Datasette Agent, a conversational AI assistant for Datasette, has been released. It enables asking questions about data stored in Datasette and generates charts via the datasette-agent-charts plugin. The demo runs on Gemini 3.1 Flash-Lite.
Release of datasette-agent-sprites 0.1a0, a Datasette Agent plugin for running commands in a Fly Sprites sandbox.
Cohere releases Command A+, its most powerful language model to date, as open source under Apache 2.0 license.
Anthropic is approaching its first profitable quarter with projected operating profit of $559 million on $10.9 billion Q2 revenue. Profitability is accelerated by coding tools and agentic Claude usage, which at times exceeded available compute capacity.
SpaceX IPO filing reveals xAI losses of $6.36 billion in 2025, a $15 billion/year Anthropic compute deal, and targets a $2 trillion valuation. Musk retains 85.1% voting power through dual-class shares.
LLM Planner is an interactive guide to match hardware or open-weights models. 60+ build configs, 50+ models, sourced tokens/sec, power draw, multi-region pricing, 150+ reviewer YouTube videos. Bidirectional modes: "which rig for this model/budget" or "what models run on my GPU". Data updated weekly, public GitHub repo.
Google officially announces ads will be integrated into AI Mode search results. This monetization of generative search responses marks a strategic shift for the tech giant amid competition from chatbots.
OpenAI GPT-next solved the 80-year-old Erdős planar unit distance problem for under $1000. Significant result at the intersection of AI and mathematics.
Qwen 3.7 Max from Alibaba is now available on Vercel AI Gateway. The model, designed as an agent foundation, excels at frontend prototyping, multi-file engineering, and office workflow automation through multi-agent orchestration.
Theoretical paper on out-of-distribution generalization in RL. Authors extend state abstraction framework to POMDPs and introduce successor-weighted model reduction. They prove smaller abstract state spaces improve generalization to more complex tasks.
M-ORE, an online editor for multimodal LLMs, corrects MLLMs from a continuous stream without degradation. Uses proximal-projection formulation with Sherman-Morrison recursion to avoid cross-modal conflicts and inter-edit interference. Code released.
LEAP couples a domain-specialized LLM with active learning to discover perovskite precursor additives for solar cell optimization. The model extracts mechanistic knowledge from literature and generates interpretable descriptors integrated into Bayesian optimization. Experimental validation: PCE 20.13–20.87% vs 19.25% control, champion 21.32%.
CP-MoE introduces a continual learning framework for LLMs and VLMs using Mixture-of-Experts architecture. A transient expert captures early task-specific updates and guides their integration into stable experts via consistency-preserving routing bias and regularization. Validated on SuperNI and VQA v2, CP-MoE reduces catastrophic forgetting while preserving cross-task knowledge transfer.
Kernel Discovery, an LLM-driven evolutionary framework, automates Gaussian kernel design for high-dimensional Bayesian optimization. The method generates novel mathematical forms via LLM, converts them to validated code, and uses LOO-CRPS criterion to penalize overfitting. On 5 benchmarks, it achieves average rank 1.2 out of 17.
Joint training of autonomous vehicle and 12 pedestrians using MARL (MAPPO) in simulation. SDC reaches 78% goal completion with 14% collision rate vs 35%/33% for rule-based baseline. Jaywalkers (13% of crossings) account for 62% of collisions. Co-training reduces collisions by 30% vs single-agent RL.
MedicalBench is a benchmark for extracting implicit medical concepts from electronic health records (MIMIC-IV). It formulates the task as verification of note-concept pairs with sentence-level evidence identification. State-of-the-art LLMs show modest performance, highlighting the difficulty of implicit medical reasoning.
Systematic comparison of two approaches for structured outputs from Claude: text instructions + post-generation parsing (65–70% success) vs tool_use with typed schemas and enum constraints (90–95%+). API-level validation eliminates parsing errors and drastically reduces downstream debugging.
Developer unlocks full 40 CUs on salvaged PS5 GPU boards (AMD BC-250, ~$50-150) by modifying two amdgpu driver registers. Results: 372 tok/s Vulkan (24→40 CUs) and 466 tok/s at 2 GHz. Custom HIP kernel for gfx1013 in progress with +63% perf gain on Q6_K.
HalBench: open-source benchmark measuring sycophancy and hallucinations across 3,200 false-premise prompts tested on 4 models (Sonnet 4.6, Grok 4.3, GPT-5.4, Gemini 3.1 Pro). Sonnet 4.6 scores 0.565/1, Grok 4.3 0.498, GPT-5.4 0.381, Gemini 3.1 Pro 0.339. Dataset, code, and results public.
Cohere releases Command A+, its first open-weights MoE model. Optimized for efficiency and latency, runs on 1-2 GPUs with advanced quantization. Apache 2.0 license, designed for agents and small teams.
ByteShape releases Qwen 3.6 35B GGUF quantizations in NTP and MTP variants. Benchmarked across RTX 4090/5090, Intel i7/Ultra 7, Ryzen 9, Raspberry Pi 5. Key finding: largest model stays competitive on quality and speed. MTP delivers 20–40% GPU generation speedup but increases memory footprint. NTP recommended for CPU.
Stability AI launches Stable Audio 3.0 with three open-weight models. The models generate music tracks up to six minutes long, trained entirely on licensed data.
Release of datasette-agent-charts 0.1a1. New features: sequential/categorical color schemes for charts, execute-sql permission checks, interactive tooltips, fixed waffleY chart description bug.
Microsoft releases an MCP server for Azure DevOps, enabling AI agents to access Azure DevOps capabilities directly.
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.
Celonis acquires Ikigai Labs and launches the Celonis Context Model (CCM). MIT joins as investor in exchange for a patent license. Announced May 12, 2026 from Munich.
OpenCompass is an open-source LLM evaluation platform featuring a modular architecture with 5 core components: configuration system, task partitioning, execution/scheduling, task execution unit, and result visualization. Supports rule-based, LLM-as-a-Judge, and cascaded evaluators across multi-domain benchmarks (knowledge, reasoning, code, science).
LLMs fail to identify and share statistical strength between distinct presentations of the same concept (same facts in English and Swahili, functions in Python and Haskell). They create redundant parallel internal representations, saturating model capacity. Small models exhibit near-total compartmentalization in early multilingual learning.
PROWL introduces a KL-constrained adversarial curriculum to improve robustness of action-conditioned video world models. A policy exposes high-error trajectories of a diffusion-based model while a Prioritized Adversarial Trajectory (PAT) buffer re-ranks data by prediction error and learning progress. Evaluation on MineRL demonstrates improved robustness on out-of-distribution trajectories.
Multi-Token Residual Prediction (MRP) is a lightweight module that accelerates diffusion language models by predicting logit residuals between consecutive denoising steps without re-running the backbone. Tested on SDAR 1.7B–8B, MRP achieves 1.42× lossless speedup in speculative decoding on reasoning and code generation benchmarks.
FormalASR introduces two compact models (0.6B and 1.7B parameters) that directly transcribe spoken Chinese into formal written text without an ASR+LLM pipeline. Fine-tuned on WenetSpeech-Formal and Speechio-Formal using supervised fine-tuning of Qwen3-ASR, they achieve 37.4% relative CER reduction over verbatim baselines and improve ROUGE-L and BERTScore.
DMPO (Distribution-Matching Policy Optimization) solves mode collapse in on-policy RL methods like GRPO by approximating forward KL instead of reverse KL. On text and vision NP-Bench, DMPO achieves 43.9% and 43.1% Quality Ratio (vs 40.1% and 38.4% for GRPO), with +2.0% gains on mathematical reasoning.
Theoretical work on adaptive exploration under uncertainty. Distinguishes volatility (reward drift) from stochasticity (observation noise): volatility enhances optimal exploration, stochasticity suppresses it. Introduces CAUSE, a closed-form exploration bonus via control-as-inference, validated on Gaussian state-space bandits with latent dynamics.
New paper on multi-task machine unlearning: removing training data from shared models without degrading other tasks. Proposes interference-aware framework combining task-aware gradient projection and instance-level orthogonalization. Reduces interference by 30–53% on vision benchmarks.
Theoretical paper on emergence of superposition in Transformers for depth reasoning. Identifies a Möbius attractor under S_n symmetry and Cascade Supervision enabling gradient descent to converge to equal-weight superposition state on Erdős-Rényi graphs. Analytical predictions validated experimentally (final cosine 0.37 vs 0.69).
Group-equivariant architectures improve neural CFD surrogates when training data lacks strong regularities, but degrade performance on strongly aligned datasets. AB-GATr, an E(3)-equivariant geometric algebra transformer, outperforms data augmentation on automotive aerodynamics and hemodynamics benchmarks.
Survey of ~120 studies on mathematical reasoning in LLMs. Structured analysis of datasets, architectures, training strategies, and evaluation protocols. Identifies recurring failure modes: reasoning faithfulness, benchmark biases, generalization limitations.
English explanations for auditing multilingual LLMs mask a trade-off: higher span agreement with human rationales but weaker causal grounding in model predictions. Across 3 tasks and 5 languages, comprehensiveness degrades up to 5.7x in English-pivot conditions despite stable task accuracy. Authors recommend auditing in input language rather than English pivots.
Novel world model architecture combining three specialized memory experts via diffusion: short-term (local dynamics), long-term (external history), spatial (geometric coherence). Eliminates transformer/RNN trade-off and scales without quadratic cost.
Empirical study of multi-model LLM scheduling challenges on heterogeneous hardware. Authors quantify layer offloading impact (non-linear throughput degradation, model-dependent sensitivity) and preemption costs (dominated by state reload). Identifies critical features for next-generation schedulers.