b10203
[SYCL] Support q2 mul_mat (#26231) support q2_0 in mul_mat support more q2_0 case Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABL
Knowledge catalogue
[SYCL] Support q2 mul_mat (#26231) support q2_0 in mul_mat support more q2_0 case Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABL
sycl : support dev2dev memcpy by DEV2DEV_MEMCPY_FORWARD (#26234) Co-authored-by: Neo Zhang Jianyu jianyu.zhang@intel.com Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple S
ggml-zendnn : group matmul direct API for mul_mat_id (#25918) ggml-zendnn : group matmul API for mul_mat_id ggml-zendnn : scale MUL_MAT_ID fallback threshold by expert count Website: https://llama.app
[SYCL] support the missed types in cpy (#26005) support the missed types in cpy use correct funct rm unused code Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (
cuda: extract Q2_0 elements via __byte_perm (#25603) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFr
server: correct accepted tokens when need draft token replay (#26320) spec: correct accepted tokens when need draft token replay cont : naming Co-authored-by: Georgi Gerganov ggerganov@gmail.com Websi
vulkan: update vulkan sdk to 1.4.357.0 (#26303) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramewo
Support rotated kv cache quant (#26180) Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64) iOS XCFramework Linux
mtmd: add n_embd_head (#26342) Co-authored-by: Daniel Han unslothai@gmail.com Website: https://llama.app macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED m
vulkan: Introduce driver version check for Windows Intel GPU to mitigate crashing (#25192) Removed crash guard for Intel Crash fixed from driver 32.0.101.8860 Added driver version check for windows Ch
vulkan: add POOL_1D op (#25431) vulkan : add pool1d push constants and pipeline field Declared data structures needed for POOL1D OP, which are the vk_op_pool1d_push_constants struct and pipeline_pool1
arXiv:2205.06947v3 Announce Type: replace-cross Abstract: CT-based bronchial tree analysis is essential for diagnosing lung and airway diseases, yet automatic bronchus classification remains challengi
arXiv:2607.27628v1 Announce Type: new Abstract: Low-light image enhancement (LLIE) methods involve tunable parameters that are typically fixed, often leading to performance degradation when applied ac
arXiv:2607.26639v1 Announce Type: cross Abstract: A self-check defense asks the target model to assess a request before answering it; SAGE, the strongest published instance, reports an average 99% def
arXiv:2607.27366v1 Announce Type: new Abstract: While data synthesis for large language models (LLMs) is prevalent, it primarily targets domains with verifiable answers, overlooking open-ended humanit
arXiv:2607.27945v1 Announce Type: cross Abstract: Sequence models must decide what to write into memory and what to retain. In quantum and quantum-inspired sequence learning, nonlinear recurrent updat
arXiv:2607.27594v1 Announce Type: new Abstract: Post-training alignment in large reasoning models (LRMs) has significantly improved their adaptability to diverse safety compliance settings. However, a
arXiv:2607.27898v1 Announce Type: new Abstract: Remote sensing images acquired by unmanned aerial vehicles (UAVs) and satellites are often degraded by adverse weather, illumination variation, and imag
arXiv:2510.25333v2 Announce Type: replace Abstract: Recent years have witnessed the rapid development of LLM-based agents, which shed light on using language agents to solve complex real-world problem
A100 with 40gb VRAM: 162GB Q8_K_XL ~16.1 tok/s generation Only 15.8GB of 40GB VRAM used with all experts on CPU NOTE just tested coding on linux box DeepSeek-V4-Flash-0731 runs losslessly on the singl
Source: https://x.com/deepseek_ai/status/2083084415157022911 & https://deepswe.datacurve.ai/ just combined data view. DeepSeek claims, not verified by DeepSWE yet. submitted by /u/sdexca [link] [comme
DeepSeek V4 Flash: Preview → 2026-07-31 Benchmark Preview 0731 Δ Terminal Bench* 56.9 82.7 +25.8 Toolathlon 51.8 70.3 +18.5 NL2Repo — 54.2 new Cybergym — 76.7 new DeepSWE — 54.4 new Agent Last Exam —
🚀 DeepSeek-V4-Flash Official API is now LIVE in public beta! 🔷 We’ve massively upgraded its Agent capabilities—benchmark scores are now far surpassing the V4-Pro-Preview. Check out the massive perform
While waiting for some of the quants to drop, I load the API with $50 and ran it on SlopCodeBench Just vibe reading the results it seems like Opus 4.8 < Deepseek < Opus 5 https://github.com/michaelasp
Artificial Analysis: DeepSeek V4 Flash scores 50 on the Artificial Analysis Intelligence Index, matching Gemini 3.6 Flash and up 10 points from the preview launch in April — DeepSeek V4 Flash 0731 is
arXiv:2607.28229v1 Announce Type: new Abstract: The web is increasingly accessed by AI agents rather than humans. Every agent needs knowledge, especially in the life-sciences, where agentic pipelines
arXiv:2607.28020v1 Announce Type: new Abstract: Learned video compression relies on accurate temporal modeling to remove redundancy between adjacent frames. However, most existing codecs infer motion
arXiv:2607.26120v1 Announce Type: new Abstract: Large Language Models (LLMs)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under asymmetric i
arXiv:2607.26512v1 Announce Type: new Abstract: AI agents can draft claims faster than authors can check whether the cited or retrieved evidence supports them. We study evidence-ledger adjudication: a
arXiv:2607.26611v1 Announce Type: new Abstract: AI-assisted coding increasingly translates informal user intent into executable software, yet coding requests often contain ambiguities that recur in us
arXiv:2607.28127v1 Announce Type: new Abstract: Recent advances in Generative AI have substantially improved financial sentiment analysis through post-trained financial large language models (LLMs). H
arXiv:2607.28022v1 Announce Type: new Abstract: Large language model training in open-ended domains lacks verifiable rewards, making task preferences difficult to formalize as effective supervision. C
arXiv:2607.28097v1 Announce Type: new Abstract: Mathematically equivalent expert-reduction orders can produce observably different sparse-MoE executions. We isolate this effect in native DeepSeek-V4-F
arXiv:2607.28505v1 Announce Type: new Abstract: The rise of generative artificial intelligence (GenAI) in academic writing and publishing (AWP) raises questions about linguistic inclusivity and the le
arXiv:2607.27422v1 Announce Type: new Abstract: Good action rankings do not make a contrastive critic safe to maximize. These critics increasingly act as value-like objectives for best-of-K selection,
Abner Li / 9to5Google: Google starts rolling out access to Gemini Spark for Google AI Pro subscribers to over 160 countries and adds a Chrome auto browse integration on desktop — Gemini Spark is getti
arXiv:2607.28456v1 Announce Type: cross Abstract: Solving large, sparse linear systems is a core task in scientific computing, and efficient iterative solvers rely critically on effective and robust p
arXiv:2607.28537v1 Announce Type: cross Abstract: Metallic magnets exhibit complex spin dynamics governed by electronically generated interactions. Predictive simulations of such dynamics typically re
arXiv:2607.27652v1 Announce Type: new Abstract: Reinforcement learning (RL) search agents commonly model retrieval as free-form natural-language query generation and optimize multi-turn interactions u
arXiv:2607.27928v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) is fundamentally influenced by the distributional composition of multi-domain pre-training data. While m
Hermes and Pi Agent led on the average cost per task, while Claude Code cost about 3.7x as much as Pi: - 0.39 Hermes Agent - 0.40 Pi Agent - 0.47 Codex - 0.51 OpenCode - 0.54 Kimi Code - 1.47 Claude C
i see your moore's law and i raise you 20x GPT-5.4 full at xhigh scored 51, exactly where Luna max sits today. GPT-5.4 costs 2.50/15; Luna now costs 0.20/1.20. In other words, roughly four months late
If LoRA is underperforming, don't reach for more expensive full parameter fine-tuning right away. We ran three cheap tests (data coverage, optimization, rank) to see if we could close the gap between
'Intelligence too cheap to meter' battle is on! Given that DeepSeek-V4-Flash-Preview is already great for agentic tasks, there is no doubt this new checkpoint must be an absolute beast. 20+ point jump
arXiv:2607.28401v1 Announce Type: new Abstract: Effective flood monitoring is critical for minimizing the impacts of flood disasters on populations and infrastructure. Yet reliable remote sensing acro
arXiv:2607.17751v2 Announce Type: cross Abstract: We present MagicSelector, a joint optimization framework integrating Counterfactual task decomposition, Progressive reranking, and Dynamic Top-K, desi
It’s an MoE with ~3B active params and a 30B n-gram lookup table offloaded to RAM for fast 256k context on a 24GB GPU. Reminds me of Gemma 4’s PLE trick. Initial analysis suggest it wont be replacing
Neat work on long-horizon agents. Splitting a hard task across agents is typically how standard multi-agent work. The usual design lets them exchange findings only at phase boundaries, through staged
OK, GPT-5.6 Luna is a bit of a beast. Given the 80% price drop today I decided to try it in Datasette Agent, and it's furiously quick and generates all the SQL, HTML and JavaScript (for Datasette Apps
arXiv:2607.28437v1 Announce Type: new Abstract: Molecular optimization is commonly performed under a limited oracle budget, which makes deciding what to evaluate as important as deciding what to gener
arXiv:2607.28623v1 Announce Type: new Abstract: We present PAC-MAN, a perception-aware CBF-RL framework that couples control-barrier safety with deployment-realistic onboard sensing for whole-body hum
Period. If LoRA is underperforming, don't reach for more expensive full parameter fine-tuning right away. We ran three cheap tests (data coverage, optimization, rank) to see if we could close the gap
arXiv:2607.27265v1 Announce Type: new Abstract: Real-time bidding is central to computational advertising, comprising three elements: Supply Side Platform (SSP) selling ad impressions, Demand Side Pla
arXiv:2607.28422v1 Announce Type: new Abstract: Fault-tolerant quantum computing (FTQC) relies on quantum error correction to suppress physical errors and preserve logical information at scale. In pra
arXiv:2602.04635v2 Announce Type: replace Abstract: Robots are finding wider adoption in human environments, increasing the need for natural human-robot interaction. However, understanding a natural l
arXiv:2607.27775v1 Announce Type: new Abstract: Generative models synthesize magnitude spectra with high fidelity, while phase is delegated to a recovery module---Griffin--Lim, a vocoder, or a latent
arXiv:2603.01229v3 Announce Type: replace Abstract: Robotic manipulation policies have made rapid progress in recent years, yet most existing approaches give limited consideration to memory capabiliti
arXiv:2607.28538v1 Announce Type: new Abstract: Classifying pathological scars from clinical photographs requires distinguishing keloids from hypertrophic scars despite limited expert-labeled data and
arXiv:2607.27273v1 Announce Type: new Abstract: Post-training of large language models is expensive, and existing efficiency improvements mainly focus on selecting informative samples or designing tra
SenseNova released U1.5-Lite-Preview Benchmarks: Qwen-Image-Bench from 47.14 to 55.20. ImgEdit-Bench from 3.90 to 4.37. GEdit-Bench-en from 7.47 to 8.17. Key updates: 4K native generation with better