đ WELCOME TO METAMESH.BIZ +++ OpenAI taking a 10% stake in AMD for 6GW of Instinct GPUs because apparently NVIDIA needs competition anxiety too +++ Anthropic drops Sonnet 4.5 and Claude Code 2.0 while OpenAI counters with GPT-5 Pro and Sora 2 (the model arms race continues unabated) +++ Musk burning $18B on 300K more chips for Colossus 2 because why build one massive cluster when you can build two +++ THE FUTURE IS VERTICALLY INTEGRATED AND HORIZONTALLY DESPERATE +++ đ âĸ
đ WELCOME TO METAMESH.BIZ +++ OpenAI taking a 10% stake in AMD for 6GW of Instinct GPUs because apparently NVIDIA needs competition anxiety too +++ Anthropic drops Sonnet 4.5 and Claude Code 2.0 while OpenAI counters with GPT-5 Pro and Sora 2 (the model arms race continues unabated) +++ Musk burning $18B on 300K more chips for Colossus 2 because why build one massive cluster when you can build two +++ THE FUTURE IS VERTICALLY INTEGRATED AND HORIZONTALLY DESPERATE +++ đ âĸ
đŦ "Every deal increases the perceived valuation, which then becomes collateral for the next one."
âĸ "If they are able to shut out Google/X.AI from the market, there really aren't any viable firms to keep financing next generation models on a pure compute scaling basis."
+++ Claude gets a major upgrade with Sonnet 4.5 and enhanced coding abilities that let it actually build and run apps, not just suggest code snippets. +++
"We're covering everything new with Claude for developers, including the launch of Claude Sonnet 4.5, major updates to Claude Code, powerful new API capabilities, and exciting features in the Claude app.
Helpful Resources:
* Claude Developer Discord - [https://anthropic.com/discord](https://anthro..."
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¤ Tianyi Jiang, Yi Bin, Yujuan Ding et al.đ 2025-10-02
⥠Score: 8.0
"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..."
đĄ AI NEWS BUT ACTUALLY GOOD
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via Arxivđ¤ Tianyu Fu, Zihan Min, Hanling Zhang et al.đ 2025-10-03
⥠Score: 7.8
"Multi-LLM systems harness the complementary strengths of diverse Large
Language Models, achieving performance and efficiency gains unattainable by a
single model. In existing designs, LLMs communicate through text, forcing
internal representations to be transformed into output token sequences. This..."
via Arxivđ¤ Yuxiao Qu, Anikait Singh, Yoonho Lee et al.đ 2025-10-02
⥠Score: 7.7
"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¤ Ej Zhou, Caiqi Zhang, Tiancheng Hu et al.đ 2025-10-03
⥠Score: 7.7
"Confidence calibration, the alignment of a model's predicted confidence with
its actual accuracy, is crucial for the reliable deployment of Large Language
Models (LLMs). However, this critical property remains largely under-explored
in multilingual contexts. In this work, we conduct the first large-..."
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..."
via Arxivđ¤ Imene Kerboua, Sahar Omidi Shayegan, Megh Thakkar et al.đ 2025-10-03
⥠Score: 7.6
"Web agents powered by large language models (LLMs) must process lengthy web
page observations to complete user goals; these pages often exceed tens of
thousands of tokens. This saturates context limits and increases computational
cost processing; moreover, processing full pages exposes agents to sec..."
via Arxivđ¤ JosÊ Cambronero, Michele Tufano, Sherry Shi et al.đ 2025-10-03
⥠Score: 7.5
"Agentic Automated Program Repair (APR) is increasingly tackling complex,
repository-level bugs in industry, but ultimately agent-generated patches still
need to be reviewed by a human before committing them to ensure they address
the bug. Showing unlikely patches to developers can lead to substantia..."
via Arxivđ¤ Qiwei Di, Kaixuan Ji, Xuheng Li et al.đ 2025-10-03
⥠Score: 7.1
"LLM inference often generates a batch of candidates for a prompt and selects
one via strategies like majority voting or Best-of- N (BoN). For difficult
tasks, this single-shot selection often underperforms. Consequently,
evaluations commonly report Pass@$k$: the agent may submit up to $k$ responses,..."
via Arxivđ¤ Hongxiang Zhang, Yuan Tian, Tianyi Zhangđ 2025-10-03
⥠Score: 7.1
"To solve complex reasoning tasks for Large Language Models (LLMs),
prompting-based methods offer a lightweight alternative to fine-tuning and
reinforcement learning. However, as reasoning chains extend, critical
intermediate steps and the original prompt will be buried in the context,
receiving insu..."
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..."
via Arxivđ¤ Yilun Hao, Yongchao Chen, Chuchu Fan et al.đ 2025-10-03
⥠Score: 7.0
"Vision Language Models (VLMs) show strong potential for visual planning but
struggle with precise spatial and long-horizon reasoning. In contrast, Planning
Domain Definition Language (PDDL) planners excel at long-horizon formal
planning, but cannot interpret visual inputs. Recent works combine these..."
đ¯ Energy consumption of AI âĸ Environmental impact of AI âĸ Potential AI bubble burst
đŦ "the energy used to extract raw materials, manufacture chips and components, and construct facilities is substantial"
âĸ "Compute has an expiration date like old milk. It won't physically expire but the potential economic potential decreases as tech increases"
"The emergence of reinforcement learning in post-training of large language
models has sparked significant interest in reward models. Reward models assess
the quality of sampled model outputs to generate training signals. This task is
also performed by evaluation metrics that monitor the performance..."
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¤ Guanhua Huang, Tingqiang Xu, Mingze Wang et al.đ 2025-10-03
⥠Score: 6.8
"Reinforcement Learning with Verifiable Rewards (RLVR) has propelled Large
Language Models in complex reasoning, yet its scalability is often hindered by
a training bottleneck where performance plateaus as policy entropy collapses,
signaling a loss of exploration. Previous methods typically address t..."
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..."
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¤ Runzhe Zhan, Yafu Li, Zhi Wang et al.đ 2025-10-02
⥠Score: 6.8
"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¤ Ziyin Zhang, Zihan Liao, Hang Yu et al.đ 2025-10-02
⥠Score: 6.8
"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¤ Phuc Minh Nguyen, Chinh D. La, Duy M. H. Nguyen et al.đ 2025-10-02
⥠Score: 6.7
"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¤ Suyuchen Wang, Tianyu Zhang, Ahmed Masry et al.đ 2025-10-03
⥠Score: 6.7
"GUI grounding, the task of mapping natural-language instructions to pixel
coordinates, is crucial for autonomous agents, yet remains difficult for
current VLMs. The core bottleneck is reliable patch-to-pixel mapping, which
breaks when extrapolating to high-resolution displays unseen during training...."
via Arxivđ¤ Cuong Chi Le, Minh V. T. Pham, Cuong Duc Van et al.đ 2025-10-03
⥠Score: 6.6
"Large Language Models (LLMs) achieve strong results on code tasks, but how
they derive program meaning remains unclear. We argue that code communicates
through two channels: structural semantics, which define formal behavior, and
human-interpretable naming, which conveys intent. Removing the naming..."
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¤ Mykyta Ielanskyi, Kajetan Schweighofer, Lukas Aichberger et al.đ 2025-10-02
⥠Score: 6.6
"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..."
"Hello again, I've been testing more models on FamilyBench, my benchmark that tests LLM ability to understand complex tree-like relationships in a family tree across a massive context. For those who missed the initial post: this is a Python program that generates a family tree and uses its structure ..."
via Arxivđ¤ Katherine Thai, Bradley Emi, Elyas Masrour et al.đ 2025-10-03
⥠Score: 6.5
"A significant proportion of queries to large language models ask them to edit
user-provided text, rather than generate new text from scratch. While previous
work focuses on detecting fully AI-generated text, we demonstrate that
AI-edited text is distinguishable from human-written and AI-generated te..."
"Tried llama.cpp with 2 models(3 quants) & here results. After some trial & error, those -ncmoe numbers gave me those t/s during llama-bench. But t/s is somewhat smaller during llama-server, since I put 32K context.
I'm 99% sure, below full llama-server commands are not optimized ones. Even..."
đ¯ SHAP Maintenance âĸ Explainer Performance âĸ Community Involvement
đŦ "I guess you are part of that new team that re-ignited maintenance?"
âĸ "People interested in contributing could appreciate knowing where to start."
via Arxivđ¤ Hima Jacob Leven Suprabha, Laxmi Nag Laxminarayan Nagesh, Ajith Nair et al.đ 2025-10-03
⥠Score: 6.5
"The integration of Large Language Models (LLMs) into multiagent systems has
opened new possibilities for collaborative reasoning and cooperation with AI
agents. This paper explores different prompting methods and evaluates their
effectiveness in enhancing agent collaborative behaviour and decision-m..."
via Arxivđ¤ Cai Zhou, Chenxiao Yang, Yi Hu et al.đ 2025-10-03
⥠Score: 6.5
"Diffusion language models, especially masked discrete diffusion models, have
achieved great success recently. While there are some theoretical and primary
empirical results showing the advantages of latent reasoning with looped
transformers or continuous chain-of-thoughts, continuous diffusion model..."
via Arxivđ¤ Zichen Chen, Jiefeng Chen, Sercan Ã. Arik et al.đ 2025-10-03
⥠Score: 6.4
"Deep research has revolutionized data analysis, yet data scientists still
devote substantial time to manually crafting visualizations, highlighting the
need for robust automation from natural language queries. However, current
systems struggle with complex datasets containing multiple files and iter..."
via Arxivđ¤ Dong Lao, Yuxiang Zhang, Haniyeh Ehsani Oskouie et al.đ 2025-10-03
⥠Score: 6.3
"We propose a test-time defense mechanism against adversarial attacks:
imperceptible image perturbations that significantly alter the predictions of a
model. Unlike existing methods that rely on feature filtering or smoothing,
which can lead to information loss, we propose to "combat noise with noise..."
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¤ Linh The Nguyen, Chi Tran, Dung Ngoc Nguyen et al.đ 2025-10-02
⥠Score: 6.3
"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¤ 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¤ 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¤ Qing Huang, Zhipei Xu, Xuanyu Zhang et al.đ 2025-10-03
⥠Score: 6.1
"With the rapid advancements in image generation, synthetic images have become
increasingly realistic, posing significant societal risks, such as
misinformation and fraud. Forgery Image Detection and Localization (FIDL) thus
emerges as essential for maintaining information integrity and societal
secu..."
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..."