Gemma 4 with quantization-aware training
Google releases Gemma 4 with quantization-aware training (QAT). Collections available on Hugging Face: Google's Q4_0 and mobile versions, plus Unsloth collections.
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Google releases Gemma 4 with quantization-aware training (QAT). Collections available on Hugging Face: Google's Q4_0 and mobile versions, plus Unsloth collections.
Satya Nadella publicly criticizes an internal memo proposing to make Microsoft's AI agent Scout deliberately addictive. The CEO states AI should empower users and Scout should reduce screen time, not increase it.
agent-sh is a shell with an embedded lightweight AI agent accessible via > key. Provides contextual awareness for quick terminal problems (rsync flags, diagnostics) without overhead. New command-suggest extension helps generate commands. npm install, works with local models.
New IronWorm malware compromised 36 NPM packages in a supply-chain attack. Infected packages distributed malicious code to developers using these dependencies.
Unsloth released MTP GGUF weights for Gemma 4 in three sizes: 31B, 26B-A4B, and 12B, with Q8, F16, and BF16 quantizations available on Hugging Face.
Google launches Fitbit Air, a modular connected bracelet allowing users to create customized wearables. The company adopts an open approach in a typically closed sector.
llama.cpp now supports model hot-swapping in under 30 seconds with a clean API that works with OpenWebUI and Hermes. The operation has become significantly faster compared to a few months ago.
Nemotron 3 Ultra is now available on HuggingChat, served by Together AI. Users report impressive speed/performance ratio.
The FBI, NSA, and CISA warn of a major cybersecurity threat targeting critical infrastructure. Federal agencies alert to imminent risk of attacks against energy and fuel distribution systems.
KVarN (Huawei's KV-cache quantization) implemented in llama.cpp fork. 3-5× KV cache compression with speed-up. KLD benchmarks show q5 quality at 4-bit, q4 quality at 3.5-bit. Available in BeeLlama.cpp v0.3.2 with --cache-type-k/v kvarn4 flags.
User finds MoE models like Qwen 35B deliver superior performance on consumer hardware by offloading experts to RAM. On Ryzen 7 3800X + 7900XT + 32GB, the 35B is significantly faster than 27B despite lower GPU VRAM usage.
IBM and Google Cloud strengthen their partnership with a new enterprise AI offering combining business expertise and cloud solutions. Goal: accelerate AI adoption in organizations.
CPU inference benchmark for Parakeet TDT 0.6B on 2 x86-64 vCPUs (7.7GB RAM). ONNX Runtime FP32 achieves RTF 0.328 (37% faster than HF Transformers bfloat16 at 0.519) but peaks at 2.7GB memory. GGUF Q6_K reduces to 928MB but doubles RTF to 0.708. Methodological note: espeak-ng inflates WER to 20.9% vs gTTS 4.65%.
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 launches 7 MAI models: MAI-Thinking-1 (1T params, 256K context) matches leading models on software engineering benchmarks; MAI-Code-1-Flash (5B active params) integrated into GitHub Copilot and VS Code; MAI-Image-2.5 for text-to-image and editing; MAI-Transcribe-1.5 (SOTA, 5x faster); MAI-Voice-2 for speech generation. No open weights announced, proprietary licenses.
MemPalace is a benchmarked open-source AI memory system, free to use. Designed to improve information retention and recall in AI applications.
Official OpenAI GitHub repository for plugins. Contains documentation, examples, and resources for developing extensions compatible with ChatGPT and OpenAI models.
CopilotKit is a frontend framework for building generative UI with AI agents. Supports React and Angular via the AG-UI protocol.
Agent-Reach is a CLI tool enabling AI agents to access Twitter, Reddit, YouTube, GitHub, Bilibili, and XiaoHongShu without API fees. Single interface for multiple platforms.
Plannotator enables visual annotation and review of coding agent plans and code diffs, team sharing, and one-click feedback to agents.
CopilotKit is a frontend framework for building generative UI with AI agents. Supports React and Angular, introduces the AG-UI Protocol.
vLLM-Omni extends the vLLM framework to support efficient inference of omnimodal models (text, vision, audio). Performance optimization and memory management for production deployment.
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.
Agent-Reach is a CLI tool enabling AI agents to access Twitter, Reddit, YouTube, GitHub, Bilibili, and XiaoHongShu without API fees. Single interface for multi-platform web search and content retrieval.
MemPalace is a benchmarked open-source AI memory system, free to use. Designed to improve information retention and recall in AI applications.
Anthropic has reportedly stationed about six engineers at the NSA to adapt its Mythos model for offensive cyber operations against China and Iran. Anthropic's restrictions on AI use apply explicitly only to US citizens.
Andreas Kling announces Ladybird will no longer accept public pull requests. The project closes external contributions because AI makes it impossible to verify contributor good faith. Only people responsible for changes can introduce them.
Pearl, an "AI mining" platform, was tested with a $50 budget. Result: 320,000 GPUs produced zero measurable AI output. The article questions the viability of the business model.
SupraLabs releases Supra-50M-Reasoning, a reasoning variant of Supra-50M-Instruct. Fine-tuned on 500 synthetic samples generated by Qwen3 1.7B over 6 epochs, it produces a full thinking chain before each answer. Model and dataset are open-source.
Google Gemma 4 12B runs locally on laptops for agentic workflows. The model provides reasoning and task execution capabilities without cloud dependency.
Lassie raises $35 million from Andreessen Horowitz to accelerate development of AI agents for automating SME operations.
Benchmark of Gemma 4 12B on Python bug-hunting task. Model finds 6 bugs vs 14 for Qwen 35B. Default LM Studio settings disable reasoning. Fix: enable enable_thinking in Jinja template, set thought tokens (<|channel>thought / <channel|>), use temperature 1.0, top_p 0.95, top_k 64.
Article exploring how LLMs perform arithmetic operations without directly manipulating numbers. Analyzes the internal mechanisms and representations used by language models to solve mathematical problems.
Trapilot AI offers an AI-native SEO platform to replace fragmented traditional tools. The startup aims to consolidate SEO features under a unified AI-driven interface.
A developer built a sub-5 MB Edge AI module for offline Morse code recognition on Android using LiteRT, running entirely on-device. The project questions whether small specialized models solving practical tasks locally are being overlooked amid the focus on larger local LLMs.
Lowfat is a pluggable CLI filter that reduces LLM token consumption by 91.8%, according to its creator. The tool integrates into existing pipelines to optimize requests before sending to models.
User benchmark of RTX Pro 4500 Blackwell 32GB vs RTX 5060 Ti 16GB. Pro 4500 delivers ~2x performance on dense models, 3-6x on MoE prompt processing, 1.8-2.6x on token generation. Key advantage: full models in VRAM without KV quantization.
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.
Wired uncovers hidden facial recognition code in Meta's AI application. The code was not publicly documented, raising questions about Meta's transparency regarding its vision capabilities and data collection practices.
User adopts Gemma 4 12B (Unsloth Q5_K_XL) as primary local coding model. Switches from Q4_K_XL (61 t/s, 23 syntax errors) to Q5 (50 t/s, one-shot results). Uses 15.7 GB VRAM with 32k context window. Prefers over Qwen 3.6 27B due to plug-and-play compatibility versus complex tool call configuration.
Anthropic AI discovered a Zcash counterfeit vulnerability, triggering a 30% price drop in ZEC. The critical flaw impacts the privacy protocol's security.
Dashlane discloses technical details of the attack that compromised its encrypted password vaults. The password manager explains the method used by attackers to access sensitive user data.
Quantizing spec draft in llama.cpp with MTP reduces available context size. With q4_0, context drops from 91648 tokens (default fp16) to 83200 tokens. Developer am17an confirmed this counterintuitive behavior.
The Pentagon operates an AI propaganda mill targeting Latin America, according to reporting. The initiative uses language models to generate disinformation content at scale for regional social media platforms.
Developer builds exploration tool for Kokoro with MIT-licensed code. GitHub repo (brosoundml) and HuggingFace models released. Windows CPU/CUDA binaries provided. Author plans similar tools for other models.
ElevenLabs launches Flows Agent, an AI agent capable of automatically building workflows. The platform promises to automate process creation without manual intervention.
Open-source GGUF quantization tool (MIT license) for creating NVFP4 and MXFP6 models. Uses imatrix and logits KLD to evaluate and blend multiple quantization methods per layer. Shows better performance than ModelOpt on Qwen 27B. Includes detailed reports and reproducible validation.
Google Magenta RealTime 2: open-source, local live music generation models. Real-time music synthesis without cloud dependency, runs on local hardware.
Google Magenta RealTime 2 offers open-source, local music generation models for real-time synthesis on laptops. Enables building and playing AI musical instruments without cloud dependency.
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.
CVT-RL, a policy-gradient algorithm with dense verifiable rewards, improves long-horizon language agent RL. On QA, ALFWorld, ScienceWorld, and web/tool tasks, task success rises from 71.8% (non-causal RL) to 78.9%, evidence F1 from 78.9 to 82.8, and measured hacking from 7.2% to 3.9%. Statistical tests yield p<0.01 after Holm correction.
ArcANE is an automatically constructed benchmark evaluating whether role-playing language agents maintain character psychological consistency across narrative phases. Built on 17 novels and 80 principal characters, it tests responses across story phases and unseen scenarios. Conditioning on Character Arc outperforms all other context strategies across 6 models and 6 context modes.
InfoShield minimizes mutual information between speech representations and sensitive attributes (gender, age) for depression screening. TimeAwareMINE with cross-modal attention reduces gender inference from 92.6% to 55.5% and age inference from 55.7% to 30.3%, achieving F1=0.784 on Androids Corpus.
Method for mispronunciation detection and diagnosis using language-specific statistical graphs. Approach captures phoneme confusion patterns and systematic pronunciation differences across native language (L1) backgrounds. F1-score of 59.52% on L2-ARCTIC benchmark.
TensorBench is a benchmark of 199 coding tasks (feature additions and refactoring) on an open-source compiler-based tensor framework extending PyTorch. Evaluation of 7 coding agents: pass rates from 64.8% (strongest) to 22.1% (weakest), with low inter-agent agreement (κ=0.05 for top two agents).
Alpha-RTL introduces TTT-RTL, a test-time reinforcement learning framework for LLM-based RTL generation optimization. On RTLLM v2.0 (Nangate 45nm), TTT-RTL reduces PPA product by 65.1% versus reference and outperforms frozen-policy baselines by 26.1%. On XuanTie C910 FPU (Sky130), achieves 59.4% ADP reduction. Adaptive KL-budget controller stabilizes policy updates.
LLMs rely excessively on morphological affixes to reason about drugs, generating plausible clinical content for fictitious names (e.g., wugcillin). A study across 653 drugs shows models induce drug meaning primarily via affixes without explicit indication, creating a safety risk in medical domains.
ERP study on 24 children (12 autistic, 12 controls) examining processing of recursive locative constructions in Mandarin. Autistic children show reduced structural prediction (attenuated P200), increased semantic integration (enhanced N400), and diminished syntactic reanalysis (reduced P600), with greater inter-individual variability in hemispheric lateralization.
RidgeFT, a lifelong learning framework without exemplar replay, attributes machine-generated text to specific LLM sources. The method freezes an initially trained encoder, stores class-wise sufficient statistics, and uses closed-form ridge regression to adapt to new generators while retaining recognition of previous ones.
GATE (Grounding After Test from Execution) bootstraps missing groundings in text-to-SQL via execution feedback. The system keeps multiple grounding hypotheses open, executes already-grounded parts to obtain observations, then validates and stores confirmed hypotheses. This execution-grounded memory accumulates and improves performance on real-world and controlled benchmarks.
VSRAQ, a post-training quantization method for Mixture-of-Experts models, preserves expert selection during quantization by aligning routing values and top-k decision boundaries. Reduces quantization-induced degradation with no inference overhead.
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.
Staged factorial screening study for hyperparameter optimization in budget-constrained micro-pretraining on single GPU. 613 experiments test batch, depth, and width effects across 2-24 hours. Factors D, A, B, C remain significant after statistical correction; random search converges to similar regions without causal attribution.
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.
Epidemiological study of model collapse from synthetic data training. Bilayer SIR/SIRS framework models cross-contamination between data corpora and AI models. GPT-2 experiments on WikiText and Shakespeare (192 runs) confirm dose-response degradation; R₀ > 1 indicates supercritical dynamics. Synthetic-text detection and filtering identified as highest-leverage interventions.
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.
Novel family of structured Bayesian priors combining total variation (TV) and ℓp norms for T₁ medical imaging. Inference via NUTS, evaluated on synthetic/real brain, cardiac, and breast datasets. Reduces uncertainty and bias versus ML and alternative priors.
CausalPOI introduces a spatio-temporal graph-based causal representation learning framework to forecast check-in patterns for newly introduced POIs. The model leverages functional interaction graphs and constructs treatment/control graphs to simulate counterfactual scenarios, capturing causal effects of urban interventions and outperforming baselines on SafeGraph datasets.
Efficient Operator Search introduces a differentiable framework to automatically optimize token-reduction operators in multimodal foundation models. The method searches where to reduce tokens, how many to retain, and how to process reduced information. Experiments demonstrate competitive accuracy-efficiency trade-offs, especially under aggressive visual-token reduction.
Branched Neural Rough Differential Equations (B-NRDEs) extend NRDEs to capture Itô dynamics on manifolds. They use a Hopf-algebraic framework based on Grossman-Larson and Munthe-Kaas-Wright rooted trees, exactly preserving manifold constraints. Validated on rough Bergomi volatility, SO(3) dynamics forecasting, and SPD covariance matrices.
New training method to distinguish temporary computation from persistent state in language models. Counterfactual Erasure RL (CERL) rewards models when answers remain correct after erasing intermediate thoughts. Evaluation on mathematics, logic, and scientific QA shows reduced dependence on hidden computations without accuracy loss.
PyCC.id is a Python library for hypothesis-driven equation discovery from time-series data. It uses structural skeletons inspired by characteristic curves to constrain the inverse problem and verify structural identifiability of candidate ODE models. Supports neural networks, symbolic regression, and sparse regression paradigms.
Agentic Monte Carlo (AMC) optimizes black-box LLM agents without parameter access. The method uses Sequential Monte Carlo to sample from the optimal policy by learning a value function to steer the agent, leaving the underlying model unchanged. Validated on AgentGym, AMC outperforms prompting baselines and GRPO.
Theoretical study of extreme learning regimes in large weight-tied linear autoencoders. Authors identify five fundamental regimes (large-data, small-data, mean-field, narrow-latent, free) associated with faces of a triangular prism. Explicit expressions derived for loss evolution under gradient flow.
SHALA-LLM is a reinforcement learning framework that treats label ambiguity as useful information rather than noise. On NLI and emotion recognition tasks, it reduces Jensen-Shannon Distance by 62.1% on ChaosNLI and improves F1 by 16.7% by learning directly from annotator distributions.
DP-MacAdam combines adaptive clipping and adaptive momentum in DP-SGD. The algorithm reuses the same mean and variance estimates to adjust the clipping threshold and accelerate training. Empirical evaluation shows improved model utility vs DP-SGD, AdaClip, DP-Adam without manual threshold tuning.
GOTabPFN introduces a feature compression method for tabular foundation models in high-dimensional, low-sample-size regimes. Graph-guided Ordering with Local Refinement (GO-LR) orders features, then Neuro-Inspired Subunit Compression pools them into meta-features. Results: improved stability and accuracy under tight token budgets on tabular benchmarks.
LEVANTE-bench benchmarks vision-language models against children aged 5-12 (N=1547) on cognitive tasks across 3 countries. Larger models show better overall alignment, but smaller models match younger children's error patterns better. VLMs struggle on matrix reasoning and mental rotation tasks.
Study of triple-latent sequence models maintaining running token state and compressed pair-memory pathway to capture higher-order token interactions. Improvements on byte-level WikiText-2 and MiniMind benchmark, with gated associative retrieval extension improving recall but remaining seed-sensitive and slow.
Interpretability framework for automated rubric-based assessment of classroom transcripts. Combines SHAP attributions with LLM-generated rationales. On 6k annotated segments: fine-tuned models outperform LLMs in accuracy but compress scores; SHAP identifies driving sentences with robust cross-architecture transfer, unlike LLM rationales.
LANTERN is a lightweight memory layer that archives every conversation turn and restores relevant details after compaction via hybrid retrieval, requiring zero LLM calls and adding <25ms latency per turn. On 94 multi-turn conversations (1,894 validated facts), LANTERN-Rerank recovers 78.3% of lost facts, significantly outperforming MemGPT (72.4%, p<0.0001) at a fraction of inference cost.
PERSUASIONTRACE is a framework for studying persuasion in human-LLM interactions. It records multi-turn belief changes, annotates rhetorical strategies (logos/pathos/ethos), and evaluates simulators by fidelity to real human belief dynamics. A Bayesian model maintaining explicit latent belief state achieves 81/100 human-likeness vs 64 for vanilla LLMs.
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.
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.
PRIG, a gradient attribution method, localizes ambiguity in LLM prompts by training a linear probe to distinguish clear from ambiguous prompts, then attributes the probe score to token representations. Evaluated on synthetic datasets (coding, math, writing) and a human-written gold benchmark, PRIG achieves 0.840 AUROC on combined synthetic benchmark and 0.891 AUROC on gold set.
CHASE is a co-evolutionary red-blue teaming framework training an attacker and defender via GRPO to improve LLM robustness against prompt-rewriting attacks (persona modulation, fictional framing). Evaluated on BeaverTails and JailbreakBench, it reduces StrongREJECT score by 43.2% with 0% false refusals on benign prompts.
AURA is a situated LLM agent system that detects implicit needs beyond literal query content. Between scene perception and tool use, it generates a structured IntentFrame with a gap score to control probing budget. On a 100-query benchmark, AURA improves implicit-need coverage by +0.07 (p < 10^-6) versus ReAct-style probing.
Purdue University deploys GPT-4o, GPT-5-mini, and GPT-5.2 to evaluate 1,200 applications for the SURF 2026 program. Models score statements of purpose across 6 rubric categories (0-3 scale), generating scores and rationales in 4.6 hours. GPT-5.2 shows strongest rubric adherence. Final coordinator review takes 4 hours versus multi-week effort in prior cycles.
arXiv paper demonstrating that optimal hyperparameters for LLM continued pre-training follow predictable scaling laws. Two-stage framework: empirical law discovery via proxy models, then state-aware prediction using validation loss and equivalent pre-training compute. Reduces hyperparameter search overhead by 90% while maintaining performance.
AdaPlanBench is an interactive benchmark evaluating LLM agents' ability to adaptively plan and replan under progressively revealed world and user constraints. Built on 307 household tasks, it tests 10 leading models: best achieves 67.75% accuracy. Performance degrades with constraint accumulation, particularly for user constraints.
QueryAgent-R1 is a memory-augmented agentic framework for e-commerce query recommendation. It optimizes alignment between generated queries and retrieved products through chain-of-retrieval validation against real inventory. Using RL with consistency rewards, it improves Query CTR by 2.9% and guided CVR by 3.1% in production A/B tests.
Multilingual Alzheimer's Disease detection from speech using cross-linguistic transfer learning. Transformer-based models trained on English, Chinese, Arabic, and Hindi achieve 82% F1 score with strong cross-language generalization. 0.5-second inference time enables real-time screening applications.
A4D introduces a functional latent space organized around object affordances rather than appearance to improve robot planning generalization. The system achieves 94% accuracy on existing affordances (+15 points vs SOTA) and 90% on new affordances using only 10% of original training data, with 100x faster inference.
New pre-training approach combining MLM (Masked Language Modeling) and JEPA (Joint Embedding Predictive Architecture) for text encoders. Hybrid model trained on English Wikipedia with identical compute budget. Results: more uniform embeddings (-0.16 vs -0.05), richer spectral geometry, better semantic-to-lexical balance on GLUE benchmarks.
SA-AH-GRPO, an extension of GRPO, applies asymmetric entropy-based token discounting for LLM reinforcement learning. On GSM8K, Qwen 2.5-3B achieves Pass@1=0.858 with 3.6× variance reduction vs standard GRPO while preserving gradients on correct trajectories.
PJ-RoPE unifies RoPE's Fourier phase, Jordan-RoPE's finite jets, and ALiBi's affine recency into a single learnable relative-position space. The framework separates scalar PJ-bias kernels from exact PJ-rotary feature transforms, with adaptive sector diagnostics and LC/rapidity coordinates to stabilize high-order jets.
Mamba-Assisted Closure (MAC) framework for reduced-order modeling of high-dimensional dynamical systems. Uses Mamba sequence model to predict non-Markovian closure term inspired by Mori-Zwanzig formalism. Outperforms GRU and Wilks method on viscous Burgers equation and chaotic Lorenz '96 system.
Mechanistic cross-architecture study on 3 1B-class models (Pythia, OLMo, OLMoE) testing whether circuit identification via pattern selectivity + causal ablation yields reproducible findings. Result: same task, same behavioral capability, different implementations across models. Five-category taxonomy (primary cause, secondary cause, correlate, interferer, null) with quantitative thresholds introduced.