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.
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 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.
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.
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.
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.
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.
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.
MOCHA is a multi-objective optimization algorithm for refining LLM agent skills. It uses Chebyshev scalarization and exponential annealing to explore the complete Pareto front, including non-convex regions. On 6 tasks, MOCHA improves performance by 7.5% on average (up to 14.9% on FEVER) while discovering twice as many Pareto-optimal skill variants as baselines.
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.
Study of jailbreak attacks against Large Reasoning Models (LRMs) using reinforcement learning. Researchers show attack success rate correlates with model attention patterns. They propose an RL method incorporating attention signals into the reward function, tested on 5 LRMs with superior results in effectiveness, efficiency, and transferability.
GUIDE, an automated bidding framework, combines a Decision Transformer with a Q-value module and Inverse Dynamics Module to balance exploration and safety. Tested on Taobao, it achieves +4.10% ad GMV, +3.52% ROI, and +1.40% clicks in real-world deployment.
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.
Google released Gemini 3.5 Flash at general availability during Google I/O. The model (ID: gemini-3.5-flash) supports 1,048,576 input tokens and 65,536 max output tokens, with knowledge cutoff January 2025. Deployed across Google Search, Gemini app, and via API for developers and enterprises. Pricing increased versus previous Flash versions.
llm-gemini 0.32a0 released. Compatible with llm>=0.32a0 alpha. Adds ability to stream reasoning tokens.
Google restructures AI subscriptions with three tiers ($7.99–$99.99/month) based on compute consumption instead of daily limits. Launches Gemini Omni and Gemini Spark agent at I/O 2026.
Google announces at I/O three new models: Gemini 3.5 Flash, Gemini Omni (multimodal), and Gemini Spark, a personal agent running continuously in the cloud. The Gemini app receives a major redesign.
A developer built a minimal ML compiler in pure Python/CUDA without external dependencies. It lowers transformers (TinyLlama, Qwen2.5-7B) through 6 successive IRs down to CUDA kernels. On RTX 5090, achieves 0.96× PyTorch production stack performance, with 32/84 kernel shapes beating hand-optimized kernels (up to 5.6× speedup).
Andrej Karpathy joins Anthropic as a senior researcher. The former Tesla AI co-founder and prominent machine learning figure joins the company's research team.
Andrej Karpathy, prominent AI researcher and former OpenAI core team member, joins Anthropic to focus on frontier LLM research. He views the coming years as "especially formative" for the field.
Andrej Karpathy joins Anthropic as Senior Research Scientist. The former Tesla AI co-founder and director of AI at Tesla brings deep learning and AI systems expertise to the company.
NVIDIA Labs releases Sana, a linear diffusion transformer for efficient high-resolution image synthesis. Architecture reduces computational complexity while maintaining visual quality.
Anthropic expands Claude Managed Agents with self-hosted sandboxes and MCP tunnels. Companies can now execute their AI agents' tools in their own infrastructure while keeping agent control with Anthropic.
TABOM, a post-training method for Diffusion Language Models, aligns optimization with the multi-step easy-to-hard decoding trajectory observed at inference. Via Boltzmann modeling of unmasking preferences, it derives a tractable pairwise ranking objective that reduces training-inference discrepancy and improves performance on new domains.
VGGT-CD is a training-free pipeline for 3D change detection from multi-view images. It decouples cross-temporal registration from dynamic-change interference via joint keyframe inference and dense reconstruction purification. On the World Across Time benchmark, it reduces Absolute Trajectory Error by 44% outdoors and 59% indoors, 6× faster.
RAT (Randomized Advantage Transformation) estimates Tikhonov-regularized natural policy gradients via direct backpropagation without explicit Fisher matrix construction. The method applies the Woodbury formula and randomized block Kaczmarz iterations on on-policy mini-batches. Results match or exceed established natural-gradient methods on continuous and visual control benchmarks.