HFEPX · Eval paper review
Yating Pan, Jiajun Zhang, Jun Wang, Qi Su
Published
May 29, 2026
Citations
0
Trust level
Low
Usefulness score
0/100 (Low)
Extraction confidence
15% (Low)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Aug 31, 2026
This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.
Use this as background context only. Do not make protocol decisions from this page alone.
All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.
Best use
Background context only
Use if you need
A secondary eval reference to pair with stronger protocol papers.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
This paper looks adjacent to evaluation work, but not like a strong protocol reference.
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
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 (Scholarly-Primitives-Inspired Research Engine), 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.
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
None explicit
No explicit feedback protocol extracted.
"LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and quantitative signals."
None explicit
Validate eval design from full paper text.
"LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and quantitative signals."
Not reported
No explicit QC controls found.
"LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and quantitative signals."
Not extracted
No benchmark anchors detected.
"LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and quantitative signals."
Not extracted
No metric anchors detected.
"LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and quantitative signals."
No benchmark or dataset names were extracted from the available abstract.
No metric terms were extracted from the available abstract.
LLM-based research agents have advanced rapidly in science and engineering, where research is organized around executable experiments, code, and quantitative signals.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Human feedback protocol is explicit
No explicit human feedback protocol detected.
Evaluation mode is explicit
No clear evaluation mode extracted.
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
Metric reporting is present
No metric terms extracted.
