đ WELCOME TO METAMESH.BIZ +++ Huawei quietly drops SINQ quantization claiming 70% memory reduction (your GPU thanks you) +++ Open source Hunyuan 3.0 dethroned every proprietary image model including Nano Banana (the revolution will be MIT licensed) +++ Sam Altman promises revenue sharing for Sora rightsholders because nothing says "disruption" like licensing deals +++ THE FUTURE RUNS ON 30% OF THE RAM AND TWICE THE IRONY +++ đ âĸ
đ WELCOME TO METAMESH.BIZ +++ Huawei quietly drops SINQ quantization claiming 70% memory reduction (your GPU thanks you) +++ Open source Hunyuan 3.0 dethroned every proprietary image model including Nano Banana (the revolution will be MIT licensed) +++ Sam Altman promises revenue sharing for Sora rightsholders because nothing says "disruption" like licensing deals +++ THE FUTURE RUNS ON 30% OF THE RAM AND TWICE THE IRONY +++ đ âĸ
đ¯ Logical reasoning with LLMs âĸ Evaluating LLM capabilities âĸ Integrating symbolic and statistical AI
đŦ "The natural source of doubt is: who's going to read a bunch of SMT rules manually and be able to accurately double-check them against real-world understanding?"
âĸ "LLMs are statistical language models (d'uh) not reasoners after all."
đ OPEN SOURCE
Huawei SINQ quantization method
3x SOURCES đđ 2025-10-03
⥠Score: 8.8
+++ New quantization method cuts LLM memory by up to 70% and runs 30x faster than AWQ, no calibration data needed. Open source, so we'll know soon enough. +++
"Open source code repository or project related to AI/ML."
đŦ Reddit Discussion: 6 comments
đ BUZZING
đ¯ Quantization Speed âĸ Model Compression âĸ Quantization Standards
đŦ "Speeding up quantisation is cool but not that impressive IMO, since it's a one time operation."
âĸ "Quantization is starting to feel like that '14 competing standards' xkcd"
via Arxivđ¤ Enxin Song, Wenhao Chai, Shusheng Yang et al.đ 2025-10-02
⥠Score: 8.1
"Video understanding in multimodal language models remains limited by context
length: models often miss key transition frames and struggle to maintain
coherence across long time scales. To address this, we adapt Native Sparse
Attention (NSA) to video-language models. Our method, VideoNSA, adapts
Qwen..."
via Arxivđ¤ Justin Cui, Jie Wu, Ming Li et al.đ 2025-10-02
⥠Score: 7.7
"Diffusion models have revolutionized image and video generation, achieving
unprecedented visual quality. However, their reliance on transformer
architectures incurs prohibitively high computational costs, particularly when
extending generation to long videos. Recent work has explored autoregressive..."
"Some quotes from the author that I found insightful about the paper:
Most prior hallucination detection work has focused on simple factual questions with short answers, but real-world LLM usage increasingly involves long and complex responses where hallucinations are much harder to detect.
Traine..."
via Arxivđ¤ Ziyin Zhang, Zihan Liao, Hang Yu et al.đ 2025-10-02
⥠Score: 7.1
"We introduce F2LLM - Foundation to Feature Large Language Models, a suite of
state-of-the-art embedding models in three sizes: 0.6B, 1.7B, and 4B. Unlike
previous top-ranking embedding models that require massive contrastive
pretraining, sophisticated training pipelines, and costly synthetic trainin..."
via Arxivđ¤ Gonzalo Gonzalez-Pumariega, Vincent Tu, Chih-Lun Lee et al.đ 2025-10-02
⥠Score: 7.1
"Computer-use agents (CUAs) hold promise for automating everyday digital
tasks, but their unreliability and high variance hinder their application to
long-horizon, complex tasks. We introduce Behavior Best-of-N (bBoN), a method
that scales over agents by generating multiple rollouts and selecting amo..."
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"Arena evals (e.g., Chatbot Arena) let users pick which model's response is better, or call it a draw. Most leaderboards then shove this into Elo, same as chess. The assumption: a draw = two models are equally strong. The paper ["Drawing Conclusions from Draws: Rethinking Preference Semantics in Aren..."
via Arxivđ¤ Ruohao Guo, Afshin Oroojlooy, Roshan Sridhar et al.đ 2025-10-02
⥠Score: 6.9
"Despite recent rapid progress in AI safety, current large language models
remain vulnerable to adversarial attacks in multi-turn interaction settings,
where attackers strategically adapt their prompts across conversation turns and
pose a more critical yet realistic challenge. Existing approaches tha..."
via Arxivđ¤ Phuc Minh Nguyen, Chinh D. La, Duy M. H. Nguyen et al.đ 2025-10-02
⥠Score: 6.8
"Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key
method for improving Large Language Models' reasoning capabilities, yet recent
evidence suggests it may paradoxically shrink the reasoning boundary rather
than expand it. This paper investigates the shrinkage issue of RLVR by..."
via Arxivđ¤ Kyoungjun Park, Yifan Yang, Juheon Yi et al.đ 2025-10-02
⥠Score: 6.8
"With the rapid advancement of AI-generated videos, there is an urgent need
for effective detection tools to mitigate societal risks such as misinformation
and reputational harm. In addition to accurate classification, it is essential
that detection models provide interpretable explanations to ensure..."
via Arxivđ¤ Anna Kuzina, Maciej Pioro, Paul N. Whatmough et al.đ 2025-10-02
⥠Score: 6.8
"Large Language Models (LLMs) excel at multi-step reasoning problems with
explicit chain-of-thought (CoT), but verbose traces incur significant
computational costs and memory overhead, and often carry redundant, stylistic
artifacts. Latent reasoning has emerged as an efficient alternative that
intern..."
đ¯ Drug discovery using AI âĸ Validation of AI predictions âĸ Limitations of AI models
đŦ "AI can also provide mechanistic explanations, which are critical for moving a molecule through the development pipeline."
âĸ "Currently, we can't just assume that these AI models are totally right, but the notion that it could be right took the guesswork out of our next steps."
via Arxivđ¤ Yuxiao Qu, Anikait Singh, Yoonho Lee et al.đ 2025-10-02
⥠Score: 6.7
"Reasoning requires going beyond pattern matching or memorization of solutions
to identify and implement "algorithmic procedures" that can be used to deduce
answers to hard problems. Doing so requires realizing the most relevant
primitives, intermediate results, or shared procedures, and building upo..."
via Arxivđ¤ Tianyi Jiang, Yi Bin, Yujuan Ding et al.đ 2025-10-02
⥠Score: 6.6
"Large Language Models (LLMs) have demonstrated remarkable reasoning abilities
on complex problems using long Chain-of-Thought (CoT) reasoning. However, they
often suffer from overthinking, meaning generating unnecessarily lengthy
reasoning steps for simpler problems. This issue may degrade the effic..."
via Arxivđ¤ Hala Sheta, Eric Huang, Shuyu Wu et al.đ 2025-10-02
⥠Score: 6.6
"We introduce VLM-Lens, a toolkit designed to enable systematic benchmarking,
analysis, and interpretation of vision-language models (VLMs) by supporting the
extraction of intermediate outputs from any layer during the forward pass of
open-source VLMs. VLM-Lens provides a unified, YAML-configurable i..."
via Arxivđ¤ Linh The Nguyen, Chi Tran, Dung Ngoc Nguyen et al.đ 2025-10-02
⥠Score: 6.5
"We introduce AccurateRAG -- a novel framework for constructing
high-performance question-answering applications based on retrieval-augmented
generation (RAG). Our framework offers a pipeline for development efficiency
with tools for raw dataset processing, fine-tuning data generation, text
embedding..."
via Arxivđ¤ Litu Rout, Andreas Lugmayr, Yasamin Jafarian et al.đ 2025-10-02
⥠Score: 6.3
"We study the problem of posterior sampling using pretrained discrete
diffusion foundation models, aiming to recover images from noisy measurements
without retraining task-specific models. While diffusion models have achieved
remarkable success in generative modeling, most advances rely on continuous..."
via Arxivđ¤ Wen Yang, Junhong Wu, Chong Li et al.đ 2025-10-02
⥠Score: 6.3
"Recent advancements in Reinforcement Post-Training (RPT) have significantly
enhanced the capabilities of Large Reasoning Models (LRMs), sparking increased
interest in the generalization of RL-based reasoning. While existing work has
primarily focused on investigating its generalization across tasks..."
via Arxivđ¤ Runzhe Zhan, Yafu Li, Zhi Wang et al.đ 2025-10-02
⥠Score: 6.3
"Reinforcement learning from verifiable rewards (RLVR) is an emerging paradigm
for improving the reasoning ability of large language models. However, standard
on-policy training discards rollout experiences after a single update, leading
to computational inefficiency and instability. While prior work..."
via Arxivđ¤ Raphael Tang, Crystina Zhang, Wenyan Li et al.đ 2025-10-02
⥠Score: 6.3
"In arena-style evaluation of large language models (LLMs), two LLMs respond
to a user query, and the user chooses the winning response or deems the
"battle" a draw, resulting in an adjustment to the ratings of both models. The
prevailing approach for modeling these rating dynamics is to view battles..."
via Arxivđ¤ Mykyta Ielanskyi, Kajetan Schweighofer, Lukas Aichberger et al.đ 2025-10-02
⥠Score: 6.3
"Hallucinations are a common issue that undermine the reliability of large
language models (LLMs). Recent studies have identified a specific subset of
hallucinations, known as confabulations, which arise due to predictive
uncertainty of LLMs. To detect confabulations, various methods for estimating
p..."
via Arxivđ¤ Qin Shi, Amber Yijia Zheng, Qifan Song et al.đ 2025-10-02
⥠Score: 6.3
"We propose the task of knowledge distillation detection, which aims to
determine whether a student model has been distilled from a given teacher,
under a practical setting where only the student's weights and the teacher's
API are available. This problem is motivated by growing concerns about model..."
via Arxivđ¤ Yu-Chien Liao, Jr-Jen Chen, Chi-Pin Huang et al.đ 2025-10-02
⥠Score: 6.3
"Updating diffusion models in an incremental setting would be practical in
real-world applications yet computationally challenging. We present a novel
learning strategy of Concept Neuron Selection (CNS), a simple yet effective
approach to perform personalization in a continual learning scheme. CNS
un..."
via Arxivđ¤ Runqian Wang, Yilun Duđ 2025-10-02
⥠Score: 6.1
"We introduce Equilibrium Matching (EqM), a generative modeling framework
built from an equilibrium dynamics perspective. EqM discards the
non-equilibrium, time-conditional dynamics in traditional diffusion and
flow-based generative models and instead learns the equilibrium gradient of an
implicit en..."