Evidence grounding makes generated content verifiable by linking statements to their sources. However, effective verifiability requires two properties that existing methods fail to jointly provide: *verification granularity*, the ability to localize fine-grained evidence for each statement, and *verification completeness*, the ability to capture both support and contradiction. Existing methods either operate at granularities too coarse for rapid inspection or are support-only. We formalize *polarity-aware evidence grounding*: linking each response sentence to its contextual evidence with a supporting, contradicting, or irrelevant label. We construct **eTracer-Bench**, a human-annotated benchmark with 7,905 supporting and contradicting sentence pairs across seven domains, covering both explicit and implicit contradictions. We further propose **eTracer**, a post-hoc framework whose context-aware signed scorer recovers the full evidence map in a single forward pass per response, with localization and polarity emerging jointly from one mechanism. **eTracer** achieves the best overall Citation F1 of **82.39**, driven by a Contradict F1 of **89.20**. A pre-registered within-subjects user study (n = 30) shows that polarity-aware grounding nearly doubles contradiction detection over the document-level baseline (**27.9% → 51.9%**, Cohen’s d = 0.82, p = 0.0003), and suggests a false-completeness bias under which support-only grounding fails to improve over this baseline (**23.4% vs. 27.9%**). Dataset and code: https://github.com/chubohao/eTracer.

ArchEHR-QA is a grounded question-answering (QA) task for electronic health records (EHRs) comprising four subtasks: (1) question rewriting, (2) evidence identification, (3) grounded answer generation, and (4) answer-evidence alignment. In this work, we present a modular pipeline centered on retrieval-augmented generation (RAG). For Subtask 1, RAG few-shot prompting outperformed both PEFT and prompt-only baselines on the development set; however, Claude few-shot proved substantially more robust on the test set, ranking 6th out of 13 participating teams (score: 26.94). For Subtask 2, a union ensemble of open-weight LLMs (GPT-OSS-120B and Qwen3-30B-A3B) achieved a 56.7 micro-F1, rivaling the proprietary Claude Opus 4.6 while demonstrating higher recall (53.6). For Subtask 3, our RAG few-shot approach using Claude Opus 4.5 achieved the 1st place out of 13 participating teams (score: 36.33). Finally, for Subtask 4, a zero-shot Claude Opus 4.6 configuration ranked 2nd out of 16 participating teams (score: 81.3).

While document summarization with LLMs has enhanced access to textual information, concerns about the factual accuracy of these summaries persist (e.g., hallucination), especially in the medical domain. Tracing source evidence from which summaries are derived enables users to assess their accuracy, thereby alleviating this concern. In this paper, we introduce TracSum, a novel benchmark for traceable, aspect-based summarization, in which generated summaries are paired with sentence-level citations, enabling users to trace back to the original context. First, we annotate 500 medical abstracts for seven key medical aspects, yielding 3.5K summary-citations pairs. We then propose a fine-grained evaluation framework for this new task, designed to assess the completeness and consistency of generated content using four metrics. Finally, we introduce a summarization pipeline, Track-Then-Sum, which serves as a baseline method for comparison. In experiments, we evaluate both this baseline and a set of LLMs on TracSum, and conduct a human evaluation to assess the evaluation results. The findings demonstrate that TracSum can serve as an effective benchmark for traceable, aspect-based summarization tasks. We also observe that explicitly performing sentence-level tracking prior to summarization enhances generation accuracy, while incorporating the full context further improves summary completeness. Source code and dataset are available at https://github.com/chubohao/TracSum.

Automatically answering patient questions based on electronic health records (EHRs) requires systems that both identify relevant evidence and generate accurate, grounded responses. We present a three-part pipeline developed by WisPerMed for the ArchEHR-QA 2025 shared task. First, a fine-tuned BioClinicalBERT model classifies note sentences by their relevance using synonym-based and paraphrased data augmentation. Second, a constrained generation step uses DistilBART-MedSummary to produce faithful answers strictly limited to top-ranked evidence. Third, we align each answer sentence to its supporting evidence via BiomedBERT embeddings and ROUGE-based similarity scoring to ensure citation transparency. Our system achieved a 35.0% overall score on the hidden test set, outperforming the organizer’s baseline by 4.3 percentage points. Gains in BERTScore (+44%) and SARI (+119%) highlight substantial improvements in semantic accuracy and relevance. This modular approach demonstrates that enforcing evidence-awareness and citation grounding enhances both answer quality and trustworthiness in clinical QA systems.