16 citations · 20 across the 21 of their papers we have counts for
23 papers
NeoHorse-1: Towards Recursive Self-Improvement via Agentic Post-Training with Routing Harness
NeoHorse Team, Guoliang Cao, Guohao Dai +34
Recursive self-improvement (RSI) requires a concrete mechanism through which an AI system observes its capabilities and converts that evidence into the next round of learning. We p…
Verilog-Evolve: Feedback-Driven and Skill-Evolving Verilog Generation
Zehua Pei, Hui-Ling Zhen, Yu Zhang +3
Large language models (LLMs) have improved Verilog generation from natural-language specifications, but most pipelines still treat generation as isolated sampling followed by funct…
GeoCycler: Reward-Aligned 3D Diffusion for Constraint-Conditioned Cyclic Peptide Design
Jingjie Zhang, Hanqun Cao, Haosen Shi +10
Cyclic peptides are attractive therapeutic modalities because their closed-ring topology can improve stability and target specificity. However, de novo cyclic peptide design remain…
MetaMoE: Diversity-Aware Proxy Selection for Privacy-Preserving Mixture-of-Experts Unification
Weisen Jiang, Shuhao Chen, Sinno Jialin Pan
Mixture-of-Experts (MoE) models scale capacity by combining specialized experts, but most existing approaches assume centralized access to training data. In practice, data are dist…
FocuSFT: Bilevel Optimization for Dilution-Aware Long-Context Fine-Tuning
Zehua Pei, Hui-Ling Zhen, Xianzhi Yu +3
Large language models can now process increasingly long inputs, yet their ability to effectively use information spread across long contexts remains limited. We trace this gap to h…
MemDLM: Memory-Enhanced DLM Training
Zehua Pei, Hui-Ling Zhen, Weizhe Lin +4
Diffusion Language Models (DLMs) offer attractive advantages over Auto-Regressive (AR) models, such as full-attention parallel decoding and flexible generation. However, standard D…
Beyond Speedup -- Utilizing KV Cache for Sampling and Reasoning
Zeyu Xing, Xing Li, Hui-Ling Zhen +2
KV caches, typically used only to speed up autoregressive decoding, encode contextual information that can be reused for downstream tasks at no extra cost. We propose treating the…
SCOPE: Prompt Evolution for Enhancing Agent Effectiveness
Zehua Pei, Hui-Ling Zhen, Shixiong Kai +4
Large Language Model (LLM) agents are increasingly deployed in environments that generate massive, dynamic contexts. However, a critical bottleneck remains: while agents have acces…
MetaDefense: Defending Finetuning-based Jailbreak Attack Before and During Generation
Weisen Jiang, Sinno Jialin Pan
This paper introduces MetaDefense, a novel framework for defending against finetuning-based jailbreak attacks in large language models (LLMs). We observe that existing defense mech…
PreMoE: Proactive Inference for Efficient Mixture-of-Experts
Zehua Pei, Ying Zhang, Hui-Ling Zhen +6
Mixture-of-Experts (MoE) models offer dynamic computation, but are typically deployed as static full-capacity models, missing opportunities for deployment-specific specialization.…
Reinforcing Compositional Retrieval: Retrieving Step-by-Step for Composing Informative Contexts
Quanyu Long, Jianda Chen, Zhengyuan Liu +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across numerous tasks, yet they often rely on external context to handle complex tasks. While retrieval-augme…
KVTuner: Sensitivity-Aware Layer-Wise Mixed-Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference
Xing Li, Zeyu Xing, Yiming Li +6
KV cache quantization can improve Large Language Models (LLMs) inference throughput and latency in long contexts and large batch-size scenarios while preserving LLMs effectiveness.…
Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis
Zehua Pei, Hui-Ling Zhen, Lancheng Zou +5
Scaling large language models (LLMs) improves performance but significantly increases inference costs, with feed-forward networks (FFNs) consuming the majority of computational res…
State Chrono Representation for Enhancing Generalization in Reinforcement Learning
Jianda Chen, Wen Zheng Terence Ng, Zichen Chen +2
In reinforcement learning with image-based inputs, it is crucial to establish a robust and generalizable state representation. Recent advancements in metric learning, such as deep…
From Pruning to Grafting: Dynamic Knowledge Redistribution via Learnable Layer Fusion
Zehua Pei, Hui-Ling Zhen, Xianzhi Yu +3
Structured pruning of Generative Pre-trained Transformers (GPTs) offers a promising path to efficiency but often suffers from irreversible performance degradation due to the discar…
Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners
Wen Zheng Terence Ng, Jianda Chen, Sinno Jialin Pan +1
Deploying a safe mobile robot policy in scenarios with human pedestrians is challenging due to their unpredictable movements. Current Reinforcement Learning-based motion planners r…
Large Language Models Know What Makes Exemplary Contexts
Quanyu Long, Jianda Chen, Wenya Wang +1
In-context learning (ICL) has proven to be a significant capability with the advancement of Large Language models (LLMs). By instructing LLMs using few-shot demonstrative examples,…
Overcoming Negative Transfer by Online Selection: Distant Domain Adaptation for Fault Diagnosis
Ziyan Wang, Mohamed Ragab, Wenmian Yang +4
Unsupervised domain adaptation (UDA) has achieved remarkable success in fault diagnosis, bringing significant benefits to diverse industrial applications. While most UDA methods fo…
Does In-Context Learning Really Learn? Rethinking How Large Language Models Respond and Solve Tasks via In-Context Learning
Quanyu Long, Yin Wu, Wenya Wang +1
In-context Learning (ICL) has emerged as a powerful capability alongside the development of scaled-up large language models (LLMs). By instructing LLMs using few-shot demonstrative…
Backdoor Attacks on Dense Retrieval via Public and Unintentional Triggers
Quanyu Long, Yue Deng, LeiLei Gan +2
Dense retrieval systems have been widely used in various NLP applications. However, their vulnerabilities to potential attacks have been underexplored. This paper investigates a no…
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning
Quanyu Long, Wenya Wang, Sinno Jialin Pan
Large language models (LLMs) have showcased their capability with few-shot inference known as in-context learning. However, in-domain demonstrations are not always readily availabl…
SOUL: Towards Sentiment and Opinion Understanding of Language
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan +1
Sentiment analysis is a well-established natural language processing task, with sentiment polarity classification being one of its most popular and representative tasks. However, d…
Multilingual Jailbreak Challenges in Large Language Models
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan +1
While large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, they pose potential safety concerns, such as the ``jailbreak'' problem, wherein mal…