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

arXiv: https://arxiv.org/abs/2606.22151 · HTML: https://mlsanity.com/paper/2606.22151/

## Key facts

- No citing papers in the map yet (data through 14 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.

## Placed in

- Retrieval-augmented generation / [Agentic RAG, agents and memory](https://mlsanity.com/rag/agentic-rag-agents-and-memory/)
- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 18 of 18)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [AgentBench: Evaluating LLMs as Agents](https://mlsanity.com/paper/2308.03688/) (2023)
- [Reasoning with Language Model is Planning with World Model](https://mlsanity.com/paper/2305.14992/) (2023)
- [ART: Automatic multi-step reasoning and tool-use for large language models](https://mlsanity.com/paper/2303.09014/) (2023)
- [Corrective Retrieval Augmented Generation](https://mlsanity.com/paper/2401.15884/) (2024)
- [ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models](https://mlsanity.com/paper/2305.18323/) (2023)
- [OpenAgents: An Open Platform for Language Agents in the Wild](https://mlsanity.com/paper/2310.10634/) (2023)
- [System for systematic literature review using multiple AI agents: Concept and an empirical evaluation](https://mlsanity.com/paper/2403.08399/) (2024)

## 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.