Multi-Agent Protocol Distillation (MAPD), a joint distillation and RL framework uses a structured, style-normalized protocol as an intermediate representation that generalizes robustly across diverse proprietary teachers while effectively mitigating the student policy from style drift and verbosity degeneration.
Junlin Liu, Jiangwang Chen, Zixin Song et al.· arXiv.org· 6 citations
PReM (Preserve and Refresh Memory), a context-compression framework that maintains the long context as the model's internal layer-wise KV memory and learns what to preserve and when to refresh it, is introduced.
Bohan Yu, Lei Shen, Chenxi Zhou et al.· arXiv.org· 0 citations
RuleWeaver is introduced, a benchmark construction framework for evaluating rule-centered scenario reasoning that starts from corpus-derived IF-THEN Meta Rules, progressively augments them into complex rules, and composes these rules into rule-centered scenario QA instances.
Bo-Han Yu, Shi-Yang Li, Pengfei Cao et al.· 0 citations
DINO-MAC is a simple yet effective model for robust deepfake detection under degraded conditions and is the first-place winner of the “NTIRE 2026 - Robust Deepfake Detection Challenge @ CVPR 2026”, significantly outperforms all competitors.
Chenfan Qu, Lianwen Jin, Junchi Li et al.· 3 citations
Hetero-Designer is proposed, a novel pipeline that efficiently encodes intricate dependencies among queries, LLMs and roles through a novel Binary-Star Transformer and constructs Hetero-MAS in an autoregressive graph generation process.
Zhiheng Zhang, Yuanzhe Zhang, Bohan Yu et al.· Annual Meeting of the Associ...· 0 citations
DynaRule is proposed, an end-to-end framework that injects the given rules into the KV cache and turns retrieval into an internal, learnable, step-wise process, and can re-attend to the most relevant rules at each step, dynamically replacing outdated ones to support more stable multi-step reasoning.
Bo-Han Yu, Pengfei Cao, Chen Han et al.· 1 citation
This work systematically study weight-only post-training quantization across bit-widths, quantization methods, model scales and downstream tasks and establishes that quantization degradation is governed by how errors are introduced at the source and how they accumulate across the network.
Chen-Xi Zhou, Pengfei Cao, Jin Ye et al.· 0 citations
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