Automatic extraction of Medical Decision Trees (MDTs) is an important task for Clinical Decision Support Systems, but remains challenging due to the complex logical structures involved. Recent approaches based on Large Language Models (LLMs) commonly adopt generate-then-revise pipelines with self-correction to improve reasoning accuracy. However, we find that applying revision uniformly across all samples can be harmful, as it often alters already correct and stable predictions, leading to over-correction on simple cases. To alleviate this issue, we propose R³-MDT, an adaptive inference framework that dynamically selects reasoning strategies based on sample difficulty. We use model uncertainty and output consistency as internal signals to estimate difficulty and route samples into Easy, Medium, and Hard categories. For Easy samples with confident and consistent predictions, we directly retain the initial outputs. Medium-difficulty samples, which exhibit ambiguity among multiple plausible candidates, are handled via reranking to select the optimal decision tree. Hard samples, characterized by high uncertainty and structural inconsistency, are revised to rectify intractable generation failures. By adaptively selecting inference strategies based on difficulty levels, R³-MDT avoids unnecessary revisions while effectively correcting underlying reasoning failures. Experiments on the Text2DT benchmark show that R³-MDT alleviates the over-correction problem and achieves state-of-the-art performance, while auxiliary language- and task-shift evaluations indicate the broader transferability of its routing principle.

Retrieval-Augmented Generation (RAG) has been widely adopted to enhance large language models (LLMs) by incorporating external knowledge. However, the two main existing paradigms struggle with multi-hop reasoning: aggregate-first approaches suffer from high construction costs and limited adaptability to dynamic knowledge, while dynamic-first approaches rely heavily on LLM reasoning and are prone to error propagation across reasoning steps. To address these limitations, we propose SR-RAG, a symbolic reasoning framework for multi-hop question answering. SR-RAG integrates the advantages of both paradigms by dynamically generating sub-questions, performing information retrieval and symbolic encoding based on an on-the-fly graph, and using a symbolic verifier to formally validate intermediate reasoning steps to ensure the correctness of intermediate answers and the completeness of the reasoning chain . We evaluate SR-RAG on multiple multi-hop benchmarks and a medical dataset. Experimental results demonstrate that it significantly improves both accuracy and robustness.

Named Entity Recognition (NER) and Relation Extraction (RE) are two fundamental and interdependent tasks in information extraction (IE), aiming to identify entities and relations from unstructured text. Recently, generative methods have become mainstream instead of discriminative methods for IE, especially joint multi-task IE, due to their promising performance and flexibility. For joint NER and RE, existing methods suffer from misalignment between entities and relations, as well as misalignment among relations. To address these issues, we propose AnchorAlign, a novel generative method enhanced by anchor alignment. Specifically, we first introduce an anchor entity selection mechanism to identify key entities in the text as anchor points, which serve as semantic pivots to bridge the two tasks. Then, we design a dual-level anchor alignment module: at the semantic level, we construct a cross-task semantic alignment space to align the semantic representations of anchor entities and their associated relations; at the generation level, we introduce an anchor-guided generation constraint to guide the model to generate entities and relations with strict alignment based on the anchor points. Extensive experiments on five benchmark datasets show that AnchorAlign outperforms state-of-the-art baselines, demonstrating its effectiveness. Our work provides a new perspective for optimizing the joint modeling of NER and RE, and has potential to be extended to more complex multi-task IE such as NER and Event Extraction (EE).

Retrieval-Augmented Generation (RAG) grounds large language models (LLMs) in external evidence, but existing RAG systems remain poorly matched to visually rich documents. OCR-based methods discard layout and non-textual visual cues, and although vision-language models (VLMs) can better understand native visual information, existing visual retrieval approaches often compress entire pages into global embedding vectors, obscuring fine-grained spatial information such as table cells and local annotations. Meanwhile, discrete Top-K selection in cascaded architectures blocks gradient flow between retrieval and generation, preventing joint optimization.In this work, we propose E2E-VisRAG, an end-to-end visual RAG framework that operates directly on native visual patches. Building upon patch-level visual encoding, we introduce a differentiable Top-K selection mechanism to enable joint training of retrieval and generation, and employ a gated residual fusion module to inject retrieval-enhanced visual features while suppressing noise. This design abandons the traditional paradigms of relying on OCR, global embeddings, and retrieval–generation separation, maximizing the preservation of original document visual semantics. Experiments on Vidore and MMLongBench-Doc show that, under matched 3B generator capacity, E2E-VisRAG outperforms non-end-to-end visual retrieval baselines, improving both Recall@1 and Accuracy@1 over OCR-based, global-embedding, and patch-level retrieval methods.

Utilizing Large Language Models (LLMs) for complex tasks is challenging, often involving a time-consuming and uncontrollable prompt engineering process. This paper introduces a novel human-LLM interaction framework, Low-code LLM. It incorporates six types of simple low-code visual programming interactions to achieve more controllable and stable responses. Through visual interaction with a graphical user interface, users can incorporate their ideas into the process without writing trivial prompts. The proposed Low-code LLM framework consists of a Planning LLM that designs a structured planning workflow for complex tasks, which can be correspondingly edited and confirmed by users through low-code visual programming operations, and an Executing LLM that generates responses following the user-confirmed workflow. We highlight three advantages of the low-code LLM: user-friendly interaction, controllable generation, and wide applicability. We demonstrate its benefits using four typical applications. By introducing this framework, we aim to bridge the gap between humans and LLMs, enabling more effective and efficient utilization of LLMs for complex tasks. The code, prompts, and experimental details are available at https://github.com/moymix/TaskMatrix/tree/main/LowCodeLLM. A system demonstration video can be found at https://www.youtube.com/watch?v=jb2C1vaeO3E.