5 citations · 12 across the 12 of their papers we have counts for
13 papers
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Ang Li, Ben Liu, Bin Han +215
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…
Test-time Recursive Thinking: Self-Improvement without External Feedback
Yufan Zhuang, Chandan Singh, Liyuan Liu +5
Modern Large Language Models (LLMs) have shown rapid improvements in reasoning capabilities, driven largely by reinforcement learning (RL) with verifiable rewards. Here, we ask whe…
Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills
Pengcheng Jiang, Jiacheng Lin, Zhiyi Shi +31
Large language model (LLM) agents are moving beyond prompting alone. ChatGPT marked the rise of general-purpose LLM assistants, DeepSeek showed that on-policy reinforcement learnin…
Training Language Models to Generate Quality Code with Program Analysis Feedback
Feng Yao, Zilong Wang, Liyuan Liu +7
Code generation with large language models (LLMs), often termed vibe coding, is increasingly adopted in production but fails to ensure code quality, particularly in security (e.g.,…
Text Generation Beyond Discrete Token Sampling
Yufan Zhuang, Liyuan Liu, Chandan Singh +2
In standard autoregressive generation, an LLM predicts the next-token distribution, samples a discrete token, and then discards the distribution, passing only the sampled token as…
Vector-ICL: In-context Learning with Continuous Vector Representations
Yufan Zhuang, Chandan Singh, Liyuan Liu +2
Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities on textual data. We explore whether these capabilities can be extended to continuous vecto…
GRIN: GRadient-INformed MoE
Liyuan Liu, Young Jin Kim, Shuohang Wang +14
Mixture-of-Experts (MoE) models scale more effectively than dense models due to sparse computation through expert routing, selectively activating only a small subset of expert modu…
Model Tells Itself Where to Attend: Faithfulness Meets Automatic Attention Steering
Qingru Zhang, Xiaodong Yu, Chandan Singh +6
Large language models (LLMs) have demonstrated remarkable performance across various real-world tasks. However, they often struggle to fully comprehend and effectively utilize thei…
Learning a Decision Tree Algorithm with Transformers
Yufan Zhuang, Liyuan Liu, Chandan Singh +2
Decision trees are renowned for their ability to achieve high predictive performance while remaining interpretable, especially on tabular data. Traditionally, they are constructed…
Tell Your Model Where to Attend: Post-hoc Attention Steering for LLMs
Qingru Zhang, Chandan Singh, Liyuan Liu +4
In human-written articles, we often leverage the subtleties of text style, such as bold and italics, to guide the attention of readers. These textual emphases are vital for the rea…
Fast-ELECTRA for Efficient Pre-training
Chengyu Dong, Liyuan Liu, Hao Cheng +3
ELECTRA pre-trains language models by detecting tokens in a sequence that have been replaced by an auxiliary model. Although ELECTRA offers a significant boost in efficiency, its p…
Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs
Suyu Ge, Yunan Zhang, Liyuan Liu +3
In this study, we introduce adaptive KV cache compression, a plug-and-play method that reduces the memory footprint of generative inference for Large Language Models (LLMs). Differ…
Sparse Backpropagation for MoE Training
Liyuan Liu, Jianfeng Gao, Weizhu Chen
One defining characteristic of Mixture-of-Expert (MoE) models is their capacity for conducting sparse computation via expert routing, leading to remarkable scalability. However, ba…