5 citations · 15 across the 19 of their papers we have counts for
24 papers
Improving OCR Faithfulness via Gated and Attenuated On-Policy Distillation
Baode Wang, Zuming Huang, Kexuan Ren +2
Vision-language models may rewrite anomalous text in images into linguistically plausible expressions, compromising OCR transcription faithfulness. Sequence-level task rewards and…
FA-Bench: A Benchmark for Phone- and Word-Level Timestamp Accuracy in Forced Alignment and ASR on Clean and Noisy Speech
Wei Chu, Yuanzhe Dong, Ke Tan +11
Forced alignment estimates the timestamps of each word, phone or character in speech given its transcript. Published comparisons normalize transcripts, split the data and match bou…
Train4Merge: RL or SFT---Which Yields Better Teachers for OPD-Based Model Merging?
Jingyuan Huang, Zuming Huang, Yucheng Shi +4
Domain experts trained from a shared checkpoint can transfer their specialized capabilities to a single student through on-policy distillation (OPD). Existing research primarily fo…
Infinity-Parser2 Technical Report
Zuming Huang, Jun Huang, Kexuan Ren +12
We present Infinity-Parser2, a large multimodal model that couples a controllable data-synthesis pipeline with multi-task reinforcement learning for end-to-end document parsing, ad…
Trust the Right Teacher: Quality-Aware Self-Distillation for GUI Grounding
Jingyuan Huang, Zuming Huang, Yucheng Shi +4
GUI grounding requires vision-language models (VLMs) to identify small target elements in high-resolution screenshots and predict precise screen coordinates. On-policy self-distill…
CTkvr: KV Cache Retrieval for Long-Context LLMs via Centroid then Token Indexing
Kuan Lu, Shuhang Lin, Sai Wu +7
Large language models (LLMs) are increasingly applied in long-context scenarios such as multi-turn conversations. However, long contexts pose significant challenges for inference e…
MOTION: ML-Assisted On-Device Low-Latency Motion Recognition
Veeramani Pugazhenthi, Wei-Hsiang Chu, Junwei Lu +5
The use of tiny devices capable of low-latency gesture recognition is gaining momentum in everyday human-computer interaction and especially in medical monitoring fields. Embedded…
Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing
Baode Wang, Biao Wu, Weizhen Li +8
Document parsing from scanned images into structured formats remains a significant challenge due to its complexly intertwined elements such as text paragraphs, figures, formulas, a…
OleSpeech-IV: A Large-Scale Multispeaker and Multilingual Conversational Speech Dataset with Diverse Topics
Wei Chu, Yuanzhe Dong, Ke Tan +7
OleSpeech-IV dataset is a large-scale multispeaker and multilingual conversational speech dataset with diverse topics. The audio content comes from publicly-available English podca…
The Choice of Divergence: A Neglected Key to Mitigating Diversity Collapse in Reinforcement Learning with Verifiable Reward
Long Li, Zhijian Zhou, Jiaran Hao +9
A central paradox in fine-tuning Large Language Models (LLMs) with Reinforcement Learning with Verifiable Reward (RLVR) is the frequent degradation of multi-attempt performance (Pa…
Infinity Parser: Layout Aware Reinforcement Learning for Scanned Document Parsing
Baode Wang, Biao Wu, Weizhen Li +8
Automated parsing of scanned documents into richly structured, machine-readable formats remains a critical bottleneck in Document AI, as traditional multi-stage pipelines suffer fr…
Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning
Shuyao Xu, Cheng Peng, Jiangxuan Long +3
Recent advances in model distillation show that data from advanced reasoning models can effectively train smaller student models. However, standard practices discard incorrect reas…
Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents
Rui Xu, MingYu Wang, XinTao Wang +4
Recent advances in LLM-based role-playing language agents (RPLAs) have attracted broad attention in various applications. While chain-of-thought reasoning has shown importance in m…
To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization
Haozhe Wang, Long Li, Chao Qu +4
Recent advances in mathematical problem-solving with language models (LMs) integrate chain-of-thought (CoT) reasoning and code execution to harness their complementary strengths. H…
SCP-116K: A High-Quality Problem-Solution Dataset and a Generalized Pipeline for Automated Extraction in the Higher Education Science Domain
Dakuan Lu, Xiaoyu Tan, Rui Xu +5
Recent breakthroughs in large language models (LLMs) exemplified by the impressive mathematical and scientific reasoning capabilities of the o1 model have spotlighted the critical…
An Attentive Dual-Encoder Framework Leveraging Multimodal Visual and Semantic Information for Automatic OSAHS Diagnosis
Yingchen Wei, Xihe Qiu, Xiaoyu Tan +4
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder caused by upper airway blockage, leading to oxygen deprivation and disrupted sleep. Traditional diagnos…
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models
Siming Huang, Tianhao Cheng, J. K. Liu +16
Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems. While open-access code LLMs are in…
BTBR: A Bayesian-Theory-Driven Probabilistic-Fuzzy Framework for Implicit Bias Removal in Large Language Models
Yongxin Deng, Xihe Qiu, Xiaoyu Tan +8
Large language models (LLMs) may encode biased associations from heterogeneous training corpora that are not immediately visible under ordinary prompting, but can surface when the…
Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Bootstrapped Prolog-based Chain-of-Thought
Xiaoyu Tan, Yongxin Deng, Xihe Qiu +7
Large language models have demonstrated remarkable capabilities as general-purpose assistants, excelling in a wide range of reasoning tasks and supporting various aspects of daily…
Robust Deep Hawkes Process under Label Noise of Both Event and Occurrence
Xiaoyu Tan, Bin Li, Xihe Qiu +3
Integrating deep neural networks with the Hawkes process has significantly improved predictive capabilities in finance, health informatics, and information technology. Nevertheless…
Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models
Xihe Qiu, Haoyu Wang, Xiaoyu Tan +6
Effective collaboration in multi-agent systems requires communicating goals and intentions between agents. Current agent frameworks often suffer from dependencies on single-agent e…
Struct-X: Enhancing Large Language Models Reasoning with Structured Data
Xiaoyu Tan, Haoyu Wang, Xihe Qiu +4
Structured data, rich in logical and relational information, has the potential to enhance the reasoning abilities of large language models (LLMs). Still, its integration poses a ch…
MINDECHO: Role-Playing Language Agents for Key Opinion Leaders
Rui Xu, Dakuan Lu, Xiaoyu Tan +5
Large language models~(LLMs) have demonstrated impressive performance in various applications, among which role-playing language agents (RPLAs) have engaged a broad user base. Now,…
InfMLLM: A Unified Framework for Visual-Language Tasks
Qiang Zhou, Zhibin Wang, Wei Chu +3
Large language models (LLMs) have proven their remarkable versatility in handling a comprehensive range of language-centric applications. To expand LLMs' capabilities to a broader…