-
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 • 25
Kush
Stalin16
AI & ML interests
None yet
Recent Activity
updated a collection 14 days ago
Reasoning Models updated a collection 17 days ago
T2I Models updated a collection 23 days ago
Agents Organizations
None yet
-
GUI-G^2: Gaussian Reward Modeling for GUI Grounding
Paper • 2507.15846 • Published • 135 -
WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent
Paper • 2508.05748 • Published • 142 -
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 • 162
-
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 • 25
-
GUI-G^2: Gaussian Reward Modeling for GUI Grounding
Paper • 2507.15846 • Published • 135 -
WebWatcher: Breaking New Frontier of Vision-Language Deep Research Agent
Paper • 2508.05748 • Published • 142 -
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 • 162
datasets 0
None public yet