1 citations · 1 across the 8 of their papers we have counts for
9 papers
Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
Wei-Lin Chen, Liqian Peng, Tian Tan +5
Large language models (LLMs) have demonstrated impressive reasoning capabilities by scaling test-time compute via long Chain-of-Thought (CoT). However, recent findings suggest that…
MathlibLemma: Folklore Lemma Generation and Benchmark for Formal Mathematics
Xinyu Liu, Zixuan Xie, Amir Moeini +5
While the ecosystem of Lean and Mathlib has enjoyed celebrated success in formal mathematical reasoning with the help of large language models (LLMs), the absence of many folklore…
SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature
Yiming Ren, Junjie Wang, Yuxin Meng +11
Evaluating whether multimodal large language models truly understand long-form scientific papers remains challenging: answer-only metrics and synthetic "Needle-In-A-Haystack" tests…
RAST: Reasoning Activation in LLMs via Small-model Transfer
Siru Ouyang, Xinyu Zhu, Zilin Xiao +3
Reinforcement learning (RL) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs), as evidenced by recent successes such as OpenAI…
ProxyThinker: Test-Time Guidance through Small Visual Reasoners
Zilin Xiao, Jaywon Koo, Siru Ouyang +3
Recent advancements in reinforcement learning with verifiable rewards have pushed the boundaries of the visual reasoning capabilities in large vision-language models (LVLMs). Howev…
PLPHP: Per-Layer Per-Head Vision Token Pruning for Efficient Large Vision-Language Models
Yu Meng, Kaiyuan Li, Chenran Huang +4
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a range of multimodal tasks. However, their inference efficiency is constrained by the large n…
LLM Alignment as Retriever Optimization: An Information Retrieval Perspective
Bowen Jin, Jinsung Yoon, Zhen Qin +5
Large Language Models (LLMs) have revolutionized artificial intelligence with capabilities in reasoning, coding, and communication, driving innovation across industries. Their true…
Unchosen Experts Can Contribute Too: Unleashing MoE Models' Power by Self-Contrast
Chufan Shi, Cheng Yang, Xinyu Zhu +6
Mixture-of-Experts (MoE) has emerged as a prominent architecture for scaling model size while maintaining computational efficiency. In MoE, each token in the input sequence activat…
Rapid Mobile App Development for Generative AI Agents on MIT App Inventor
Jaida Gao, Calab Su, Etai Miller +2
The evolution of Artificial Intelligence (AI) stands as a pivotal force shaping our society, finding applications across diverse domains such as education, sustainability, and safe…