Xiaoqing Wu

Xiaoqing Wu, Feifei Li, Haoliang Ming et al.

Evidence construction systems--chunk retrieval, agent memory, knowledge-graph traversal, and thematic indexing--are evaluated on separate benchmarks with incompatible corpora and metrics, making cross-paradigm diagnosis impossible. We introduce AuthTrace, the first diagnostic benchmark that places all major paradigms on a single corpus and query set by exploiting the dual nature of single-author collections. Built on thematically dense corpora where all texts share style, topic, and vocabulary, AuthTrace provides 2,099 instances with exhaustive gold evidence and a fan-in gradient as the primary diagnostic axis. Comparing eight systems across two QA models, we find that (1) evidence recall--not precision--is the dominant predictor of answer quality (r = 0.96); (2) fan-in exposes paradigm-specific collapse patterns, with flat retrieval degrading 3x faster than structured-evidence systems; and (3) full-context prompting fails uniformly, establishing evidence construction as a necessary capacity beyond raw corpus exposure.

Haoliang Ming, Feifei Li, Xiaoqing Wu et al.

LLM agents require retrieval to behave less like one-shot context fetching and more like reasoning: searching, reading, traversing, and deciding when evidence is sufficient. However, Retrieval-Augmented Generation (RAG) typically organizes external knowledge as flat chunks retrieved by embedding similarity, exposing a retrieval-as-lookup interface that is poorly aligned with tool-using agents. We propose LLM-Wiki, an agent-native retrieval system that operationalizes the Retrieval-as-Reasoning paradigm by treating external knowledge as a compilable, composable, and self-evolving structure rather than a static retrieval index. LLM-Wiki compiles documents into structured Wiki pages with bidirectional links, exposes search, read, and link-following operations through standard tool-calling interfaces, and introduces an Error Book for persistent structural and semantic self-correction. On HotpotQA, MuSiQue, and 2WikiMultiHopQA, LLM-Wiki outperforms seven baselines, including HippoRAG 2, LightRAG, and GraphRAG, with gains of 2.0-8.1 F1 points over the strongest graph-based baseline and larger gains over Dense RAG. On AuthTrace, LLM-Wiki achieves the best overall accuracy, with especially strong gains on multi-document structured queries, showing that compilation-based knowledge organization generalizes beyond chain-style multi-hop reasoning.

Shuhao Chen, Weisen Jiang, Changmiao Wang et al.

Inpatient medication recommendation requires clinicians to repeatedly select specific medications, doses, and routes as a patient's condition evolves. Existing benchmarks formulate this task as admission-level prediction over coarse drug codes with multi-hot diagnostic and procedure code inputs, failing to capture the per-timepoint, information-rich nature of real prescribing. We propose RxEval, a prescription-level benchmark that evaluates LLM prescribing capability by multiple-choice questions: each question presents a detailed patient profile and time-ordered clinical trajectory, requiring selection of specific medication-dose-route triples from real prescriptions and patient-specific distractors generated via reasoning-chain perturbation. RxEval comprises 1,547 questions spanning 584 patients, 18 diagnostic categories, and 969 unique medications. Evaluation of 16 LLMs shows that RxEval is both challenging and discriminative: F1 ranges from 45.18 to 77.10 across models, and the best Exact Match is only 46.10%. Error analysis reveals that even frontier models may overlook stated patient information and fail to derive clinical conclusions.