Tianyi Zhou

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commented a paper 28 days ago

Routing Manifold Alignment Improves Generalization of Mixture-of-Experts LLMs

Paper • 2511.07419 • Published 29 days ago • 25 •

2

commented a paper about 1 month ago

ChartAB: A Benchmark for Chart Grounding & Dense Alignment

Paper • 2510.26781 • Published Oct 30 •

1

commented 7 papers 5 months ago

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs

Paper • 2507.07996 • Published Jul 10 • 34 •

14

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs

Paper • 2507.07996 • Published Jul 10 • 34 •

14

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs

Paper • 2507.07996 • Published Jul 10 • 34 •

14

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs

Paper • 2507.07996 • Published Jul 10 • 34 •

14

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs

Paper • 2507.07996 • Published Jul 10 • 34 •

14

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs

Paper • 2507.07996 • Published Jul 10 • 34 •

14

Where to find Grokking in LLM Pretraining? Monitor Memorization-to-Generalization without Test

Paper • 2506.21551 • Published Jun 26 • 28 •

2

commented 4 papers 6 months ago

FaSTA$^*$: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient Multi-turn Image Editing

Paper • 2506.20911 • Published Jun 26 • 41 •

2

FaSTA$^*$: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient Multi-turn Image Editing

Paper • 2506.20911 • Published Jun 26 • 41 •

2

Where to find Grokking in LLM Pretraining? Monitor Memorization-to-Generalization without Test

Paper • 2506.21551 • Published Jun 26 • 28 •

2

Optimizing Length Compression in Large Reasoning Models

Paper • 2506.14755 • Published Jun 17 • 10 •

2

commented 7 papers 8 months ago

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents

Paper • 2504.15785 • Published Apr 22 • 22 •

4

WALL-E 2.0: World Alignment by NeuroSymbolic Learning improves World Model-based LLM Agents

Paper • 2504.15785 • Published Apr 22 • 22 •

4

Exploring Expert Failures Improves LLM Agent Tuning

Paper • 2504.13145 • Published Apr 17 • 12 •

4

Exploring Expert Failures Improves LLM Agent Tuning

Paper • 2504.13145 • Published Apr 17 • 12 •

4

ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness

Paper • 2504.10514 • Published Apr 10 • 48 •

4

ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness

Paper • 2504.10514 • Published Apr 10 • 48 •

4

ColorBench: Can VLMs See and Understand the Colorful World? A Comprehensive Benchmark for Color Perception, Reasoning, and Robustness

Paper • 2504.10514 • Published Apr 10 • 48 •

4