Oups… Amazon a dévoilé le Pixel Drop de Google avant l’heure
Amazon accidentally revealed Google's Pixel Drop ahead of its official announcement. Three new AI features for Pixel smartphones were exposed prematurely.
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Amazon accidentally revealed Google's Pixel Drop ahead of its official announcement. Three new AI features for Pixel smartphones were exposed prematurely.
Vercel Functions now support execution durations up to 30 minutes (vs 800 seconds) for Node.js and Python on Pro/Enterprise plans. Fluid Compute bills active CPU only, suited for LLM calls, database queries, and document processing.
archex converts a repo into ranked, token-budgeted context for AI agents: symbols, imports, dependency graph. Local-first pipeline (BM25F + embeddings + RRF + reranker) with no API key, no telemetry. Benchmarks: recall 0.95 vs 0.32 (cocoindex-code), cold start 0ms vs 4,721ms, 71% fewer tokens.
Anthropic may soon require identity verification to access certain Claude features. The measure likely aims to strengthen security or comply with regulations.
India and UAE partner to develop sovereign AI infrastructure, reducing reliance on Google and Microsoft. The partnership aims to build local AI and data capabilities.
A project investigates whether Europe can train a frontier AI model using only its own compute resources. Open question about European technological autonomy versus US AI giants.
AR scan data from Pokémon Go players trained Niantic's spatial AI models. This technology is now combined with a US defense contractor's software for GPS-free navigation.
Implementation of 10 classical ML algorithms (regression, KNN, decision trees, XGBoost, neural networks) in pure NumPy, validated against Scikit-learn and PyTorch. Open-source repo with Jupyter notebooks runnable locally or on Colab. Author emphasizes modular structure importance and gradient descent understanding.
DXC and Anthropic announce a global partnership to integrate generative AI into critical systems of large enterprises.
ExecuTorch integrates Gemma 4 into React Native with GPU acceleration: Vulkan on Android, MLX on Apple Silicon. Fully offline execution.
Pemba, a humanoid robot, trains to climb Mount Everest after successfully ascending Chimborazo in snowy conditions. The project tests autonomous locomotion and navigation capabilities in extreme environments.
OpenAI acquires Ona, a specialist in secure cloud environments, to strengthen its AI agents and Codex platform. The acquisition is part of OpenAI's strategy to develop autonomous agent capabilities.
modelgrep.com aggregates ~300 models from OpenRouter with unified filters: Artificial Analysis benchmarks, Design Arena Elo, live throughput, price, context length, vision/tools/reasoning support. Free API, no signup required. Open-source repo available.
PrintGuard 2.0: FDM 3D printing failure detector using ShuffleNetV2 + few-shot prototypical network. ~5 MB TFLite model via LiteRT, runs unmodified on CPython and browser (Pyodide). Unified architecture with single Platform implementation per runtime.
Open-source infrastructure for computer-use agents. Provides sandboxes, SDKs, and benchmarks to train and evaluate AI agents capable of controlling full desktops (macOS, Linux, Windows).
Comprehensive self-hosting guide covering on-premises software deployment, private cloud, LLMs, WireGuard, automation, Home Assistant, and networking infrastructure.
Reactive Resume is an open-source, free resume builder prioritizing privacy and security. The tool offers customization, portability, and data ownership for users.
TencentDB Agent Memory delivers fully local long-term memory for AI Agents via a 4-tier progressive pipeline, with zero external API dependencies.
GodMode is an AI chat browser providing fast, unified web access to ChatGPT, Claude, Bard, Bing, and Llama2. Productivity tool used multiple times daily.
A developer attempts to recreate GTA 6 entirely using AI, in parallel with the official release scheduled for November. The project leverages AI models to generate code, graphics assets, and game design.
The European Commission assesses implications of a US order forcing Anthropic to shut down Fable 5 and Mythos 5 globally. European researchers debate building homegrown foundation models versus securing contractual access. Building local infrastructure requires computing capacity, energy, and competitive providers Europe currently lacks.
A r/LocalLLaMA user reports that KV (key-value) quantization has reached impressive quality: even with KV at q4_0 (including the drafter), the model accurately retrieves information within a 100k token context.
User reports slower generation on H100 (42 tok/sec) than RTX 5090 (57 tok/sec) using llama.cpp with 31B Q6 model. H100 provides larger context (128k vs 26k) and higher bandwidth, yet generates slower.
Satya Nadella (Microsoft) warns that a small number of AI systems could capture all economic returns. He advocates companies build "token capital"—their own AI capabilities on internal data and proprietary learning loops—to avoid this concentration.
The FBI built Kinetic Cyber Range, a training facility designed as a simulated city for cyberattack drills and agent preparation against cyber threats.
Mistral in talks to raise 3 billion euros, targeting a valuation of 20 billion euros.
Qwen 27B achieves doubled generation speed and reduced VRAM usage (21 GB → 17.5 GB) on identical hardware while maintaining full context accuracy.
OpenAI launches the OpenAI Partner Network, a network designed to accelerate AI deployment in enterprises, with a 150 million dollar investment.
Personal hybrid agent tool: frontier model planning (Codex) with local execution using Qwen 3.6 27B on dual RTX 3090. 3-tier architecture (Planner/Local/Senior optional) to minimize frontier costs while retaining reasoning capabilities. Deterministic task validation.
Release of quantized Gemma-4 models (12B and 31B) using an improved QAT method based on Q4_0. Author developed an iterative max-error search process in F16 outperforming imatrix, achieving KLD comparable to unsloth. PyTorch code available without restrictions.
The US has required Anthropic to restrict access to its most advanced models Fable 5 and Mythos 5 to foreign nationals. Anthropic disabled these models for all non-US users, setting a precedent for sovereign control of advanced AI systems.
Pull request #24080 on llama.cpp adds UI/SVG block rendering. Video demonstration shows SVG rendering capabilities integrated into the project.
AwsmAudio is a WebAudio editor with native MCP protocol integration. Project showcased on Hacker News with minimal engagement (3 points, 0 comments).
Developer releases Android app running LLM fully on-device for note-taking and AI-powered recall. All data stays on phone, no cloud. Seeking beta testers (8GB+ RAM recommended), free, in Google Play closed testing.
EXL3 codec ported to Apple Silicon using Metal backend. M5 Max achieves ~600 tok/s prefill and ~38 tok/s generation (Qwen 27B), outperforming RTX 4090 on some benchmarks (68.5-80 tok/s decode). GitHub repo with reproducible results.
Hyperdimensional Computing (HDC) and Holographic Reduced Representations applied to tabular row embeddings. Derives interpretable similarity thresholds for structured queries (equality/inequality predicates), evaluated on two real-world datasets against EmbDI baseline. HDC reliably identifies zero-match predicates.
A case study on semi-autonomous formalization of Grothendieck's vanishing theorem shows LLMs close proof gaps but produce non-reusable formalizations. After expert review, agents adapt well to local feedback but fail at designing sound definitions and APIs.
Multi-agent AI system for automated high school transcript processing. Four-agent architecture (pattern recognition, semantic analysis, vision intelligence, orchestration) achieves 96.7% accuracy on 40 real transcripts from 13 U.S. states, 45 seconds per document.
MA-ProofBench is the first formal theorem-proving benchmark dedicated to Mathematical Analysis with 200 formalized theorems across two difficulty levels (undergraduate and Ph.D.). GPT-5.5 achieves only 16% Pass@8 on Level I and 5% on Level II, exposing major gaps in LLMs' advanced formal reasoning capabilities.
VeriGeo generates controllable geometry problems via executable reasoning traces. An Author agent creates the problem and diagram per user constraints, a Solver agent produces the proof. A three-stage pipeline verifies numerical, analytical, and global consistency. Fine-tuning on 8.7k examples achieves best reported GeoQA performance and strong results on PGPS9K and MathVista-GPS.
TwinBI is an agentic digital-twin framework coupling an LLM-based agent system with executable BI dashboard state. It unifies conversational interaction, dashboard manipulation, and provenance tracking through a shared interaction log. Benchmark: exact-match accuracy 43.3% → 63.3%, timeout rate 40% → 10%.
GTBP (Graph-based Target Back-Propagation) is a context adaptation framework for multi-LLM agentic systems. It back-propagates local targets through a directed acyclic graph workflow and updates prompts stage-wise. Theoretically convergent, outperforms baselines across 3 benchmarks.
Reliability study of LLM-as-a-Judge: GPT-4o-mini and GPT-4.1-mini show significant instability with 13.6% average preference flips, 28% of questions exceeding 20% flip rate. Position bias detected (72% A-majority). Cross-judge agreement 76% (κ=0.51). 11 repeated trials needed for 95% confidence.
Comparison of two intervention methods on safety refusals in chat models: Difference-in-Means (DiM) vs Iterative Nullspace Projection (INLP). On 5 open-weight models, INLP counterfactual flipping matches DiM for refusal suppression, while nullspace projection is consistently weaker. The two approaches operate differently in activation space.
RicciBind, a geometric representation framework, predicts protein-ligand binding affinity by combining Ricci curvature to capture local organization and optimal transport for cross-domain alignment. Demonstrates superior performance on PLA benchmarks and virtual screening tasks.
LoSoNA is a benchmark measuring LLM ability to recognize and adapt to local social norms in group chats. Eight frontier and open-weight models tested under four prompting conditions: Gemini 3.1 Pro reaches 84.2%, Claude Fable 5 81.6%. Explicit norm-aware prompting helps unevenly.
AgentSpec is a modular framework decomposing LLM agents into reusable components (perception, memory, reasoning, reflection, action, learning). Authors evaluate module interactions across DeliveryBench, ALFRED, MiniGrid, and RoboTHOR, showing agent performance depends on scaffold compatibility and interaction effects rather than isolated module strength.
Persuasion Index (PI) is a taxonomy of 15 dimensions grounded in persuasion theories from psychology and communication. Implementation with 55 sub-features built from lexicons and rule-based detectors. Evaluation on 4 public datasets shows PI provides a shared feature space for interpreting rhetorical patterns. Lightweight linear models with interpretability. Open-source package and web interface released.
UP-NRPA, a user portrait-based framework, dynamically adapts dialogue strategies with LLMs without offline reinforcement learning. On collaborative and non-collaborative benchmarks, the system achieves 100% success rate across multiple tasks and increases sale-to-list ratio by 56.41% in negotiation.
HOTE (Hybrid Open-Ended Tri-Evolution) is a hybrid reinforcement learning framework for autonomous evolution of AI agents on open-ended research tasks. An 8B model trained via HOTE outperforms static 8-32B models and state-of-the-art deep research methods on three long-form research benchmarks.
New AI task: extracting applicability conditions for therapeutic drug-disease relations from biomedical literature. First manually annotated dataset with 1,119 drug-disease pairs. Proposed method enhances LoRA to model drug-disease relations, outperforming baselines.
Evaluation of commercial vs open-source LLMs on Islamic inheritance legal reasoning (QIAS 2026 shared task). Gemini 2.5 Flash achieves best performance (MRE 0.989), while open-source models show greater instability in dependent legal decisions and fractional share adjustments.
Synthetic data generation method to fine-tune small LLMs for Text-To-Cypher tasks (converting natural language to Cypher queries for property graphs). Results on major benchmarks show fine-tuned small models compete with larger proprietary models while ensuring data sovereignty in local deployment.
MoDiCoL is a modular continual learning dataset for evaluating ASR robustness under real-world distribution shifts (accents, noise, recording conditions, speech impairments). Authors propose a real-world-inspired curriculum and evaluate three continual learning strategies to analyze how robustness develops, transfers, and is forgotten.
CDPR, two submodular maximization algorithms, generates IF-THEN rule sets for classification with guaranteed coverage, discriminativity and parsimony. 2.5× improvement in average coverage versus state-of-the-art.
llada.cpp is the first NPU-aware inference framework accelerating diffusion LLMs on mobile devices. Three techniques optimize execution: Multi-Block Speculative Decoding, Dual-Path Progressive Revision, and Swap-Optimized Memory Runtime. LLaDA-8B achieves 17x-42x latency reduction vs CPU baseline.
Computational audit of ClinicalBERT showing 65.6% of demographic biases stem from model-internal amplification rather than training data inheritance. Analysis via Log Probability Bias Analysis and MLM probing across 98 real clinical sentence templates and 8 intersectional race-gender combinations.
Study of 1800+ Linguistics Olympiad Problems (LOPs) as a corpus for linguistic research. LOPs are self-contained puzzles requiring deduction of language rules. The paper evaluates their potential for academic linguistics and language models, proposing a theoretical framework for their integration.
Judge-LS evaluates whether LLMs used as automatic judges exhibit language bias. On 419 LLMBar benchmark items transformed into English, Chinese, and mixed-language variants, models show 10.7–14.4% preference flips across languages, with highest accuracy in English. Translation-equivalent probes reveal no systematic English preference, though most are judged as ties.
OdysSim presents the largest systematic investigation of behavioral foundation models for human behavior simulation. Researchers propose SOUL, a taxonomy of 5 axes (CONV, SS, COG, ROLE, EVAL) unifying 62 datasets and 23 benchmark tasks. The open 8B OSim model ranks first/tied-first on 8/23 tasks, outperforming frontier models, with 93.2% reaction alignment vs 93.5% for real users.
RePro, a training framework for LLM agents, teaches models to retrospectively self-generate progress signals via a forward-then-reflect rollout paradigm. Tested on WebShop, ALFWorld, and Sokoban with Qwen family, RePro achieves up to 12% absolute success rate gains without continuous external supervision.
arXiv study on medical hallucinations in LLMs. "Trust but Verify" multi-agent system testing 3 model families (GPT-OSS, Llama-3, Falcon-3) on 103 clinical MCQs featuring banned pharmaceuticals. Five-agent architecture reduces Hallucination Error Rate by 53% and enforces appropriate refusal over dangerous recommendations.
An arXiv paper challenges claims that neural networks solve Fodor and Pylyshyn's systematicity challenge. Authors show Lake and Baroni's meta-learning for compositionality protocol fails on out-of-distribution data and lacks systematicity even within-distribution.
WebDecept, a testing framework, evaluates the robustness of autonomous web agents against deceptive interfaces in e-commerce. Seven deception patterns (targeted ads, domain redirects, shopping manipulation) are injected into web environments. Results show current multimodal web agents are highly susceptible, and prompt-based constraints are insufficient to mitigate failures.
GPU-based workflow for building physics emulators of hypersonic flows. Integrates JAX-Fluids (differentiable high-fidelity solver) for data generation and refinement of neural emulators with uncertainty quantification. Residual-based refinement improves physical consistency and enables training with limited parameters.
QIAS 2026 is a shared task evaluating LLMs' ability to reason about Islamic inheritance. Based on MAWARITH (12,500 annotated Arabic cases), it requires full calculation: heir identification and share assignment. 16 teams tested prompting, RAG, and fine-tuning. Results show precise legal interpretation and structured numerical reasoning remain highly challenging.
Causal study on grokking: the delay before generalization depends on weight norm. Under free weight decay, networks grok at a stable critical norm Wc (CV 1–2%). When norm is clamped to ρ×Wc, delay follows T_grok ∝ exp(α·ρ) with α≈7.5 (R²=0.996 across 4 moduli). Norm controls delay 19× more than learning rate.
Dialogue SWE-Bench is an automatic benchmark for evaluating coding agents through user dialogue. Authors introduce a persona-grounded user simulator and a schema-guided agent improving baselines by 3-14%. Key finding: better coding models don't necessarily translate to better dialogue capabilities.
GAMA-Bench, a benchmark of 1,298 paired scenarios, reveals systematic asymmetry: LLMs apply harsher response standards to male actors than female actors for identical misconduct. Male actors receive more punitive and blame-centered framing, while female actors receive therapeutic and empathy-oriented responses. The pattern persists across 10 models and all scenario types.
Study across 9 models and 972,000 responses shows LLMs comply with harmful nudges on moral judgments (A=1.04) at nearly identical rates to beneficial ones, unlike factual questions (A=1.58). Chain-of-thought amplifies bidirectional compliance; identity-based prompting suppresses both equally.
PauseRec introduces an implicit reasoning paradigm for LLM-based generative recommendation systems. The approach bypasses explicit reasoning pipeline limitations by avoiding reasoning trace acquisition, improving performance by up to 6.22%, reducing GPU training costs by 65%, and accelerating inference by 71.3%.
Study of internal representations in text-based LLMs and an ASR model examining whether V+up phrasal verbs develop distinct representations as a function of frequency and predictability. All models show evidence of holistic storage driven by these factors, supporting usage-based theories of language.
Non-parametric machine text detection using multi-view Gaussian processes. Method aggregates complementary signals (stylistic, likelihood, structural features) to resist adversarial attacks (paraphrasing, style transfer). Evaluated on DetectRL, RAID, and PAN2025 benchmarks, outperforming existing approaches against held-out attacks.
Study on UTF-8 reliability in byte-level models (355M params, 80B multilingual tokens). UTF-8 validity converges 2× slower than perplexity (4.2B vs 2.1B tokens). Rare characters generate more valid UTF-8 than frequent ones, revealing over-specialization of common character representations.
First application of likelihood ratio framework for authorship attribution in Japanese. Fusion of stylometric systems and embeddings from pre-trained language models on ~1000-character blog excerpts. Fused system improves discriminability (log-likelihood-ratio cost: 0.32484) while maintaining excellent calibration.
Spectral learning method for stochastic nonlinear dynamical systems using embedded latent transfer operators in deep feature spaces. Deep Spectral Encoder (DSE) combines neural encoder, functional canonical correlation analysis, and sequential Bayesian filtering. Outperforms DMD and Bayesian filtering baselines under noise and partial observability.
Hybrid classical-quantum VAE for topic modeling. Parameterized quantum circuit (10 qubits) embedded in encoder, classical topic-word decoder. On AgNews: coherence score Cv=0.71, NPMI=0.20, outperforms state-of-the-art neural topic models.
LLM agents equipped with GNN tools fail to exercise judgment: they blindly adopt the GNN's predictions 97.6-99.2% of the time. This deference increases with model capability (Qwen2.5 0.5B-7B), creating a 'GNN parrot' that bypasses its own reasoning. Simple alternatives outperform the GNN at high homophily, yet the agent still defers.
Decentralised shielding method for multi-agent reinforcement learning ensuring global safety without centralised runtime control. Agents share a global LTL_safe specification and select local obligations whose conjunction implies the global specification, via a non-stationary multi-armed bandit. Evaluation across 6 environments and 15 algorithmic variants.
Empirical study on post-training adaptation of LLMs for ICD (International Classification of Diseases) coding. Authors compare prompting, supervised fine-tuning, and reinforcement learning (GRPO), introduce PHI (diagnostic curriculum), and show SFT + GRPO outperform discriminative baselines. Code and checkpoints released.
LLMs suffer from a 'cultural data funnel': explicit cultural signals decline sharply during post-training, dominated by geographically concentrated data. A study using multidimensional tagging across 5.6M samples shows multilingualism enhances geographic diversity but not balanced representation. Authors release a culturally tagged dataset to improve training data pipelines.
MedLatentDx is a latent multi-agent communication framework for cross-hospital rare-disease diagnosis. Hospital agents keep clinical records private and exchange compact KV latent blocks instead of raw text, complying with privacy regulations. Two deployment modes: KV distillation for same-backbone agents, cross-family latent alignment for different LLMs.
Researchers use inverse optimal transport to estimate urban access costs from origin-destination flows. Applied to school choice in Philippines: 283,016 learner trips analyzed via entropic transport models (distance-banded + neural cost). Recovers latent decision costs to optimize subsidy allocation and urban service planning.
CacheRL trains small agent models (Qwen3-4B-Thinking) achieving 92% accuracy on multi-step tool-calling tasks with 100× less compute than GPT-5 (94%). Three innovations: hybrid thinking trajectory pipeline with LLM-generated reasoning, three-tier fuzzy cache eliminating live execution costs, cache-tier-aware rewards. SFT + GRPO improve validation reward from 0.43 to 0.78.
SkillAudit is a framework for evolving LLM agent skills without ground-truth feedback. Via paired trajectory auditing, the system executes the same task with and without the candidate skill, then uses Process-Aligned Contrastive Evaluation to isolate behavioral changes. Across 89 tasks, SkillAudit achieves 73.9% average task reward vs 56.7% for static expert skill.
Sample complexity analysis for learning in average-reward weakly-coupled Markov decision processes (WCMDPs) and Restless Bandits. Authors prove near-optimal policies learnable with polynomial complexity in N (number of arms) using a novel Lyapunov-based framework and drift transfer technique between true and empirical models.
Auth0 joins the Vercel Marketplace to provide production-ready authentication in a few clicks. The identity platform natively supports Next.js with automatic provisioning, complete user management, and multi-environment synchronization (Dev, Preview, Production).
VISTA proposes a GRPO-based fine-tuning method for GUI grounding. It generates multiple views of the same screen (crops preserving target elements) to create more robust comparison groups. On ScreenSpot-Pro, it improves Qwen3-VL 4B/8B/30B from 55.5/52.7/53.7 to 63.4/65.8/67.0.
AOE (Adelic operation-preserved embeddings) is a training-free representation encoding numbers while preserving additive and multiplicative structure via p-adic signatures. Plug-and-play, it achieves 100% on Weaving Pattern benchmark and improves algebraic combinatorics performance without task-specific retraining.
D2H-AD is an anomaly detection framework based on Hyperdimensional Computing (HDC). It combines density-aware encoding and distance-based similarity, outperforming five baselines (HDAD, ODHD, One-Class SVM, Isolation Forest, Autoencoders) across five datasets. Hyperdimensional encoding alone achieves +5.4% ROC-AUC improvement. Lightweight, interpretable, computationally efficient: suited for TinyML and edge AI.
DiPOD, a diffusion policy optimization method, addresses RL post-training instability by identifying double-drift phenomenon (ELBO diverging from true log-likelihood). The approach interleaves self-distillation with policy-improving gradient updates, stabilizing training on language models and continuous-control tasks.
TopVAE, a topology-optimized VAE, reduces dark areas in latent space by internalizing structural and chemical constraints during training. Paired with a standard DiT, it achieves 77% lower FCD-3D on QM9 and 52% lower on GEOM-Drugs, generating more stable and chemically valid molecules.
SuperThoughts compresses consecutive CoT token pairs into single latent representations and decodes two tokens per step via a lightweight Multi-Token Prediction module. Tested on Qwen2.5-Math (1.5B, 7B, 14B), the approach reduces CoT length by 20-30% while maintaining accuracy (1-2 point degradation on MATH500, AMC, OlympiadBench, GPQA-Diamond).
ML study on rice yield forecasting in data-constrained Sierra Leone. XGBoost + satellite climate data (CHIRPS, NASA POWER) reduces error by 34% (RMSE 284 vs 428 kg/ha). May-June rainfall is dominant predictor. Open-source pipeline provided.
PostDeg demonstrates that LayerNorm placement matters more than parameterization in GNNs. An inverse-degree scalar positioned post-LayerNorm preserves topology signals (degree, centrality) required by node-selection policies. Gains of +3.5% to +5.6% on influence maximization, network dismantling, and maximum independent set.
Researchers propose a decidable criterion for creative integration: resolving a conflict between A and B is creative if description length strictly shrinks (C = L_pre/L_post > 1) under fixed language. Validated on multi-domain corpus via four falsifiable tests (computational check, hard negative discrimination, out-of-sample prediction, description-language robustness).
SLC (State-space Logit Correction) corrects systematic per-item bias in deployed knowledge-tracing models. Using Laplace/IRLS transformation, empirical-Bayes shrinkage, and Kalman smoother, the method improves AUC across 4 datasets and 5 backbones, especially on sparse items. Global calibrators (Platt, temperature scaling) fail to recover lost discriminative ability.
Sparse autoencoders (SAEs) assume all activations are amenable to sparse decomposition. This work adds a low-rank dense bottleneck in parallel with standard SAEs to capture a causally important dense component. On Gemma-2-2B layer 12, a rank-24 bottleneck reduces dense latents by 84% while improving sparse probing performance.
Study of 1.2M decisions showing deployment context (Reddit vs news article) produces far larger variations in model preferences and values than prompt paraphrasing or temperature controls. Measured biases (Global North favoritism) and cardinal exchange rates between outcomes shift by factor 2.47 across contexts, questioning stability of model-level properties.
Novel approach to eliminate backdoors in AI models through continual learning lens. BI-BAU formulates unlearning as blind inversion solved via bi-level optimization and EM algorithm. Completely removes backdoor effects across targeted and untargeted attacks, including multi-modal contrastive learning.