmax Transformers Open-weight pretraining recipes are actively replacing sliding window attention with Gated DeltaNet linear recurrence, demonstrating superior scaling metrics and formal expressivity over pure transformer baselines.
Sub-Bit and Vector Space Transformations Target Pre-RoPE Keys KV cache compression research is shifting away from naive scalar quantization toward pre-RoPE covariance grouping and sub-bit vector quantization, absorbing mathematical transforms directly into projection matrices to eliminate runtime kernel overhead.
Multi-Tiered Memory Architecture Enables Frontier MoE Local Serving Local inference runtimes are bypassing discrete GPU VRAM limits by treating VRAM, system RAM, and NVMe SSDs as unified multitier memory hierarchies specifically optimized for sparse expert routing and offloaded n-gram tables.
In-Process Agent Hooks Introduce Fine-Grained Delegated Governance Agent CLI frameworks are expanding mod hook interfaces to surface agent identifiers, organizational approval ceilings, and background tool calls, preventing subagent permission escalation in multi-agent workflows.
Execution Trace Simulation Replaces Brute-Force Evaluator Models Agent evaluation frameworks are abandoning expensive frontier LLM judge calls in favor of specialized, trace-aware RL models and git worktree sandboxes that treat execution spans as filesystem state.
What to Expect
2026-10-31—Expected public weights release of Reflection AI's Beam 501B MoE model under Apache 2.0 license.
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