Knowledge catalogue
arXiv:2510.03481v3 Announce Type: replace Abstract: We present an optimization-based framework for robust permissive synthesis for Interval Markov Decision Processes (IMDPs). While robust IMDP control
arXiv:2609.12752v1 Announce Type: new Abstract: Objective Prediction models are commonly trained using objectives such as Bernoulli negative log-likelihood (NLL), although downstream clinical decision
arXiv:2609.12366v1 Announce Type: new Abstract: We present ORQA, a method for testing occupation-level knowledge in large language models. Prior methods either map abstract LLM skills to occupations v
arXiv:2609.12917v1 Announce Type: cross Abstract: Handwritten mathematical expression recognition (HMER) is conventionally scored by exact-match rates and string-similarity metrics that are blind to w
arXiv:2609.12913v1 Announce Type: new Abstract: Dense retrieval systems increasingly rely on multi-billion-parameter language models, whose memory and computational requirements make large-scale index
arXiv:2609.12837v1 Announce Type: new Abstract: LiDAR-based SLAM (Simultaneous Localization and Mapping) and LIO (LiDAR-inertial odometry) algorithms are often used for precise navigation of unmanned
arXiv:2609.12345v1 Announce Type: new Abstract: Existing agent benchmarks mainly evaluate final task success or tool-call correctness, providing limited insight into whether agents can reliably diagno
arXiv:2609.12473v1 Announce Type: new Abstract: Scene-level point cloud understanding remains challenging due to diverse geometries and spatial layouts. While pre-trained 3D point cloud foundation mod
arXiv:2609.12181v1 Announce Type: new Abstract: Vision-language and language models increasingly interpret materials data, yet benchmarks report that they hallucinate invalid properties and violate ph
arXiv:2609.12550v1 Announce Type: new Abstract: Quantized Mixture-of-Experts (MoE) services can hold several pre-materialized instances of one base model, but quantization damage varies sharply across
arXiv:2609.12202v1 Announce Type: new Abstract: Characterizing two-dimensional (2D) quantum materials by optical microscopy requires localizing exfoliated flakes and determining their layer thickness
arXiv:2509.24367v2 Announce Type: replace Abstract: Deepfake generators evolve rapidly, making exhaustive data collection and repeated retraining impractical. Unlike generic multi-task settings, deepf
arXiv:2609.12123v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is an effective approach for adapting large pretrained models by learning low-rank weight updates. In practice, the LoRA rank
arXiv:2609.12552v1 Announce Type: new Abstract: Open-vocabulary detection accepts any class list at inference, and promptable segmentation returns regions without class names: the taxonomy has left th
arXiv:2609.12686v1 Announce Type: cross Abstract: Large language models (LLMs) process long contexts, including long documents and lengthy conversations, but face token-level memory usage that increas
arXiv:2609.12418v1 Announce Type: cross Abstract: Neural architecture search (NAS) evaluates candidate networks, but fully training enough architectures to rank an entire space is expensive. Zero-cost
A new paper (I'm not a coauthor BTW -- I just found it interesting) argues, basically, that RSI1 is not on the horizon2, because current (at the time the study was done) agents cannot do open-ended ML
Hello Reddit. Posting this for fun. I thought it was a lonely and silly journey to set up Qwen 3.8 Next on a system that doesn't really run it properly—it was a challenge that might help the community
Now that the dust has settled a bit - here's a write-up on running Qwen3.8-Flash-Next (125B-A6B MoE + 51B n-gram table) on relatively middle-tier hardware (RTX 4070 12GB + 64GB DDR5-5600 + Gen4 NVMe o
arXiv:2502.07250v3 Announce Type: replace Abstract: Modern machine learning models excel at detecting individual actions, sounds, or scene attributes from short, localized observations. However, many
arXiv:2609.12691v1 Announce Type: new Abstract: Instruction-guided image editing has a training-time blind spot. Generative editors are never required to semantically verify whether their outputs actu
arXiv:2609.12331v1 Announce Type: new Abstract: Simulated students generated by computational models provide a practical way to evaluate tutoring strategies and pedagogical approaches used by human an
arXiv:2501.04234v2 Announce Type: replace-cross Abstract: Modern artificial intelligence is supported by machine learning models (e.g., foundation models) that are pretrained on a massive data corpus
arXiv:2609.12623v1 Announce Type: cross Abstract: Full-duplex spoken dialogue models support low-latency turn taking, interruption handling, and backchanneling, yet a key capability remains underexplo
arXiv:2512.07197v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful explicit representation enabling real-time, high-fidelity 3D reconstruction and novel view sy
arXiv:2505.13569v3 Announce Type: replace Abstract: The use of machine learning for modeling, understanding, and controlling large-scale physics systems is quickly gaining in popularity, with examples
arXiv:2609.12653v1 Announce Type: new Abstract: Russian state propaganda spreads across many languages and online spaces. Yet, most computational work examines only one such space, usually social medi
arXiv:2609.13005v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance on a wide range of natural language tasks, and recent benchmarks suggest that they are inc
arXiv:2504.04981v3 Announce Type: replace Abstract: This paper studies continual test-time adaptation (CTTA), the task of adapting a model to constantly changing unseen domains in testing while preser
arXiv:2603.08045v2 Announce Type: replace-cross Abstract: This paper presents a systematic framework for computing formally guaranteed trajectory tracking error bounds for autonomous helicopters based
I know Sage and Triton are nothing new but if you're a beginner or doing a fresh re-install like me, these resources helped me get faster gens on the 4-step turbo workflow I use (will post a link to t
arXiv:2609.12397v1 Announce Type: new Abstract: Multi-modal image generation, particularly subject-driven customization, has garnered growing attention in recent years. Despite the rapid advancement o
Hi everybody, we post-trained Qwen 3.8 27B to be more efficient by figuring out which tokens were linked to overthinking and penalizing them without 'attacking' the reasoning length directly then fixe
arXiv:2601.04339v3 Announce Type: replace Abstract: Unified multimodal generation architectures that jointly produce text and images have recently emerged as a promising direction for text-to-image (T
Overview llama.cpp 0.4.1 adds Maple 20B-A1B, Tencent Hy 4, and Spark2.5 support, improves JSON schema handling, chat parsing, logging, and server child-process management, and updates ggml to v0.24.0.
Bryan Shan / SemiAnalysis: Vera Rubin NVL72 inference tests show up to 7x better token throughput per MW vs. Blackwell on a 1.6T DeepSeek model, above Huang's 3x claim for 1T-3T LLMs — Jensen Sandbagg
arXiv:2609.12343v1 Announce Type: new Abstract: Feed-forward Gaussian splatting models have demonstrated remarkable effectiveness in reconstructing three-dimensional (3D) objects from a few two-dimens
arXiv:2609.12663v1 Announce Type: cross Abstract: Audio-based Multimodal Large Language Models (MLLMs) can generate detailed natural-language descriptions of complex acoustic scenes, yet it remains un
arXiv:2511.07318v3 Announce Type: replace Abstract: Despite substantial advances, large language models (LLMs) continue to exhibit hallucinations, generating plausible yet incorrect responses. In this
arXiv:2609.12591v1 Announce Type: new Abstract: Foundation models are increasingly adapted through fine-tuning, model editing, and alignment procedures while retaining previously acquired capabilities
arXiv:2609.12475v1 Announce Type: new Abstract: Comprehensive benchmark suites are essential for improving large language models (LLMs), but many widely used benchmarks are redundant, making evaluatio
1) The HuggingFace attack was a felony under the Computer Fraud and Abuse Act. So were Anthropic’s Claude gaining “unauthorized access to the production infrastructure of three different organization(
The first generation of our 150M model has just been released Its performance is similar to that of GPT2-Small The benchmarks: PIQA: 62.24% Hellaswag: 32.20% Arc-Easy: 44.91% Arc-Challenge: 25.00% Ari
common : implement common_schema internal representation for JSON schemas (#28736) common : implement common_schema types common : implement a json schema optimizer common : reduce optimizations commo
common: add LOG_JSON macro to log structured data (#28586) add LOG_JSON macro fit: add demo LOG_JSON Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/4711287
chat : improve parsing of complex types in qwen3-coder (#28742) chat : improve schema support in qwen3 parser cont : clean up grammar a bit Website: https://llama.app Attestations: https://github.com/
opencl: apply the noshuffle row-alignment rule to q4_K, q5_K and q8_0, not just q6_K (#28575) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/47132459 macOS
vulkan: workaround NV queuesubmit driver bug (#28830) There is a driver bug where two queues on the same VkDevice simultaneously submitting can break some internal synchronization. Until it's fixed, a
tests : reduce FA test sizes (#28842) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/47152933 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (a
sycl : Fix get mem error (#28227) fix for unsupport zes API optimize the code adjust the log level rm unused head files Update docs/backend/SYCL.md Co-authored-by: Titaniumtown titaniumtown@proton.me
ggml-cpu(s390x): guard VXE-only repack helpers (#28775) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/47190488 macOS/iOS: macOS Apple Silicon (arm64) macO
models : guard the expert FFN size fallback in nemotron-h against a zero divisor (#28779) The NextN/MTP tail loop derives the expert FFN size as n_ff/n_expert_used when expert_feed_forward_length give
tests : exclude HY_V4 from WebGPU test-llama-archs tests (#28855) Co-authored-by: Stanisław Szymczyk sszymczy@gmail.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/a
A few people here mentioned interest in a way to test their custom Pi setups, so I figured I’d drop this here: RoastMyHarness The basic idea is a small engine that sets up an environment to run DeepSW
Don't upvote, just looking for CPU ram options for running qwen flash next, and maybe larger models (so maybe between 128 and 256gb total ram) with an existing cmp 170hx. Use case is agentic coding so
VLLM Benchmark: Prefill, Prompt processing - avg, 871.93 tok/s (3 hours constant running xhigh) - 10K prompt, 1000.26 tok/s (16 runs) - 90K prompt, 743,59 tok/s (16 runs) Decode, tok gen - avg, 38.39
deepseek v4.1 flash has arrived on together ai it beats gpt-5.6 sol on agentic benchmarks at one third the cost per task the model supports a 1m context window and native multimodal input, with 552b t
Excited to continue our partnership with the @alibaba_cloud team on this one, bringing the latest version of Qwen's flaship model to the Fireworks community. Start building with Qwen3p8 today: https:/
It's still a dream for many folks to run medium size(30B range) models @ Q8 with Unquantized KVCache (256K Context) on their GPUs. It would be awesome to have DeepSeek-V4.1-Flash's KVCache + Engram fo
Hi 👋 long time lurker It’s been a few years since the last post about Horse Racing within this sub. A few years ago I was reading a popular science book and came across the story Bill Benter and the s