14 papers
Do Language Models Need Music Supervision? Verifiable Rewards for Multi-Constraint Symbolic Music Generation
Haoyue Liu, Ye Chen, Zhichao Wang +3
Language models can now generate symbolic music from natural-language instructions. However, existing models still struggle to satisfy multiple explicit musical constraints simulta…
Why Sample What You Can Enumerate? Exact Policy Optimization for Genomic Tool Selection
Haoyue Liu, Xiaoyu Ma, Ye Chen +2
Reinforcement learning over a frozen reasoner has become a common recipe for teaching a policy which external tools to invoke. We show that this recipe becomes structurally mismatc…
SEPO: Evidence-Grounded Prompt Optimization via Structural Editing
Xiaoyu Ma, Haoyue Liu, Yiwen Li +4
Existing API-only prompt optimisers are often described as interpretable, but in practice, this usually means only post-hoc inspectability: each iteration still rewrites the prompt…
Do SpeechLMs Hear Their Own Opinions? Diagnosing and Mitigating Previous-Belief Contamination in Streaming Emotion Understanding
Haoyue Liu, Zhichao Wang, Ye Chen +2
Streaming emotion understanding uses historical state while continuously interpreting current audio, often feeding the model's previous prediction back as context. We show that thi…
Which Negatives Matter? Ask Your Text Encoder: Adaptive Similarity Margins for Dense-Caption Retrieval
Haoyue Liu, Ye Chen, Zhichao Wang +1
Dense-caption retrieval has recently been improved by introducing segmentation, edge maps, LLM-filtered captions, and cross-modal modules into contrastive fine-tuning. However, the…
Select Smarter, Not More: Prompt-Aware Evaluation Scheduling with Submodular Guarantees
Xiaoyu Ma, Yiwen Li, Haoyue Liu +4
Automatic prompt optimization (APO) hinges on the quality of its evaluation signal, yet scoring every prompt candidate on the full training set is prohibitively expensive. Existing…
Adaptive Prompt Structure Factorization: A Framework for Self-Discovering and Optimizing Compositional Prompt Programs
Haoyue Liu, Zhichao Wang, Yongxin Guo +2
Automated prompt optimization is crucial for eliciting reliable reasoning from large language models (LLMs), yet most API-only prompt optimizers iteratively edit monolithic prompts…
PIDP-Attack: Combining Prompt Injection with Database Poisoning Attacks on Retrieval-Augmented Generation Systems
Haozhen Wang, Haoyue Liu, Jionghao Zhu +3
Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of applications. However, their practical deployment is often hindered by issues such as o…
The End of Manual Decoding: Towards Truly End-to-End Language Models
Zhichao Wang, Dongyang Ma, Xinting Huang +6
The "end-to-end" label for LLMs is a misnomer. In practice, they depend on a non-differentiable decoding process that requires laborious, hand-tuning of hyperparameters like temper…
Multi-Physics: A Comprehensive Benchmark for Multimodal LLMs Reasoning on Chinese Multi-Subject Physics Problems
Zhongze Luo, Zhenshuai Yin, Yongxin Guo +3
While multimodal LLMs (MLLMs) demonstrate remarkable reasoning progress, their application in specialized scientific domains like physics reveals significant gaps in current evalua…
Federated Unlearning with Gradient Descent and Conflict Mitigation
Zibin Pan, Zhichao Wang, Chi Li +4
Federated Learning (FL) has received much attention in recent years. However, although clients are not required to share their data in FL, the global model itself can implicitly re…
FedMABA: Towards Fair Federated Learning through Multi-Armed Bandits Allocation
Zhichao Wang, Lin Wang, Yongxin Guo +2
The increasing concern for data privacy has driven the rapid development of federated learning (FL), a privacy-preserving collaborative paradigm. However, the statistical heterogen…
Save It All: Enabling Full Parameter Tuning for Federated Large Language Models via Cycle Block Gradient Descent
Lin Wang, Zhichao Wang, Xiaoying Tang
The advent of large language models (LLMs) has revolutionized the deep learning paradigm, yielding impressive results across a wide array of tasks. However, the pre-training or fin…
FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems
Lin Wang, Zhichao Wang, Xi Leng +1
Preserving privacy and reducing communication costs for edge users pose significant challenges in recommendation systems. Although federated learning has proven effective in protec…