activity
20242026
most citedInjecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey

10 citations · 12 across the 16 of their papers we have counts for

collaborators
Showing 2026Show all

10 papers · 1 filter

cs.AI2026

RIFAR: Reliability-Informed and Forgetting-Aware Replay for Continual Robot Learning with World-Action Models

Zirui Song, Tu Lin, Zirong Song +11

Genuine embodied agency requires robots to turn continuous real-world experience into lasting, transferable skills. This demands continual learning that integrates new capabilities…

cs.CL2026

Style Wins, Substance Loses: A Diagnosis of LLM-as-Judge in Idea Generation

Fengxian Ji, Yuke Li, Jingpu Yang +8

However, whether these judges truly evaluate the scientific substance of ideas or are influenced by superficial stylistic presentation remains an open question. To address this que…

cs.LG2026

Parametric Memory Decoding for Zero-Shot Routing in LoRA-Based External Parametric Memory

Fengxian Ji, Zhuohan Xie, Jingpu Yang +3

With the rise of parametric memory, LoRA-based External Parametric Memory (EPM) has emerged as a modular solution, but existing routing methods often introduce additional training,…

cs.AI2026

Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments

Xianhui Meng, Zirui Song, Yuchen Zhang +10

Large Language Models (LLMs) have demonstrated remarkable capabilities in 3D indoor synthesis for Manhattan environments. However, existing methods often fail to capture plausible…

cs.CL2026

The Cylindrical Representation Hypothesis for Language Model Steering

Lang Gao, Jinghui Zhang, Wei Liu +7

Steering is a widely used technique for controlling large language models, yet its effects are often unstable and hard to predict. Existing theoretical accounts are largely based o…

cs.LG2026

Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning

Puning Yang, Junchi Yu, Qizhou Wang +3

Mitigating sensitive and harmful outputs is fundamental to ensuring safe deployment of LLMs. Existing approaches typically follow two paradigms: Knowledge Deletion (KD), which eras…

cs.CV2026

TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards

Mingxuan Cui, Jingpu Yang, Fengxian Ji +7

Faithful text rendering remains a persistent weakness of large text-to-image generative models, as it requires both semantic instruction following and fine-grained glyph-level stru…

cs.CV2026

FineState-Bench: Benchmarking State-Conditioned Grounding for Fine-grained GUI State Setting

Fengxian Ji, Jingpu Yang, Zirui Song +5

Despite the rapid progress of large vision-language models (LVLMs), fine-grained, state-conditioned GUI interaction remains challenging. Current evaluations offer limited coverage,…

cs.CV2026

ServImage: An Image Generation and Editing Benchmark from Real-world Commercial Imaging Services

Fengxian Ji, Jingpu Yang, Zirui Song +5

Recent image generation and editing models demonstrate robust adherence to instructions and high visual quality on academic benchmarks. However, their performance on paid, real-wor…

cs.CV2026

FlashSign: Pose-Free Guidance for Efficient Sign Language Video Generation

Liuzhou Zhang, Zeyu Zhang, Biao Wu +10

Sign language plays a crucial role in bridging communication gaps between the deaf and hard-of-hearing communities. However, existing sign language video generation models often re…