Large Language Models (LLMs) are increasingly explored for mental health assessment and psychiatric decision support, but autoregressive generation remains vulnerable to hallucination, semantic drift, and weak traceability. These risks carry particular consequences in psychiatric settings because decisions depend on structured diagnostic criteria, longitudinal symptom relations, exclusion rules, and clinically interpretable evidence. Retrieval-Augmented Generation (RAG) can improve factual grounding, yet pipelines centered on dense semantic retrieval do not naturally represent the discrete topology, hierarchical organization, and multi-hop dependencies of clinical ontologies and diagnostic rules. We therefore treat neuro-symbolic RAG as an information-fusion problem for explainable mental-health applications. We organize the literature around three fusion loci: pre-retrieval fusion, intra-retrieval alignment and re-ranking, and post-retrieval decoding and decision-level fusion. These loci are compared through source heterogeneity, fusion granularity, constraint strength, and evaluative consequences. The central questions are where neural and symbolic signals meet in the RAG pipeline, which mechanism families recur at each fusion locus, and how those choices shape strengths, risks, and evaluation burdens in mental health applications. To answer them, we draw selectively on adjacent clinical, biomedical, information retrieval, and general RAG literatures. The evaluation lens moves beyond task accuracy to include faithfulness, logical consistency, uncertainty and abstention behavior, and auditability; these dimensions structure a stage-aware evaluation framework. We close by outlining challenges in temporal reasoning, patient-specific graph construction, evaluation standardization, and clinically deployable human-in-the-loop neuro-symbolic systems.
Multi-attribute decision-making (MADM) faces the fundamental challenge of fusing uncertain and often conflicting evidence from diverse sources. The Dempster-Shafer evidence theory provides a rigorous framework for this task, wherein discounting evidence to quantify its reliability is crucial for robust fusion. However, the prevailing decision pathway involving discounted evidence fusion suffers from a dual computational bottleneck: the fusion process itself scales exponentially with the discernment frame (O(2|Θ|)), and optimizing discount factors within this model is equally prohibitive, hindering real-time deployment. We observe that decision-making often requires only a partial order among alternatives. This “order-preserving” requirement opens a new avenue for complexity reduction. First, we derive an equivalent model based on Pignistic Probability, which linearizes the fusion structure. Then, under strict order-consistency, we devise an efficient numerical approximation, reducing complexity to linear in the number of sources (O(|Θ|)). Building on this, we further propose a computationally efficient strategy for optimizing discount factors. Extensive evaluations on 12 public datasets demonstrate that our method outperforms 11 baselines in decision accuracy while also delivering significantly greater computational efficiency relative to other discount factor optimization methods. This work offers a theoretically grounded fast fusion framework for evidence-based reasoning, highlighting its potential to support scalable and efficient multi-attribute decision-makingMADM in future real-time systems.
In emergency disaster response, resource allocation conflicts stemming from functional specialization often trigger noncooperative behaviors that severely compromise group decision-making efficiency. Existing social network group decision-making methods face two critical limitations: static trust models fail to capture the dynamic evolution of decision-makers’ credibility, and penalty mechanisms tend to provoke psychological resistance in high-pressure environments. To address these issues, this study develops an incentive-driven consensus framework. The approach integrates subjective trust relationships with objective behavioral similarity to construct a dynamically updated composite trust scoring mechanism, which enables adaptive allocation of decision makers’ weights. Furthermore, it introduces a harmony degree-based feedback mechanism incorporating a maximum individual cooperation willingness detection model to accurately identify noncooperative decision-makers. The core contribution lies in its weight incentive mechanism, which stimulates cooperation by increasing the decision weights of noncooperators, thereby transforming the traditional penalty paradigm into a reputation-based incentive paradigm. Case studies and comparative analyses demonstrate that the framework not only achieves consensus at lower adjustment but also effectively transforms noncooperative behavior into proactive collaboration while ensuring fairness.
In large-scale and long-term robotic tasks, multi-robot localization and mapping is essential for environmental perception and collaborative navigation. However, under real-world conditions such as GPS-denied environments, unknown initial poses, and environmental changes, establishing stable inter-robot associations remains challenging, often causing accumulated localization errors and reducing global map consistency. To address this, we present Multi-Robot LiDAR-based localization and mapping with rotation-invariant Deep Relocalization (MRDR-SLAM). The system comprises four modules: single-robot odometry, intra-robot loop closure, inter-robot retrieval and matching, and multi-robot back-end optimization and mapping. At the core of the framework is a lightweight rotation-invariant feature extraction network (LRI-Net), which extracts rotation-invariant local geometric features and constructs global descriptors to enable efficient and robust inter-robot association, cross-robot loop detection, and map registration. On the public Snail Radar dataset, LRI-Net improves mean Recall@Top1 by 11.2% over the second-best method across four scenarios. On both self-collected and public multi-robot datasets, MRDR-SLAM achieves best or competitive trajectory accuracy in multiple scenarios, with clear improvements in multi-agent group alignment and global map consistency.
Driven by advances in machine learning and sensors, data-driven state estimation has become a key technology for supporting operational decisions in safety-critical systems. However, a single point estimate fails to reveal the uncertainty and confidence associated with a prediction, which can lead to overconfident decisions; as reliance on such estimates grows, the quantification and management of uncertainty becomes a prerequisite for reliable operation. This comprehensive review analyzes uncertainty management in data-driven state estimation, taking battery state estimation, in which current state estimation and future state prediction coexist, as a representative case study. This paper organizes uncertainty along two axes, its source (Aleatoric/Epistemic) and the mechanism that handles it (memory-based before modeling, model-based within the model), and applies this taxonomy to analyze how the dominant source and the management strategy suited to it shift along the prediction horizon. In doing so, it goes beyond a simple classification of techniques to present the correspondence between uncertainty sources and management strategies, and it summarizes the main open challenges and future research directions. The insights of this paper provide a foundation for reliable and risk-aware decision-making in predictive maintenance and safety-critical systems beyond batteries.
