Computational value analysis has largely treated values as instances of predefined universal taxonomies, making it difficult to discover culturally specific value dimensions not fully represented in existing schemes. We formulate value analysis as an open-ended discovery problem grounded in the argumentative contexts in which values are expressed. To this end, we propose an LLM-assisted framework that discovers human value dimensions directly from text without relying on predefined taxonomies. It extracts Premise-Conclusion-Stance (P-C-S) argumentative edges under explicit argument tree constraints, expands each edge into structured value-semantic descriptions spanning goal, principle, tradeoff, and perspective dimensions, and induces coherent value clusters via co-association consensus clustering over the embedding representations. We evaluate the framework across ten languages using external label alignment, internal structural stability metrics, and human judgment. Results show that the framework is robust and transferable, with the induced value structures achieving low hallucination and strong alignment with human judgments while revealing culturally distinctive value orientations that both align with and meaningfully complement established psychological frameworks. By shifting computational value analysis from taxonomy-driven classification toward open-ended discovery, our framework offers a new approach to cross-cultural value research, with implications for culturally aware AI alignment.

LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and quantitative signals. Humanities scholarship, however, requires interpretive, evidence-grounded arguments over primary sources, whose value rests on faithful quotation, verifiable provenance, and deep interpretation. Existing research agents use general planning, tool use, and reflection, leaving these scholarly operations implicit. To address this gap, we introduce **SPIRE** (**S**cholarly-**P**rimitives-**I**nspired **R**esearch **E**ngine), a multi-agent framework that realizes Scholarly Primitives, a typology of basic humanities scholarship practices, as cooperating roles over a multi-scale close-reading substrate of passages, intra-context graph communities, and cross-context semantic clusters. On a benchmark of peer-reviewed papers in classical Chinese and Greco-Roman Latin scholarship, SPIRE recovers substantially more cited primary-source evidence and receives higher blind human and LLM ratings on four scholarly quality dimensions than LLM, RAG, and generic agentic baselines. Retrieval-tier and agent ablations, with retrieval-volume and answer-length controls, trace its advantage to targeted retrieval, structured evidence selection, and claim-evidence binding. Code and benchmark data are released at https://github.com/YatingPan/SPIRE.

Sequence labeling models often benefit from incorporating external knowledge. However, this practice introduces data heterogeneity and complicates the model with additional modules, leading to increased expenses for training a high-performing model. To address this challenge, we propose a dual-stage curriculum learning (DCL) framework specifically designed for sequence labeling tasks. The DCL framework enhances training by gradually introducing data instances from easy to hard. Additionally, we introduce a dynamic metric for evaluating the difficulty levels of sequence labeling tasks. Experiments on several sequence labeling datasets show that our model enhances performance and accelerates training, mitigating the slow training issue of complex models.