1 citations · 2 across the 15 of their papers we have counts for
15 papers
MVEI & EmObserver: Empowering MLLM-Oriented Visual Emotional Intelligence via Emotion Statement Judgement
Daiqing Wu, Dongbao Yang, Jiashu Yao +4
Affective Image Content Analysis (AICA) aims to recognize and understand emotions elicited by visual content, representing an indispensable step toward Artificial General Intellige…
Learning from Own Solutions: Self-Conditioned Credit Assignment for Reinforcement Learning with Verifiable Rewards
Yingyu Shan, Yuhang Guo, Zihao Cheng +7
Reinforcement learning with verifiable rewards (RLVR) has driven substantial progress in training LLMs for reasoning tasks, but representative methods such as GRPO assign uniform c…
PEC-Home: Interpretation of Progressively Elliptical Commands in Smart Homes
Yingyu Shan, Zeming Liu, Silin Li +4
Recent advancements in Large Language Models (LLMs) have empowered home assistants with natural language interaction capabilities. However, current assistants overlook the progress…
Benchmarking Living-Screen-Native GUI Agents on Short-Video Platforms
Jiashu Yao, Heyan Huang, Daiqing Wu +5
GUI agents today assume a static screen, where the world is frozen between two actions. However, real interfaces such as short-video applications violate this assumption, as their…
Utilizing and Calibrating Hindsight Process Rewards via Reinforcement with Mutual Information Self-Evaluation
Jiashu Yao, Heyan Huang, Zeming Liu +1
To overcome the sparse reward challenge in reinforcement learning (RL) for agents based on large language models (LLMs), we propose Mutual Information Self-Evaluation (MISE), an RL…
Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization
Jiashu Yao, Heyan Huang, Daiqing Wu +2
To encourage diverse exploration in reinforcement learning (RL) for large language models (LLMs) without compromising accuracy, we propose Policy Split, a novel paradigm that bifur…
HierarchicalKV: A GPU Hash Table with Cache Semantics for Continuous Online Embedding Storage
Haidong Rong, Jiashu Yao, Matthias Langer +12
Traditional GPU hash tables preserve every inserted key -- a dictionary assumption that wastes scarce High Bandwidth Memory (HBM) when embedding tables routinely exceed single-GPU…
Echo: Towards Advanced Audio Comprehension via Audio-Interleaved Reasoning
Daiqing Wu, Xuan Zhang, Dongbao Yang +7
The maturation of Large Audio Language Models (LALMs) has raised growing expectations for them to comprehend complex audio much like humans. Current efforts primarily replicate tex…
Incorporating Self-Rewriting into Large Language Model Reasoning Reinforcement
Jiashu Yao, Heyan Huang, Shuang Zeng +6
Through reinforcement learning (RL) with outcome correctness rewards, large reasoning models (LRMs) with scaled inference computation have demonstrated substantial success on compl…
HomeSafeBench: Benchmarking Embodied Vision-Language Models in Free-Exploration Home Safety Inspection
Jiashu Yao, Haoyu Wen, Siyuan Gao +3
Safety hazards in the home are a leading cause of preventable domestic injuries, motivating an automated inspector that actively explores a home and reports hazards before they cau…
HomeBench: Evaluating LLMs in Smart Homes with Valid and Invalid Instructions Across Single and Multiple Devices
Silin Li, Yuhang Guo, Jiashu Yao +2
Large language models (LLMs) have the potential to revolutionize smart home assistants by enhancing their ability to accurately understand user needs and respond appropriately, whi…
ReFF: Reinforcing Format Faithfulness in Language Models across Varied Tasks
Jiashu Yao, Heyan Huang, Zeming Liu +4
Following formatting instructions to generate well-structured content is a fundamental yet often unmet capability for large language models (LLMs). To study this capability, which…
Optimizing Chain-of-Thought Reasoning: Tackling Arranging Bottleneck via Plan Augmentation
Yuli Qiu, Jiashu Yao, Heyan Huang +1
Multi-step reasoning ability of large language models is crucial in tasks such as math and tool utilization. Current researches predominantly focus on enhancing model performance i…
FAME: Towards Factual Multi-Task Model Editing
Li Zeng, Yingyu Shan, Zeming Liu +2
Large language models (LLMs) embed extensive knowledge and utilize it to perform exceptionally well across various tasks. Nevertheless, outdated knowledge or factual errors within…
Deterministic Reversible Data Augmentation for Neural Machine Translation
Jiashu Yao, Heyan Huang, Zeming Liu +1
Data augmentation is an effective way to diversify corpora in machine translation, but previous methods may introduce semantic inconsistency between original and augmented data bec…