16 citations · 49 across the 24 of their papers we have counts for
24 papers
Search More, Think Less: Rethinking Long-Horizon Agentic Search for Efficiency and Generalization
Qianben Chen, Tianrui Qin, King Zhu +21
Recent deep research agents primarily improve performance by scaling reasoning depth, but this leads to high inference cost and latency in search-intensive scenarios. Moreover, gen…
Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space
Xingwei Qu, Shaowen Wang, Zihao Huang +16
Large Language Models (LLMs) apply uniform computation to all tokens, despite language exhibiting highly non-uniform information density. This token-uniform regime wastes capacity…
TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling
Yizhi Li, Qingshui Gu, Zhoufutu Wen +14
Recent advancements in aligning large language models via reinforcement learning have achieved remarkable gains in solving complex reasoning problems, but at the cost of expensive…
Aligning Instruction Tuning with Pre-training
Yiming Liang, Tianyu Zheng, Xinrun Du +12
Instruction tuning enhances large language models (LLMs) to follow human instructions across diverse tasks, relying on high-quality datasets to guide behavior. However, these datas…
KARPA: A Training-free Method of Adapting Knowledge Graph as References for Large Language Model's Reasoning Path Aggregation
Siyuan Fang, Kaijing Ma, Tianyu Zheng +4
Large language models (LLMs) demonstrate exceptional performance across a variety of tasks, yet they are often affected by hallucinations and the timeliness of knowledge. Leveragin…
LIME: Less Is More for MLLM Evaluation
King Zhu, Qianbo Zang, Shian Jia +18
Multimodal Large Language Models (MLLMs) are evaluated on various benchmarks, such as image captioning, visual question answering, and reasoning. However, many of these benchmarks…