AI evidence extraction

Novelty-Aware Agentic Retrieval: Comparing Research Contributions Through Structured Multi-Step Reasoning

Shou-Tzu Han · ArXiv.org · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

Methodology & findings

Study design

Prototype system evaluation on a 100-paper corpus using: (a) automated retrieval and compliance metrics, (b) graded-relevance retrieval quality with author-assigned labels (Precision@5, nDCG@5, Recall@5, MRR), (c) schema failure analysis, (d) query-sensitivity analysis, (e) ReAct sparse-query stress test, (f) deterministic gap-matrix construction and validation, (g) baseline RAG comparison, (h) BM25/dense/hybrid retriever comparison, (i) component ablation, (j) qualitative case study, (k) single-rater external usefulness assessment, and (l) extended seven-query evaluation for robustness..

Primary method

Design science approach with iterative prototyping, component-based architecture design, and evaluation through automated metrics and user assessment

Main result

The central result is that "the system supports five structured comparison capabilities that a standard RAG baseline supports none of, while remaining query-sensitive. Across the three main queries, no paper appears in all three top-5 sets and the mean pairwise Jaccard similarity is 0.12." On graded-relevance metrics, "the ranker attains mean Precision@5 of 1.000 and nDCG@5 of 0.752 on the three main queries, ahead of BM25, dense, and hybrid retrieval; over all ten queries, Precision@5 remains high but non-saturated at 0.980, with nDCG@5 of 0.739." Additionally, "a validation of 20 sampled empty gap-matrix cells yields gap precision of 0.600."

Research paradigm

Design science / Pragmatist

Author conclusions

"We presented the Novelty-Aware Research Agent, a prototype agentic retrieval system that layers structured multi-step reasoning on a RAG pipeline to compare research contributions across a retrieved corpus." The system "demonstrates query-sensitive retrieval: no paper appears in all three main top-5 sets, and an extended ten-query evaluation preserves this pattern with mean pairwise Jaccard similarity of 0.115 and 18 query-exclusive papers among 29 distinct retrieved papers." The key payoff is that "the system supports 5/5 structured comparison capabilities versus 0/5 for a baseline RAG system." The authors conclude: "These results support treating retrieval output as structured data for contribution-level comparison: the payoff is a single inspectable map of what a retrieved set covers and what it leaves open, in place of a ranked list the researcher would otherwise cross-reference by hand."

Risk of bias

Single-rater external usefulness assessment (not multi-rater); Author-assigned relevance labels (potential author bias in labeling); Limited corpus size (100 papers) may not generalize; Evaluation on earlier 55-paper version for external usefulness assessment, not final system; No independent benchmark or third-party evaluation; Author-assigned relevance labels (single annotator, potential author bias); Single external rater for usefulness assessment; Ratings collected on earlier 55-paper system version, not final outputs; Small corpus (100 papers) limits generalizability; Graded relevance labels may not represent consensus quality standards; Single author-assigned graded relevance labels (no inter-rater agreement reported); Single external rater for usefulness assessment (not multiple independent raters); Author familiarity with system and corpus may bias qualitative case study interpretation; Limited corpus (100 papers) may not represent full domain diversity; Earlier 55-paper version used for external usefulness assessment (not final system); Manual validation of only 20 gap-matrix cells (small sample for precision estimation)

Limitations

"As a prototype, the system has two scope limits that frame its future work
First, the 100-paper corpus, though substantially larger and more diverse than the initial set, remains modest relative to large-scale IR benchmarks
Second, evaluation uses author-assigned relevance labels and manually labeled gap-validation judgments, with usefulness assessed by a single external rater whose ratings were collected on an earlier 55-paper version of the outputs
additional independent raters and a broader multi-rater human study on the final outputs would further strengthen the conclusions." Additionally, "the latency–structure trade-off inherent to agentic retrieval" is noted, where the system takes roughly "seven times longer" (23 seconds versus 3-4 seconds for baseline RAG).

Open questions raised

Large-scale evaluation beyond 100-paper corpus
Independent human assessment with multiple raters
Set-aware differentiation pass that explicitly contrasts papers against one another rather than in isolation
Richer gap characterization with deeper methodological analysis rather than high-level labels
Evaluation on larger corpus scales to demonstrate robustness of query-sensitivity and schema compliance
Independent human assessment with multiple raters beyond single-rater external validation

Data: 100-paper corpus on agentic AI and retrieval (described in Section 4.2, corpus details not separately released); 100-paper agentic AI and retrieval corpus (used in evaluation); Seven additional queries (R4-R10) evaluated on same corpus; 100-paper corpus (agentic AI and retrieval domain) - described but full corpus list not explicitly provided in paper; Code and live demo referenced: https://github.com/52147/Novelty-Aware-Research-Agent and https://debrah1-novelty-aware-research-agent.hf.spaceCode: https://github.com/52147/Novelty-Aware-Research-Agent; Live demo: https://debrah1-novelty-aware-research-agent.hf.space; https://debrah1-novelty-aware-research-agent.hf.space (live demo)Extracted from: pdfAgreement 62%

Explore related topics

RAG for Research Literature Discovery Design Science