Papers by Jusheng Zhang
OSC: Cognitive Orchestration through Dynamic Knowledge Alignment in Multi-Agent LLM Collaboration (2025.findings-emnlp)
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| Challenge: | Prior work has advanced agent selection and result aggregation, efficient linguistic interactions for deep collaboration among expert agents remain a critical bottleneck. |
| Approach: | They propose a knowledge-aware adaptive collaboration framework to enhance cognitive synergy in multi-agent systems with large language models. |
| Outcome: | The proposed framework improves synergy between agents and language models by enabling agents to dynamically perceive their collaborators’ cognitive states. |
Reinforcement Learning for Diffusion LLMs via Energy-Based Gibbs Alignment (2026.acl-long)
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| Challenge: | Diffusion Large Language Models (dLLMs) offer parallel decoding and bidirectional context modeling . aligning dLLms with reinforcement learning (RL) remains a challenge . |
| Approach: | They propose a variational framework that reformulates RL for dLLMs as a distribution matching problem. |
| Outcome: | The proposed framework reformulates RL for dLLMs as a distribution matching problem. |
Towards More Efficient Post-training via Fourier Domain Adapter Framework (2025.findings-emnlp)
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| Challenge: | FDA reparameterizes the core projection operation of the adapter module directly in the Fourier domain. |
| Approach: | They propose a framework that reparameterizes the core projection operation of the adapter module directly in the Fourier domain. |
| Outcome: | The proposed framework outperforms existing parameter-efficient fine-tuning methods on GLUE, E2E NLG, and instruction tuning benchmarks. |
Nash-Pruned CredMAS: Dynamic Panel Pruning for VLM-MAS using Nash-based Selection and Doubly-Robust Credits (2026.findings-acl)
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| Challenge: | Multi-Agent Systems (MAS) are expensive due to static panel designs, where all N agents communicate at every T round. |
| Approach: | They propose an economic framework that transforms agent selection into a dynamic resource allocation game. |
| Outcome: | The proposed system reduces token consumption by over 25% on challenging benchmarks while reducing token consumption. |
CCG: Rare-Label Prediction via Neural SEM–Driven Causal Game (2025.findings-emnlp)
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| Challenge: | Multi-label classification (MLC) faces persistent challenges from label imbalance, spurious correlations, distribution shifts, especially in rare label prediction. |
| Approach: | They propose a Causal Cooperative Game framework that models multi-player cooperative process for multi-label classification. |
| Outcome: | The proposed framework improves rare label prediction and overall robustness compared to baselines. |
Provably Safe Offline-to-Online RL: Decoupling Learning from Data-Driven Safety Enforcement (2026.acl-long)
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| Challenge: | Hybrid offline–online reinforcement learning (O2O RL) promises both sample efficiency and robust exploration, but suffers from instability due to distribution shift between offline and online data. |
| Approach: | They propose a framework that decouples policy optimization from safety enforcement . they propose dynamic curricula that gradually extend temporal horizons and anneal offline–online data mixing . |
| Outcome: | The proposed framework preserves the exploratory value of online interactions without collapsing to conservative policies. |
RACC: Regret-Aware Confidence Calibration for Consistent Masked Discrete Diffusion Decoding (2026.findings-acl)
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| Challenge: | Masked Discrete Diffusion Models (MDMs) enable parallel generation via iterative refinement, but their current decoding paradigms are static and myopic. |
| Approach: | They propose a Regret-Aware Confidence Calibration framework that aligns decoding decisions with the model’s latent self-correction capabilities. |
| Outcome: | The proposed framework aligns decoding decisions with model’s latent self-correction capabilities. |
DrDiff: Dynamic Routing Diffusion with Hierarchical Attention for Breaking the Efficiency-Quality Trade-off (2025.emnlp-main)
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Jusheng Zhang, Yijia Fan, Kaitong Cai, Zimeng Huang, Xiaofei Sun, Jian Wang, Chengpei Tang, Keze Wang
| Challenge: | et al., 2019; Brown e.t al, 2023; Touvron e t al; 2024; OpenAI, 2024) Large Language Models (LLMs) have demonstrated remarkable capabilities in knowledge encoding and contextual understanding during their pretraining phase. |
| Approach: | They propose a dynamic expert scheduling mechanism that allocates computational resources based on text complexity and a hierarchical sparse attention mechanism that adjusts attention patterns according to a variety of input lengths. |
| Outcome: | The proposed framework overpowers existing methods on long-text generation benchmarks. |