Literature-Grounded Novelty Assessment of Scientific Ideas (2025)

Abstract: Automated scientific idea generation systems have made remarkable progress, yet the automatic evaluation of idea novelty remains a critical and underexplored challenge. Manual evaluation of novelty through literature review is labor-intensive, prone to error due to subjectivity, and impractical at scale. To address these issues, we propose the **Idea Novelty Checker**, an LLM-based retrieval-augmented generation (RAG) framework that leverages a two-stage retrieve-then-rerank approach. The Idea Novelty Checker first collects a broad set of relevant papers using keyword and snippet-based retrieval, then refines this collection through embedding-based filtering followed by facet-based LLM re-ranking. It incorporates expert-labeled examples to guide the system in comparing papers for novelty evaluation and in generating literature-grounded reasoning. Our extensive experiments demonstrate that our novelty checker achieves approximately 13% higher agreement than existing approaches. Ablation studies further showcases the importance of the facet-based re-ranker in identifying the most relevant literature for novelty evaluation.

## introduction novelty evaluation is foundational for determining whether ideas in scientific research, product development, or creative ideation introduce meaningful innovation relative to prior work. yet, as the volume of published literature grows exponentially, manual verification of originality becomes impractical. this is further complicated by the inherent subjectivity of novelty judgments, which is why experts can more easily decide on similarity of two ideas @xcite and often struggle to articulate why one idea is more novel than another. further the evaluation becomes subjective as it also depends on personal knowledge and intuition gained from scientific literature @xcite . automated systems attempt to address this challenge by defining novelty as differences observed while comparing new ideas against prior work with similarity measures, but they exhibit critical limitations. prior work has evolved from using ngram frequency and lexical metrics (tf-idf, lda) @xcite to semantic embeddings @xcite that capture similarity but don't capture paraphrased variations of ideas and papers. moreover, while recent approaches have adopted llm-augmented pipelines to generate numerical scores @xcite @xcite @xcite @xcite @xcite @xcite or provide binary classifications (novel versus not novel) @xcite @xcite , they do not ground the rationales in existing works and frequently fail to capture subtle variations in phrasing, resulting in the misclassification of well-documented ideas as novel @xcite . this shortcoming makes it difficult for researchers to distinguish novel ideas from incremental contributions or subtle cases of plagiarism @xcite . moreover, all these approaches hinge on the successful retrieval of relevant literature for a given idea, a task that remains inherently challenging @xcite @xcite @xcite . prior works @xcite extract keywords from the idea to search for papers, so important work can easily be missed if the relevant paper does not have the exact keyword. this undermines the reliability of the novelty evaluation process. we address these gaps with idea novelty checker, a retrieval-augmented llm pipeline that assesses an idea's novelty by comparing it to a set of the most relevant papers. first, idea novelty checkercollects a broad set of relevant papers using keyword and snippet-based retrieval, as well as by retrieving papers similar to any seed papers provided. next, an embedding-based similarity search filters this large collection, and a facet-based llm re-ranker @xcite further narrows the set by comparing idea facets (purpose, mechanism, evaluation, and application) with those in the retrieved papers. finally, expert-annotated in-context examples of novel and not novel ideas guide the system in generating literature-grounded rationales, mitigating subjectivity in novelty judgments. in our experiments, we compared idea novelty checkerwith baselines such as zero-shot prompting, prompt optimization approaches (dspy and textgrad), and expert-based openreview examples. our results show that expert-annotated in-context examples significantly improve classification performance. comparisons with systems like ai scientist and ai researcher further demonstrate that our idea novelty checkerachieves higher agreement with expert judgments, and our ablation studies shows that the combined retrieval and twostage re-ranking are critical for identifying the most relevant papers. our contributions are as follows: ## formative study on challenges in evaluating novelty evaluating idea novelty in scientific literature is inherently challenging because the criteria for novelty are subjective and can be defined in multiple ways. we conducted a formative study, referred to as the expert-annotated study throughout the paper, where the first and second authors reviewed the novelty of ideas based on the most relevant papers. to assess idea novelty relative to existing literature, our study engaged experts who evaluated 51 ideas, comprising of 46 generated by the scideator system @xcite ) and 5 adapted from accepted and rejected papers from openreview (iclr 22, neurips 23). 2 each idea was classified into one of three categories: novel, moderately novel, or not novel. for every idea, we identified the most relevant papers through a two-step process: candidate papers were initially gathered using keyword-based queries and subsequently re-ranked using an llm-based reranker @xcite according to their overall relevance to the idea. the experts achieved a moderate agreement (cohen's @xmath0.64). a key challenge identified was that experts sometimes relied on their broader domain knowledge rather than restricting their judgments to the most relevant papers, as the top papers alone were often not sufficient. additionally, using three categories led to disagreements, as the distinction between novel and moderate novelty is itself subjective. building on these observations, we conducted a second study to minimize the influence of external knowledge. in this study, experts were instructed to base their judgments solely on the provided papers, and the categories were simplified to just two: novel and not novel. inspired by prior work @xcite @xcite @xcite @xcite that categorizes research ideas into core facets such as purpose (the problem being addressed by the paper) and mechanism (the proposed solution to the problem), we define novelty as follows: an idea is considered novel if it differs from all retrieved papers in at least one core facet for the topic at hand-namely, purpose (i.e., a distinct objective), mechanism (i.e., a distinct technical approach), or evaluation (i.e., a distinct validation method). an idea is also considered novel if it uniquely combines these facets or applies them to a new application domain. using this controlled framework, we reannotated a set of ideas and evaluated 51 ideas, comprising of 34 new ones generated by @xcite and 17 from the previous study where external knowledge had influenced novelty judgments. by narrowing the focus to the relevant papers alone, we observed fewer disagreements and achieved a higher agreement rate (cohen's @xmath1.68). of the 8 instances of disagreement, in 4 cases one expert overlooked details from the paper, in 2 cases the experts differed in their perception of subtle contributions to novelty, and in the remaining 2 cases no specific comments were provided. this formative study highlights that a robust novelty checker depends critically on high-quality retrieval and a well-defined notion of novelty. these findings directly inform our methodology described in the following section. ## methodology: idea novelty checker based on our formative study findings, our novelty checker is designed with two key components that address the two critical challenges: c1 ensuring high-quality retrieval of papers relevant to the idea for novelty assessment and c2 establishing clear criteria for judging novelty. the challenge c1 arises from the vast space of overlapping papers-there are hundreds of millions of potential matches. to address this, our system first filters the scientific literature to collect the most relevant papers for a given idea (see section 4.1 and step 1 and 2 in figure 1 ). the input idea is then compared to each paper in this collection by prompting an llm (see section 4.2). in tackling challenge c2 , which arises from the inherent subjectivity and multiple definitions of novelty, the novelty checker leverages expertlabeled examples of novel and not novel ideas from the formative study. it generates reasoning grounded not only in comparisons against the most relevant papers but also in the standardized definition of novelty introduced earlier, which helps counteract subjectivity (see step 3 in figure 1 ). below, we detail these two components. this idea is not novel because… this idea is novel because… this idea is not novel because… papers related to input papers from semantic scholar papers related to input papers snippet api idea-snippet papers ## implementation & baselines dataset: from our formative study, we collected 67 consensus-labeled examples (39 labeled as novel and 28 as non-novel). we split into training and test sets (35 for training and 32 for testing) with a balanced distribution of novel and non-novel ideas. please refer to ## experiments in this section, we first compare different baselines on the dataset for novelty evaluations (section 6.1). next we present our findings from ablation studies that shows the imporance of each component in our approach (section 6.2). ablations studies we supplement these findings with qualitative examples of expert-labeled ideas and compare our setup with recent novelty checkers (section 6.3). we conclude with insights from prompt optimization experiments that highlights the sensitivity of llms to prompt variations for novelty evaluation tasks (section 6.4). ## conclusion in this work, we propose idea novelty checker, a retrieval-augmented pipeline for evaluating the novelty of scientific ideas and generating literaturegrounded rationales. our formative study highlighted two main challenges in evaluating novelty: (1) retrieving the most relevant papers from a vast corpus, and (2) establishing a fixed notion of novelty due to its inherent subjectivity. to address the latter, we incorporate expert-annotated examples in our novelty checker where we consider an idea to be novel within a given topic domain if it (1) differs from all retrieved papers in at least one core facet-namely, purpose (a new objective), mechanism (a distinct technical approach), or evaluation (a distinct validation method); (2) uniquely combines these facets; or (3) applies them to a new application domain. our experiments on an expert-annotated dataset demonstrate that idea novelty checker outperforms two well-known recent baselines, and our ablation studies confirm the importance of each component in our system. furthermore, qualitative comparisons and analyses of prompt sensitivity provide additional insights into novelty evaluation. 103 while idea novelty checker is superior in many aspects, it also has some limitations. for instance, due to context size constraints (with fifteen incontext examples for both novel and not novel categories), our analysis is restricted to the top 10 retrieved papers, which may disproportionately influence the overall novelty assessment. additionally, our definition of novelty relies on expert annotations, and the same annotators who provided the incontext examples also classified the test ideas. this could potentially give our approach an advantage in understanding our view of novelty. moreover, many of the ideas used for testing were generated by the same system @xcite that produced the in-context examples, although some ideas were sourced from openreview. in future work, we aim to address these limitations by expanding the literature scope using tools such as deepresearch and scholarqa and further refining our novelty evaluation to view novelty as a continuum rather than binary classification. highlighted text shows unique instructions used to evaluate the novelty of ideas. the final test accuracy was 0.78125, showing that none of the optimized prompts (1 to 12) improved over the original.