π WELCOME TO METAMESH.BIZ +++ Claude's Excel plugin leaking data like a startup's cap table after Series A (enterprise security theater continues) +++ DeepSeek writing vulnerable code when you mention Taiwan because geopolitical censorship makes terrible debugging partners +++ AI-Newton discovering physics laws from scratch while human physicists still arguing about string theory funding +++ Jailbreaking LLMs with haikus because apparently models respect meter more than safety guardrails +++ WE'VE TAUGHT MACHINES TO DO SCIENCE BUT NOT TO RESIST POETRY +++ π β’
π WELCOME TO METAMESH.BIZ +++ Claude's Excel plugin leaking data like a startup's cap table after Series A (enterprise security theater continues) +++ DeepSeek writing vulnerable code when you mention Taiwan because geopolitical censorship makes terrible debugging partners +++ AI-Newton discovering physics laws from scratch while human physicists still arguing about string theory funding +++ Jailbreaking LLMs with haikus because apparently models respect meter more than safety guardrails +++ WE'VE TAUGHT MACHINES TO DO SCIENCE BUT NOT TO RESIST POETRY +++ π β’
+++ OpenAI's latest model can help researchers think faster, but the gap between "assistant" and "autonomous" remains as wide as the hype cycle, per their surprisingly honest assessment. +++
"A new report from OpenAI and a group of outside scientists shows how GPT-5, the companyβs latest AI large language model (LLM), canΒ help with researchΒ from black holes to cancerβfighting cells to math puzzles."
+++ Allen Institute drops another competent open-weight model that actually benchmarks well against Llama, proving the open-source tier keeps raising the floor while commercial labs nervously refresh their slides. +++
"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 β reflects on what worked/failed β curates strate..."
π¬ Reddit Discussion: 16 comments
π GOATED ENERGY
"So apparently weβve reached the stage of AI evolution where you donβt need elaborate prompt injections, roleplay, DAN modes, or Base64 sorcery to jailbreak a model.
All you need is⦠a rhyming stanza.
A new paper just dropped:
βAdversarial Poetry as a Universal Single-Turn Jailbreak Mechanism in La..."
via Arxivπ€ Jing Bi, Filippos Bellos, Junjia Guo et al.π 2025-11-19
β‘ 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 Arxivπ€ Priyanka Kargupta, Shuyue Stella Li, Haocheng Wang et al.π 2025-11-20
β‘ Score: 7.0
"Large language models solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reasoning. We synthesize cognitive science research into a taxonomy of 28 cognitive elements spanning computational constraints, me..."
π¨ CREATIVE
Meta Segment Anything Model 3
2x SOURCES ππ 2025-11-20
β‘ Score: 6.9
+++ Meta upgraded its visual foundation model to handle text prompts alongside traditional inputs, unifying image/video segmentation tasks. Reddit enthusiasm noted, skepticism about real-world performance pending. +++
"Metaβs 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..."
"Metaβs latest models in the Segment Anything family, SAM 3 and SAM 3D, introduce text based segmentation, faster processing, and early 3D reconstruction features.
We tested them across mixed scenarios to see how they actually behave outside controlled demos.
**Here is what we found across the..."
π― SAM 3D Objects performance β’ SAM usage restrictions β’ Accessing SAM model checkpoints
π¬ "We are running a few early tests on production style datasets and the text prompts feel much more stable than SAM 1 and 2"
β’ "am currently living in china"
via Arxivπ€ ΓloΓ―se Benito-Rodriguez, Einar Urdshals, Jasmina Nasufi et al.π 2025-11-20
β‘ Score: 6.9
"Understanding Large Language Models (LLMs) is key to ensure their safe and beneficial deployment. This task is complicated by the difficulty of interpretability of LLM structures, and the inability to have all their outputs human-evaluated. In this paper, we present the first step towards a predicti..."
via Arxivπ€ Kevin Qinghong Lin, Siyuan Hu, Linjie Li et al.π 2025-11-19
β‘ 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..."
"I just installed the MCP for letting Claude Code drive Chrome from https://github.com/ChromeDevTools/chrome-devtools-mcp. Now the dev loop is complete: Claude is porting my app for me, and for each piece of work fires it up in the browser, checks it works, checks the console logs for errors.
Even ..."
via Arxivπ€ Yushi Huang, Zining Wang, Zhihang Yuan et al.π 2025-11-19
β‘ 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 Arxivπ€ Irmak Guzey, Haozhi Qi, Julen Urain et al.π 2025-11-20
β‘ Score: 6.8
"Learning multi-fingered robot policies from humans performing daily tasks in natural environments has long been a grand goal in the robotics community. Achieving this would mark significant progress toward generalizable robot manipulation in human environments, as it would reduce the reliance on lab..."
via Arxivπ€ Elias Hossain, Md Mehedi Hasan Nipu, Maleeha Sheikh et al.π 2025-11-20
β‘ Score: 6.8
"We propose MedBayes-Lite, a lightweight Bayesian enhancement for transformer-based clinical language models designed to produce reliable, uncertainty-aware predictions. Although transformers show strong potential for clinical decision support, they remain prone to overconfidence, especially in ambig..."
via Arxivπ€ Xiaoshuai Hao, Lei Zhou, Zhijian Huang et al.π 2025-11-20
β‘ Score: 6.8
"We open-source MiMo-Embodied, the first cross-embodied foundation model to successfully integrate and achieve state-of-the-art performance in both Autonomous Driving and Embodied AI. MiMo-Embodied sets new records across 17 embodied AI benchmarks in Task Planning, Affordance Prediction and Spatial U..."
via Arxivπ€ Medha Kumar, Zifei Xu, Xin Wang et al.π 2025-11-19
β‘ Score: 6.8
"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..."
via Arxivπ€ Ali Taghibakhshi, Sharath Turuvekere Sreenivas, Saurav Muralidharan et al.π 2025-11-20
β‘ Score: 6.7
"Training a family of large language models targeting multiple scales and deployment objectives is prohibitively expensive, requiring separate training runs for each different size. Recent work on model compression through pruning and knowledge distillation has reduced this cost; however, this proces..."
"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 Arxivπ€ Qinghao Hu, Shang Yang, Junxian Guo et al.π 2025-11-20
β‘ Score: 6.7
"The emergence of Large Language Models (LLMs) with strong reasoning capabilities marks a significant milestone, unlocking new frontiers in complex problem-solving. However, training these reasoning models, typically using Reinforcement Learning (RL), encounters critical efficiency bottlenecks: respo..."
via Arxivπ€ Sirui Chen, Mengshi Zhao, Lei Xu et al.π 2025-11-19
β‘ 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 Arxivπ€ Michael McCabe, Payel Mukhopadhyay, Tanya Marwah et al.π 2025-11-19
β‘ Score: 6.7
"Foundation models have transformed machine learning for language and vision, but achieving comparable impact in physical simulation remains a challenge. Data heterogeneity and unstable long-term dynamics inhibit learning from sufficiently diverse dynamics, while varying resolutions and dimensionalit..."
via Arxivπ€ Yicheng He, Chengsong Huang, Zongxia Li et al.π 2025-11-19
β‘ 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..."
via Arxivπ€ Mateusz ChiliΕski, Julita OΕtusek, Wojciech JaΕkowskiπ 2025-11-20
β‘ Score: 6.6
"Arctic-Extract is a state-of-the-art model designed for extracting structural data (question answering, entities and tables) from scanned or digital-born business documents. Despite its SoTA capabilities, the model is deployable on resource-constrained hardware, weighting only 6.6 GiB, making it sui..."
via Arxivπ€ Yi Zhang, Che Liu, Xiancong Ren et al.π 2025-11-20
β‘ Score: 6.6
"Developing a universal and versatile embodied intelligence system presents two primary challenges: the critical embodied data bottleneck, where real-world data is scarce and expensive, and the algorithmic inefficiency of existing methods, which are resource-prohibitive. To address these limitations,..."
via Arxivπ€ Sen Chen, Tong Zhao, Yi Bin et al.π 2025-11-20
β‘ Score: 6.4
"Developing intelligent agents capable of operating a wide range of Graphical User Interfaces (GUIs) with human-level proficiency is a key milestone on the path toward Artificial General Intelligence. While most existing datasets and benchmarks for training and evaluating GUI agents are static and id..."
"It's called OCR Arena, you can try it here: https://ocrarena.ai
There's so many new OCR models coming out all the time, but testing them is really painful. I wanted to give the community an easy way to compare leading foundation VLMs and open source OCR models side-by-side. You can upload any doc, ..."
π¬ Reddit Discussion: 47 comments
π BUZZING
π― OCR model comparison β’ OCR model performance β’ OCR model costs
π¬ "Wow, Gemini costs $3 and has an 82% win rate, and GPT-5.1 only costs $1 and has a 77% win rate."
β’ "Gemini 3 is really strong, but very expensive + slow which doesn't make it great for a lot of use cases compared to Paddle or dots.ocr"
+++ Meta's new model reconstructs full 3D geometry and texture from single images, trained on unprecedented scale of annotated data. Finally, a use case for all those pictures gathering dust in your phone. +++
via Arxivπ€ SAM 3D Team, Xingyu Chen, Fu-Jen Chu et al.π 2025-11-20
β‘ Score: 6.1
"We present SAM 3D, a generative model for visually grounded 3D object reconstruction, predicting geometry, texture, and layout from a single image. SAM 3D excels in natural images, where occlusion and scene clutter are common and visual recognition cues from context play a larger role. We achieve th..."
via Arxivπ€ Ziyu Guo, Renrui Zhang, Hongyu Li et al.π 2025-11-20
β‘ Score: 6.1
"Recent advances in visual generation have increasingly explored the integration of reasoning capabilities. They incorporate textual reasoning, i.e., think, either before (as pre-planning) or after (as post-refinement) the generation process, yet they lack on-the-fly multimodal interaction during the..."