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Rong Fu

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InfoMamba: An Attention-Free Hybrid Mamba-Transformer Model

A consistency boundary analysis is presented that characterizes when diagonal short-memory SSMs can approximate causal attention and identifies structural gaps that remain and proposes InfoMamba, an attention-free hybrid architecture that consistently outperforms strong Transformer and SSM baselines.

Youjin Wang, Jiaqi Zhao, Rong Fu et al. · 0 citations
#artificial intelligence Preprint Feb 2026

ASA: Backbone-Training-Free Representation Engineering for Tool-Calling Agents

Activation Steering Adapter (ASA), a training-free, inference-time controller that performs a single-shot mid-layer intervention and targets tool domains via a router-conditioned mixture of steering vectors with a probe-guided signed gate to amplify true intent while suppressing spurious triggers is proposed.

Youjin Wang, Run Zhou, Rong Fu et al. · 4 citations · ⚡2
Preprint Jul 2026

SQuaD-SQL: Efficient Text-to-SQL with Small Language Models via LLM-Guided Knowledge Distillation

SQuaD-SQL (Small-Qualified and Distilled for SQL), a novel approach that empowers small language models to approach the performance of LLMs on the Text-to-SQL task while significantly improving efficiency through knowledge distillation and synthetic data generation, is introduced.

Wangyu Wu, Xiaojian Lin, Rong Fu et al. · 0 citations
Preprint Aug 2026

MotionCraft: Latent World Modeling with Sparse Attention for Visual Upscaling

MotionCraft is presented, a controllable VSR framework that formulates restoration as motion-aware latent state prediction inspired by world models and integrates adaptive sparse attention with an explicit user-accessible control interface to deliver temporally consistent, high-quality reconstructions under streaming c...

Rong Fu, Chun-Lei Meng, Yangcheng Zeng et al. · 1 citation
Preprint Jul 2026

Degeneracy-Guided List Compression for Greedy Graph Coloring

P-SAPST Lite replaces peeling with a degree order and provides a lower latency order choice within the same framework and complements edge oblivious streaming APST by addressing an offline regime in which structural plans can be reused.

Rong Fu, Yongtai Liu, Xiaowen Ma et al. · 0 citations

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