Discovering governing Partial Differential Equations (PDEs) from observational data is a fundamental challenge in AI for Science. While Symbolic Regression (SR) dominates this task, existing token-level methods trigger a combinatorial explosion of search spaces, frequently yielding mathematically valid yet physically inconsistent candidates. To overcome this, we propose Modular Symbolic Regression with Physics Priors (MSR-PP), a knowledge-guided framework that conceptualizes one-dimensional PDEs as structured compositions of semantic modules (e.g., convection, diffusion) rather than random symbol sequences. MSR-PP employs an RL-optimized LSTM agent to sequentially construct equation skeletons based on syntactic and semantic dependencies, utilizing a modular search strategy to significantly prune the search space. Addressing the critical scarcity of standardized, high-fidelity benchmarks for 1D PDEs, we constructed an extended dataset for rigorous evaluation. Extensive experiments demonstrate that MSR-PP outperforms state-of-the-art baselines. Real-world validation on the NGSIM US101 traffic dataset shows MSR-PP successfully identifies a convection-diffusion model featuring a critical second-order term. Notably, the negative coefficient of this term (-u_xx) aligns with theoretical models, accurately capturing the string instability and wave-amplifying dynamics characteristic of stop-and-go traffic. Moreover, the discovered equation exhibits robust out-of-distribution (OOD) generalization on the unseen NGSIM I-80 dataset, underscoring MSR-PP's capability to uncover physically meaningful laws. Datasets: https://github.com/jinyangdu5/MSR-PP.
Jinyang Du, Chunguo Wu, Xiaohu Shi et al.· Proceedings of the 32nd ACM...· 0 citations
Retrieval-Augmented Generation (RAG) has emerged as a powerful architecture for Question Answering (QA) by integrating external information into Large Language Models (LLMs). However, false, inaccurate, and misleading information in news and social media poses a serious challenge to real-world RAG systems, especially in multi-hop QA, where complex multi-step reasoning can be misled by even a single deceptive misinformation segment in the retrieved documents. Existing approaches mainly rely on implicit alignment or explicit regulation, but their limited ability to assess fine-grained information reliability makes them vulnerable to deceptive misinformation that is semantically relevant to the question yet factually incorrect, leading to erroneous answers. To address this limitation, we propose ReliableRAG, which, to the best of our knowledge, is the first reliability-driven framework that mitigates deceptive misinformation in multi-hop QA through fine-grained evaluation of individual triples. ReliableRAG first extracts information segments from source documents and represents them as structured triples. It then quantifies triple reliability by combining query-triple semantic relevance with triple credibility, retaining only the top-$K$ reliable and non-redundant triples. Based on these refined triples, ReliableRAG autoregressively constructs robust reasoning chains to consolidate trustworthy evidence and filter deceptive misinformation, producing accurate answers faithful to reliable information. Experiments on three multi-hop QA datasets show that ReliableRAG outperforms existing methods, substantially improving the factual reliability and robustness of RAG systems under deceptive misinformation injection.
Jingjing Jiang, Xuan Wu, Wenhao Song et al.· 0 citations
