3149 articles
Japan successfully tested a ramjet engine designed for Mach-5 aircraft. The trial validates hypersonic propulsion technology, a key milestone toward next-generation supersonic aircraft.
Nathan Witkin (NYU Stern) harshly critiques METR's AI time horizons graph. Errors include: unmeasured human baselines merely estimated, hourly-paid benchmarkers incentivized to work slowly, biased sample toward authors' peers, and failure to account for familiarity advantage (5-18x faster). Witkin concludes the graph contains too many compounding errors to be salvaged.
DCGAN with 12.6M parameters runs on RISC-V CH32H417 microcontroller (512KB SRAM). Generates 64×64 cat faces in 26 seconds using pure C inference engine with int8 per-channel quantization. Weights streamed from SD card via double buffering. Z vector seeded with 200 bytes quantum random data (ANU QRNG). No existing frameworks (TFLite, CMSIS NN) — built from scratch.
Controlled study on TypeScript codebase (25 sections, 3,250 files): LLM (Kimi K2.6) equipped with structural graph (Blueprint: Universal Ctags + ast-grep + BM25) consumed 54% more input tokens (63,541 vs 41,327) but explored deeper (6 turns vs 5). Graph costs ~6,500 tokens and increases model's navigational confidence.
Fine-tuned Qwen 3.5 0.8B on Pangram's EditLens dataset to detect AI-generated content. Chrome extension 'Slop Hammer' released for local inference (~1s on M1), 400MB model. 20h training on single RTX 3090. Limitation: dataset built with older LLMs, struggles with GPT-5.5.
A user sequenced a complete human genome to 30× coverage at home using open-source tools and accessible hardware. Demonstrates democratization of genome sequencing outside professional laboratories.
Spice is an open-source project adding an explicit decision layer above AI agents. It documents observations, considered options, reasoning for choices, and rejected trade-offs before execution, making agent behavior less of a black box. Compatible with Claude Code, Codex, and other agents.
CUDA implementation of Fast Walsh-Hadamard Transform (FWHT) for llama.cpp optimizing KV-cache quantization. 1-2% speedup on prefill, 7-9% on token generation with RTX 5090 and q8_0 quantization.
Researcher launches red-teaming challenge on Llama 3.1 8B to stress-test SAFi, a runtime governance engine designed to enforce alignment of autonomous agents. 10 prompts to break a Socratic Tutor Agent (make it give direct answers or go off-topic from science/math). Open-source code available.
Uber's COO states it's becoming harder to justify massive AI spending. The company questions the ROI of 'token maxxing'—accumulating compute capacity without clear use cases.
Llama.cpp reportedly preparing a fix for split mode tensor crashes on multi-GPU setups. Split tensor mode delivers ~35% throughput gain over layer mode but crashes every 90-120 minutes due to VRAM exhaustion.
Delta Attention Residuals improves residual connections by routing over layer deltas (vᵢ = hᵢ₊₁ − hᵢ) instead of cumulative hidden states. Results: −8.2% PPL at 7.6B, 1.8× sharper cross-layer routing (max weight 0.2→0.6), <0.01% parameter overhead. Code and paper released.
Spice is an open-source decision layer above AI agents. It makes the decision process explicit (observations, options, justifications, trade-offs) before execution, rather than treating agents as black boxes. Compatible with Claude Code, Codex, and other executors.
Call for papers for the 2nd Workshop on Efficient Reasoning at COLM 2026 (October 9). Deadline: July 12, 2026. Topics: multimodal reasoning under efficiency constraints, dataset curation, algorithmic innovations, fast inference (pruning, compression, KV-cache), benchmarks, on-device deployment, safety, real-time applications (healthcare, robotics).
KVANTA, an open-source web-based KV cache calculator (Apache 2.0) for LLM/VLM from Hugging Face. The tool aims to outperform existing calculators with an improved interface.
A r/LocalLLaMA user reports Qwen 3.6 35B A3B outperforms other local models (Gemma 4, GLM 4.7 Flash) for agentic tasks, with fewer infinite loops and broken tool calls. Tested on Hermes Agent and Pi using IQ4_NL quantization.
RTPurbo transforms full-attention LLMs into sparse models in hundreds of training steps. The method exploits three observations: only certain heads require full attention, long-range retrieval uses a 16D subspace, and token selection is query-dependent. Results: 9.36x prefill speedup at 1M context, 2.01x decode speedup, accuracy preserved.
A Republican senator from Louisiana voted in favor of a Meta data center project and allegedly profited financially from the decision. The article reveals a potential conflict of interest spanning two years.
GitHub repo 'Local-LLM-Toolkit' shared documenting optimization techniques for local LLMs on Mac Studio M4 Max 128GB. Includes C and Swift code for performance improvements.
Financial Times reports Heretic, a GitHub tool, removes guardrails from Llama 3.3 in under 10 minutes. Creator Philipp Emanuel Weidmann confirms 3,500 'decensored' models created and 13 million downloads since launch.
Endeavour Energy, Australia's major electricity distributor, migrated its outage map to Next.js on Vercel. Results: sub-1s page loads during peak storm traffic, 5-minute data sync cycles, 38% faster deployments. Supabase handles real-time data layer.
Small specialized models (Gemma 4 31B at 86.4% on tau2-bench, Qwen 27B outperforming 397B models) now dominate agentic benchmarks. Yet the industry keeps deploying expensive frontier models: frontier labs profit from per-token billing, creating misalignment between technical performance and market adoption.
Numind releases NuExtract3, a 4B open-weight VLM based on Qwen3.5-4B (Apache-2.0 license). The model extracts structured data and converts documents/images to Markdown. Trained for 3 days on 8xH100, it handles PDFs, forms, tables with multiple quantizations (GPTQ, W8A8, FP8, Q4, Q6) for self-hosting from 4GB VRAM.
Pope Leo raises concerns about opaque AI systems controlled by few firms risking 'new forms of dehumanization'. He calls for greater transparency and responsible AI governance.
A 2013 Mac Pro with D700 GPUs (12 GB VRAM) now runs LLMs via Vulkan after recent driver support. Qwen 3.5 9B achieves 11 t/s, Qwen 2.5 Coder 22 t/s at 70k context. User reports Qwen 3.5 outperforms Claude Sonnet 4.6 on C#/.NET planning tasks.
User reports memory leak in llama.cpp with Qwen3.6-27B-MTP-GGUF after 20-40 minutes of active use. Process gradually consumes more system RAM despite various configuration attempts (--no-mmap, --cache-ram 0, without MTP). Issue persists across multiple builds and Docker images.
OSCAR RotationZoo provides precomputed rotation matrices for INT2 KV-cache quantization. The method achieves ~7× KV-cache memory compression with single-digit accuracy drop on GPQA for dense reasoning models (Qwen3-4B, Qwen3-8B, GLM-4.7). Code and rotations available on HuggingFace.
Researchers filmed immune system cells destroying melanoma cancer cells in real time. Immune checkpoint inhibitors, used in medicine for 15 years, enable this therapeutic action now visualized directly.
Call for papers for the U&ME (Unlearning & Model Editing) workshop at ECCV 2026. Organizers seek submissions on unlearning, model editing, model merging, compression, and lifelong learning. Work-in-progress and exploratory ideas welcome.
Stop-slop is a skill file designed to detect and remove common AI-generated text markers from prose, such as repetitive phrases and generic formulations.
Gstack: 23 opinionated Claude Code tools configured from Garry Tan's setup, covering CEO, designer, engineering manager, release manager, doc engineer, and QA roles.
Agent harness performance optimization system. Integrates skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, and Cursor.
Anthropic releases claude-cookbooks, a collection of notebooks and recipes demonstrating practical and creative ways to use Claude.
Airi is a self-hosted, open-source AI companion capable of real-time voice chat, Minecraft and Factorio gameplay. Supports Web, macOS, and Windows. Inspired by Neuro-sama.
llmfit: CLI tool to test hundreds of LLM models and providers on your hardware. One command to identify what runs locally.
Meetily is an open-source, self-hosted meeting assistant built on Rust. 4x faster transcription than Whisper/Parakeet, speaker diarization, Ollama-based summarization. 100% local processing, no cloud required.
IronClaw is an Agent OS emphasizing privacy, security, and extensibility. Open-source project hosted on GitHub.
OB1 (Open Brain) offers a unified infrastructure layer: one database, one AI gateway, one chat channel. Compatible with any AI model, no middleware or SaaS required.
CodebuffAI: command-line tool for code generation. Generates code directly from the terminal.
Gstack: Garry Tan's Claude Code setup with 23 opinionated tools automating CEO, designer, engineering manager, release manager, doc engineer, and QA roles.
Airi is a self-hosted AI companion supporting real-time voice chat, Minecraft and Factorio gameplay. Web, macOS and Windows support. Open-source project inspired by Grok and Neuro-sama.
OpenBB is an open-source financial data platform for analysts, quants and AI agents. It provides unified access to market data through a GitHub-hosted repository.
TrendRadar is an AI-driven trend monitor aggregating multi-platform news via RSS with smart alerts. Filters by keywords, translates and analyzes articles via AI, supports MCP for natural language dialogue, Docker deployment with local/cloud data, integrations with WeChat/Feishu/DingTalk/Telegram/Slack.
Google DeepMind's AlphaProof Nexus autonomously solved nine open Erdős problems, including two unsolved for 56 years, for a few hundred dollars per problem. The system uses the Lean compiler to automatically verify each proof step, with a 2.5% success rate.
NuExtract3 is a 4B vision-language model for document understanding. It combines structured extraction (text/images + JSON template → JSON output) and image-to-Markdown conversion, with multilingual support and reasoning/non-reasoning modes. Available in GGUF, NVFP4, MLX, VLLM.
George Hotz warns that AI coding agents will be "one of the most costly mistakes" in software development. After six months of testing, he concludes LLMs deliver fast prototypes but generate bugs that become increasingly hard to spot. His stance reflects deep divisions in the AI community over LLMs' role.
Developer builds sandbox framework for AI agents on headless Linux with GPU passthrough, sudo access, and host OS isolation. VM-based architecture enables autonomous web browsing, Docker execution, and parallel sessions. Code released on GitHub.
Release of MiMo-V2.5-coder, a coding-optimized model for 128 GB RAM systems. Positioned as alternative to Qwen 3.6 and DeepSeek-4, featuring reliable tool calling and fast performance.
Mininglamp AI added W8A8 activation quantization to MLX via Cider, a custom SDK with Metal kernels. On M5 Pro, prefill improved from 2.84s to 2.52s for a 4B VLM. Works with any MLX model, but INT8 TensorOps requires M5+.
Leading AI models like GPT and Gemini routinely cite text passages that don't support their answers, even when answers are correct. Researchers at Peking University term this "attribution hallucination" and introduce CiteVQA benchmark to systematically test for it.
A learning repo « MCP from Scratch » teaches Model Context Protocol in plain Node.js, from raw JSON-RPC to a working local agent loop (plan → act → observe) using node-llama-cpp and GGUF models. Designed to expose underlying mechanics without heavy abstractions.
Qwen 3.6 benchmarks on 2x RTX PRO 6000 with vLLM. Qwen 3.6 27B BF16 reaches 1800 tps (64 concurrency, MTP-2). Qwen 3.6 35B BF16 reaches 3500 tps generation (128 concurrency, MTP-Off) with 30k tps prompt processing.
llama.cpp PR #22929 optimizes checkpoint creation to avoid full context re-processing when conversation history is edited. Use case: agentic coding with 70k tokens. Improves responsiveness by reprocessing only changed portions, tested for 2 weeks.
User reports 1000 tokens/s generation on Qwen 3.6 27B with V100s at batch 128, and 80 t/s single-user (batch 1) without MTP. Processing throughput reaches 3000 t/s.
Developer builds custom C++ inference engine for MiniCPM-V 4.6 on Orange Pi AIPro (Ascend 310B NPU, $149). Bypasses heavy frameworks with optimized AscendC kernels, achieving 5.90 tokens/s vs 2.88 baseline (170ms per step). Open-source on GitHub.
Inventory of text and speech resources for Hausa (80-100M speakers) and Fongbe (2M speakers). Hausa has diverse parallel corpora and text collections (news, encyclopedic, educational). Fongbe lacks text data but benefits from recent speech collection initiatives. Both languages represented in Masakhane benchmarks (NER, POS tagging).
BOHM is a hierarchical attribution method for compound AI systems that extracts component contributions directly from routing weights without evaluating arbitrary subsets. Zero marginal cost, compatible with opaque third-party APIs. On 18 LLMs (880 LiveCodeBench problems), Kendall tau=0.928 vs SHAP tau=0.980 at 9,000x more evaluations.
Red-teaming study of 30+ open-source LLMs (10 families, 5 countries) measuring capacity to generate biased political content via jailbreaks. Findings: systematic asymmetries (left-leaning bias), Overton Window contraction with model size, substantial regional differences, variable jailbreak potency across model families.
Theoretical work adapting rare switching to linear bandits with Gaussian noise for privacy. Standard determinant-based rules fail due to loss of design matrix monotonicity. Proposed solution: generalized Rayleigh quotient-based rule, validated by Codex.
New LIFT method for fine-tuning diffusion language models (DLMs). Analysis shows vanilla SFT ignores token learnability based on masking. LIFT aligns learning with diffusion steps: easy tokens when input is masked, hard tokens with more context. Up to 3x gains on AIME'24/25 vs SFT baselines.
CFD (Computable Fair Division) proposes a framework for fairly allocating GPU and bandwidth resources among AI agents. The Boltzmann-Softmax β parameter controls the efficiency-fairness tradeoff. AHC++ adjusts β in real time to maintain diversity without major throughput degradation. Simulations: 100x agents = +5.5x execution time.
LFRAG introduces a multimodal RAG system using block-level instead of page-level retrieval. A semantic-layout fusion encoder integrates local semantics with global context. On LFDocQA benchmark, LFRAG improves answer accuracy by 7.20% and reduces token consumption by 73.07%.
KPI2KVI transforms natural language service descriptions into Key Value Indicator (KVI) estimates using a deterministic multi-agent LLM workflow. The system elicits missing context, extracts relevant KVI categories, generates service-specific KPIs, collects values through interactive dialogue with intelligent estimation, and computes interval-valued KVIs with traceable explanations.
Theoretical paper proposing a Cognitive Kardashev Scale to quantify AI compute capacity civilisations could sustain. Based on four parameters (total power, cognition share, energy efficiency, brain reference), the study estimates current humanity at K≈0.73 (Type I). At Type I with 1% power allocation, each human would have access to one personal AI's worth of cognition.
Co-ReAct integrates step-level rubrics to guide ReAct agents in multi-step search-intensive reasoning tasks. A rubric generator trained with GRPO optimizes list-wise Spearman rank-correlation against multi-judge expert consensus. Measured improvements on DeepResearchBench and SQA-CS-V2 across 8B/14B and frontier models.
EDGE-OPD improves on-policy self-distillation (OPSD) by using guided rollouts and evidence masking to efficiently transfer privileged context (persona, private fact, worked solution) without degrading general model capabilities. Experiments show standard OPSD fails on rare-identity tasks while EDGE-OPD succeeds.
Ontological Knowledge Blocks (OKBs): programmable governance infrastructure compiling regulatory obligations into machine-checkable constraints over structured evidence graphs. Uses RDF/OWL, SHACL, and PROV-O. Prototype evaluated on HPC resource allocation with 24 validation runs and 4 governance profiles. SHACL validation latency: 12.6–100.3 ms.
LLM-based multi-agent systems fail despite correct plan execution when agents misjudge their knowledge (epistemic miscalibration). Authors propose EPC-AW workflow that selects plans stable across agents and refines epistemic state over time using past discrepancies. 9.75% improvement in system-level success.
SpaceNum evaluates spatial numerical understanding in VLMs through bidirectional tasks (Num2Space, Space2Num). Current models largely fail to ground numbers in spatial meaning, performing near random chance. They rely on shallow spatial cues and fail to build stable coordinate-aware representations.
GENSTRAT introduces a benchmark for evaluating strategic reasoning in LLMs using procedurally generated card games. Evaluation of 9 models (GPT-5, Claude, Gemini-3.1-Pro) across 36,000+ matches. Methodology decomposes competence across 6 axes and measures local volatility (jaggedness) to diagnose real-world deployments.
ImProver 2 is a neurosymbolic framework for automated proof optimization in Lean 4. A 7B-parameter model trained outperforms orders-of-magnitude larger models and is competitive with mid-tier frontier models. The scaffold exposes formal structure alongside lightweight informal abstractions.
Study of 20 commercial and open-source LLMs across 182 religious pairings. Models exhibit persistent asymmetries: they favor conversions to Catholicism, Bahá'í, Sikhism and discourage conversions to Atheism, Agnosticism, Jehovah's Witnesses. Grok 4.20 shows strongest asymmetries. Patterns reproducible across question phrasings.
GEMQ introduces global expert-level mixed-precision quantization for MoE-LLMs. The method uses global linear-programming formulation to estimate expert importance and fine-tunes routers to adapt routing to quantized experts. Results: significant memory reduction and inference acceleration with minimal accuracy loss.
Theoretical study showing that network depth induces implicit low-rank bias, promoting alternatives to neural collapse. Analysis of unregularized deep UFM (unconstrained feature model) training dynamics reveals how depth favors softmax codes over classical structured geometries.
SF-NorMuon, a schedule-free spectral optimizer, matches or exceeds tuned AdamW on 125M and 772M parameter language models without requiring a predefined learning-rate schedule. Theoretical proof of stationarity guarantee and identification of weight decay as essential for long-horizon stability.
Gen-ROTDA, a robust optimal transport method, adapts Citi Bike demand prediction models across years (2021-2026). It transfers residuals rather than raw demand and uses a label-preserving feature generator. Gen-ROTDA achieves lowest MAE on the 2025-2026 task and outperforms non-robust OT variants under abnormal data.
DFKI-MLT applies activation steering to multilingual LLMs to improve cultural awareness in SemEval-2026 Task 7. The method adds language-specific steering vectors to the residual stream without parameter updates. Result: 86.96% accuracy on MCQ track (7th/17), but modest and heterogeneous improvements varying by language-region pair and layer selection.
Study showing task-relevant information is distributed non-monotonically across layers in foundational models. Introduces LOES (Layer-wise Optimal Embedding Selection), a spectral method identifying task-discriminative subspaces, and GeoReg, geometric regularization enforcing simplicial structure on class manifolds. Consistent improvements across architectures and modalities.
World Machine is a transformer-based generative world-modeling architecture with latent states for time series. It reduces the quadratic complexity of standard transformers and adapts to varying amounts of observed data. Validated on synthetic dataset Toy1D.
Theoretical study on multiclass classifier calibration. Authors characterize elicitation complexity for discrete orderable properties (mode, rankings) using Lipschitz continuous properties as intermediary, solving exponential complexity blowup in number of classes.
HawkesLLM models semantic uncertainty propagation in multi-agent text simulation systems. A multivariate Hawkes process captures temporal dependencies between text-generating agents, while an LLM generates each new event from compact selected memory. Evaluation on GDELT dataset shows improved late-stage semantic alignment under memory constraints.
An arXiv study shows that many recent unsupervised feature selection methods are outperformed by random feature selection in both performance and efficiency. Authors propose using random selection as a mandatory baseline to evaluate novel methods.
Prototype-Based Sparse Steering applies Sparse Autoencoders to LLM attention query activations to decompose representations into interpretable features. Gradient-based optimization during inference aligns sparse representations with target behavior prototypes. Validated on Textualized Gridworld (planning constraints) and educational domain (cognitive complexity via Bloom's Taxonomy).
Learned Relay Representations (Relay) enables Masked Diffusion Models to propagate latent information across denoising steps via a differentiable per-token channel trained with truncated BPTT. Applied to Fast-dLLM v2, it outperforms supervised finetuning on coding tasks and reduces inference latency by 32%.
Theoretical study on balancing relational generalization and memorization in learning systems. Authors introduce transitive inference with exceptions task and analytically characterize kernel ridge regression models across representations. Validation on pretrained language models shows successful generalization depends on representational geometry, with systematic errors predicted by theory.
Two-stage federated recommendation pipeline for mobile devices: collaborative filtering on non-sensitive app-context data in cloud, then on-device re-ranking with sensitive mobile signals. Validated on MovieLens, UCI HAR, and proprietary dataset. Production-ready Kotlin Multiplatform library for Android/iOS.
Tensor Cache introduces a two-level cache for transformers: sliding-window local attention (L1) plus fixed-size outer-product fast-weight memory (L2) storing evicted KV pairs as a matrix. A learned gate fuses outputs. Improves memory-quality tradeoff on long-context models.
On 1-3B models, CoT in arithmetic relies on a positional shortcut: the model simply copies the number in the final position before the answer delimiter, regardless of intermediate reasoning. This strategy accounts for 54-92 pp of accuracy on GSM8K. Replacing that number with an incorrect value collapses performance even with correct steps.
Method to quantify uncertainty in language models by analyzing layer-wise representation trajectories. Eleven geometric features extracted from MLP updates outperform maximum softmax probability (MSP) by up to 21 AURC points, revealing where and how errors emerge across depth.
DreamerNLplus combines DeBERTa, Random Forest, Llama 3.1, and RAG to model mental health dynamics from social media timelines (CLPsych 2026). Ranks 2nd in summarization, 1st in Improvement, 3rd in Deterioration. Code released.
Study showing explicit reasoning (CoT) benefits only specific tasks. Authors propose EDRM, a training-free framework using early-stage entropy dynamics to adaptively route to CoT or direct inference. Across 15 benchmarks and 4 LLMs, EDRM achieves 41–55% token reduction while improving accuracy up to 4.7%.
Reproducible pipeline for building a Universal Dependencies parsing resource for Katharevousa Greek (post-junta parliamentary texts). Dataset of 1,697 annotated sentences, comparison of 6 architectures (spaCy, Stanza, XLM-R, mBERT, etc.). Best model (XLM-R): 0.5162 LAS vs 0.4183 baseline. Code and annotations released open-access.
Paper formalizing cultural adaptation for LLMs in political discourse analysis. Identifies English dominance bias and systematic failures across linguistic/institutional contexts. Proposes evaluation matrix (cultural fidelity, calibration, democratic safety) and methodologies: participatory datasets, culturally-aware transfer learning, culturally-measurable benchmarks.
MaR (Metacognition-as-Reward) is an RL framework improving LLM reasoning via two dimensions: metacognitive knowledge (identifying task-relevant information) and metacognitive regulation (planning the reasoning process). Tested on 22 benchmarks, Qwen3.5-9B + MaR achieves up to 7.7% gain over base model and 11.0% over vanilla DAPO, surpassing GPT-OSS-120B on average.
Researchers reveal that LLM-generated texts contain hidden human-like spans that complicate detection. They propose a stacked model-agnostic framework using a hard-EM procedure to iteratively filter human-like subsequences and enhance existing detectors, working also in training-free mode.
SciAtlas is a large-scale academic knowledge graph integrating 43M papers across 26 disciplines, 157M entities, and 3B triplets. It features a neuro-symbolic retrieval algorithm with tri-path collaborative recall and graph reranking to enhance semantic search and reduce inference costs for AI agents in automated scientific research.
IDS (Inductive Deductive Synthesis) is a multi-agent LLM system jointly synthesizing implementation and formal proof for distributed systems. On 7 key-value store specifications, IDS achieves 7/7 in 6.8h/$106, versus 2/7 for GPT-5.4 and Claude Opus 4.6. Result is 200x faster than expert effort, 17% cheaper than SOTA agents.
EVE-Agent is a self-evolving agent that generates its own questions, answers, and verifiable evidence spans without human annotations. An evidence verifier rewards text spans based on their marginal contribution to correct answers. The training curriculum becomes auditable and trustworthy without external oracles.
Paper introduces parallel context compaction for long-horizon LLM agents to address latency and unpredictability of sequential summarization. Enables fine-grained control over summary volume and targeted prompt engineering per block. Evaluated on HotpotQA and LoCoMo benchmarks across 8B-120B models (dense and MoE architectures).
Paper on aircraft disassembly scheduling for end-of-life aircraft. Proposes Constraint Programming and MIP models to handle thousands of tasks with precedence constraints, technician certifications, aircraft balance, and space limitations. Tested on real instances up to 1450 tasks from industrial partner.