๐ WELCOME TO METAMESH.BIZ +++ Agentic systems cracking ARC-AGI while teen mental health chatbots can't crack basic warning signs (therapeutic breakthrough pending) +++ Stanford's ACE framework proves your local LLM can match GPT-4 if you just let it learn from its mistakes like a proper intern +++ White House drafting orders to sue states for AI regulation because federal preemption is the new federalism +++ Allen Institute's Olmo 3 joining the "we're better than Llama" support group while Meta ships SAM 3 for when you need AI to know where your cat ends and your couch begins +++ THE MACHINES ARE LEARNING TO LEARN WHILE WE'RE STILL LEARNING TO REGULATE +++ ๐ โข
๐ WELCOME TO METAMESH.BIZ +++ Agentic systems cracking ARC-AGI while teen mental health chatbots can't crack basic warning signs (therapeutic breakthrough pending) +++ Stanford's ACE framework proves your local LLM can match GPT-4 if you just let it learn from its mistakes like a proper intern +++ White House drafting orders to sue states for AI regulation because federal preemption is the new federalism +++ Allen Institute's Olmo 3 joining the "we're better than Llama" support group while Meta ships SAM 3 for when you need AI to know where your cat ends and your couch begins +++ THE MACHINES ARE LEARNING TO LEARN WHILE WE'RE STILL LEARNING TO REGULATE +++ ๐ โข
+++ Meta upgraded Segment Anything from "click pixels" to "describe what you want" across images and video, proving that foundation models work better when you stop making users think like programmers. +++
๐ฌ "This feels like a seminal moment for computer vision."
โข "It feels really magical to go from an unlabeled video to a fine-tuned realtime segmentation model with minimal human intervention in just a few minutes."
"**Abstract**: *We present Segment Anything Model (SAM) 3, a unified model that detects, segments, and tracks objects in images and videos based on concept prompts, which we define as either short noun phrases (e.g., โyellow school busโ), image exemplars, or a combination of both. Promptable Concept ..."
๐ฌ Reddit Discussion: 19 comments
๐ BUZZING
๐ฏ Model Evolution โข Prompting Capabilities โข Tracking Performance
๐ฌ "It's a shame that Meta laid off some of the people on this team."
โข "Insane how fast SAM is evolving."
๐ฏ Rapid prototyping and distillation โข Transformative potential of SAM3 โข Challenges of deploying SAM3
๐ฌ "This feels like a seminal moment for computer vision."
โข "You can use the big, powerful, expensive SAM3 model to create a dataset to train the small, fast, cheap RF-DETR model."
"Metaโs Segment Anything Model 3 (SAM 3) is a 848M parameter vision foundation model that upgrades Segment Anything from promptable visual segmentation to Promptable Concept Segmentation, unifying image and video detection, segmentation and tracking from text prompts, exemplars, points and boxes. Tra..."
"I implemented Stanford's Agentic Context Engineering paper. The framework makes agents learn from their own execution feedback through in-context learning instead of fine-tuning.
**How it works:**
Agent runs task โ reflects on what worked/failed โ curates strate..."
via Arxiv๐ค Keya Hu, Ali Cy, Linlu Qiu et al.๐ 2025-11-18
โก Score: 7.0
"The Abstraction and Reasoning Corpus (ARC) is designed to promote research on abstract reasoning, a fundamental aspect of human intelligence. Common approaches to ARC treat it as a language-oriented problem, addressed by large language models (LLMs) or recurrent reasoning models. However, although t..."
via Arxiv๐ค Jing Bi, Filippos Bellos, Junjia Guo et al.๐ 2025-11-19
โก Score: 7.0
"Test-time thinking (that is, generating explicit intermediate reasoning chains) is known to boost performance in large language models and has recently shown strong gains for large vision language models (LVLMs). However, despite these promising results, there is still no systematic analysis of how..."
via Arxiv๐ค Medha Kumar, Zifei Xu, Xin Wang et al.๐ 2025-11-19
โก Score: 6.9
"Strong reasoning capabilities can now be achieved by large-scale reinforcement learning (RL) without any supervised fine-tuning. Although post-training quantization (PTQ) and quantization-aware training (QAT) are well studied in the context of fine-tuning, how quantization impacts RL in large reason..."
"I implemented the code execution mode that Anthropic talked about in a recent blog post. Here is how it works.
Basically I build a docker container with Claude code and a configured MCP server inside it. I had Claude create a wrapper.py script that essentially accepts TCP or http connection and us..."
via Arxiv๐ค Kevin Qinghong Lin, Siyuan Hu, Linjie Li et al.๐ 2025-11-19
โก Score: 6.9
"Computer-Use Agents (CUA) are becoming increasingly capable of autonomously operating digital environments through Graphical User Interfaces (GUI). Yet, most GUI remain designed primarily for humans--prioritizing aesthetics and usability--forcing agents to adopt human-oriented behaviors that are unn..."
via Arxiv๐ค Yushi Huang, Zining Wang, Zhihang Yuan et al.๐ 2025-11-19
โก Score: 6.8
"Mixture-of-Experts (MoE) Multimodal large language models (MLLMs) excel at vision-language tasks, but they suffer from high computational inefficiency. To reduce inference overhead, expert skipping methods have been proposed to deactivate redundant experts based on the current input tokens. However,..."
via Arxiv๐ค Tao Yang, Dandan Huang, Yunting Lin et al.๐ 2025-11-18
โก Score: 6.8
"Rare diseases affect hundreds of millions worldwide, yet diagnosis often spans years. Convectional pipelines decouple noisy evidence extraction from downstream inferential diagnosis, and general/medical large language models (LLMs) face scarce real world electronic health records (EHRs), stale domai..."
via Arxiv๐ค Alexis Audran-Reiss, Jordi Armengol Estapรฉ, Karen Hambardzumyan et al.๐ 2025-11-19
โก Score: 6.7
"AI research agents offer the promise to accelerate scientific progress by automating the design, implementation, and training of machine learning models. However, the field is still in its infancy, and the key factors driving the success or failure of agent trajectories are not fully understood. We..."
via Arxiv๐ค Ali Amin, Raichelle Aniceto, Ashwin Balakrishna et al.๐ 2025-11-18
โก Score: 6.7
"We study how vision-language-action (VLA) models can improve through real-world deployments via reinforcement learning (RL). We present a general-purpose method, RL with Experience and Corrections via Advantage-conditioned Policies (RECAP), that provides for RL training of VLAs via advantage conditi..."
via Arxiv๐ค Sirui Chen, Mengshi Zhao, Lei Xu et al.๐ 2025-11-19
โก Score: 6.7
"Recent advances in large language models (LLMs) have greatly improved their reasoning and decision-making abilities when deployed as agents. Richer reasoning, however, often comes at the cost of longer chain of thought (CoT), hampering interaction efficiency in real-world scenarios. Nevertheless, th..."
via Arxiv๐ค Yicheng He, Chengsong Huang, Zongxia Li et al.๐ 2025-11-19
โก Score: 6.6
"Reinforcement learning (RL) provides a principled framework for improving Vision-Language Models (VLMs) on complex reasoning tasks. However, existing RL approaches often rely on human-annotated labels or task-specific heuristics to define verifiable rewards, both of which are costly and difficult to..."
๐ฏ AI model capabilities โข Challenges with AI code generation โข Comparison of Codex and Claude
๐ฌ "Codex is extremely, painfully, doggedly persistent in following every last character of them"
โข "Hallucinations and ignored requirements are big problems that are very annoying to deal with"
via Arxiv๐ค Chia-Yu Hung, Navonil Majumder, Haoyuan Deng et al.๐ 2025-11-18
โก Score: 6.1
"Vision--language--action (VLA) models have recently shown promising performance on a variety of embodied tasks, yet they still fall short in reliability and generalization, especially when deployed across different embodiments or real-world environments. In this work, we introduce NORA-1.5, a VLA mo..."