OS-Genesis
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3 items
•
Updated
OS-Copilot
community
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Recent Activity
OS-Copilot's activity
QiushiSun
updated
a
collection
about 21 hours ago
zy001
authored
a
paper
17 days ago
Symbol-LLM
posted
an
update
about 1 month ago
Post
947
🥳 Thrilled to introduce our recent efforts on bootstrapping VLMs for multi-modal chain-of-thought reasoning !
📕 Title: Vision-Language Models Can Self-Improve Reasoning via Reflection
🔗 Link: Vision-Language Models Can Self-Improve Reasoning via Reflection (2411.00855)
😇Takeaways:
- We found that VLMs can self-improve reasoning performance through a reflection mechanism, and importantly, this approach can scale through test-time computing.
- Evaluation on comprehensive and diverse Vision-Language reasoning tasks are included !
📕 Title: Vision-Language Models Can Self-Improve Reasoning via Reflection
🔗 Link: Vision-Language Models Can Self-Improve Reasoning via Reflection (2411.00855)
😇Takeaways:
- We found that VLMs can self-improve reasoning performance through a reflection mechanism, and importantly, this approach can scale through test-time computing.
- Evaluation on comprehensive and diverse Vision-Language reasoning tasks are included !
Symbol-LLM
posted
an
update
about 2 months ago
Post
2151
🚀 Excited to introduce a new member of the OS-Copilot family: OS-Atlas - an open-sourced foundational action model for GUI agents
📘 Paper: OS-ATLAS: A Foundation Action Model for Generalist GUI Agents (2410.23218)
🔗 Website: https://osatlas.github.io
😇 TL;DR: OS-Atlas offers:
1. State-of-the-Art GUI Grounding: Helps GUI agents accurately locate GUI elements.
2. Strong OOD Performance and Cross-platform Compatibility: Excels in out-of-domain agentic tasks across MacOS, Windows, Linux, Android, and Web.
3. Complete Infrastructure for GUI Data Synthesis:
You can easily build your own OS agent upon it!
📘 Paper: OS-ATLAS: A Foundation Action Model for Generalist GUI Agents (2410.23218)
🔗 Website: https://osatlas.github.io
😇 TL;DR: OS-Atlas offers:
1. State-of-the-Art GUI Grounding: Helps GUI agents accurately locate GUI elements.
2. Strong OOD Performance and Cross-platform Compatibility: Excels in out-of-domain agentic tasks across MacOS, Windows, Linux, Android, and Web.
3. Complete Infrastructure for GUI Data Synthesis:
You can easily build your own OS agent upon it!
cckevinn
authored
a
paper
about 2 months ago
xufangzhi
authored
a
paper
about 2 months ago
zy001
authored
a
paper
about 2 months ago
QiushiSun
authored
2
papers
about 2 months ago
zy001
authored
2
papers
3 months ago
zy001
authored
2
papers
4 months ago
Symbol-LLM
posted
an
update
5 months ago
Post
2119
🔥Thrilled to release our 8B version of Symbol-LLM-Instruct !
It follows the two-stage training strategy proposed in the original paper and is continually optimized on LLaMA3-Chat-8B model.
Symbol-LLM was accepted by ACL'24 main conference ! See you in Thailand !
Paper link: https://arxiv.org/abs/2311.09278
Paper Title: Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
It follows the two-stage training strategy proposed in the original paper and is continually optimized on LLaMA3-Chat-8B model.
Symbol-LLM was accepted by ACL'24 main conference ! See you in Thailand !
Paper link: https://arxiv.org/abs/2311.09278
Paper Title: Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
- 1 reply
QiushiSun
authored
3
papers
5 months ago
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills
Paper
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2306.07285
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Published
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2
Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models
Paper
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2405.12939
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Published
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1
Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models
Paper
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2406.11736
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Published
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5
Symbol-LLM
posted
an
update
6 months ago
Post
1914
📍Excited to make public a series of checkpoints !
- Final checkpoints after self-training with ENVISIONS framework
- Cover math, logic, and agent domains
- Include 7B / 13B
📕 Check our paper:
Title: Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models
Link: https://arxiv.org/abs/2406.11736
- Final checkpoints after self-training with ENVISIONS framework
- Cover math, logic, and agent domains
- Include 7B / 13B
📕 Check our paper:
Title: Interactive Evolution: A Neural-Symbolic Self-Training Framework For Large Language Models
Link: https://arxiv.org/abs/2406.11736
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- 2 replies