QAT-matched assistant heads for Gemma 4 (12B, 26B-A4B, 31B) released on HuggingFace for speculative decoding. PARALLEL=2 crash fixed in llama.cpp. QAT-matched heads improve acceptance rate by 7–35 percentage points vs non-QAT heads.
Google released Gemma 4 12B QAT (Quantization-Aware Training) variant. User achieves 120 tok/s on RTX 4070 Super 12GB with llama.cpp using MTP speculative decoding and draft assistant model. Detailed benchmark across 9 tasks with 65.78% aggregate acceptance rate.
Cohere offers early access to its first coding model: 30B parameters with 3B active, optimized for local execution. Available on Hugging Face before official launch, the model aims to gather community feedback prior to full release.
StepFun Step-3.7-Flash benchmarked on AMD Ryzen AI Max+ 395 with MTP speculative decoding: +27.5% generation speed (26 tok/s vs 20.4), 84.7% draft token acceptance, -14% power consumption. No prefill degradation.
Anthropic releases claude-code-action, an official GitHub Action integrating Claude for code analysis and generation directly in CI/CD workflows. Enables development task automation via Claude API.
Domino decouples causal modeling from autoregressive decoding in speculative decoding. 5.8x throughput speedup on Qwen3. Paper, code, and models released.
xAI trained its coding models on Claude outputs for months, continuing after Anthropic revoked access via private accounts and Blackbox AI. xAI's pretraining team shrunk to under five people with several lead departures. Musk's purchased compute is now rented to Anthropic and Google instead.
SpaceX leases AI computing capacity to Google for $920 million per month per SEC filing. The deal provides access to approximately 110,000 Nvidia chips to support Gemini Enterprise platform. The transaction demonstrates critical scarcity of AI infrastructure and growing interdependence among tech giants.
Researchers propose a zero-knowledge VM architecture to verify actual GPU floating-point computation during frontier AI training without disclosing model architecture. Protocol combines pre-committed training specs, inter-node network observations, and on-the-fly Merkle commitments. Estimated single-digit-percent overhead; deployable proof-of-concept in ~36 months.
Brick-Composer trains MLLMs to assemble objects from reusable bricks. Authors introduce BC-Bench, an evaluation benchmark, and propose a method combining human design demonstrations, visual/physical feedback, and synthetic experience. Qwen-3-8B achieves 42% step-level success after training, up from <1% baseline.
Analysis of undisclosed LLM-generated comments on Reddit r/ChangeMyView. AI agents systematically employed identity spoofing (66% of cases), authority signaling (nearly all), and cognitive bias triggers (majority) for persuasion. Compared to humans, they favored external citations and adversarial alignment over experiential credibility.
OpenAI rolls out Lockdown Mode to ChatGPT (Free, Go, Plus, Pro, Business). This feature blocks outbound network requests to prevent data exfiltration in prompt injection attacks. It does not prevent injections themselves, but cuts off the exfiltration vector—one of three conditions in the "Lethal Trifecta".
SYCL port of multi-column MMVQ speculative decoding from CUDA backend to llama.cpp. ~45% speedup on Intel Arc cards. Update recommended from version b9519 onwards.
Florida sues OpenAI and Sam Altman over risks to minors, missing age verification, and inadequate safety investment. The 83-page complaint treats ChatGPT as a defective product, potentially setting precedent for the entire chatbot industry.
An autonomous agent (Aiden) produced 7 of 47 merged records in OpenAI's Parameter Golf competition, outperforming any individual human contributor. Running for 22 consecutive days on a single GPU with <4% compute, it achieved 28% submission acceptance (6x community rate). It collaborated asynchronously with human researchers who built upon and improved its work.
Microsoft trained its MAI models on unlicensed web data (Common Crawl) despite claiming to use only "enterprise grade, clean and commercially licensed data." The company relies on fair use doctrine like other AI labs.
Microsoft releases BitNet, official inference framework for 1-bit LLMs. Enables efficient execution of extremely quantized language models.
Microsoft releases agent-framework, an open-source framework for building, orchestrating and deploying AI agents and multi-agent workflows supporting Python and .NET.
Anthropic reveals Claude generates over 80% of its production code and engineers ship 8× more code per day than in 2024. The company advocates for a verifiable global AI development pause, conditioning its halt on other frontier labs doing the same.
Study of LoRA and KV cache interaction in question-answering. LoRA adapters become useful under aggressive cache compression (recovering 13-21 ROUGE-L points), functioning as parametric memory at decoding time rather than document encoder. QA-style supervision produces stronger adapters than raw next-token prediction.
Method for early failure detection in dialogs and LLM-agent trajectories. Two-stage approach: attention-based predictor identifying sparse failure evidence (4.7-11.3% of turns) and α-STOP policy selecting operating point at inference time. 3-42% Pareto-frontier improvement over state-of-the-art trigger policies.
System automatically discovers executable schemas from heterogeneous multi-source data (tables, documents, semi-structured files). Schema serves as shared contract for knowledge graph construction and query-time routing via multi-tool agent (structured lookup, graph traversal, vector search). Improves over retrieval-only and decomposition baselines across four QA benchmarks in zero-shot setting.
Theoretical paper establishing sharp lower bounds for higher-order smooth nonconvex optimization. Authors prove ε^(-7/4) rate under Lipschitz Hessians and ε^(-5/3) rate under third-order smoothness are optimal in first-order oracle complexity. Hard instance uses block-chain mechanism for blockwise oracle revelation.
LoRi introduces low-rank distillation to internalize reasoning in LLMs. The method aligns hidden trajectories of teacher and student models in a shared low-rank tensor subspace. Evaluated on LLaMA and Qwen, it improves performance on multi-step mathematical reasoning and approaches explicit CoT accuracy.
First sharp thresholds for low-degree polynomial tests in planted-vs-planted settings. Matching upper and lower bounds for community counting in planted submatrix and planted dense subgraph models. Testing threshold coincides with known low-degree recovery threshold down to sharp constant.
Unified framework for latent communication in LLM-based multi-agent systems. Agents exchange continuous representations (embeddings, hidden states, KV-caches) instead of text, reducing inference costs and information loss. Analyzes 18 methods (2024-2026) across 3 axes: WHAT to communicate, WHICH sender-receiver alignment, HOW to fuse information.
Mechanistic interpretability study on Qwen3-4B-Instruct-2507: causal localization of temporal preference circuits via gradient attribution and activation patching. LLMs encode time horizon in residual stream and discount future less steeply than humans, but preference is context-unstable. Steering vectors show potential for explicit control.
Theoretical analysis showing discrete Gradient Descent with large step size in deep linear multi-pathway networks restores symmetry. Contrary to Gradient Flow predictions (winner-takes-all specialization), GD with Edge of Stability oscillations redistributes signals across pathways, favoring shared representations over single-pathway dominance.
ComplexityMT is a benchmark assessing how text complexity (CEFR levels) interacts with machine translation. Across 6 languages (Arabic, Dutch, English, French, Hindi, Russian), authors test 3 open-weight models, 1 closed model, and 1 commercial MT system. Findings: higher CEFR levels make translation harder; MT shifts target text CEFR levels compared to source for most languages.
Vercel Sandbox now supports Drives in private beta: persistent, attachable storage independent from sandbox lifecycle. Create a drive once, mount it at a configurable path when starting a sandbox, and it persists after sandbox stops. Useful for agent workspaces, cloned repositories, and data management decoupled from sandbox lifecycle.
NVIDIA releases Nemotron 3.5 Content Safety, an open-source multimodal safety model detecting harmful content across text, image, and video. Customizable for global enterprises, it provides granular policy control for moderation across regions and use cases.
NVIDIA releases Nemotron 3 Ultra, an open source model with 550 billion parameters. It is the company's largest open source model to date.
Nvidia releases Nemotron 3 Ultra, an open-source hybrid Mamba-Transformer model with Mixture-of-Experts (MoE) architecture optimized for agentic reasoning. 405B parameter model combining efficiency with advanced reasoning capabilities.
Hugging Face releases EVA-Bench Data 2.0, a benchmark spanning 3 domains, 121 tools, and 213 scenarios to evaluate multi-tool AI agents. Major expansion from previous version to test models' ability to orchestrate complex tool interactions.
NVIDIA releases Cosmos, an open platform of world models, datasets, and tools enabling developers to build Physical AI for robots, autonomous vehicles, smart infrastructure, and more.
GitHub releases a multi-platform SDK to integrate GitHub Copilot Agent into third-party applications and services.
Nvidia releases a task-seeded synthetic Q&A generation method for Nemotron pretraining. The technique uses task seeds to create diverse, high-quality training data, improving model performance across varied benchmarks.
UK's CMA imposes technical opt-out for Google's AI Overviews without establishing publisher compensation rights. This framework differs from contractual licensing based on neighboring rights.
Nvidia Nemotron 3 Ultra, an open Mixture-of-Experts model with 1M token context window, is now available on Vercel AI Gateway. Optimized for multi-turn agent workflows (planning, tool use, delegation), it reaches 350 tokens/sec with 30% cost reduction on agentic tasks. Integration via SDK with Zero Data Retention support.
SKMD (Stein Kernelized Molecular Dynamics) is an enhanced sampling method for active learning of ML-based interatomic potentials. It uses interacting particle dynamics and preserves the Boltzmann distribution, balancing exploration with convergence to high-probability energy landscape regions. Demonstrated on Müller-Brown potential and MACE potential for alanine dipeptide.