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Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learning
Paper • 2507.17512 • Published • 37 -
Too Good to be Bad: On the Failure of LLMs to Role-Play Villains
Paper • 2511.04962 • Published • 57 -
10 Open Challenges Steering the Future of Vision-Language-Action Models
Paper • 2511.05936 • Published • 6 -
How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities
Paper • 2603.02578 • Published • 17
Kush
Stalin16
AI & ML interests
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Recent Activity
updated
a collection
about 6 hours ago
Model Evaluation updated
a collection
about 6 hours ago
Edu updated
a collection
1 day ago
T2I Models Organizations
None yet
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GUI-G^2: Gaussian Reward Modeling for GUI Grounding
Paper • 2507.15846 • Published • 133 -
WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent
Paper • 2508.05748 • Published • 141 -
Mobile-Agent-v3: Foundamental Agents for GUI Automation
Paper • 2508.15144 • Published • 65 -
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 160
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Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learning
Paper • 2507.17512 • Published • 37 -
Too Good to be Bad: On the Failure of LLMs to Role-Play Villains
Paper • 2511.04962 • Published • 57 -
10 Open Challenges Steering the Future of Vision-Language-Action Models
Paper • 2511.05936 • Published • 6 -
How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities
Paper • 2603.02578 • Published • 17
-
GUI-G^2: Gaussian Reward Modeling for GUI Grounding
Paper • 2507.15846 • Published • 133 -
WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent
Paper • 2508.05748 • Published • 141 -
Mobile-Agent-v3: Foundamental Agents for GUI Automation
Paper • 2508.15144 • Published • 65 -
AgentFly: Fine-tuning LLM Agents without Fine-tuning LLMs
Paper • 2508.16153 • Published • 160
datasets 0
None public yet