The rapid spread of misinformation has made fact verification a critical necessity. LLM-based methods have become the mainstream approach for fact verification, but they still suffer from hallucinations. To mitigate hallucinations, researchers incorporate KG on LLM reasoning with a graph-based constrained decoding. However, these methods with constrained decoding suffer from error cascading due to the inherent knowledge gaps of the LLM, which cause it to select wrong branches during beam search in the constrained token space. To tackle error cascading, we think that the decoding process should be dynamically guided by step-level plausibility assessment. In light of this, we propose TrustFV (https://anonymous.4open.science/r/Trust_FV-6239), a fact verification framework that step-wise beam search in token-level constrained decoding with risk-averse pruning for trustworthy verification. Specifically, TrustFV integrates (1) token-level constrained decoding on a Knowledge Graph Tries (KG-Tries) to ground generation in retrieved evidence from KG, (2) an Uncertainty-aware Process Reward Model (UPRM) trained via Monte Carlo rollouts and ordinal regression to estimate both expected success and uncertainty per step, and (3) a step-wise beam search that actively prunes high-risk branches via the step-level signal of UPRM, producing faithful and plausible reasoning paths. Experiments show that TrustFV achieves state-of-the-art performance on FactKG and CWQ datasets.

Conversational stance detection, which aims to identify users’ attitudes in conversation threads, has attracted increasing attention in recent years. Yet, mainstream LLM-based approaches remain prone to biases caused by spurious correlations. Existing debiasing methods either rely on indiscriminate multi-step prompting, which can induce unnecessary reasoning and overthinking, or require expensive fine-tuning on curated data. To address these challenges, we propose ExpCSD, an experience-based debiasing framework grounded in latent thinking paths that follows a debias-only-when-needed principle. ExpCSD first assesses whether an LLM’s latent thinking path is biased and selectively retrieves and injects historically similar debiasing experiences only when necessary, without updating model parameters. Experiments on public benchmarks show that ExpCSD outperforms strong baselines in stance detection accuracy while avoiding overthinking and costly fine-tuning.

The identification of harmful memes extends beyond a mere classification task, encompassing challenges related to multi-perspective semantic comprehension and hierarchical reasoning. Prevailing approaches predominantly depend on modal alignment or black-box classifiers, which fail to capture implicit biases and lack interpretability. In this study, we propose BPDMoE-Hate, a novel framework grounded in dual-space mixture-of-experts, which innovatively conceptualizes harmful meme detection as an integrated process of “viewpoint decoupling and hierarchical fusion”. Our approach generates adversarial binary perspectives via Visual-Language Models (VLMs) and incorporates an adaptive viewpoint gating to facilitate viewpoint selection, thereby enabling the model to autonomously discern implicit semantic inclinations. Moreover, we propose the Hyperbolic-Euclidean space expert to effectively capture the hierarchical structural relationships and semantic correlations between multimodal and viewpoint features, thereby enabling interpretable reasoning at the geometric representation level. Empirical evaluations conducted on three mainstream datasets demonstrate that BPDMoE-Hate not only substantially surpasses existing methodologies in performance but also offers visual explanations for viewpoint selection and hierarchical structuring, thereby advancing the field of interpretable multimodal content analysis.