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Renjun Xu

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4 papers

2 author rows

AAMAS Conference 2026 Conference Paper

SocraticAgent: An Autonomous Agent for Unlocking Latent Knowledge in LLMs

Yang Yan
Yu Lu
Renjun Xu
Zhenzhong Lan

Reasoning failures in Large Language Models (LLMs) used by autonomous agents are often attributed to knowledge deficits, leading to a reliance on solutions like Retrieval-Augmented Generation (RAG) or parametric fine-tuning. This paper empirically demonstrates that this assumption is often flawed. We identify a quantifiable "knowledge recall gap": while modern LLMs possess 90-97% of the necessary facts for a task, they spontaneously apply only 57- 64% of this knowledge during reasoning. This reveals a significant performance gap rooted in a failure of recall, not a fundamental absence of knowledge. To address this, we introduce SocraticAgent, a zero-shot autonomous agent that emulates Socratic inquiry by guidinganLLMtofirstdeconstructaproblemandcomprehensively detail the internal knowledge required for its solution. Through a deterministic two-action cycle of (1) knowledge deconstruction and (2) grounded reasoning, it procedurally closes this recall gap without any model updates. Across a diverse suite of LLMs, SocraticAgent significantly improves reasoning accuracy, outperforming standard prompting and noisy external retrieval. Critically, our agentic, process-driven approach achieves performance competitive with expensive, data-dependent fine-tuning methods, but does so at inference time without any parametric changes. Our work demonstrates that a deliberative agentic process can serve as a powerful substitute for parametric memory adaptation. This paves the way for adaptable, capable autonomous reasoning systems, positioningagent-drivendeliberationasakeymechanismforunlocking latent knowledge within LLMs. Code and prompts are available at https: //github. com/kuri-leo/BigFive-LLM-Predictor.

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UAI Conference 2023 Conference Paper

E(2)-Equivariant Vision Transformer

Renjun Xu
Kaifan Yang
Ke Liu 0012
Fengxiang He

Vision Transformer (ViT) has achieved remarkable performance in computer vision. However, positional encoding in ViT makes it substantially difficult to learn the intrinsic equivariance in data. Ini- tial attempts have been made on designing equiv- ariant ViT but are proved defective in some cases in this paper. To address this issue, we design a Group Equivariant Vision Transformer (GE-ViT) via a novel, effective positional encoding opera- tor. We prove that GE-ViT meets all the theoreti- cal requirements of an equivariant neural network. Comprehensive experiments are conducted on standard benchmark datasets, demonstrating that GE-ViT significantly outperforms non-equivariant self-attention networks. The code is available at https: //github. com/ZJUCDSYangKaifan/GEVit.

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IJCAI Conference 2022 Conference Paper

S2SNet: A Pretrained Neural Network for Superconductivity Discovery

Ke Liu
Kaifan Yang
Jiahong Zhang
Renjun Xu

Superconductivity allows electrical current to flow without any energy loss, and thus making solids superconducting is a grand goal of physics, material science, and electrical engineering. More than 16 Nobel Laureates have been awarded for their contribution in superconductivity research. Superconductors are valuable for sustainable development goals (SDGs), such as climate change mitigation, affordable and clean energy, industry, innovation and infrastructure, and so on. However, a unified physics theory explaining all superconductivity mechanism is still unknown. It is believed that superconductivity is microscopically due to not only molecular compositions but also the geometric crystal structure. Hence a new dataset, S2S, containing both crystal structures and superconducting critical temperature, is built upon SuperCon and Material Project. Based on this new dataset, we propose a novel model, S2SNet, which utilizes the attention mechanism for superconductivity prediction. To overcome the shortage of data, S2SNet is pre-trained on the whole Material Project dataset with Masked-Language Modeling (MLM). S2SNet makes a new state-of-the-art, with out-of-sample accuracy of 92% and Area Under Curve (AUC) of 0. 92. To the best of our knowledge, S2SNet is the first work to predict superconductivity with only information of crystal structures. This work is beneficial to superconductivity discovery and further SDGs. The code and datasets are available at https: //github. com/supercond/S2SNet

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IJCAI Conference 2020 Conference Paper

Joint Partial Optimal Transport for Open Set Domain Adaptation

Renjun Xu
Pelen Liu
Yin Zhang
Fang Cai
Jindong Wang
Shuoying Liang
Heting Ying
Jianwei Yin

Domain adaptation (DA) has achieved a resounding success to learn a good classifier by leveraging labeled data from a source domain to adapt to an unlabeled target domain. However, in a general setting when the target domain contains classes that are never observed in the source domain, namely in Open Set Domain Adaptation (OSDA), existing DA methods failed to work because of the interference of the extra unknown classes. This is a much more challenging problem, since it can easily result in negative transfer due to the mismatch between the unknown and known classes. Existing researches are susceptible to misclassification when target domain unknown samples in the feature space distributed near the decision boundary learned from the labeled source domain. To overcome this, we propose Joint Partial Optimal Transport (JPOT), fully utilizing information of not only the labeled source domain but also the discriminative representation of unknown class in the target domain. The proposed joint discriminative prototypical compactness loss can not only achieve intra-class compactness and inter-class separability, but also estimate the mean and variance of the unknown class through backpropagation, which remains intractable for previous methods due to the blindness about the structure of the unknown classes. To our best knowledge, this is the first optimal transport model for OSDA. Extensive experiments demonstrate that our proposed model can significantly boost the performance of open set domain adaptation on standard DA datasets.

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