BOOM! I Got a 4x AI Speed Improvement!
NEw Paper: AutoMem Turns Memory Management into a Trainable Cognitive Skill, Boosting Long-Horizon Agents 2-4x
arxiv.org/abs/2607.01224
@Machine_learn
NEw Paper: AutoMem Turns Memory Management into a Trainable Cognitive Skill, Boosting Long-Horizon Agents 2-4x
arxiv.org/abs/2607.01224
@Machine_learn
❤3
Dataset Name: Real / Fake Job Posting Prediction
Basic Description: Dataset of real and fake job postings
📖 FULL DATASET DESCRIPTION:
==================================
This dataset contains 18K job descriptions out of which about 800 are fake. The data consists of both textual information and meta-information about the jobs. The dataset can be used to create classification models which can learn the job descriptions which are fraudulent.
The University of the Aegean | Laboratory of Information & Communication Systems Security http://emscad.samos.aegean.gr/
The dataset is very valuable as it can be used to answer the following questions:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (17 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/shivamb/real-or-fake-fake-jobposting-prediction
📊 Additional information:
==================================
File count not found
Views: 341,000
Downloads: 41,400
@Machine_learn
Basic Description: Dataset of real and fake job postings
📖 FULL DATASET DESCRIPTION:
==================================
This dataset contains 18K job descriptions out of which about 800 are fake. The data consists of both textual information and meta-information about the jobs. The dataset can be used to create classification models which can learn the job descriptions which are fraudulent.
The University of the Aegean | Laboratory of Information & Communication Systems Security http://emscad.samos.aegean.gr/
The dataset is very valuable as it can be used to answer the following questions:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (17 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/shivamb/real-or-fake-fake-jobposting-prediction
📊 Additional information:
==================================
File count not found
Views: 341,000
Downloads: 41,400
@Machine_learn
❤2
🔥 MemGUI-Agent: An End-to-End Long-Horizon Mobile GUI Agent with Proactive Context Management
💡 The paper introduces MemGUI-Agent, a mobile GUI agent designed to address the limitations of existing agents on long-horizon tasks. Current agents struggle with retaining intermediate facts across many steps and app transitions, leading to unreliable performance. This limitation is attributed to the ReAct-style prompting approach, which passively accumulates per-step records, causing prompt explosion and dilution of critical cross-app facts.
To address this issue, the authors propose MemGUI-Agent, which uses proactive context management through Context-as-Action, or ConAct. ConAct casts context management as first-class actions emitted by the same policy that selects UI actions. This approach maintains three structured context fields: folded action history, folded UI state, and recent step record, preserving critical UI facts while keeping context compact.
The authors also introduce MemGUI-3K, a dataset with 2,956 trajectories and full ConAct annotations for supervised training and offline analysis. Training an 8B model on MemGUI-3K results in MemGUI-8B-SFT, an 8B MemGUI-Agent that achieves the best open-data 8B performance on MemGUI-Bench and generalizes to the out-of-distribution MobileWorld benchmark.
The contributions of the paper are threefold. Firstly, it identifies the limitations of existing mobile GUI agents on long-horizon tasks and attributes them to the ReAct-style prompting approach. Secondly, it proposes MemGUI-Agent with proactive context management through ConAct, which addresses the limitations of existing agents. Finally, it introduces MemGUI-3K, a dataset for supervised training and offline analysis, and demonstrates the effectiveness of MemGUI-8B-SFT, an 8B MemGUI-Agent trained on this dataset. The code, data, and trained models will be released to facilitate further research and development.
📅 Published on Jun 18
🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.19926
• PDF: https://arxiv.org/pdf/2606.19926
• Project Page: https://memgui-agent.github.io/
🤖 Models citing this paper:
• https://huggingface.co/lgy0404/MemGUI-8B-SFT
📊 Datasets citing this paper:
• https://huggingface.co/datasets/lgy0404/MemGUI-3K
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@Machine_learn
💡 The paper introduces MemGUI-Agent, a mobile GUI agent designed to address the limitations of existing agents on long-horizon tasks. Current agents struggle with retaining intermediate facts across many steps and app transitions, leading to unreliable performance. This limitation is attributed to the ReAct-style prompting approach, which passively accumulates per-step records, causing prompt explosion and dilution of critical cross-app facts.
To address this issue, the authors propose MemGUI-Agent, which uses proactive context management through Context-as-Action, or ConAct. ConAct casts context management as first-class actions emitted by the same policy that selects UI actions. This approach maintains three structured context fields: folded action history, folded UI state, and recent step record, preserving critical UI facts while keeping context compact.
The authors also introduce MemGUI-3K, a dataset with 2,956 trajectories and full ConAct annotations for supervised training and offline analysis. Training an 8B model on MemGUI-3K results in MemGUI-8B-SFT, an 8B MemGUI-Agent that achieves the best open-data 8B performance on MemGUI-Bench and generalizes to the out-of-distribution MobileWorld benchmark.
The contributions of the paper are threefold. Firstly, it identifies the limitations of existing mobile GUI agents on long-horizon tasks and attributes them to the ReAct-style prompting approach. Secondly, it proposes MemGUI-Agent with proactive context management through ConAct, which addresses the limitations of existing agents. Finally, it introduces MemGUI-3K, a dataset for supervised training and offline analysis, and demonstrates the effectiveness of MemGUI-8B-SFT, an 8B MemGUI-Agent trained on this dataset. The code, data, and trained models will be released to facilitate further research and development.
📅 Published on Jun 18
🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.19926
• PDF: https://arxiv.org/pdf/2606.19926
• Project Page: https://memgui-agent.github.io/
🤖 Models citing this paper:
• https://huggingface.co/lgy0404/MemGUI-8B-SFT
📊 Datasets citing this paper:
• https://huggingface.co/datasets/lgy0404/MemGUI-3K
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@Machine_learn
GitHub
Hugging Face
The AI community building the future. Hugging Face has 458 repositories available. Follow their code on GitHub.
❤3
با عرض سلام اکانت @Raminmousa به دلیل جابجایی در زمان لاگین تلگرام تعلیق شده و در تلاش برای رفع این مشکلیم. دوستانی که باهام پروژه دارن لطفا با این ایدیم در ارتباط باشن
@Raminmousa1
@Raminmousa1
❤4
Machine learning books and papers pinned «با عرض سلام اکانت @Raminmousa به دلیل جابجایی در زمان لاگین تلگرام تعلیق شده و در تلاش برای رفع این مشکلیم. دوستانی که باهام پروژه دارن لطفا با این ایدیم در ارتباط باشن @Raminmousa1»
🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨
We found this interactive website that shows you visually how transformer models work. 🌐📊
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
@Machine_learn
We found this interactive website that shows you visually how transformer models work. 🌐📊
Transformer Explainer:
https://poloclub.github.io/transformer-explainer/
@Machine_learn
poloclub.github.io
Transformer Explainer: LLM Transformer Model Visually Explained
An interactive visualization tool showing you how transformer models work in large language models (LLM) like GPT.
❤2👍1
Forwarded from Machine learning books and papers
Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models
Read
@Machine_learn
Read
@Machine_learn
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