4 citations · 7 across the 17 of their papers we have counts for
18 papers
Long-Horizon-Terminal-Bench 2.0: Automating Diverse Task Construction and Measuring Sustained Agent Progress
Zongxia Li, Zhongzhi Li, Yucheng Shi +14
Long-horizon terminal benchmarks measure whether an agent can sustain progress through extended workflows with process rewards, not only whether its final state passes a binary che…
Parallel-Probe: Towards Efficient Parallel Thinking via 2D Probing
Tong Zheng, Chengsong Huang, Runpeng Dai +9
Parallel thinking has emerged as a promising paradigm for reasoning, yet it imposes significant computational burdens. Existing efficiency methods primarily rely on local, per-traj…
RelayLLM: Efficient Reasoning via Collaborative Decoding
Chengsong Huang, Tong Zheng, Langlin Huang +3
Large Language Models (LLMs) for complex reasoning is often hindered by high computational costs and latency, while resource-efficient Small Language Models (SLMs) typically lack t…
Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following
Tianyi Xiong, Yi Ge, Ming Li +13
Large multimodal models (LMMs) are increasingly adopted as judges in multimodal evaluation systems due to their strong instruction following and consistency with human preferences.…
Dual-Uncertainty Guided Policy Learning for Multimodal Reasoning
Rui Liu, Dian Yu, Tong Zheng +8
Reinforcement learning with verifiable rewards (RLVR) has advanced reasoning capabilities in multimodal large language models. However, existing methods typically treat visual inpu…
Parallel-R1: Towards Parallel Thinking via Reinforcement Learning
Tong Zheng, Hongming Zhang, Wenhao Yu +7
Parallel thinking has emerged as a novel approach for enhancing the reasoning capabilities of large language models (LLMs) by exploring multiple reasoning paths concurrently. Howev…
CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models
Runpeng Dai, Linfeng Song, Haolin Liu +8
Reinforcement Learning with Verifiable Rewards (RLVR) is a powerful paradigm for enhancing the reasoning ability of Large Language Models (LLMs). Yet current RLVR methods often exp…
R1-RE: Cross-Domain Relation Extraction with RLVR
Runpeng Dai, Tong Zheng, Run Yang +2
Relation extraction (RE) is a core task in natural language processing. Traditional approaches typically frame RE as a supervised learning problem, directly mapping context to labe…
Learning to Reason via Mixture-of-Thought for Logical Reasoning
Tong Zheng, Lichang Chen, Simeng Han +2
Human beings naturally utilize multiple reasoning modalities to learn and solve logical problems, i.e., different representational formats such as natural language, code, and symbo…
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation
Yingfeng Luo, Tong Zheng, Yongyu Mu +8
The field of neural machine translation (NMT) has changed with the advent of large language models (LLMs). Much of the recent emphasis in natural language processing (NLP) has been…
Towards Optimal Multi-draft Speculative Decoding
Zhengmian Hu, Tong Zheng, Vignesh Viswanathan +5
Large Language Models (LLMs) have become an indispensable part of natural language processing tasks. However, autoregressive sampling has become an efficiency bottleneck. Multi-Dra…
Asymmetric Conflict and Synergy in Post-training for LLM-based Multilingual Machine Translation
Tong Zheng, Yan Wen, Huiwen Bao +2
The emergence of Large Language Models (LLMs) has advanced the multilingual machine translation (MMT), yet the Curse of Multilinguality (CoM) remains a major challenge. Existing wo…
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning
Bei Li, Tong Zheng, Rui Wang +8
Residual networks, as discrete approximations of Ordinary Differential Equations (ODEs), have inspired significant advancements in neural network design, including multistep method…
A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution
Zhengmian Hu, Tong Zheng, Heng Huang
Authorship attribution aims to identify the origin or author of a document. Traditional approaches have heavily relied on manual features and fail to capture long-range correlation…
Incorporating Probing Signals into Multimodal Machine Translation via Visual Question-Answering Pairs
Yuxin Zuo, Bei Li, Chuanhao Lv +3
This paper presents an in-depth study of multimodal machine translation (MMT), examining the prevailing understanding that MMT systems exhibit decreased sensitivity to visual infor…
PartialFormer: Modeling Part Instead of Whole for Machine Translation
Tong Zheng, Bei Li, Huiwen Bao +4
The design choices in Transformer feed-forward neural networks have resulted in significant computational and parameter overhead. In this work, we emphasize the importance of hidde…
EIT: Enhanced Interactive Transformer
Tong Zheng, Bei Li, Huiwen Bao +2
Two principles: the complementary principle and the consensus principle are widely acknowledged in the literature of multi-view learning. However, the current design of multi-head…
Learning Multiscale Transformer Models for Sequence Generation
Bei Li, Tong Zheng, Yi Jing +3
Multiscale feature hierarchies have been witnessed the success in the computer vision area. This further motivates researchers to design multiscale Transformer for natural language…