Vercel warns of inference theft: attackers exploit exposed AI endpoints to resell API calls at discount, generating tens of thousands in losses. Protection requires per-request verification (not per-session) via deep analysis, integrated in a few lines of code.
OpenAI launches Rosalind Biodefense, expanding trusted access to GPT-Rosalind for vetted developers and U.S. government partners advancing biodefense, public health, and pandemic preparedness.
Vercel Sandbox now supports installing and running Docker inside sandboxes without touching the host system. Enables testing containerized services like Redis/Postgres, validating container images before deployment, and previewing containerized applications. Also adds FUSE filesystem drivers and VPN client support.
OpenAI publishes guidance for third-party AI evaluations, covering assessment of model capabilities, safeguards, and validity for frontier systems.
ByteDance is developing custom Arm and RISC-V processors to reduce inference costs for its models. The group processes 120 trillion tokens/day with Doubao and aims to reduce Nvidia GPU dependency by optimizing server infrastructure.
Cognition and OpenInspect showcase async agents as emerging paradigm: 80% of Devin commits from agents, automated spec-to-PR workflows, full VMs, persistent agent memory, and PMs shipping code directly.
LiquidAI releases LFM2.5-8B-A1B, a hybrid 8B model optimized for on-device inference (CPU/GPU). Extended architecture with reinforcement learning, compatible with llama.cpp/MLX/vLLM/SGLang. Performance competitive with larger models on agentic tasks and complex instruction following.
Comparative test of Qwen 3.6 35B across output formats (raw text, markdown, HTML, HTML+CSS). Markdown achieves best quality (78/100 per ChatGPT-4o) with 1,496 output tokens in 23s. HTML+CSS generates 10,290 tokens in 82s but lower quality score (58/100). Measurements include reasoning tokens, throughput, and total time.
Writ is an enforcement layer for AI coding agents using a local Neo4j knowledge graph and hybrid RAG. A 5-stage retrieval pipeline (BM25, HNSW vector similarity, graph traversal, reciprocal rank fusion) surfaces only relevant rules per task. 30 bash hook scripts enforce execution: no code without approved plan, mandatory tests, static analysis required.
Google unveils Coral Board at Google I/O, a compact single-board computer designed to run Gemma 3 locally on-device.
Tomesphere: Chrome extension + website indexing 3M arxiv papers with LLM-curated summaries, OpenReview reviews, GitHub repos, HuggingFace models, citation graphs and SPECTER2 semantic neighbors. Free, no signup.
Sigilant-sweep, an open-source CLI for llama.cpp and vLLM, benchmarks 16 configurations (quantizations, KV cache, context). On Qwen2.5-7B, Q4_K_M beats Q8_0 by 230ms TTFT and +10.7 TPS. Tool measures TPS, TTFT, PPL with p50/p95 and weighted scoring (latency/quality/balanced).
Tomesphere: Chrome extension + web layer enriching arxiv with LLM-curated TLDRs, OpenReview reviews, GitHub/HuggingFace links, citation graphs, SPECTER2 semantic neighbors. 3M papers indexed, free, no signup.
Mistral rebrands Le Chat as Vibe and integrates it into a multiplatform work agent. Work Mode connects to Google Workspace, Outlook, Slack and GitHub to handle emails, reports and pull requests. Pro subscription drops from €17.99 to €14.99. Mistral positions itself against agent offerings from OpenAI, Google and Anthropic.
Krasis v1.0, LLM runtime for models exceeding VRAM, achieves 12.48 tokens/s on RTX 3070 Mobile 8GB with Qwen3.6-35B-A3B (Q4). Full Rust implementation (no Python in hot path) and separate prefill/decode optimizations. Benchmarks: 222 pp, 12.48 tg on laptop; 10,030 pp, 124.9 tg on RTX 5090 32GB.
Amazon MGM Studios and AWS launch a creators' fund and in-house AI platform called 'Project Nara'. Three animated series are in production with five-week timelines for pilots. Amazon claims the only end-to-end AI content ecosystem in the industry.
Qwen releases Q-Judger, a vision-language model based on Qwen3.6-27B for automated evaluation of AI-generated images. The model assesses 5 dimensions (quality, aesthetics, alignment, real-world fidelity, creative generation) using chain-of-thought reasoning and outputs structured JSON scores.
ElevenLabs releases Music v2, an AI music generation model enabling seamless genre transitions (opera, heavy metal, rap) within single compositions. New inpainting feature allows regenerating specific sections independently.
Theoretical paper decomposing the pre-softmax attention matrix QK^T into symmetric and skew-symmetric components. The symmetric part governs the energy landscape, the skew-symmetric part drives circulation. Authors propose Hopfield-style stability measures to quantify fidelity-diversity trade-offs in generation and a controllable mechanism to modulate this trade-off.
Variance-Regularised Pruning (VR) is a model compression method for affective computing that prioritises cross-user stability over sparsity alone. Tested on the AGAIN dataset (9 game environments), VR maintains competitive CCC performance at 80% sparsity without additional fine-tuning, suited for resource-constrained embedded systems.
EvaluatorDPT is a bounded decision-control model predicting YES, NO, or TBD (learned deferral). Using a transformer encoder with structured auxiliary heads, it achieves Accuracy=0.8260 and Macro F1=0.8252 on 44,597 test samples. The interface enables inspectable routing and auditable decision control for production AI systems.
Hyperparam introduces three JavaScript libraries (<70 KB) to query Parquet and Apache Iceberg directly from object storage in client-side applications (Claude Code, Cursor). The system runs LLM-shaped async UDFs 300x faster than DuckDB-WASM on filter-bounded queries and reduces costs of a ten-task agent analyst suite by two-thirds.
HEAL is a decentralized learning framework combining federated learning, gossip learning, and epidemic learning through a self-organizing P2P overlay. It dynamically promotes nodes as aggregators and demonstrates performance equivalent to standard federated learning while being resilient to failures and churn.
SignGAD introduces a self-designing agentic framework for few-shot graph anomaly detection. Instead of fixed pipelines, it designs task-conditioned detection workflows by selecting suitable graph encodings and detector designs. A guarded refit strategy refines selected workflows under limited supervision, outperforming state-of-the-art methods on real-world datasets.
Comparative study of Liquid Neural Networks (LNN/CfC) vs LSTM across four sequential modalities (N-MNIST, QuickDraw, IAM, PhysioNet Sepsis-3). LNNs model hidden state evolution as continuous differential equations. Results: LNNs outperform LSTM in parameter efficiency and robustness to missing data, especially in clinical environments.
E³-Agent is an executable and evolving agent for edge generative inference resource management. It pairs a fast-path router (millisecond dispatch) with a slow-path LLM meta-controller driven by events, learning online from execution feedback. Evaluated in simulation, it reduces latency by 65-73% versus static baselines across dynamic scenarios (semantic shifts, device churn, hidden drift).
Theoretical paper positioning Metric-Aware Principal Component Analysis (MAPCA) within geometric deep learning framework. MAPCA parameterises PCA by a positive-definite metric matrix, with solutions equivariant under the orthogonal group preserving the metric. A uniqueness theorem characterises Invariant PCA as the unique linear data-derived metric equivariant under arbitrary diagonal rescaling.
New Architecture-driven Shift (ADS) metric for efficient pre-trained model selection in continual learning. ADS decouples logit shift into architecture and data dependencies, showing their combination captures logit shift tendency with minimal samples. Validated across 175+ architectures with Spearman correlation ≥0.731.
Hierarchical framework for compact LLMs in resource-constrained agentic systems. Model distillation + oracle-controller loop monitors protocol validity, projects histories into feasible prompt domain, triggers lightweight fine-tuning under drift. Separates schema learning from semantic adaptation. Evaluated on Multi-Fidelity Bayesian Optimization with improved reliability and cost-efficiency.
VibeSearchBench evaluates LLM agents on collaborative multi-turn search in real-world context. The benchmark comprises 200 bilingual (Chinese/English) tasks across 20 domains with schema-free knowledge graphs. Seven frontier models tested achieve max F1 of 30.30, exposing gaps in long-context reasoning and proactive intent elicitation.
Paper proposes steganographic mechanism to trace origin of synthetic information generated by AI. An encoder hides an invisible signature in each generation, enabling parent model identification via decoding. Theoretical analysis and empirical evaluations across multiple projectors and stegosystems.
Soro is a family of Tajik-specialized LLMs based on Gemma 3, trained on 1.9B Tajik tokens (web, PDFs, educational materials). After supervised instruction tuning on 40K examples, Soro outperforms Gemma 3 on author-created Tajik benchmarks while maintaining English performance. FP8/INT4 quantization validated for edge deployment in schools.
Comparative study of local explainability techniques (LIME, SHAP, Feature Ablation) reliability across 32 tabular datasets. Results show explanation quality does not systematically correlate with model predictive performance, but depends instead on dataset complexity and feature distributions.
SDR (Supervised Distributional Reduction) combines optimal transport and dependence maximization to learn target-aware representations. The algorithm extends the Fused Gromov-Wasserstein objective with an explicit dependence term, producing compact embeddings that capture both geometric structure and predictive signal. Application to Gaussian Process modelling with adaptive kernels.
Agyn is an open-source platform for deploying AI agents in production. It provides a stateful serverless runtime on Kubernetes, agent definition via Terraform, and a zero-trust security model. Agyn is agent-, model-, and cloud-agnostic.
E²-LoRA, a low-rank adaptation method for continual learning, concentrates energy of parameter updates into principal ranks to minimize task interference. A dynamic rank allocation strategy balances model stability and plasticity.
Theoretical paper on spectral control of stochastic gradient noise via entry-wise clipping. Shows that simple entry-wise clipping balances matrix structure and computational cost, with O(ε⁻⁴) convergence guarantees under Cauchy-contaminated noise. Empirical gains: ~7% token savings on NanoGPT with smooth shrinkage, ~2% additional when combined with Muon.
SparseOpt, a sparsity-aware optimizer, addresses gradient skew induced by Batch Normalization in Dynamic Sparse Training. Experiments on ResNet (CIFAR-100, ImageNet) show faster convergence and improved generalization. First systematic study of interactions between Batch Normalization, sparse layers, and DST.
A physics-informed machine learning (PIML) framework for thermal modeling of a lunar rover. An adaptive neural network determines 3D finite-difference meshing based on thermal loads, improving accuracy by 50% vs coarse-mesh physics models and 39% vs pure ANN, while being 3x faster than high-fidelity simulations.
RL framework for sim-to-real policy transfer via probabilistic latent embeddings and dynamic adaptation. Uses meta-RL and CMDPs to infer latent environment representation, with distributional RL formulation dynamically adjusting risk levels based on latent context estimation accuracy.