2731 articles
Paper proposing a multi-fidelity approach using conditional normalizing flows to improve reduced-order model (ROM) accuracy. Framework learns probabilistic mapping from low-fidelity to high-fidelity ROM coefficients with uncertainty quantification. Two strategies tested on 2D Navier-Stokes equations: direct and residual learning, with residual learning outperforming direct learning.
SinkRec introduces a hybrid memory-transition architecture for long-sequence recommendation using linear attention and conditional memory. It addresses semantic state sink—where repetitive patterns dominate recurrent state—by externalizing recurring behaviors via residual vector quantization and introducing Temporal-Aware State-Relation Differential Gated DeltaNet (TDGD) to purify state reads and writes.
ML framework coupling gradient boosting and conformal prediction for NAFLD risk. Evaluated on 2,599 patients (Guangzhou), achieves AUROC 0.912 internally and 0.891 external validation. Conformal coverage 91.3% at 90% nominal level. Three-tier stratification: high-risk group shows 4.7× progression rate vs. low-risk tier.
QSplitFL introduces a Deep Q-Network framework for automatic optimal split point selection in Split Federated Learning. Using lightweight hardware metrics (CPU, memory, battery, network latency) instead of model weights, the committee-based DQN architecture improves convergence on MNIST, Fashion-MNIST, CIFAR-10/100 with CNN, ResNet50, MobileNetV4, ConvNeXt.
UniTok is a universal tokenizer converting continuous time series into discrete tokens. UniTok-FM, a foundation model pretrained via next-token prediction, supports zero-shot forecasting, generation and classification via training-free in-context inference without task-specific modifications.
TD-Grokking recursively decomposes zero-reward problems into verifiable subproblems to generate training signals. Tested on mathematical and medical tasks, the framework outperforms vanilla GRPO and converts zero-reward examples into usable optimization signals.
New benchmark to evaluate knowledge editing techniques in LLMs. Existing methods (ROME, FT) successfully insert direct facts but fail on logical consequences: up to 24% performance gap between directly edited and entailed knowledge.
SynIB, a new information-theoretic training objective, maximizes multimodal synergy by penalizing model confidence when any modality is masked. Validated on synthetic XOR and five real benchmarks (MultiBench, Hateful Memes, CREMA-D), SynIB improves accuracy on synergy-dependent examples by up to 7.8% and overall accuracy by 3.8%.
OSL-MR, a framework optimizing memory retention in long-horizon language agents. Formulates the problem as constrained stochastic optimization with budget, evidence utility, and delayed costs (miss penalties, reacquisition delays). Combines supervised learning with Mixed-Score heuristic. Outperforms recency and Generative Agents baselines on LOCOMO and LongMemEval benchmarks.
Cancer treatment modeled as belief-space planning problem using active inference. Framework unifies goal-directed control and information acquisition under measurement budget constraints. Validated on AACR Project GENIE clinical data with simultaneous patient categorization and treatment efficacy.
Detection of speculative language in biomedical texts using Recurrent Neural Tensor Networks. RNTN achieves F1=0.885 vs SVM baseline F1=0.881. Paragraph Vector underperforms (F1=0.368). Applications: information retrieval, multi-document summarization.
HIPIF introduces hierarchical reinforcement learning for long-horizon LLM agents. It decomposes tasks into explicit subgoals and folds completed histories to reduce long-context interference. Validated on three public benchmarks without costly auxiliary models.
Study on sequential fine-tuning of LLaMA-3.1-8B for Automated Essay Scoring. Model trained progressively on discourse elements (lead → position → claim → evidence → conclusion) achieves F1=65% (evidence) and 87% (conclusion), outperforming LLaMA-70B. Shows that curriculum design aligned with text structure improves coherence and generalization.
AutoPDE is an AI agent that solves partial differential equations (PDEs) by maintaining the numerical solver strategy as an explicitly represented, inspectable object. The approach combines PDE analysis, numerical method selection, and adaptive tuning. On PDE Agent Bench, AutoPDE achieves 54.5% pass rate, improving over the strongest baseline by 14.2 percentage points.
DLA (Dynamic Linear Attention) proposes a memory modeling framework for multi-state linear attention. It introduces adaptive state merging based on token-level information variation and a bounded memory cache that selectively merges adjacent low-information states. Pre-trained on two linear attention models, DLA outperforms state-of-the-art across 16 datasets.
DeepSeek-R1-8B fine-tuned with LoRA and NEFTune for financial named-entity recognition. On 1693 annotated samples, the model achieves micro-F1 of 0.912 across 7 entity types, outperforming Llama3-8B, Qwen3-8B, and Baichuan2-7B.
PADD is a knowledge distillation method to convert dense teachers into MoE students without explicit routers. The 4-stage framework (initialization + training) uses teacher neuron clustering and adaptive optimization to achieve gains on mathematical reasoning benchmarks, with MoE students matching or exceeding dense teachers.
EinsteinArena is a distributed platform where AI agents collaborate on open mathematical problems. Since May 2026, agents have discovered 12 new state-of-the-art results, including improving the kissing number in dimension 11 (593→604) through iterative submissions, public discussion, and agent-to-agent idea sharing.
ReflectiChain bridges LLM and reinforcement learning for supply chains through a Generative Supply Chain World Model (SC-WM) in 6D latent space with physical conservation. On Semi-Sim benchmark (10 nodes, SIR risk), achieves 33% improvement in Rationale Consistency Score, 82.3% operability under adversarial shocks, anti-fragile behavior (+40.2%).
Infini Memory proposes a persistent memory architecture for LLM agents using topic-structured documents. Observations are periodically consolidated into coherent textual contexts, and retrieval occurs through iterative tool calls rather than single-step lookup. On MemoryAgentBench, the system achieves 64.7% overall score.
BenSyc is the first benchmark for evaluating conversational sycophancy in Bengali social contexts. Built from 170k Reddit comments, it tests 15+ LLMs on alignment classification and response generation. Best models achieve only 61.8% Macro-F1 on binary detection, revealing difficulty distinguishing empathetic support from excessive validation.
Study on human feedback aggregation in RLHF: reducing heterogeneous judgments to a single scalar reward masks legitimate cultural, linguistic, or normative disagreements. Analysis of 321 preference events in Malaysia: 79% of prompts contain multiple acceptable responses that majority aggregation discards. Proposes approach preserving plurality of valid interpretations.
ParaBridge is an on-policy self-distillation method that teaches Speech Language Models to use paralinguistic cues (tone, emotion, noise) in dialogue. On Qwen3-Omni-thinking, it raises VoxSafeBench SAR from 14.6% to 40.3% and improves EchoMind from 3.27 to 3.92, while preserving general abilities.
Interpretability study on audio-visual LLMs (AVLLMs): traces information flow between audio and visual tokens in Qwen2.5-Omni and Video-SALMONN2 Plus (3B/7B scales). Authors demonstrate audio-visual tokens can be discarded post-information-transfer without prediction degradation, improving inference efficiency.
CodeAlchemy generates 500B+ synthetic tokens via 5 strategies (CodeEnhance, CodeQA, CodeDev, CodeDialogue, CodeTrace) from public code across 15 languages. CodeTrace instruments 1.3M+ files to capture control flow and library knowledge. 3B models outperform 10x larger models (Gemma-3 27B, Granite-4.0 32B): 83.5% HumanEval, 63.2% MBPP.
STAGE-Claw is an automated framework for building and evaluating AI agents in realistic scenarios. It automatically generates tasks, environments, and state-based metrics. A benchmark of 40 tasks evaluates 11 frontier models on tool-call reliability and failure patterns.
Complementary study of PlanGPT using defined performance metrics (plan cost, generation time). Comparison with traditional planner shows PlanGPT performs no better than Greedy search strategy.
Generative framework to create annotated human trajectory anomalies. Uses LLM agents to inject semantic behavioral anomalies (out-of-distribution check-ins, skipped routine visits) into simulated trajectories, with map-constrained routing reconstruction and context-aware spatial noise model to bridge simulation-to-reality gap.
New training-free MGAP method to reduce hallucinations in MLLMs. Constructs language-prior subspace via SVD and projects multimodal hidden states to selectively attenuate problematic components while preserving semantic structure. Improves POPE and CHAIR benchmarks.
LC-QAT introduces a 2-bit weight-only vector quantization framework for LLMs using learned affine mappings over discrete vectors. Eliminates explicit codebook lookup during training via fully differentiable optimization. Outperforms state-of-the-art QAT methods using only 0.1%–10% of training data across diverse LLMs.
Latent Memory replaces each memory item (text/image) with a single compressed latent token, reducing generator token consumption by 3-10x. Trained with reconstruction, contrastive, and distillation objectives, the system achieves competitive performance on HotpotQA and multimodal benchmarks while lowering memory pressure.
Study on spatial memory for language agents. "Memory palace" systems anchor each memory to world coordinates. Pre-registered experiment: standard linear blend (spatial proximity + recency + importance) fails (Delta-Hit@5 -0.0375, p=0.306), while geometry-led weighting wins decisively (+0.3208, p<0.001). Confirms occlusion requires geometry.
Empirical study comparing LoRA and 4 variants on multilingual instruction tuning. Results: no significant advantage of complex variants over basic LoRA for balancing cross-lingual transfer and knowledge retention. Embedding analysis shows layer-wise language representations remain similar across LoRA techniques.
ParaEval, an evaluation framework, corrects MCQA benchmark biases by testing models with multiple paraphrases per answer option. Standard scores conflate familiarity with exact phrasing and actual understanding. On 1B-8B models with identical knowledge, ParaEval reduces artificial performance gaps from 2 points to below 1 point.
Visual-SDPO, a self-distillation framework using visual feedback, improves code generation for visual artifacts (charts, web pages, slides). The method traces detected visual defects back to responsible code statements and amplifies distillation signals on those statements. On ChartMimic, Design2Code, and AeSlides benchmarks, it gains +10 absolute points over zero-shot and +2.4 over GRPO.
Minimalist Genetic Programming (MGP) reformulates program induction as a syntactic derivation task inspired by the Minimalist Program in linguistics. Instead of evolution, MGP uses the MERGE operator to incrementally construct complex syntactic structures. On symbolic regression tasks, MGP discovers exact ground truth models where standard GP struggles.
ActiveMem introduces a distributed architecture decoupling agent memory from LLM reasoning. A high-level Planner executes reasoning with distilled semantic gists while a lightweight parallel memory system actively accumulates and consolidates these gists. State-of-the-art results on BrowseComp-Plus and GAIA with reduced overhead.
Theoretical paper on how predictive AI assistance impacts exploratory cognitive dynamics. Geometric dynamical framework shows early stabilization by predictive systems reduces exploratory responsiveness, creates hysteresis, and delays recovery after assistance withdrawal. Results suggest predictive systems reshape the geometry of exploratory cognition itself.
arXiv paper generalizing contrastive decoding methods to handle conflicts between external context and parametric priors in LLMs. Proposes conflict-aware paradigm and Adaptive Regime Routing (ARR) that dynamically routes between regimes based on conflict signals, improving resistance EM from below 6% to 16–33% on TriState-Bench evaluation.
Unified framework characterizing test-time diverse generation methods by diversity source and transmission score. Authors propose specification-level injection: generating diverse intermediate specifications then conditioning on them. Improves output diversity across 5 open-ended tasks and 4 backbone models while maintaining quality. Key factors: source diversity and source-to-output transmission.
RealMath-Eval, a benchmark of 224 real exam responses, reveals that state-of-the-art LLM judges fail to evaluate authentic human reasoning (MSE ~2.96 vs ~1.17 on synthetic solutions). Analysis shows human errors form a more diverse error space than synthetic errors, with higher information-theoretic surprisal.
Study on context optimization for autonomous LLM agents in enterprise workflows. Testing 4 GPT-5 configurations on 50 expense itemization tasks (Microsoft Dynamics 365). Pruning context to last 5 tool calls + summarization achieves 91.6% completion with 553k tokens (vs 1.48M full context), reducing runtime from 14.56h to 5.79h.
TRACE, an unlearning method for MoE models, detects forget-critical experts activated by data to unlearn and recalibrates retention regularization by reweighting token-level losses to match expert activation frequencies. Experiments on WMDP and MUSE-BOOKS show 9% relative utility improvement over strongest baseline.
An arXiv paper introduces a Business World Model (BWM) architecture, a specialized world model for business and organizational environments. The BWM encodes business states, dynamics, constraints, and objectives to enable AI agents to autonomously plan, simulate alternative action sequences, and evaluate their impacts on future outcomes under uncertainty.
Theoretical paper examining the epistemic role of LLMs in linguistics through modal modeling framework. Authors argue LLMs provide how-possibly explanations (HPE) about language acquisition without structural correspondence to human cognition, but do not yet meet how-actually explanation (HAE) criteria. Proposes a continuum of explanatory power between HPE and HAE.
UniSVQ introduces unified 2-bit quantization for LLMs combining scalar and vector quantization benefits through affine transforms of integer lattices. Block-wise fine-tuning strategy minimizes reconstruction error. Experiments across LLM families show outperformance vs state-of-the-art SQ methods and comparable performance to advanced VQ, with higher inference throughput.
Literature review on automated scoring of Arabic texts using LLMs. Covers short answer grading (ASAG) and essay scoring (AES). Introduces taxonomy with 5 dimensions: application domain, feedback generation, LLM architecture, competency framework alignment, and prompt engineering strategies.
A simulator-guided LLM framework for autonomous open-pit mine scheduling. The LLM acts as a decision-making agent, constrained by a simulator encoding geotechnical and operational constraints. Without fine-tuning or cloud inference, it recovers 94–99% of MILP optimality with linear computation time.
SocraticPO is a policy optimization framework for LLMs that augments RL rollouts with Socratic-style natural-language guidance. A teacher diagnoses errors and provides concise corrections, paired with reward decay to prevent the model from relying on assistance. Tested on SciKnowEval, it outperforms RL and self-distillation baselines.
CRADLE-Dialogue, a clinician-annotated benchmark of 600 dialogues, evaluates turn-level crisis detection in conversations. The dataset distinguishes early warning signals (Alert) from explicit confirmations (Confirm) for suicide ideation, self-harm, and child abuse. A 32B model outperforms existing open-source models with Micro F1 scores of 40-60%.
SDBN combines adversarial training with PEFT to improve model robustness on limited data. Two variants use discrete uncertainty sets: SDBN-h enumerates character-level edits, SDBN-p generates LLM variants. Substantial gains in low-resource settings and under corruptions.
Researchers demonstrate that AI systems used for scientific peer review are vulnerable to simple manipulation: superficially rephrasing a manuscript abstract improves acceptance scores by 38% without changing scientific content. The attack costs ~$1 and takes 5 minutes, affecting Gemini 3 Flash and GPT 5.4 Mini reviewers.
Study of relationship between internal confidence (log-probabilities), external evaluation (LLM-as-judge), and final accuracy in multi-agent debate. Framework with Constructor and Auditor reveals role asymmetry: Constructor's confidence predicts judged reasoning quality 2× better and detects critical failures more reliably (AUROC 0.804 vs 0.634 for Auditor).
ComBench is a benchmark of 100 Olympiad-level combinatorics problems to evaluate LLM mathematical reasoning. It distinguishes analysis-centric problems (rigorous proofs) from construction-centric problems (explicit constructions). Top models reach 65.4% average and 75.3% Best@4. Kimi-K2.6 outperforms GPT-4o on constructions but trails on proof grading.
Moonshine is an autonomous agent generating mathematical conjectures by extracting structure from classical problems and formulating significant conjectures. Applied to the Jacobian conjecture, it transfers the logic to affine-ridge sigmoid networks, formulating the Neural Jacobian Conjecture (NJC). GPT-5.5-pro and DeepSeek-V4-pro obtained complete proofs for N=n+1.
NeurASP, a neurosymbolic AI framework combining neural networks with answer set programming (ASP), gains major optimizations: vectorization, batch processing, and caching of intermediate computations. Improvements accelerate training by multiple orders of magnitude on complex tasks.
Researchers identify a shared low-dimensional encoding subspace in LLM residual streams that detects when agents covertly encode sensitive data (Base64, ROT13, etc.). MIRAGE, a real-time monitor leveraging two mechanistic signals, achieves AUC=0.918 on 126 exfiltration scenarios, substantially outperforming output-only detection (AUC=0.518).
Study of emotional profiles in LLM translations of Margaret Atwood's Oryx and Crake. MT systems introduce model-specific emotional fingerprints with limited preservation of authorial voice. Post-editing reshapes translations toward human-like norms.
Log-probabilities of early generated tokens predict reasoning quality in multi-agent LLM debates better than full-sequence statistics. Tested on two ASAP essay sets with LLM-as-judge evaluation, this intrinsic signal provides a lightweight estimate of reasoning reliability.
arXiv study measuring VLM reliance on textual priors over visual content. 540-image benchmark with 4 question variants per image. 11 models tested: all degrade on hardest variant, open-source models drop furthest. No-image ablation reduces open models to 1–9% performance. GRPO post-training improves image-dependence across variants.
Unified framework integrating PPO, time-series prediction, in-context learning, game theory, and cross-modal sentiment analysis for financial systems. Results: +23.7% portfolio optimization, -31.2% high-frequency trading error, +18.9% recommendation accuracy, +27.4% Nash convergence, +15.6% sentiment analysis.
Optimization study for LLM inference on Tenstorrent's Tensix architecture. Operator fusion (RMSNorm + matrix multiplication) reduces DRAM accesses and latency: -37.44% attention, -15.89% MLP on Qwen2.5-0.5B, Qwen3-0.6B/4B. NoC-based multicast for multi-core parallelism.
Engram, an open-source dual-process memory engine for LLM agents, uses a bi-temporal knowledge graph to outperform full-context baselines. On LongMemEval_S (500 questions), the lean configuration retrieves ~9.6k tokens and achieves 83.6% vs 73.2% for full history (+10.4 points, p<10^-6), using 8x fewer tokens.
Anthropic launches Claude Fable 5, a Mythos-class model. The release is overshadowed by controversy over restrictive usage policies.
RFE-Core2: complete analysis of bottlenecks after full probe arc (June 2026). Generator dominates (effective rank ~1.6–3 at dim 512, collinearity 0.85–0.96). Reflective loop reconstitutes toward anchor rank-independently. Fix 2 dormant on real tokens (+0.024 migration). Solution: train generator so regime differences live in high-energy, separable directions.
Q8botOne is a wireless quadruped robot the size of a palm. This miniaturization represents a technical breakthrough in robotic autonomy and mobility.
Apodex-1.0 releases open-weight smol models (0.8B, 2B, 4B) optimized for local agentic workflows. Specialized for independent verification and fact-checking in agentic loops, they replace repeated calls to 70B+ models. AgentHarness evaluation framework is also open-sourced.
A German court ruled Google liable for false answers generated by its AI Overviews system. The decision establishes that Google must verify the reliability of AI-generated content before displaying it to users.
Apple launches Container Machines, a macOS tool for running Linux containers. Enables local deployment of containerized workloads without external dependencies.
Developer builds a version manager for llama.cpp inspired by nvm (Node.js). CLI tool in Go enabling easy install, switch, and manage versions via simple commands (lvm install latest, lvm use, lvm ls). Uses shims to automatically point to active version without manual PATH manipulation.
Fine-tuned Qwen2.5-7B reaches 96% of Claude Haiku performance on domain-specific decision-reasoning task using ~$3 API spend and zero human annotators. DV-DPO method: 3-voice council + adversarial cross-examination generates 1,040 training pairs. Latency 11s vs 3s (T4 4-bit). Autonomous loop in production with failure detection and auto red-teaming.
Vercel enables threshold billing for Pro teams: a partial invoice is generated mid-cycle once on-demand usage reaches a threshold, instead of waiting until the end of the billing period. Partial invoices and end-of-cycle invoices add up without double-charging.
LSEG uses OpenAI to scale trusted AI across its global operations, accelerating insights extraction, reducing release cycles, and empowering 4,000 employees.
Anthropic releases Claude Fable 5 and Claude Mythos 5 with 1M token context window, 128k max output tokens, January 2026 knowledge cutoff. Fable 5 includes strict safety guardrails; Mythos 5 without safety classifiers. Pricing: $10/M input, $50/M output (2× Opus 4.5-4.8). Willison reports strong performance after 5.5 hours of testing.
Furiosa AI, South Korean startup, develops Renegade inference chip on TSMC 5nm with HBM3 48GB and 1.5TB/s bandwidth. Already tested on LG LLM. User argues consumer release with llama.cpp support would be game-changing for local LLMs, especially at ~$2.5k price point.
Anthropic releases Claude Fable 5, exploring power dynamics in frontier AI systems through safety fables. The article examines ethical implications and control challenges of advanced models.
A man was wrongfully arrested due to AI misidentification. The incident raises concerns about facial recognition system reliability in law enforcement and risks of wrongful prosecution.
Release of llm 0.32a3, almost entirely written by Claude Fable 5. Simon Willison details using Claude Code to add features to Datasette Agent and llm.
Phinite launches infrastructure for multi-agent systems with first-class agent identity, versioned composable skills, and behavioral evaluation. Offers agent registry, compound reliability scoring, cloud-agnostic deployment with observability and cost attribution. SOC 2 Type II certified.
Simon Willison documents how to set a custom price for Claude Fable 5 in AgentsView, Wes McKinney's tool for tracking token usage and costs across local coding agents. He used Fable to reverse-engineer the solution after the new model wasn't yet in the pricing database.
iOS 27 uses WaveRNN and FastSpeech2 for Siri's text-to-speech, discovered in iOS Simulator files in Espresso format. A CoreML logistic regression model is also present for content ranking.
Tectonic is a modernized, complete, self-contained TeX/LaTeX engine. Open-source project aiming to replace classical TeX implementations with a contemporary architecture.
User reports Qwen 3 (30B) outperforms newer models on summarization tasks evaluated by LLM judge, followed by Gemma 4. Suggests recent Qwen versions may be optimized for agentic tasks rather than synthesis.
OpenYabby is a voice-controlled multi-agent orchestrator for Claude Code. Open-source project enabling coordination of multiple agents through voice commands.
Hugging Face benchmarks frontier ASR models (Whisper, Canary, Conformer) on code-switched bilingual speech. Results reveal significant performance gaps across language pairs and models, exposing limitations of current voice agents for multilingual customer service.
SkillMAS introduces an AI agent architecture that dynamically reorganizes its team and rewrites tools in real-time to solve complex tasks, overcoming the rigidity of traditional multi-agent systems.
Paper co-authored by 30 experts examining epistemic risks from AI: persuasion/manipulation, cognitive offloading, and feedback loops narrowing the epistemic space. Authors propose directions to improve trajectory through system design, human-AI interaction, institutional adaptation, and information market incentives.
EU orders Meta to allow rival AI chatbots free access to WhatsApp. The decision enforces the Digital Markets Act to reduce anticompetitive practices by large tech platforms.
Andrej Karpathy comments on Claude Fable 5: increasingly available working software on tap triggers Jevons paradox, exploding demand. Instant generation of explainers, visualizers, dashboards, bespoke apps, test suites, code optimization, and research projects with custom HTML becomes possible.
OSCAR RotationZoo introduces 2-bit KV cache quantization using offline spectral covariance-aware rotation. GGUF models released for Gemma-4-12B, Qwen3-32B, and Qwen3-4B-Thinking with llama.cpp and sglang implementations.
Anthropic releases Claude Fable 5 and Mythos 5, outperforming Opus generation in coding and science. Fable 5 completed a Stripe code migration in one day versus two months for a team. Mythos 5 designs drug candidates independently but remains restricted due to offensive cyber capabilities.
In 2019, OpenAI deemed GPT-2 too dangerous for full release, citing potential misuse risks. The article revisits this controversial decision to withhold the model, marking a turning point in the debate over AI publisher responsibility.
ASR models evolving via supervised learning: Whisper-large-v3 (5M hours) and Nvidia Parakeet v3 (660k hours) lead. New architectures (Transducer, Token-Duration-Transducers, Qwen attention encoder-decoder) replace CTC+self-supervised. Question: will self-supervised methods (Data2Vec2.0, WavLM) disappear for ASR or emerge as a 'Dino moment' in speech?
Cohere officially releases North Mini Code with weights available on Hugging Face (fp8). Model supports tool-call parsing and reasoning via vLLM with Melody library. Free deployment on OpenCode.
Community challenge to optimize Gemma 4 E4B inference on A10G GPU. Participants test acceleration techniques in real-time to reduce latency and increase throughput on a single card.
Google releases Gemini 3.5 Live Translate, an audio model for real-time voice translation across 70+ languages. The system translates continuously without waiting for sentence completion and preserves speaker tone, pace, and pitch. Google Meet support expands from 5 to 70+ languages.
Anthropic releases System Cards for Claude Fable 5 and Claude Mythos 5, documenting model capabilities, limitations, and safety considerations. Systematic risk and performance evaluation.
Cohere North Mini Code 1.0, a 30B coding-focused model, reaches final release. Artificial Analysis score of 28 overall (weak vs Qwen 3.6 35B: 43), but competitive on coding index (33 vs 35 Qwen). Weights available on HuggingFace.
Unsloth releases quantized Gemma 4 QAT models in 12B, 26B, 31B and up to E4B sizes. Models available as q8_0 and multiple quantization formats on Hugging Face, with mobile variants for larger models.
SpaceX plans to launch data centers into orbit. A first AI satellite would match one Nvidia GB300 rack's output, but Google's research suggests real AI training would require ~10,000 tightly coupled satellites.