mlsanityData to 7 Oct 2026

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

arXiv 2606.22151 ↗First posted 20 Jun 2026cs.IRcited by 0

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

also in 1 more
Abstract (arXiv)

Scientific literature search is an information retrieval (IR) task in which ranked lists are insufficient: a researcher entering a new area needs to know not only which papers are relevant, but how they relate, where they overlap, how they differ, and what problem-method combinations are absent. Standard retrieval-augmented generation (RAG) summarizes documents independently, discarding this comparative signal. We present the Novelty-Aware Research Agent, a prototype agentic retrieval system that layers structured multi-step reasoning on a RAG pipeline through six typed-contract components: query analysis, a ReAct-style retrieval loop, relevance ranking, schema-guided contribution extraction, a three-pass comparison agent, and answer generation. Beyond returning relevant papers, it produces structured comparison artifacts: per-paper contribution records, paper-level overlaps, and a problem x method gap matrix. On a 100-paper corpus, the system supports five structured comparison capabilities that a standard RAG baseline supports none of, while remaining query-sensitive: across three main queries no paper appears in all three top-5 sets (mean pairwise Jaccard 0.12), and an extended seven-query evaluation holds the pattern across ten queries (mean Jaccard 0.115, 18 of 29 retrieved papers query-exclusive). Under author-assigned graded relevance the ranker attains mean Precision@5 1.000 and nDCG@5 0.752 on the main queries, ahead of BM25, dense, and hybrid retrieval; over ten queries Precision@5 is non-saturated at 0.980 with nDCG@5 0.739. Schema compliance is 86.7% on the main queries and 84.0% over the ten-query set, and validating 20 sampled empty gap-matrix cells yields a gap precision of 0.600. We discuss the latency-structure trade-off in agentic retrieval and identify corpus scale, author-assigned labels, and limited independent evaluation as the main limitations.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 18 of its 23 references that are in the map. First posted on arXiv 20 Jun 2026.

Builds on

18 of 23 references.

References · 18

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  5. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  6. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  7. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  8. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
  9. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  10. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  11. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,348
  12. 2023AgentBench: Evaluating LLMs as Agentscited by 1,211
6 more
  1. 2023Reasoning with Language Model is Planning with World Modelcited by 846
  2. 2023ART: Automatic multi-step reasoning and tool-use for large language modelscited by 193
  3. 2024Corrective Retrieval Augmented Generationcited by 187
  4. 2023ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Modelscited by 174
  5. 2023OpenAgents: An Open Platform for Language Agents in the Wildcited by 136
  6. 2024System for systematic literature review using multiple AI agents: Concept and an empirical evaluationcited by 13

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Agent-Orchestrated Adaptive RAG: A Comparative Study on Structured and Multi-Hop Retrieval
  2. 2026VecTree-RAG: An Agentic Retrieval-Augmented Generation Framework Combining Vector and Tree Retrieval for Efficiency and Accuracy
  3. 2026ScholarQuest: A Taxonomy-Guided Benchmark for Agentic Academic Paper Search in Open Literature Environments
  4. 2026Agent-Enhanced Heterogeneous Graph RAG for Academic Question Answering
  5. 2026A-RAG: Scaling Agentic Retrieval-Augmented Generation via Hierarchical Retrieval Interfaces
  6. 2025Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review
  7. 2026Retrieval as Reasoning: Self-Evolving Agent-Native Retrieval via LLM-Wiki
  8. 2026Structured Linked Data as a Memory Layer for Agent-Orchestrated Retrieval