Large Language Models for Scientific Idea Generation: A Creativity-Centered Survey
Authors: Fatemeh Shahhosseini
Arash Marioriyad
Ali Momen
Mahdieh Soleymani Baghshah
Mohammad Hossein Rohban
Shaghayegh Haghjooy Javanmard
Venue: Accepted by TMLR ()

Abstract: Scientific idea generation is central to discovery, requiring the joint satisfaction of novelty and scientific soundness. Unlike standard reasoning or general creative generation, scientific ideation is inherently open-ended and multi-objective, making its automation particularly challenging. Recent advances in large language models (LLMs) have enabled the generation of coherent and plausible  scientific ideas, yet the nature and limits of their creative capabilities remain poorly understood. This survey provides a structured synthesis of methods for LLM-driven scientific ideation, focusing on how different approaches trade off novelty and scientific validity. We organize existing methods into five complementary families: External knowledge augmentation, Prompt-based distributional steering, Inference-time scaling, Multi-agent collaboration, and Parameter-level adaptation. To interpret their contributions, we adopt two complementary creativity frameworks: Boden’s taxonomy to characterize the expected level of creative novelty, and Rhodes’ 4Ps framework to analyze the aspects or sources of creativity emphasized by each method. By aligning methodological developments with cognitive creativity frameworks, this survey clarifies the evaluation landscape and identifies key challenges and directions for reliable and systematic LLM-based scientific discovery.

Published in Transactions Machine Learning Research (02/2026)
Large Language Models for Scientific Idea Generation: A Creativity-Centered Survey
Fatemeh Shahhosseini
1alahhoossenni.20@gmmaa com
Department of Computer Engineering
Sharif University of Technology
Arash Marioriyad
wmsahmarriorryydd@@gaal.ooo
Department of Computer Engineering
Sharif University of Technology
Ali Momen
ali. wi.mommen&99988mmiil.com
Department of Computeer Engineering
Iran Universily of Science and Technology
Mahdieh Soleymani Baghshah
solegmani@shariff.ede
Department of Computer Engineering Sharif University of Technology
Mohammad Hossein Rohban"
rolsban Sibbrff@shrrf..cdd
Department of Computer Engineering Sharif University of Technology
Shaghayegh Haghjooy Javanmard'
sh haghjoo@med. mui. ac.
Department of Physiology IsJahan University of Medical Sciences
Reviewed on OpenReview: https: openreveew net/ forum? id= 9KMoj
Abstract
Scientific idea generation central to discovery, requiring the joint satisfaction of novelty and scientifie soundness. Unlike standard reasoning or general creative generation, scientific ideation is inherently open-ended and multi-objective, making its automation particularly challenging. Recent advances in large language mod- els (LLMss have enabled the generation of coherent and plausible scientific ideas, yet the nature and limits of their creative capabilities remain poorly understood. This survey provides a structured synthesis of methods for LLM-driven scientific ideation, focusing on how different approaches trade off novelty and scientific va- lidity. We organize existing methods into five complementary families: Exter- nal knowledge augmentation, Prompt-based distributional steering, Inference-time scaling, Multi-agent collaboration, and Parameter-lewe adaptation. To interpret their contributions, we adopt two complementary creativity frameworks: Boden's
'Corresponding Author

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Published in Transactions Machine Learning Research (02/2026)
taxonomy to characterize the expected level of creative novelty, and Rhodes' 4Ps framework to analyze the aspects or sources of creativity emphasized by cach method. By aligning methodological developments with cognitive creativity frame- works, this survey clarifies the evaluation landscape and identifies key challenges and directions for reliable and systematic LLM-based scientific discovery.
1 Introduction
Scientific discovery has long stood at the frontier of human progress, from uncovering the laws of physics to developing transformative medicines. At the heart of this process lies scientific idea generation-thae ability to propose hypotheses that are not only new but also scientifically mean- ingful. In cognitive science, this ability is captured by creativity, which defines as requiring two jointly nocessary components: novelty and valueness[Bodor 2004). Novelty refers to the degree to which an idea departs from existing knowledge or established pailerns, while valueness concerus whether the idea correct, plausible, feasible, or useful within a scientific context. Novelty without value results in arbitrary or incoherent ideas; value without novelty yiclds incremental or redundant contributions. Scientific ideation, therefore, emerges only when these two criteria coexist.
Recent advances in large language models (LLMs) offer an unprecedented opportunity to augment this process. On the valueness side, research in reasoning and factuality has improved model relia- bility. Techniques such as Shain-of-Thoughh prompting (Wei et al. 2022), Self-Consiste Consistency decod- ing (Wang et al. 2023c). ) Troe-of-Thoughts (Yao al. 2023)) and iterative refinement methods like Self-Retine (Madaan et al. 2023 enhance structured reasoning. Inference-ting scaling approaches, from repeated sampling (Brown et al.] 2024) to advanced search (Sprueill et. al.) 2023 and fron- tier RL post-training models (DeepSeek-Al et al., 2025 Yin et al., 2025b), further demonstrate that compute can unlock deeper reasoning abilities. Complementary strategics including retrieval- augmented generation (RAG) (Lewis et al. 2020) and agentic tool use inlegrate external knowledge sources and fact-checking pipelines to ensure factual grounding (Schick et al.) 2023). Collectively, these advances provide a foundation for promoting valueness and correctness in scientific ideation.
In contrast, efforts explicitly largeting novelly remain more limited and domain-deeendeet. Alignment-oriented methods push LLMs toward greater originality. Extensions of direct prefer- ence optimization (DPO) such Creative Preference Optimization (CRPO) (Ismayilzada et al.) 2025a) and Diverse Preference Optimization (DivPO) (Lanchantin et al., 2025) explicitly inject signals for novelty and diversity into the optimization process. However, such methods often excel domains like storytelling or open-ended writing, where factual complexity is limited, but struggle to generalize to science, where creativity must remain tightly coupled with empirical soundness.
The convergence of reasoning and generative creativity highlights why scientifie idea generation is uniquely challenging: it is inherently multi-objective problem where calls for a deeper understand- ing of creativity itself. Creativity, higher-order cognitive function of the human mind that LLMs do not yet possess (Liu et al. 2025a), remains poorly understood within this emerging field.
To ground our analysis, we turn to cognitive science, which offers robust frameworks for studying and categorizing creativity and helps clarify which cognitive dimensions are captured by current LLM-based approaches and which remain underexplored. Rhodes' seminal 4Ps framework (Rhodes, 1961 conceptualizes creativity as the interaction among four dimensions: the person (the individual
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or system generating ideas), the process (the cognitive or algorithmic mechanisms involved), the press (the surrounding environment and context), and the product (the evaluable outcome). While all four are jointly constitutive of creativity, later work emphasizes that the product is typically the dependent, measurable outcome, whereas person, process, and press act as the primary sources shaping its emergence (Kozbelt et al. 2010 Gruszka & Tang 2017
Complementing this perspective, Boden (Boden 2004] distinguishes three levels of creative output: combinatorial creativity, arising from novel recombinations of existing concepts; exploratory crc- ativity, which results from structured search within a given conceptual space; and transformational creativity, which alters expands the space itself. In this survey, we use Boden's taxonomy to characterize the expected creative potential of LLM-based scientific ideation methods according to their dominant mechanisms. Methods centered on rocombination are thus expected to yield combi- natorial creativity, those enabling systematic exploration to favor exploratory creativity, and those that challenge established assumptions to potentially approach transformational creativity. These assignments are conceptual ,reflccting tendencies rather than guaranteed outcomes.
Building on these foundations, this survey-unlike existing works that primarily emphasize engi-
neering pipelines, agent architectures, task coverage (Zheng et al. 2025 Luo al. 2025
Eger
et al. 2025 Han & Cheng, 2025 EmergenttMind Research Group 2024 Ren et al. 2025
examines
how approaches in LLM-driven scientific discovery manage the dual objectives of scientific valueness and novelty. We propose that these objectives can be systematically understood through the lens of output creativity levels (combinatorial, exploratory, transformational) and creativity sources (person, process, press), as illustrated in Figure 11 which maps current meth to their primary sources of creativity. Although Boden's and Rhodes' frameworks are classical, their intentionally high-level and orthogonal dimensions allow them to map naturally onto modern LLM-based systems. The 4Ps emerged empirically from analyses of creativity definitions and were independently rediscovered across multiple studies, supporting their robustness as an organizing framework (Jordanous) 2012)
Unlike alternative creativity-level frameworks such as the 4C model (Kaufman & Beghetto 2009
whose categorics span everyday and personal creativity (c.g., mini-c and littlc-c), Boden's 's taxonomy is tailored research-level idea generation, where novelty arises through recombination, structured
exploration, or transformation of conceptual spaces; as result, these frameworks continue to serve
foundational basclincs in recent studics of LLM crcativity (Nagarajan et al. 2025 Gu et al.
2025 Jackson et al. 2025 Ismayilzada et al. 2025b).
To make this analysis concrete, as illustrated in Figure 3 we categorize existing methods into five complementary families according to their primary mechanisms for influencing novelty and valueness in LLM-based ideation:
1. Knowledge and retrieval augmentation: A central line of work enhances LLMs with relational domain knowledge, compensating for the limitations of static pre-training. Adding related literature or factual resources into the model's context not only grounds outputs in established knowledge and reduces hallucinations, but also acts a source of crcativity infusion from the environment. In 4Ps, this reflects the press, balancing correct- ness with novelty derived from recombining known elements. Such methods are most likely to foster combinatorial creativity. We expand on these approaches in Section 2
2. Prompt-based distributional steering: Another family of approaches steers LLMs toward more original ideas through input manipulations. Manipulations like role priming,
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constraint-based prompting, or structured scaffolds encourage less typical outputs without parameter changes. Rooted mostly in the press, they often yicld combinatorial creativity but can also broaden into exploratory forms when prompts guide systematic exploration. Details and more examples appear in Section B
3. Search and sampling expansions: Inference-time scaling via iterative refinement or branching exploration expand the capabilities of LLMs beyond single-pass decoding by enabling dynamic exploration and without additional model training. By systematically enlarging the idea search space, these methods increase the chance of exploratory creativity, though correctness depends on strong evaluation signals. This family maps most closely to the process dimension. We discuss them further Section
4. Multi-agent and deliberative systems: Beyond single-agent reasoning, multi-agent systems emulate the collaborative dynamiis of scientific leams. Beyond automation such setups featuring debate, critique, role specialization costi emergent interactions that extend reasoning beyond individual limits, often surfacing cross-discipliaryy boundaries or unconventional ideas. These dynamics promote critical thinking, analogy-making, and out-of-the-box problem Boving-hallmarkee of transformational creativity. This line of work highlights the process dimension and is discussed further in Section
5. Parameter adaptation and learning: Fine-tuming, reinforcement learning, and hybrid alignment approaches directly modify model's parameters, internalizing new strategies and knowledge. Acting on the person dimension, as models internalize reasoning palterns, the likelihood of reaching closer higher-level creative outcomes may increase. We return to these methods in Scction 0
This taxonomy is derived by synthesizing recurring design patterns in the LLM-based scientific discovery literature and is intended to be general and extensible rather than exhaustive; hybrid and future methods may span or extend multiple categories. Table 3 summarizes how the five method categorics are empirically observed or thcorctically expectod to influence the crcativity.
Beyond methodological categorization, this survey also examines how evaluation practices shape and constrain scientific creativity. In particular, analyze the current landscape of metrics and methods for assessing generated ideas, highlighting persistent challenges of subjectivity, reliability, and comparability. These issues map naturally to the product dimension of Rhodes' 4Ps framework, reflecting how scientific; ideas are judged once produced. We discuss evaluation metrics in detail in Section
Finally, we outline open directions in Section Current methods tend to remain at the combinato- rial or exploratory level, with transformational creativity still elusive. Much progress has been made from the "process" and "press" perspectives, yet the "person" and "product" dimensions remain underexplored. For example, shifting from idea-level search to agent-level search, as it is done in recent open-ended methods (Zhang et al. 2025a),, may unlock richer person-level creativity. At a deeper level, the auto-regrossive training paradigm or Ilm attention-based architecture itsclf may impose structural limits on the creative potential of LLMs in science (Nagarajan el al. 2025 On the product side, the lack of standardized metries and benchmarks for evaluating generated scien- tific ideas leaves creativity assessment vague and subjective. Addressing these gaps could move us closer to realizing LLMs true partners in scientific discovery.
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Figure 1: Overview of the training-free methods and requirements for scientific idea generation using LLMs. (a) Knowledge augmentation: The pipeline begins with integrating external sources such as research papers, databases, and knowledge graphs to ground the model's reasoning, either through relational linking or semantic similarity-based retrieval. (b) Prompt-driven techniques: The model can be steered by modifying system prompts or input instructions, including struc- tured prompts, adversarial queries, persona and role priming, and multilingual prompting. (c) Test-time scaling via search: At inference time, search-based methods explore multiple candidate idcas through sequential refinement, parallel refinement, or branching exploration, enabling scal- able reasoning. (d) Evaluation metrics: Candidate hypotheses are evaluated based on key scientific metrics such as novelty, feasibility, and potential impact. (e) Feedback sowrces: Search and genera- tion are guided by feedback signals from human experts, scientific rules, internal model confidence, or external tools/sto mols/simulators, which iteratively refine and improve the generated ideas. (f) Gen- eration system: Single-agent systems versus multi-agent frameworks. Multi-agent setups include pipeline-oriented workflows for automation and debate- interactions, which can yield emer- gent bchaviors in LLMs. This multi-stage process collectively enables LLMs to produce creative, reliable, and high-value scientifie hepotheses.

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2 Knowledge Augmentation: Grounding LLMs in External Evidence
Scientific ideation rarely starts from a blank slate; researchers typically draw on prior literature, data, and domain knowledge to frame questions and generate hypotheses. In contrast, LLMs rely on static knowledge base limited to their pretraining cutoff, motivating the need for access to up-to-date and domain-relevant information at inference time. Incorporating external literature factual resources into the model's context grounds generation in established knowledge, reduces hallucinations, and serves source of creative inpul from the environment. From the perspec- tive of Boden's taxonomy, knowledge augmentation primarily supports combinatorial creativity by enabling novel ideas through the rocombination of existing concepts rather than transformation of the conceptual space. Indeed, Gu al.] demonstrate that retrieval-augmeeteed models can ex- plicitly enable combinatorial creativity by synthesizing research ideas across diffferett abstraction levels. This mechanism aligns with the press dimension of Rhodes' 4Ps (Rhodes) 1961), where ex- ternal context introduces novelty while preserving grounding and correctness. We survey two main approaches Semantic relrieval, which dynamically incorporates evidence from external corpora, and Relational retrieval, which provides structured grounding and discuss their limitations and open challenges. Figure a illustrates an example of knowledge augmentation with these two approaches.
2.1 Semantic Retrieval
Scientific ideation often begins by searching for nearby ideas in the existing literature. RAG systems extend LLMs with this capability: they query external sources (e.g., Semantic Scholar, EuropePMC, or vector databases) to retrieve relevant papers or text fragments based on semantic similarity. The retrieved content is then injected into the prompt, providing the LLM with context to enrich and constrain hypothesis generation.
Originally designed for open-domain question answering, RAG (Lewis et al. 2020 has become foun- dational in scientific idea generation. Early systems like PaperQA (Lála et al. 2023 and LitLLM
[ [Agarall et al. 2024) focused on raw literat ure access, while newer frameworks like Ideasynth (Pu et al. 2025), Scideator (Radensky et al. 2024). Nova (Hu et al. 2024 ResearchAgent (Baek et al. 2024 emphasize multi-stage decomposition, critique-aud-refine loops, and citation-aware retrieval. Furthermore, systems such as SciPIP (Wang al.) 2024c), SCI-IDEA (Keya al.) 2025), and Pa- perHelper (Yin et al. 2025a) push beyond simple matching by integrating novelty detection, visual scaffolding, and hybrid fine-tuning.
Across this line of work, retrieval is not merely about collecting documents- it is about curating and structuring the inspiration space. However, these methods remain rooted in similarity-driven access, which, while effective for relevance, can limit creativity by keeping exploration close to known clusters of knowledge.
2.2 Relational Retrieval
Similarity-based RAG excels at finding "neighbors" but scientific discovery often emerges from connecting distant or cross-disciplinary concepts. Knowledge Graphs (KGs) address this by encod- ing explicit relations among entities concepts, methods, results, papers forming a structured topology for exploration.
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KG-based approaches allow LLMs to traverse paths across domains, promoting more creative and meta-level reasoning than local similarity alone. In biomedicine, systems like PubTator (Wei et al.)
2013 2024 and SciSpacy (Neumann et al. 2019) extract subgraphs from literature to enable link prediction and hypothesis generation.
An early example of Relational Retrieval is Chain of Ideas (Li et al. 2024a] that by explicitly track- ing breakthroughs and their dependencies, enabled LLMs to reason over trends rather than isolated documents. This inspired subsequent work that generalized the idea to broader graph formalisms. The KG-Col framework (Xiong et al., 2024) ScciMON .0 [Wang al.) 2024a) and Grounding LLM Reasoning with Knowledge Graphs (Amayuelas et al., 2025 demonstrate that graph-structured
multi-hop reasoning improves plausibility and interpretability Large-scale efforts such as Graph of AI Ideas (GoAT) (Gao et al., 2025b) and SciMuse (Gu & Krenn 2025) show how domain-spanning
graphs can surface trends, gaps, and opportunit ies invisible to similarity-based ret.rievll
In short, KG approaches ground LLM reasoning and expand LLM ideation in relational rather than purely semantic space. By traversing structured dependencics and cross-domain links, they open the door to more multidisciplinary and potentially more creative hypothesis generation.
2.3 Takeaways and Limitations
RAG-style and graph-based retrieval answer complementary needs I.LMMassisted scientific ideation. As shown in Scenario A| Similarity-driven RAG gives fast, relevant context pulls the nearest prior work and facts the model can be accurate and up-to-date. Relational retrieval, by contrast, exposes structure and cross-domain paths that are invisible to nearest-neighbon. search, enabling multi-hop, meta-level, and multidisciplinary leaps.
Empirical evidence from GoAI study which explicitly organizes and traverses ideas using a struc- tured graph (Gao et al. 2025b) shows that GoAI achieves higher Elo-based novelty scores (3.83) and the highest overall average score (3.39) compared to less structured basclines, such as Chain-of- Ideas(Li et al. 2024a) (3.21 novelty, 3.27 average) and vanilla LLM prompting (2.44 novelty, 2.70 average). These results indicate that relational structuring of ideas does more than improve rele- vance clarity: it supports combinatorial creativity by enabling non-trivial recombination across idcas, while maintaining fcasibility and effectiveness 3
Despite these advances, heavy reliance on retrieved content can bias ideas toward well-represented clusters, reinforcing conservative or incremental thinking rather than encouraging novelty. Em- pirical studies confirm these tendencies: higher retrieval quality often reduces output diversity as models overfit to ritvee passages (Lee al. 2024); decoding strategies overweight context at the expense of parametric reasoning (Wang et al. 2025b: and when external evidence conflicts with
internal knowledge, LLMs exhibit confirmation bias rather than creative divergence (Lee et al. 2025).
Addressing these limitations may require hybrid strategics that balance grounding with exploration, incorporate richer modalities, and explicitly encourage divergence directions we revisit subse- quent sections on prompt-based creativity and inference-time scaling.
'To aid intuition, we also include scenario bozes throughout this survey. These illustrative examples are syn- thetic-non not actual system outputs- but designed to clarify how different methods might operate grounding abstract mechanisms in concrete cases, the baxes highlight contrasts between approaches and help readers better visualize potential reasoning and generative steps, without implying empirical results.
in practice. By
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Scenario: Similarity-driien vs. Relation-driven retrieval
Seed research goal: Design methods to make LLMs more reliable when solving reasoning tasks in out-of -ittr--diitriittion (OOD) settings.
Semantic-driven Retrieval. The system generates query such "robustness in LLM reasoning under OOD" and retrieves the top-k semantically similar papers from vector database or literature API.
Illustrative retrieved items:
Paper A: Chain-of-thought prompting improves reasoning in arithmetic tasks.
Paper B: Self-consistency decoding reduces reasoning errors in LLMs.
Paper C: Fine-tuning LLMs with synthetic reasoning chains.
Generated hypothesis: Combine chain-of-thouuht prompting with self-consistency decoding and fine-tune synthetic reasoning datasets improve robustness novel tasks.
Relation-driven Retrieval. The seed is mapped to nodes such "LLM reasoning" and "distribution shift" within knowledge graph of ML research concepts and papers. The system performs multi-hop traversal to find relational paths across subfields. Illustrative graph paths:
Causal representation learning
Counterfactual data
Distribution addressed shift
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Formal methods
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syuthesis
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Generated kypothesis: Develop hybrid framework where causal representation learning identifies shift-invariant features, augmented by counterfactual data generation, and pair it with neurosymbolic reasoning modules to improve LLM reasoning robustness under OOD conditions.
Similarity driven retrieval emphasizes local relevance by surfacing closely related tech- niques, while relational retrieval encourages cross-paradigm creativity, uncovering con- nections unlikely emerge from similarity alone. Despite these differences, standard greedy decoding in LLMs generally limits outputs from either approach to the combinatorial level.
Figure 2: Comparison of similarity-driven and relation-driven retrieval methods.

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(Wagg et 2024c)
Similarity- Based Retrieval
WWng et al.
2024a
(Yin et al. 2025a
Knowledge Augmentation
(Gao et al. 2025b
Relational- Based Retrieval
(Amayuelas et al. 2025
(Gu & Krenn 2025
(Liu et al. 2025b
Persona Priming
(Kim al. 2025
(Wan & Kalman
2025
Constraint- Based Queries
(uu 2024b
Prompt-Driven Driven Methods
O'Neill et al.
2025
Structured Prompting
Li et al. 2024a
(Wang
2025c
Multilingual Prompting
(Vatsal
Scientific Idea Generation
(Madaan
Xu
2023
Search Mechanism
(Li ct al.
2025d]
Inference Time Scaling
x Feedback source Abstraction level
YYng et al. 2025b
RRabby
2024
Fang et al.
2025
GGaareeb et al. 2025
Pipeline-Oriented Oriented Workflows
Schmidgall
2025
2025
Multi-Agent
Systems
Johnson
Debate- Debate-Based Loops
(Liagg et al. 2023
Surina et al. 2025
Reinforcement Learning
(Weng et al. 2025a
(aan et al. 2025
Parameter Adaptation
Xie et al. 2023
Supervised Finetuning
(Prabhakar
Structured Op- timization
(Ismayilzada et al. 2025a
(Lanchantin et al.
2025)
Figure 3: five-level taxonomy for scientific idea generation methods using LLMs.

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3 Prompt-Driven Creativity: Steering LLMs Towards Novel Ideas
Prompt engineering is a powerful and efficient method to influence LLMs without costly retraining or fine-tuning. By designing precise instructions, role contexts, or structured templates within the input prompt, researchers can steer LLMs toward generating novel ideas and creative solutions (Vatsal & Dubey 2024). As prompts function as external constraints and affordances that shape the model's generative behavior, prompt driven methods align with the Press dimension of the 4P model trying to foster novelty while maintaining grounding in the model's pretrained knowledge. Figure Tbb schematically depicts these prompt-steering strategies and their placement within the adea-generalle workflow. Building on this view, discuss four main prompt-driven techniques for diversifying outputs: Persona & Role Priming, Constraint-Basee & Adversarial Queries, Structured Creative Prompts, and Multilingual Prompting.
3.1 Persona Role Priming
Assigning expert identities or roles to LLMs helps sharpen creative output. Zhao et al. (2025) showed that role-based prompts such as instructing the model to "act as a scientist" " can boost originality significantly on psychometric creativity tests. Supporting this, recent work in 2025 pro- poses frameworks that enhance LLM aersons-awareness over multiple dialogue sessions, improving consistency and contextual relevance in outputs (Liu et al., (025b). Another study explores how persona conditioning influences model alignment and ethical decision-making, highlighting the im- portance of carefully designed persona prompts to guide LLM creativity while avoiding unintended biases (Kim et al. 2025). Moreover, studies on human-AI co-creativity show that even ambigu- ous counterintuitive personas can spur unexpected ideas, suggesting that muanced role priming strategies can be highly effective in scientific idcation (Wan & Kalman 2025
3.2 Constraint-Basee & Adversarial Queries
Prompting LLMs with constraints or adversarial instructions encourages them to explore less obvi- ous, more innovative solutions. For example, asking models to challenge core scientific assumptions or forbidding common coding constructs can drive originality. Recent work introduces Denial Prompting, method that incrementally imposes constraints on generated code to push models to- ward creative strategies (Lu et al., 2024b). This approach is evaluated using new creativity metric called NeoGauge, demonstrating that systematically denying routine solutions helps LLMs access novel arcas of their generative space (Lu et al.) 2024b). These bonstraint-based methods effectively leverage the vast internal knowledge of LLMS while navigating the lower-probability, more inventive regions of output distributions.
3.3 Structured Creative Prompts
Structured prompts scaffold LLM reasoning, fostering more deliberate and diverse ideation. By guiding models through defined reasoning stops, they emulate human metacognitive stratogics that enhance creativity. Chain-of- -hain-of-Though (CoT) prompting, where models are encouraged to "think step by step," " improves reasoning accuracy and idea diversity (Wei al., (002).. Building on this, recent advances like Consistency-Bant Based Self- Adaptieee Prompting (COSP) automate zero-shot
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prompt construction by leveraging the model's own predictions and unlabeled data, enhancing performance reasoning tasks without task-specific training (Wan et al. 2023
In scientific domains, methods like Chain-of-Tdlen -ddenss (Col), structures ideation by organizing literature into sequential knowledge chains that reflect the evolution of a research domain (Li et al. 2024a Instead of exposing LLMs to unstructured data, Col captures dependencies between past findings and emerging trends, enabling the model to identify gaps, trace idea development, and forecast future research directions. Likewise, Bit-Flip-Spark, structures idea generation into three stages: the Bil (baseline assumption), the Plip (challenging that assumption), and the Spark (novel insight that emerges) (O'Neill et al. 2025
This scaffold is designed to induce cognitive conflict by prompting models to question default assumptions and adopt alternative premises, a mechanism commonly associated with creative ideation. Prior work shows that system-level instructions and structured reasoning scaffolds can reweight exploration and promote otherwise underrepresented trajectories within a fixed pretrained space Reynolds & McDonell 2021 Yao al. 2023). In this more limitod sense, structured prompt- ing may be viewed as a proced ural step loward transformational behavior, approximating aspects of conceptual space alteration through controlled search and assumption relaxation rather than true representational change.
Scenario: When Prompting Alone Cannot Break Alignment Constraints
Task: researcher asks an LLM to "Think outside the boz and propose a completely new programming paradigm begond object-oriented functional programming.. "
Aligned LLM response (current. state). Despite being explicitly prompted to "be radically creative," " the aligned model produces only safe, incremental ideas, such as:
hybrid paradigm combining object-oriented and functional programming.
Event-driven programming with Al-assised code generation.
Domain-specific languages for scientific computing.
These outputs plansible but unsurprising, reflecting the conservative bias from alignment training (c.g., RLHF).
Unaligned / base model (hypothetical) response. less-aligned model might generate more radical and unconventional concepts, for example:
A self-modifying, biology-inspired code ecosystem where programs evolve like neural tissue.
A programming language treating time as first-class dimension
These idcas may be impractical incoherent, but they reflect true divergent exploration beyond the comfort zone of mainstream paradigms.
Figure+ Comparison between aligned and unaligned LLM responses, highlighting how alignment training suppresses divergent creative exploration.
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3.4 Multilingual Prompting
Recent studies reveal that multilingual prompting translating prompts into multiple languages and aggregating the results- significantly increase generative diversity and creativity. For instance, Wang et al. (2025c) found that cross-lingual prompting enables LLMs to draw distinct linguistic ic and cultural priors, outperforming traditional diversity techniques such as temperature sampling. Similarly, a large-scale survey by Vatsal et al. (2025) across more than 250 languages highlights multilingual prompting as a broadly effective strategy for improving comprehension, reasoning, and creative output. The MLPrommt system further demonstrated how cross-lingual prompt reformula- tions can boost LLM comprehension and reasoning in complex tasks (Wng al. 2025c). Together, these findings suggest that linguistic diversity itself acts cognitive scaffold encouraging LLMs to traverse alternative semantic frames and produce more original, cross-cultural ideas.
3.5 Takeaways and Limitations
Prompt-based techniques-sidd persona prompting, constraint based queries, structured, and multilingual prompting effectively broaden LLM creativity by diversifying perspectives, rea- soning paths, and linguistic representations. Empirical results from ZZaoo et al. (2025), using a TTCT-inspire framework with LLM-judge scoring aggregated over many samples, show that prompt-basod techniques systematically shift crcativity components. Interpreting originality as novelty and the mean of fluency, flexibility, and elaboration as valueness, instructional prompting increases novelty from 3.78 to 4.05 and valueness from 4.46 to 4.65. Chain-of-Thought prompt- ing primarily boosts valueness (4.75 vs. 4.68) with negligible change in novelty (3.76 3.78). Persona-basee prompting (e.g., natural scientist) achieve higher novelty (4.23) and valueness (4.62) than generic roles (4.1, 3.78). Overall, prompt-based methods enhance creativity by redistributing generation within existing knowledge, with limited impact on deeper exploratory novelty.
However, as shown in Scenario f even with strong creativity cues in the prompt e.g., "Be radi- cally original," "Break all current conventions"- the aligned LLM remains trapped within a safe attractor basin created during RLHFF and instruction tuning which prioritize safety and helpfulness over novelty. Some recent. researches found that RLHF reduces output entropy and semantie riety, pushing models toward safer, less creative responses (Mohammadi 2024 Kirk et al.
2023
Similarly, explicit creativity cues can sometimes reduce fluency and flexibility Zhao et al. 2025
This shows why simply prompting for creativity is insufficient: the model's sampling distribution
has been narrowed by alignment to avoid outputs that seem implausible or risky (Wolf et al. To address these limitations, inference-time scaling and multi-agent interaction offer a promising direction by expanding the search space beyond one-pass prompting, potentially enabling more novel, cross-disciplinaryy ideas (Feng et al. 2025 Baek al. 2024).
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4 Search and sampling expansions: Inference- Time Exploration of Hypothesis Spaces
Inference-time scaling methods expand the capabilities of LLMs beyond single-pass decoding by enabling dynamic and iterative exploration without additional training. From a classical AT per- spective (Russell & Norvig 2020), these approaches can be framed as search problems: LLMs act as agents traversing vast combinatorial spaces of candidate hypotheses, guided by hcuristics and evaluation functions.
By moving beyond random sampling, test-time scaling highlights the role of systematic search, where both the stralegy of erploration and the abstraction level of representalion shape the cre- ative potential of generated ideas. The search strategy saaatteyy-aniiingggggge ranging from focused local refinement to determincs the breadth and depth of traversal, while the abstraction level spanning from low-level outputs to higher-level conceptual hypotheses defines the scope of novelty that can emerge. We expect that applying more systematic search at more abstract repre- sentational levels increases the likelihood of producing exploratory ideas, aligning this process with Boden's notion of creativity as the structured traversal of conceptual space (Boden 2004).
treo-structured
Yet, exploration must be balanced against correctness and feasibility. While multi-sampling and branching strategies favor novelty and diversity, unconstrained search risks producing trivial or scientifically invalid ideas. To counter this, inference-time scaling methods incorporate feedback signals that guide pruning, refinement, and prioritization. this way, leasibility and soundness are gradually reinforced without sacrificing exploratory breadth.
Overall, existing approaches be analyzed along three complementary design dimensions. The Search mechanism defines the structure of exploration, such as local refinement, population-based scarch, or treo-structured branching. The Feedback source specifies the signals that shape traver- sal, ranging from self-consistency checks lo empirical simulations and expert judgment. Finally, the node Abstraction level captures the gramularity of ideas, from low-level token continuations to high-level hypotheses executable workflows. These dimensions jointly determine how effectively inference-ting scaling methods navigate the trade-off between novelty and fcasibility, and they pro- vide principled framework for comparing strategies of scientific idea generation within the broader context of classical Al search and creativity theory.
4.1 Search Mechanism
Search mechanisms govern how LLMs traverse the hypothesis space during inference-time scaling for scientific idcation. In this section, we analyze three primary paradigms: Local Search, Tree Search, and Population-Based Search. Each paradigm embodies distinct approach expand- ing, evaluating, and selecting candidate scientific idcas, reflecting tradc-offs between exploration, exploitation, memory cost, and computational efficiency. Figure [(c) illustrates an example of idea expansion across various search mechanisms.
4.1.1 Local Search: Single Cangie-an Refinement
Sequential refinement methods iteratively improve single candidate hypothesis, analogous hill- climbing in classical AI (Russell & Norvig 2020). This approach mirrors the scientific practice of proposing initial idca and then refining it through feodback cycles.
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For example, the Belf-Refine method (Madaan et al. 2023) uses internal feedback loops in which the LLM critiques its prior output and revises it without requiring additional training. PANEL (Li et al. 2025d) similarly decomposes reasoning into multiple steps, generating and critiquing each soquentially. ResearchAgent (Back et al. 2024) extends this paradigm by simulating peer review with multiple collaborating agents, though the refinement of each hypothesis remains sequential. CriticAL (Li et al. 2024b) integrates statistical hypothesis testing into this loop, enabling the detection logical inconsistencies or unsupported claims.
A single-path search, while simple and low-cost, is fundamentally brittle. It may terminate with a suboptimal uninteresting solution if the initial path leads to local maximum, making it an incomplete method that risks missing solution entirely, In contrast, beam search method designed to overcome this limitation by exploring multiple paths concurrently. Scenario 5 demonstrates how different search stralegies address this trade-off between efficiency and exploration.
4.1.2 Iaaulation-Baseer Search: Beam-Style Refinement
Population-based methods maintain multiple candidate hypotheses simultaneoush propagating the most promising across iterations. This aligns with Local Beam Search, a natural extension single- candidate local scarch (Russell & Norvig 2020). Maintaining a population allows models to balance exploration and exploitation, reduce the risk of local optima, and improve diversity.
Modern LLM examples include MOeSE-Chem? (Yang et al.) 2025b), which run multiple instances seeds in parallel, scoring candidates with internal or external evaluators. These approaches leverage ensemble diversity while maintaining refinement dynamics akin to local search.
Although local beam search reduces the brittleness of single-path refinement by maintaining a population of candidates, remains constrained: once a branch is discarded, it cannot be recovered (Scenario 5). This means the method may still miss promising but initially ander-performing directions. To address this limitation, researchers turn to tree-based approaches that allow more systematic exploration of alternatives.
4.1.3 Tree Search: Branching Exploration
Branching exploration maps closely tree-based informed search, where instead of tracking only a fixed-iidhh beam, the search process can expand, backtrack, and revisit multiple paths, guided by heuristics that balance depth and breadth. This structured exploration mitigates premature pruning and supports the discovery more diverse solutions. [Russell & Norvig 2020
MC-NEST (Rabby et al. 2024) exemplifies a Monte Carlo Tree Search guided by Nash equilib- rium principles, balancing exploration and exploitation through stochastic node selection strategies. Conceptually, this approach mirrors classical local search tochniques such as stochastic hill climb- ing and simulated annealing (Russell & Norvig 2020), in which the search tolerates occasional suboptimal moves to escape local minima. MAGIC (Xu et al. 2023a. applies multi-armed bandit. strategies to allocate computational resources preferentially to promising branches. Monte Carlo Thought Search (Sprueill et al. 2023) explores alternative synthesis pathways in catalysis design. Multi-agent systems, such as Virtual Scientists (Johnson & Davis 2024) and the IRTS framework Feng et al. 2025). implement branching exploration collaboratively, integrating both LLM and human feedback prune and guide search.
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Scenario:
Search Mechanisms in Hallucination Mitigation
Seed Idea:
"Design a method to reduce Ilm factual hallucinations in medical questions."
1. Local Search (Sequential Refinement): The model iteratively refines a single idea; Iteration Add RAG with PubMed.
Iteration 2: Integrate retrieval into every step of multi-hop reasoning.
Iteration Combine retrieval with a confidence estimator; rejects uncertain answers.
Focuses refining single path, which may lock the model into paradigm (e.g., RAG) and overlook other promising strategies like calibration or verification.
2. Local Beam Search (Parallel Refinement): Multiple candidate directions are ex- plored simultancously;
RAG-based factual grounding - iteration + iteration 2
Uncertainty calibration methods - iteration 1 + iteration 2 +
Training with ballucination-focusee preference data -> iteration -
After each refinement round, weaker ideas (e.g., costly preference data) are dropped, while stronger ones continue. Balances multiple strategies before committing, but innovative yet noisy ideas may be pruned prematurely.
Tree Search (Branching Exploration): Structured hicrarchical exploration of the idea space;
Branch A: Retrieval-based approaches
Hybrid BM25 + Dense retrievers
Domain-specifi knowledge bases
Branch B: Training-time alignment
Hallueinatiion-speeiii RLHF
Adversarial training with hallucination prompts
Branch C: Uncertainty-awan generation
Selective abstention
Calibrated probability outputs
Heuristics like computational cost determine branch expansion, Preserve diverse strategies and enables backtracking, though strong heuristics are required to manage combinatorial growth.
Figure 5: Comparison of sequential, parallel, and based search mechanisms for the ideation task reducing LLM hallucinations in medical question answering.
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Across these mechanisms, we see spectrum of trade-offs in how LLMs explore idea space during inferenco-time scaling. As shown in Scenario single candidate refinement offers simplicity and efficiency but risks tunnel vision, converging too quickly on narrow line of thought. Beam- Style refinement introduces diversity by allowing multiple candidates to coevolve, yet its pruning decisions are irreversible and may discard unconventional but valuable paths (Zhou & Hansen 2005 Meister et al. 2020: Cohen & Beck) 2019). Tree search provides the broadest and most systematic exploration, preserving multiple paradigms in parallel -though its ability to preserve unconventional hypotheses depends on the choice of heuristics- and enabling backtracking, mochanism shown to be critical for recovering from premature commitments in recent LLM reasoning frameworks Yaa et al. 2023), albeit at significant computational cost.
Together, these methods highlight that no search strategy alone guarantees optimal idea genera- lion. Greater systematic exploration approaching tree-like search- tends to increase the creativity level of idea into exploratory in boden framework, but this comes at the cost of efficiency. Their effoctiveness, however, depends on the reward source that guide search and defines how candidate hypotheses are prioritized and refined wwhether from internal confidence, external evaluators, or environment. feedback- we next examine the role of feedback sources in scientific ideation.
4.2 Feedback Source
Feedback sources in inference-time scaling act as evaluators that shape the trajectory of idea. ploration. While most commonly used to verify feasibility, soundness, and scientific grounding, they can also assess novelty and creativity by identifying underexplored branches or highlighting more promising directions. Crucially, each feedback source has strengths in particular domains. Model confidence signals are effective in relatively simple settings, where the model's general knowledge suffices to infer correctness. Peer evaluator agents offer greater flexibility and domain specialization, though they remain vulnerable to reward hacking. Domain rules and symbolic constraints are most effective in well-formalized fields, where correctness is clearly defined. Finally, Human evaluation is especially valuable in subjective domains where intuition, creativity, and contextual judgment are essential. Together, these complementary feedback sources provide the necessary guidance to balance exploratory breadth with scientific validity in inference- temeence--imeeeee scaling.
4.2.1 Internal Self-Evaluation
A foundational feedback souree is the model's internal confidence, cnabling self-critique and refine- ment without external input. Methods like Self-Refine (Madaan et al., 2023) leverage the LLM's ability to generate critical commentary on its own outputs and revise accordingly. PANEL (Li et al.) 2025d] decomposes reasoning into discrete steps, each followed by model-generated critiques. Crit- icAL (Li et al. 2024b) further enhances this by applying statistical reasoning- such as hypothesis testing to identify contradictions and weaknesses.
It is most effective when the model's pretrained general linguistic knowledge suffices (e.g., logical consistency, surface fluency). However, numerous studies show that LLM self-confidence is often miscalibrated: models can be markedly overconfident in incorrect outputs or under-confident in correct oncs, with and training objectives (Geng el al. 2024 Leng el al. 2025 Chhikara, 2025). As a result, reliance on internal heuristics risks bias reinforcement and even form of reward hacking, where the model over- trerstrusts its own scoring signals, strengthens spurions associations, repeatedly exploits flaws in
alignment hcavily influenced by prompt framing, distractors,
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its confidence estimates without genuinely improving hypothesis quality. n Scenario we compare the effects of internal confidence feedback versus peer-agent feedback on hypothesis evaluation.
Scenario: Internal Confidence vs Peer Agents in Hypothesis Evaluation
Suppose an LLM proposes novel compound, CoallasNoo4, potential inhibitor for cancer-related enzyme.
Internal Confidence Feedback. The model rates the compound valid because its structure "looks" consistent with patterns learned during pretraining. It deems the struc- ture plausible because it resembles valid chemical formulas and mimics motifs frequently seen known inhibitors.
Peer-Agent Feedback. A domain-specializzd peer agent reviews the same compound and identifies structural instabilities or unrealistic bond angles that make it chemically unfeasible and flags it as unstable.
Internal confidence is efficient and scalable, helping flag syntactically invalid outputs, but it may overestimate validity by focusing on surface-level coherence rather than underlying chemical feasibility. While peer-agent feedback enhances scientific rigor and domain aware- ness, though it requires additional computational and training resources to maintain reliable expert peers.
Figure 6: Contrasting internal confident and peer- based feedback in scientific hypothesis evaluation.
4.2.2 Peer LLMs Verifiers
growing line of work leverages LLMs as external evaluators, distinct from the generator itsclf. For example, FleziVe (Zhong et al. 2025) implements a solve solve-detect -erect-veriyy pipeline, while process- reward frameworks [Choudhury 2025) and diverse verifier tree search verifier models to score intermediate hypotheses and control expansions. Stability can be improved by ensemble verification /Wang et al. 2025a).
exemplifies this approach by using an LLM's general linguis- tic knowledge as a feedback signal for guiding logical consistency and language fluency. Similarly, the --M-as-a-Judge (Zheng al., 2023) paradigm formalizes this idea: one model (or ensem- ble (Chen et al. 2025a)) evaluates hypotheses produced another, effectively operationalizing externalized languago-model judgment as search heuristies.
The CRPO Ismayilzada et al. 2025a
More advanced methods first train specialized evaluators to capture problem-specific criteria before deploying them as verifiers. For example, Cyclelessaarchrr (Weng et al. 2025aa relies on trained verifier model (CycleReviewer) to judge intermediate steps and direct search trajectorics. Li et al, (2024c) similarly build a reward dataset and trains three reward models to guide idea generation dynamically, allowing fine-grained control over different evaluation dimensions. These approaches often positioned scalable, automated substitutes for human feedback, especially in domains
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where expert evaluation is costly. Peer evaluators are more flexible than self-confidence, since they may be domain-specialized and provide rationales.
However, they remain vulnerable to reward hacking: generators can learn to exploit systematic weaknesses in verifier heuristics, producing outputs that appear highly rated without being gen- uinely correct or useful. Recent studies in preference optimization and RLHF have shown that models can overfit to their evaluators, generating verbose, sycophantic, or superficially persuasive responses that maximize reward signals rather than substantive quality (Laidlaw et al. 2025 Miao et al, 2024). These findings highlight that while peer evaluators extend the evaluative horizon, their reliability depends careful design to avoid exploitation and collapse of evaluation diversity. While effective evaluators and filters, feedback sources differ fundamentally from collaborative multi-agent sctups. As outlined in Section 5 multi-agent frameworks engage additional LLMs as co-creative partners, enabling them not only to assess but also to propose, conlest, and refine ideas in a shared search process.
4.2.3 Simulators and Real Environments
Simulators serve as semi-grounded environments that test the outputs of LLMs in dynamic, feedback-rich contexts. These can be physical simulations, digital twins, or even agent-based soci- etics. Simulators and real environments provide feedback through interactive trial-and-crror, align- ing closely with notion of online search (ussell Norvig, 2020). Unlike offline planning, where a complete solution computed in advance, online search emphasizes interleaving generation with execution: the agent proposes an action, observes the environment's 's response, and adapts its tra- jectory accordingly. This makes simulators and lab-in-the-lops systems natural analogues, as they allow LLM-driven agents to iteratively refine hypotheses or strategios based on grounded feedback from physical laws, empirical trials, or synthetic environments.
Robin Ghaareeb et al. 2025) uses a flab-in-the-l the--oooo"" design, where hypotheses are tested via in silico simulators (or even robotic wet-labs), and the resulting outputs are looped back into the ideation process as reward signals. For example, a gene-editing strategy might be generated, simulated for efficacy, and then refined based on empirical response.
Similarly, AtomAgents (Ghafarollahi & Buehler 2024) combines LLMs with alysics-based alloy simulators: agents propose material compositions, query simulators for predicted properties like tensile strength, and prioritize candidates for further iteration based on performance metrics.
In more abstract contexts, CASEVO (Jiang et al. 2024) simulates agent societies debating political or scientific stances, tracking how persuasive interactions shift beliefs-modeling ideation in socially complex domains.
Recent robotics work demonstrates the coupling of simulators and real execution. SayCon Ahn et al. 2022) grounds LLM-ggnnrated high-level commands in an affordance model and low- robot controller, combining planning tools with real robot feedback. RoboTool (Xu et al. 2023b) extends this to crcative tool use, where LLMs propose robot code tested in both simulators and phys- ical hardware. Voyager (Wang el al.] 2023a) highlights how open-ended exploration Minecraft enables the synthesis of reusable tools, iteratively evaluated in a simulator to accumulate a growing library of skills.
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On the evaluation side, Torne al. (2024) analyze the fidelity of simulator-to-realig transfer, explicitly comparing robot manipulation policies in simulation versus physical exccution. Similarly, Large-Scale Simulation of LLM-Driven Generative Agents stress-tests agentic LLMs in syuthetic societies, showing how simulation feedback can approximate large-scale environment signals for long-running scientific ideation (Piao et al. 2025). As shown in the Scenario 7 the simulator evaluates stability and feasibility of candidate designs.
4.2.4 External Tools and APIs
External tools expand the feedback horizon by integrating APls, online search, and domain-specific databases. The Toolformer architecture (Schick et al. 2023) equips LLMs with the capability to query external resources dynamically, enabling verification and augmentation of generated content. Chemical synthesis platforms like Chem Crow (Bran et al. 2023 leverage cheminformaties tools and external data sources to validate reaction pathways proposed by the model. Similarly, HuggingGPT (Shen al., 2023)) coordinates wide range of specialized AI models hosted on the Hugging Face platform, allowing LLM to act as controller that routes tasks such as vision, speech, and text analysis to appropriate external tools, demonstrating how tool integration can expand LLM's capabilities beyond text-only reasoning.
Recent ovaluations focus directly on LLM-tool interaction. MINT Wang et al. 2024d system- atically studics multi-turn tool usage, showing how feedback from tool calls and natural language guidance improves decision-making. Voyager's open-ended tool synthesis also bridges this calegory, where tools (skills, functions) are generated internally but then tested through simulators and APls.
By incorporating external tools, LLMs move beyond isolated language generation toward grounded, multi-modal reasoning informed by up-to-date, external knowledge. This shift transforms them from pure text predictors into decision-theorete agents augmented with specialized oracles. This feedback source and its alternatives are examined in Sconario
4.2.5 Scientific Priors and Domain Rules
Incorporating feedback from hard-coded learned scientific priors enables models to remain within physically or logically valid domains. These systems operate without full simulation but leverage symbolic constraints or heuristic checklists to prune the hypothesis space. This connects closely to discussion of constraint satisfaction problems, where domain knowledge narrows search to feasible regions.
SCaSML (Fan et al., 2025) integrates physical laws such as energy conservation to constrain gen- eration, ensuring outputs remain within scientifically plausible bounds without requiring explicit simulation feedback. Similarly, methods like R-V (Cheng et al. 2024h embed domain logic check- points to detect and correct illogical reasoning mid-generation. The Scenario 7 illustrates how scientific priors enforce fundamental constraints and ensure theoretical validity. Domain rules are most effective in well-formalized fields (c.g., physics, chemistry, mathematics) where symbolic con- straints or laws are clear and non-negotiable. Their strength reliability: they are resistant to reward hacking and scale well. However, their rigidity limits flexibility in ill-defined or emergent domains, where strict rules may exclude innovative unconventional ideas.
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4.2.6 Human-in-the-Loop Evaluation
Human experts remain the gold standard for nuanced and context-aware evaluation. Unlike auto- mated evaluators, humans can flexibly weigh novelly, relevance, and feasibility in ways not easily reducible to rules or simulations.
Platforms like (He ct al. 2024 integrate human expertise directly into the scientific discovery pro- cess by allowing researchers to iteratively guide and critique model-generated hypotheses. IdeaSynth (Pu et al.
2025 and Scideator (Radensky et al. 2024) provide interactive environments for hypoth- refinement, allowing rescarchers blend and adapt elements from prior work. Chain-of- Table (Wang al. 2024e) offers a mixed-initiative framework where human evaluation is combined with structured scientifie tables and knowledge bases, balancing creativity with formal rigor. IRIS (Feng et al. 2025). though originally designed for self-reflection, also incorporates human evaluators in interac-
tasks requiring subjective judgment of novelty and coherence. These human-in-the-lue tions enrich the feedback loop, ensuring that the discovery process not only leverages computational power for exploration but also incorporates experiential insights that are difficult to encode explic- itly, ultimately leading to more reliable and interpretable scientific advancements.
Scenario Contrasting Evaluation feedback sources
To illustrate how different feedback sources shape idcation, consider an LLM that proposes a novel photo-catalyst for converting CO2 into useful chemicals under sunlight. The idea passed through several evaluators, each providing distinct form of guidance:
The design is tested in simulation engine, which shows promising version rates but also reveals that the material becomes unstable at high temperatures. The simulator narrows the search space to only variants that remain physically viable.
Simulator:
External database: The LLM queries scientific databases and patents. It finds no direct precedent for the exact design (a sign of novelty), but flags that similar catalysts once produced toxic byproducts. This feedback shifts exploration toward safer material choices.
Symbolic scientific rules check the proposed reaction. They detect a violation of basic energy conservation, forcing the model to reformulate the mechanism in physically consistent way. Priors act as hard constraints that keep the search scientifically valid.
Scientific priors:
Human expert: A human expert reviews the surviving ideas. Beyond technical fea- sibility, she considers manufacturability cost, and environmental impact, and directs the system toward designs using abundant, scalable matcrials.
Together, these perspectives highlight how different feedback sources contribute comple- mentary strengths: simulators ground ideas in empirical behavior, external tools provide contextual knowledge, priors ensure theoretical soundness, and humans add judgment and broader vision.
Figure 7: Contrasting evaluation feedback sources for LLM-driven scientific ideation.
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meman-inntth-loon evaluation is especially valuable in subjective or poorly formalized domains (e.g., social sciences, exploratory biology) where intuition, creativity, and context matter. It provides robustness against reward hacking and fills gaps left by incomplete simulations or priors. However, it is costly, time-consuming, and hard to scale-limiting its use in high-throughput or automated scientific ideation pipelines.
4.3 Levels of Abstraction
Inference-time scaling methods differ not only in their strategies but also in the level of abstraction at which search conducted. From thcoretical perspoctive, abstraction provides mcans of reducing or restructuring the problem space, thereby enabling broader exploration but complicating
verification (Zucker 2003 Nakar et al., 2024). Recent LLM-based methods explicitly operationalize this trade-off. For instance, Wang al. (2024b) show that scparating hypothesis generation at
abstract level from concrete program instantiation improves performance reasoning tasks, while Singhal & Shroff (2024) demonstrate that concept-level scoring functions allow more efficient traversal of search spaces than surface-level metrics. Together, these studies suggest that higher levels of abstraction broaden the search space and bring the process closer to exploratory creativity, but at the cost of increased difficulty in assessing feasibility and correctness.
In scientific idea generation field, some approaches direct LLMs toward ideating high-level scientific Hypotheses, while others instruct them to produce execulable Program simulation code. A further class operates at the Meta-level, searching over the model's own instruction modes of interaction with the environment.
4.3.1 Hypothesis-Level Ideation
At the highest level of abstraction, models function as idea generators. They propose conceptual hypotheses, causal explanations, theoretical insights without grounding them in execution or empirical validation. The focus lies in novelty, coherence, and domain relevance, supporting early- stage brainstorming or hypothcsis formulation.
For instance, MAGIC (Xu al. 2023a generates speculative hypotheses genomics and systems biology, ranking them by plausibility and novelty heuristics. VirScí (Johnson & Davis 2024) applies multi-agent debate to refine hypotheses through peer critique. IRIS (Feng et al. 2025 adds inter- nal reflection, restructuring ideas mid-generation to avoid incoherence or excessive derivativeness. These systems act as cognitive companions: their outputs are inspirations requiring downstream verification by humans or automated evaluators. An example of hypothesis-level ideation is illus- trated in Scenario
4.3.2 Program-Level Ideation
A second class of methods situates search at the programmatic layer, where models not only propose what to test but also how to test it. Here, LLMs generate structurod experiment scripts, or API call sequences that can be execuled by simulators, databases, or robot ic systems. The results are then looped back, closing the gap between hypothesis generation and empirical evaluation.
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Scenario: Hypothesis--evel Ideation (Mathematical Reasoning)
An LLM proposes the conjecture:
"For any odd prime the sum of quadratic residues modulo equals p(-1)
This is purely abstract claim without proof. The model explores nearby conjectures (e.g., cubic residues, alternate modulus conditions). Evaluation requires external proof attempts human mathematicians..
Search style: Local search over conceptual variants of conjectures.
Figure 8: Illustration of hypothesis-level idcation mathematical reasoning.
exemplify this principle by translat- ing natural-language reasoning into Python programs executed externally, outperforming direct chain-of-thouuht reasoning. HugginggPT (Shen et al.
Program-Aided Language models (PAL) (Gao et al.
2023
2023 Toolformer (Schick et al. 2023 and
Explanations as Programs (Zhang & Huang 2023) similarly teach models output modular, de- buggable tool pipelines. In science, LLM-SR (Shojaee et al. 2024) refines symbolic equations by querying solvers for constraint violations, ChemCrow (Bran et al. 2023) plans synthesis routes via cheminformatics APIs, and CodeScientist (Huang & Wu 2024 generates executable lab protocols for simulators or robotic wet-labs.
Idcation at the program level enables the use of simulators and other external tools by integrating execution directly into the generalive loop. An example of program-level ideation is illustrated Scenario This integration offers key benefits: programs are structured and interpretable, their execution provides robustness against unsupported claims, and hallucination is reduced as hypotheses must be empirically validated through execution.
Scenario: Program-Level Ideation (Mathematical Reasoning)
Instcad of stating a conjecture, the LLM outputs a structured proof plan: Represent quadratic residues as a² mod p.
2. Pair each a² with (p- (p-a)) a)².
3. Show these pairs to
4. Derive closed-form expression for the total sum.
Each step can be checked in proof assistant tested computationally. Search style: Tree beam search, where alternative proof strategies (induction direct algebraic argument) form different branches.
Figure 9: Illustration of program-leva ideation in mathematical reasoning.
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4.3.3 Meta-Level Search and seff-Adatatiion
A third, emerging frontier is meta-level search, where the object of optimization is not the hypothesis or program but the agent's 's own configuration and interaction with its environment. Here, systems explore changes to their prompting strategies, exploration parameters, reward weightings, or even architectural components, adapting the search process itself. An example of meta-level ideation illustrated in Scenario I0
The Darwin Gödel Machine (Zhang al. 2025a] exemplifies celf-modification: instead of emitting domain-level programs, it evolves its own reasoning code to improve performance. More broadly, meta-search also includes adaptive inference strategies e.g., adjusting beam widths, modifying exploration exploitation trade-olls, or re-balancing between simulator calls and heuristic priors. Such mechanisms parallel meta-ngg and utility-basedd agent adaptation, where the agent learns not only how to act, but how to search. Another notable example the Meta-rcview agent in Google's AI co-scienlisl system (Goltweis et al., 2025). This agent synthesizes feedback from various agents, identifies recurring issues, and generates comprehensive critiques and research overviews. The Meta-review agent compiles feedback across iterations to adjust the agents' prompts, ensuring continual improvement.
Meta-level search completes the abstraction spectrum: from generating hypotheses, to encoding them in executable plans, to adapting the very rules by which hypotheses and programs are gen- crated. This higher-order flexibility may prove critical for scaling LLMs into autonomous scientific explorers.
Scenario: Meta-Level Ideation (Mathematical Reasoning)
The LLM reconfigures its own reasoning process rather than directly producing conjectures or proofs:
Switch from a "direct proof-first" mode "counterexample-seatee
mode.
Invoke external theorem provers to prune invalid proof paths.
Deploy multiple agents specialized in algebraic manipulation vs. computational experi- ments.
Here, the search space over reasoning strategies themselves.
Search style: Evolutionary seinforcement-style optimization over proof-generatin poli-
cies.
Figure 10: Ilussraaioon of meta-level ideation in mathematical reasoning.
4.4 Takeaways and Limitations
Inference-time scaling techniques expand the effective search space of single LLM, giving it more opportunities to explore, refine, and evaluate potential ideas. [aroe[[s a comparison of scien- tific inference-time scaling methods. Empirical evidence from Monte Carlo Thought Search (Sprueill ct al. 2023) demonstrates the impact of test-time scaling and structured search on scientific idcation quality. Using a domain-specific reward function, the st udy shows that increasing search depth and breadth substantially improves outcomes compared single-pase pass prompting. While standard CoT achieves low rewards (2.04-2.27), adding self-consistency yields moderate gains (4.04-6.38). In
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Table 1: Comparison of represenlative scientific inference lime-scaling methods.
Search
Feedback Source
Abstraction
Main Idea /
Method
Mechanism
Level
Explanation
Self-Refine Madaan Sequential
Internal con- fidence Internal con- fidence Peer agents
Hypothesis-
Iterative self-critique loop refin- ing oulputs.
ltt al.
2023
refinement.
level
PANEI
EL
Li et
al.
Sequential
Hypothesis- level
Stepwise reasoning refinement via critique generation.
2025d]
refinement
ResearchAgent Baek
Sequential
Hypothesis-
Simulates multi-agent peer re- view for idea improvement. Applies statistical testing to cri- tique and enhance reasoning. Incorporates physical laws; re- fines via Monte Carlo correction. Refines ideas using simulated in-lab feedback.
et al. 2024
refinement
level
CriticAL
Li et al.
Sequential
Internal con- fidence Scientific rules
Hypothesis-
2024b]
refinement
level
SCaSML
Fan et al.
Sequential refinement Sequential refinement
Program-
2025)
level
Robin Ghareeb et al.
Simulator
Hypothesis-
2025
level
IdeaSynth
Pu et al.
Sequential
Human
Hypothesis-
Human-guided iterative idea re- finement.
2025
refinement Sequential refinement
Tool
level
Scideator
Smith &
Human
Hypothesis-
Joint human tool refinement of idea components.
Huang
2025
Tool
level
Agent
Lab Schmidgal
Soquential
Poer agents
Hypothesis-
Role-basod pipcline from data analysis to paper drafting. Multi-agent tuning of simulation parameters.
et al.
2025
refinement
level
Digital Twin Yang
Sequential
Simulator +
Hypothesis- level
ct al.
2025a
refinement
Peer agents
IRIS
Feng
al. Branching exploration Branching exploration
Peer agents
Hypothesis- level
MCTS- bass- branching with both agent and human feedback. Explores reasoning paths via MCTS with internal evaluation. Guides hypothesis branching via multi-armed bandit scores.
2025
Human
MC-NEST
Rabby
Internal con-
Hypothosis- level
et al.
2024
fidence
MAGIC
Xu et al.
Branching
Internal con-
Hypothesis-
2023a
exploration
fidence
level
MCTS
Sprucill
Branching
Internal con-
Hypothesis-
Applics combinatorial MCTS for catalyst. design.
al.
2023
exploration
fidence
level
DGM
Zhang al.
Branching exploration
Internal con-
Program-
Open-ended evolution: agents rewrite and test their own code. Agent-based social simulation with adaptive opinion modeling.
2025a
fidence
level
Casevo
Jiang et al.
Sequential + Simulator +
Hypothesis- level
2024
Branching
Peer agents
MOOSEChem2
Parallel ro- finement. fidence
Internal con-
Hypothesis- level
Parallel sampling of chemical hy- potheses with internal scoring.
Yang et al. 2025b
Chem Crow
Bran
Programmatic External tool planning
Program- level
Designs synthesis routes exe- cuted via cheminformatics APls. Writes lab scripts run simula- tors or robotic labs.
et al.
2023
Scientiietist
Huang
Programmatic
Simulator +
Program-
& Wu
2024
planning
In-lab run Programmatic External tool
level
LLM-SR
Shojace
Program- level
Generates and refines symbolic equations through tool feedback.
et al.
2024
planning
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contrast, structured search methods such Lrree-of-Thoughe (breadth-first search) achieve much higher rewards (9.91-13.8) by evaluating hundreds of candidate prompts (NP 253, depth 5). Monte Carlo Thought Search further improves performance (12.47-15.6) by deeper and more adaptive exploration (NP 301, depth 9). These results indicate that test-time scaling enables exploratory creativity by systematically traversing the ideation space, though gains come at the cost of increased computational expense and reliance on evaluator quality.
However, this search is ultimately confined to one model's internal distribution. For highly in- terdisciplinary or radically novel ideas, relying solely on single model's representation may be insufficient. Just breakthrough research often emerges when diverse human experts collaborate across fields, scientific ideation may require different LLMs each trained specialized in differ- ent domains-to interact, negotiate, and collectively build richer space of idcas. While scientific discovery is not purely compelilive, adversarial-style mechanisms such as debate, critique, and counterexample generation map naturally onto collaborative scientific workflows, where hypothe- ses are stress-tested, defended, and revised through peer interaction.
Thus, moving from single-agent scaling to multi-agent collaboration may bring LLM-assisted search closer to the collective, emergent creativity observed in real-world scientific communities (Chen et al., 2025b Park et al.] 2023). The next section explores these multi-agent architectures and their implications for scientific discovery.
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5 Collaborative Multi-Agent Systems
As scientific inquiry becomes increasingly interdisciplinary, data-intensive, and empirically grounded, the limitations of single LLM-no matter how capable- sypable-become more apparent. While previous strategies such prompt engineering, knowledge augmentation, and test-time individual models, they do not fully address the inherent complexity of gener- ating novel, valid, and feasible scientific hypotheses. This process often requires not just broad linguistic and conceptual knowledge, but also specialization, iterative reasoning, and interaction with real-world data or domain tools. Multi-agent LLM systems address this by simulating col- laborative research environments, where multiple LLMs, each often imbued with specialized roles, debate, critique, and build upon each other's idcas.
From the perspective of complexity science, the behavior of such systems can exceed the sum of their parts: interactions among agents give rise to emergent properties not predictable from isolated individuals (Holland 1998 Anderson 1972 Extending this perspective to collaborative multi-agent LLMs, we can conjecture that debates among agents with diverse perspectives may not only expand the idea space but also challenge entrenched domain assumptions. In doing so, such systems could approach what Boden defines as transformational creativity [Boden] (2004) that alters the very conceptual space in which ideas are peteerated-ppeninn pathways to more radical and paradign-shiftingg hypotheses. Although still speculative, this view resonates with the broader observation that emergent abilities tend to arise in sufficiently complex and interactive systems (Peruial et al., 2024 Chen et. al. 2025b). Importantly, transitioning from single-agent to multi- agent systems (MAS) represents a shift in the process of creativity itself, introducing interactions and itcrative reasoning as sources of innovation in the 4Ps framework (Rhodes) 1961).
There are two primary motivations for adopting multi-agent. approaches in scientific discovery. The first one is Automation of Complex Workflows, where specialized agents divide and execute research tasks at scale. The second one is Emergent Collaboration for Idea Generation, where interacting agents critique and build upon each other's ideas to foster creativity and discovery. Together, these approaches represent a step toward building Al systems that are not only efficient. assistants but also active, innovative collaborators in the scientific process.
5.1 Pipeline-Oriented Workflows: Automation of Complex Workflows
One prominent application of MAS is the crcation of pipelinc-oriented workflows, where specialized LLM agents collectively automate the various stages of scientific research. In these systems, each agent is assigned a distinct role-such as literature reviewer, hypothesis generator, experiment designer, or manuscript writer mirroring the division of labor found in human research teams. By decomposing the scientific process into structured steps, these pipelines enable comprehensive coverage of the research lifecycle and improve scalability and efficiency.
Recent work has demonstrated the potential of this approach for fully autonomous discovery. Al- Scientist (Lu et al.) 2024a) introduces an end-to-end framework that spans the entire process of scientific research: gathering and synthesizing literature, generating novel hypotheses, designing and simulating experiments, and even producing manuscripts and code repositorics. AI-Coscientist [oolwweis et al. 2025), establishes a structured, multi-agent pipeline for aulonomous scientific discovery. The system includes specialized agents for literature retrieval, idea generation, reflective self-ovaluation, ranking and tournament-style comparison of hypotheses, and itcrative refinement.
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through evolutionary search and debate. Similarly, Robin (Ghareeb et al. 2025) demonstrates fully integrated chain of LLM agents that autonomously identify promising targets, design and run in silico experiments, and validate findings. This system notably proposed and computationally validated a novel therapeutic candidate for dry age-related macular degeneration, illustrating the real-world impact of multi-agent pipelines.
Infrastructure efforts such as AgentLab and AgentRziv provide shared environments where au- tonomous agent laboratories can publish, retrieve, and iteratively build upon prior research results (Schmidgall al., 2025] Schmidgall Moor 2025). These platforms introduce mechanisms for reproducibility and collaborative improvement, paralleling how human scientists interact through preprint servers and open-source repositories.
Pipeline-oriented MAS primarily target efficiency and automation rather than creativity or nov- elty. By systematically orchestrating specialized agents, they represent a significant step toward accelerating the entire research lifecycle and scaling scientific discovery to levels unattainable by human researchers alone. Future advances in model capabilities may roduce the neod for explicit task division, but for now, these systems offer a practical path for handling the complexity and scale of modern scientific rescarch.
5.2 Debate and Emergent Collaboration: Fostering Creative Discovery
While pipeline-oriented systems focus on efficiency and task automation, a different motivation for multi-agent approaches foster creativity and emergent idca generation. Scientific breakthroughs rarely arise from single linear process they are often the product of discussion, critique, and synthesis among researchers with different perspectives. LLMs, trained on vast corpora of human language, encode implicit models of human reasoning and social interaction. As shown in the Generative Agents framework (Park et al., 2023). when placed in narrowly defined contexts, LLMs can produce believable patterns of human-like behavior, from cooperation to competition. This suggests that a society of LLMs interacting through dialogue may exhibit emergent behaviors such as creativity, critical reasoning, and the collaborative problem-solving dynamics characteristic of real-world scientific communities (Piao et al. 2025 Piatti el al. 2024).
In this paradigm, agents do not simply pass outputs along pipeline. Instead, they actively engage in multi-turn debates, critique one another's proposals, and iteratively refine ideas. Also, in collaborative MAS, additional LLMs are active participants in the creative process, so it stands in contrast to the single-agent generator-verifer setup (Section 4.2.1 where a verifier agent provides external feedback to guide the search space but remains a largely passive filter. This creates a richer search process, where hypotheses can be challenged, defended, and synthesized in multiple perspectives |Estonnll Liu 2025).
A common architecture for such systems is the generator discriminator loop, where one agent pro- poses ideas while others act as critics, rankers, or adversaries. Through repeated argumentation and revision, the group collectively converges on stronger and more coherent scientific hypotheses. For example, VirSei (Su et al. 2025) introduces a team brainstorming agents, critics, and judges that iteralively debate to generale, evaluate, and refine hypotheses. By framing the process as structured rounds of argument and critique, this system demonstrates how collaboration between diverse roles produce hypotheses with greater novelty and fcasibility than single-agent approaches.
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Similarly, the Multi-Agent Debate (MAD) framework (Liang al, 2023) organizes agents into adversarial roles, where each proposal is rigorously scrutinized by competing agents and adjudicated by neutral referee. This adversarial process is especially valuable for mitigating common LLM weaknesses such as hallucinations or factual inaccuracies, as challenges from other agents force the generator to strengthen its reasoning and evidence. The result is a kind of automated peer review, producing more resilient and reliable outputs.
Recent large-scale systems combine debate with structured pipelines. For instance, AT- Al-Coscientist (Gottweis et al., 2025) incorporates generation agents, reflection agents, rankers, and evolutionary search to iteratively improve generated hypotheses. Here debate is deeply embedded within the pipeline itself: the generation agent engages in simulated scientific debates to iteratively refine and strengthen its proposals. Mcanwhile, the ranking agent conducts tournament-styll debate matches, where hypotheses compete head-to-head. In this way, debale serves a dual role-both as a filter to eliminate weak ideas and as a driver of exploration, expanding the search space toward diverse, high- quality hypotheses. This integration demonstratos how adversarial interactions can be harnessod within structured multi-agent pipelines, combining the efficiency of workflow automation with the creativity and rigor of collaborative scientific discourse.
This richer interaction structure allows the system to explore complex intellectual landscapes more fully and can help transcend purely combinatorial idea generation, enabling the emergence of ex- ploratory and potentially groundbreaking hypotheses (Chen et al. 2025b
5.3 Takeaways and Limitations
MAS have demonstrated strong potential for scientific discovery by providing two key benefits: scalable automation, where specialized agents divide complex workflows into coordinated stages {Su
et al. 2025). and emergent creativity, where multi-turn debate and critique among agents generate novel exploratory hypotheses that resemble real-world scientific collaboration (Liang et al. 2023).
Empirical results from the VirSei study show that multi-agent systems consistently outperform single-agent baselines across both automated metrics and human evaluations, and this holds across different backbone models (GPT-4o, LLaMA-3.1-8B, and 70B). In particular, human ratings show clear gains in novelty (e.g., 3.78 vs. 3.10 3.10-3.22) 3.22), feasibility (5.24 vs. 4.78-4.94) and effectiveness (4.95 vs. 4.43 4.77). At the same time, improvements in the proposed metrics (higher Cl and lower CD: 7.1.1) 1 indicate more coherent and less redundant idcation. Beyond benchmarked evaluations, a particularly notable result from the AI co-scientist (Gottweis et al. 2025 provides evidence of potential transformational creativity. The system independently recapitulated an unpublished gene- transfer mechanism bacterial evolution via in silico discovery. As this mechanism was absent from public data and could not have been retrieved, the result goes beyond combinatorial recombination and points to the capacity of MAS to restructure the scientific search space, offering early empirical signals consistent with exploratory- and in rare cases, transformational-ccrati
However, MAS face notable limitations. Their reliance on brittle prompting and hand-tuned orches- tration makes them fragile and susceptible to cascading failures (Cemri et al. 2025 tse Huang et al.) 2025). Moreover, in line with Sutton's Bitter Lesson (Sutton| 2019), such human-cnginoered coor- dination mechanisms are often out performmddd over time by simpler, more general approaches that scale with better-trained models and data. MAS also resemble other test-time scaling methods, relying repeated inference and multi-round interaction to improve performance, which incurs
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substantial computational overhead. As shown by Gao al. (2025a), these advantages diminish base LLMs improve, with strong singlo-agent models frequently matching MAS performance without communication costs. Consequently, MAS are best suited for settings where interaction itself constitutes the primary source of value, such as genuine multi-perspective debate or exploratory synthesis.
To move beyond experimental prototypes toward dependable systems, LLMs must become more capable, reliable, and efficient, reducing reliance on external coordination. This motivates shift toward parameter adaptation methods that enhance models' internal reasoning abilities, enabling them to function individually competent researchers without heavy orchestration test-time compute overhead (Li et al. 2025cp. The next section examines these approaches, focusing how training- based techniques can improve hypothosis generation, reduce errors, and complement MAS by internalizing collaborative behaviors.
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Multi-Agent
Knowledge Augmentation
Systems
Inference Time Search
Process
Press
Prompt Engincering
Person
RL Post-Training
Supervised Finetuning
Architectural & Training Innovations
Conceptual mapping of I.I.Mdrrveen scientific ideation methods to their primary source of creativity, inspired by Rhodes' 4Ps framework. Here, we focus on the generative di- mensions-person,, process, and press-as sources of creativity, with product reserved for evalua- tion. Press includes knowledge augmentation and prompt engineering, supplying external context. and inspiration. Process covers multi-agent collaboration and inference-time search strategies that guide exploration. Person encompasses design choices and inductive biases in model architecture and training, including objectives, prediction formats, and alternative generative structures. In- tersections illustrate methods that combine dimensions: supervised fine-tuning (person + press) internalizes external knowledge, while reinforcement learning (person + process) internalizes search strategies, enhancing the model's reasoning and creative capacities.
Figure 11:
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6 Larameter-Level Adaptations
As scientific AI moves from experimental prototypes toward dependable infrastructure, underlying LLMs must become smarter, more reliable, and more efficient. In such settings, reliance on lest-time scaling or external tool use includnuu multi-agent interactions becomes increasingly impractical, as these approaches incur high computational costs, latency, and fragility. To overcome these limitations, is necessary to internalize both domain knowledge and effective creative pathways within the model itself. This represents paradigm shift. from relying on process as the main source of creativity to cultivating the person itself, conceptualized in the 4Ps framework (Rhodes 1961.. By incorporating both knowledge and scarch capabilities into the model, these approaches amorlize the cost of exploration: once trained, the model can generate high-quality hypotheses without repeated costly dnference-time interactions (Li et al. 2025c
In this section, we systematically categorize parameter adaptation methods into three groups: Su- pervised Fine-Tuning Approaches, which leverage curated datasets lo teach domain-specific knowledge; Reinforcement Learning-Based Methods, which optimize generative and search behavior through feedback; and Hybrid and Preference Optimization Methods, which com- bine supervised and reinforcement signals to align models with both task objectives and creative evaluation criteria.
6.1 Supervised Fine- Tuning Approaches
Supervised fine-tuning (SFT) represents one of the earliest and most widely adopted strategies for adapting LLMs specific downstream tasks. In the context of scientific discovery, SFT leverages labeled datasets- often curated from scientific literature, domain-speeific corpora, or structured instruction-folloviii examples to imbue models with domain expertise and task-relevant capabil- itics. The strength of SFT lies in its ability to exploit existing pretraining knowledge and refine it for specific scientific domains, such as chemistry, biology, or physics. However, SFT can also face limitations, including overfitting to narrow datasets or failing to generalize to novel problems outside the training distribution. Additionally, curating high-quality, large-scale datasets remains one of the most challenging aspects of using SFT, as it both costly and fabor-intensive. Recent advances have sought to mitigale these challenges by introducing techniques such instruction generation, curriculum learning, and fine-grained data curation. As a result, SFT continues to be a cornerstone of training pipelines for scientific LLMs, particularly when paired with robust evaluation and generalization strategies.
notable direction within SFT Domain-specific Adaptation. The DARWIN Xie et al. 2023
series leverages SFT on scientific literature and structured scientific datasets through an automaled Scientific Instruction Generation (SIG) model. By applying SFT on over 20,000 instruction answer pairs, the model is aligned with ground-truth knowledge in scientific domains, leading to substantial performance improvements across tasks in physics, chemistry, and materials science. Building on this paradigm, OmniScience (Prabhakar et al.) 2025) introduces more comprehensive framework for domain specialization through (I) domain- domain-adaptive pretraining on curated scientific corpora, (2) instruction tuning with tailored datasets for domain-specific tasks, and (3) bassoningbbasoe knowl- edge distillation to strengthen logical consistency and contextual relevance. Scenario 12 illustrates this by showing how curated datapoints from OmniScience and DARWIN help models shift from inconsistent answers to domain-consistent scientific rcasoning.
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Scenario: SFT-Enhanced Question Answering in Materials Science
A base LLM is asked: "When photons with eneryies just below 2.3 eV interact with thin film whose absorption sharply incraass above this threshold, what happens?" Without finetuning, the model often mispredicts (e-g.: "reflection" "photoluminescence"" After SFT, the model succeeds because its training set included domain -eaannngooudedd data- points such as:
OmniScience datapoint
Instruction (input): Read the following excerpt from materials science paper and answer the MCQs about its optical properties:
Excerpt: "The absorption coefficient of the thin film increases sharply around 2.3 eV, indicating the onset of strong interband transitions between the valence and conduction bands. Below this energy, the film shows very low absorption, which suggests that it behaves transparently the visible region."
Question: What happens when photons with energies just below 2.3 eV interact with the film?
A) They are strongly absorbed via interband transitions.
B) They are mostly transmitted (i.e. not absorbed).
C) They cause photoluminescence.
D) They are reflected with high reflection coefficient.
Response (label): B) They mostly transmitted (i.c. not absorbed).
Reasoning: Below ~23e,V, the absorption coefficient is very low, which means the film does not absorb much; hence photons in that energy range are largely transmit- ted.
DARWIN datapoint
Instruction (input):
Given a description of material, classify whether it behaves conductor, semiconductor,, or insulator.
Material: Silicon at room temperature.
Response (label): Semiconductor.
Reasoning: At room temperature, silicon has a bandgap of about 1.1 eV, which is small enough to allow limited electron excitation into the conduction band, charac- teristic of a semiconductor.
Figure 12: SFT improves LLM understanding of physical principles in materials science question answering tasks.
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Extending this approach to chemistry, ChemLM (Kallergis et al. 2025) applies -amain-adaptive pretraining followed by SFT on chemical datasets, enabling accurate molccular property prediction and identification of potent pathoblockers. Unlike conventional SFT, ChemLM leverages chemical SMILES sequences input, treating molecules as a language to capture structural patterns and relationships, which supports discovery of novel compounds. ChemMLLM (Tan et al., 2025) further enhances this paradigm through multimodal SFT across text, SMILES, and molecular images. By integrating these modalities, ChemMLLM achieves unified reasoning over chemical structure, function, and visual representation, outperforming GPT-4o by 118.9% on image-basod molccule optimization lasks. These efforts demonstrate that domain-specific SFT, when combined with modality- aware pretraining, can substantially accelerate discovery and reasoning while maintaining high accuracy.
6.2 Reinforcement Learning- Baarning-Based Methods
Reinforcement learning (RL) has emerged as pivotal approach for aligning LLMs with complex, goal-driven tasks in scientific discovery. In traditional reinforcement learning, a Markov Decision Process (MDP) serves as the mathematical framework for representing the key components of an RL algorithm: states, actions, transition probabilities, rewards, and the discount factor. In the context of language models' post-training, these elements are analogous to the input prompt plus the tokens generated far (stales), selecting the next token (action), transition probabilities condi- tioned state and action, and the accuracy of the generated response for given query (reward). RL has been widely adopted to enhance the reasoning capabilities of LLMs within this framework [DeepSeck-A) AI et al.) 2025) (Wang et al., 2023b) (Shao et al., 2024). Traditional RL frameworks have primarily focused on optimizing terminal rewards, such as achieving the correct solution or maximizing accuracy. However, scientific discovery and hypothesis generation are inherently process-oriented: intermediate steps such as problem decomposition, hypothesizing alternatives, self-correction, experiment design for hypothesis validation and citing credible sources are just as critical as the final output. To address this, increasing body of rescarch is shifting toward PRMs (Lightman et al. 2023), which evaluate not only "what" models say but also "how" they think. ORMs, though easier to implement, tend to favor shallow heuristics and are susceptible to reward hacking. In contrast, PRMs provide granular, intermediate feedback that fosters deeper chains of reasoning. This evolution in RL design- from black-box evaluation to interpretable, step-wise reward shaping-enables enables new paradigms like debate, self-play, and simulation-based learning, all of which are increasingly relevant for equipping LLMs with the capacities for rigorous and transparent scientific reasoning.
6.2.1 Algorithm Discovery and Reasoning
RL has been instrumental in enabling LLMs discover efficient algorithms and enhance scientific reasoning capabilities. For instance, [Surina et al. (2025) demonstrated that RL-based fine-tuning, integrated within evolutionary search processes, dynamically improves LLM performance in al- gorithm discovery tasks. By using FunSearch search without RL) as a baseline, they showed that the combination of RL finetuning with evolu- tionary search can enhance the performance of the model and surpass the bascline in constructing optimal algorithms for tasks like bin packing, traveling salesman problem and flatpack. Addition- ally, leveraging expert domain knowledge, RL-based fine-tuning has been successfully applied to genomics, where expert forum discussions automatically transformed into RI-friendly multiple-
Ktomera-Paredes et al.) 2024) (only evolutionary
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Table 2: Parameter-adaattaion methods for scientific idea generation and discovery.
Adaptation
Main contribution
Main
Method
Field
strategy
result
challenges
Cycle-
Automated
preference training (SimPO-based
Iterative
Automates
hypothe-
Limited cross-
Rescarcher
Scientific research
sis sis-review cycles
domain
generaliza-
Wenn et al.
RL between policy and reward)
Produces ideas near ac-
tion
2025a
cepted paper quality
Reward instability
Dynamic
Scientific idea gen- eration
SFT PPO-based controllable RL with multi-objective rewards (novelty, feasibility, usefulncss)
Dynamically adjusts idea generation
Reward hacking
Control
Li
risk
2024c et. al.
Balances novelty and feasibility
Trade-ofls between
objectives
CRPO
General creative genera-
Preference-bast based RL (modular DPO) using creativity metrics (nov- elty, diversity, surprise, quality) on MUCE dataset
Boosts originality and diversity with quality tention
LLM-judge vulner-
mayilzada
ability
et a..
2025a
Static preference
tion
Outperforms DDPO, GPT-4o
DPO,
bias
Algorithm Discov-
Comput- ational scientific
Hybrid evolutionary search RL fine-tuning (DPO forward KL)
Discovers novel algo-
High computation
rithms for TSP, Bin Pack- ing, regression
cost
ery
Surina 2025
Large scarch space Limited inter-
et al.
discovery
Improves optimality and diversity
pretability
DARWIN
Scientific reasoning
Domain SFT on ~~0K tasks + multi-task finc- luning with synthetic
Improves domain rea-
Domain overfitting
Series
Xic
soning and factual acc
Spurious learning
al.
2023
Strong on SciQ bench- mark
Data diversity de-
instruction generation
pendence
Smiley-
Domain-
SFT + DPO RL
Generates valid, diverse molecules
Dataset bias, lim-
Llama
Ca-
specific
(iMiner) for property- optimized molecule de- sign
ited coverage
vanagh
discovery,
Higher novelty and di- versity than RNN base- lines
Costly validation
et al.
2025
molecular
Chem-
Domain-
Self-supervised molec- ular pretraining + domain adaptation
Improves molecular
Limited inter- pretability
LM Kallerr-
spocific discovery, chemistry
property prediction Aids discovery of chem- ical candidates
gis el
al.
Poor cross-family generalization
2025
SFT for property prediction
Chem- MLLM
Domain- specific
Multimodal fine-tuning on text, SMILES, and molecular images; uni- fied cncoderrdocoder design
Bridges modalities for teutl-text-ssructte rea- soning
Complex alignment
Tai
High compute de- mand
et al.
2025
discovery, chemistry
Enables molecule gener- ation/ mapping
Pretraining bias
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choice questions (MCQs), significantly enhancing the model's scientific reasoning abilities et al. 2025b
Yin
6.2.2 Domain-Specitte Reasoning and Optimization
RL has also been successfully adapted for domain-specilic scientific challenges that demand inter- pretability structured optimization.
In protein discovery, RL has been coupled with Protein Language Models (pLMs) to design novel bio- logically plausible sequences optimized for therapeutic objectives. For instance, Stocco et al. (0255)
demonstrate how PPO-based fine-tuning can produce protein sequences with improved binding affinity and stability, including candidates for EGFR binders, Similarly, |Subramanian et al. (0024) applics policy-gradient training on top of pre-trained protein models to generate diverse sequences tailored to objectives such structural integrity. These studies illustrate how combining RL with MLMs enables protein discovery beyond natural sequence space.
In drug discovery, RL leverages structured and composite rewards to optimize molecular candi- dates. DrugImproverGPPT introduces Structured Policy Optimization (SPO) to align molecules with therapeutic objectives while preserving beneficial properties (Liu et al. 2025d). Similarly, PPO-based fine-tuning balances protein-target efficacy and chemical validity, enhancing novelty and trug-likeness (Ahmed & Mohammed 2025).
6.3 Hybrid and Preference Optimization Methods
Hybrid and preference optimization approaches integrate SFT, RL, and advanced aligmment meth- ods to balance complex, multi-dimensional objectives in scientific discovery. These frameworks combine the grounding benefits of SFT with the adaptive refinement capabilities of RL, while also leveraging preference-basee based optimization to address challenges of diversity, creativity, and precision.
One prominent application is research idea generation, where a two-stage framework combines SFT with controllable RL to dynamically balance novelty, feasibility, and effectiveness through multi- dimensional reward modcling (Li et al., (024c)). Similarly, molccular generation, a two-stage pipeline of SFT followed by RL significantly enhances structural diversity in generated molecule sets, outperforiing conventional decoding methods (Jang et al. 2025).
Preference optimization has emerged as particularly effective complement to these hybrid strate- gies. DPO [Rafailov et al. 2023) simplifies aligmment by directly learning from human preference pairs, avoiding the instability of complex reward models. SmileyLlama (Cavanagh et al. 2025 derived from the Llama--3.1 1-amma-.11-B---ae 1-B- Instruct model, employs a pipeline of SFT combined with DPO to generate drug-like like molecules optimized for biological activity and validated efficacy against protein targets. However, standard DPO often favors "safe" high-reward responses, limiting creativity and diversity in outputs.
To address this, several recent works propose creativity- and diversity-enhanee preference opti- mization. CrPO introduces a multii-iimensional alignment framework that injects signals of novelty, diversity, surprise, and quality into the optimization process, producing outputs that are not only accurate but also more surprising and original (Ismayilkada et al. 2025a).. DivPO extends this idea by explicitly selecting preference pairs to reward rare but high-quality responses while penalizing common low-quality ones, leading to substantial improvements in output diversity without sacri-
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ficing quality (Lanchantin et al. 2025). These approaches are particularly relevant for scientific idea generation, where generating multiple distinct but plausible hypotheses is as important as generating feasible ones.
A recent example of this direction is CycleReseerrherr, which proposes a complete framework for automating the research cycle with open-source LLMs (Weng et al. 2025a). The framework inte- grates two models: CycleResearcher, responsible for literature review, idea development, manuscript generation, and refinement; and CycleReviewer, which simulates peer review and provides itera- tive feedback via RL. The models are trained on new datasets Review-5k; containing struc- tured peer reviews, and Research- 14k, which provides outlines and segmented academic papers for fine-tuning. Through Carchor-CyelcRevievve loop allows the policy model to improve over multiple iterations, with CycleReviewer acting as a reward model to guide preference-based updates. Unlike standard DPO, which tends to reward only "safe" responses, this iterative design promotes more nuanced alignment with academic standards. In practice, the framework achieved competitive review scores, showing that LLMs can produce outputs near the quality of human preprints while still leaving room for further improvement.
iterative preference optimization approach (SimPO), the CycleRe-
Structured optimization methods further extend this landscape by employing more specialized align- ment techniques. Energy Rank Alignment (ERA) (Chennakesavalu et al. 2025) uses gradient- gradient-based optimization to align molecular and protein generation with explicit chemical objectives, balancing robustness, diversity, and property alignment. Contrastive Preference Optimization (CPO) (Gk-
oumas 2024) improves precision by explicitly contrasting optimal and suboptimal outputs, thereby enhancing reliability and mitigating issues like memorization or hallucination.
In summary, the structured exploration of training- methods- RL, SFT, hybrid ap- proaches, Aiructured optimization, and elficiency-focused curriculum learning highhighhhhhhss the rich landscape of opportunities for adapting LLMs for scientific discovery. These methodologies col- loctively enable procise alignment of model capabilitics with specific scientific discovery objectives, thereby significantly advancing automated scientific research. A concise overview of the most recent parameter-adaatation frameworks that target scientific-idea generation or discovery provided in Table 2
6.4 Takeaways and Limitations
Post-training adaptation of LLMs-using SFT, RL, or bybriddppeffereceeeee pnreferencc-oppimizatio methods is a powerful tool for scientific idea generation, but each approach has distinct limitations.
SFT instills reliable reasoning patterns and aligus outputs with human demonstrations, ensuring consistency and interpretable results on tasks close to the training distribution. However, it
constrained by static datasets and can overfit to dataset biases, limiting generalization, handling
of ambiguous prompts, and exploration of novel or high-risk hypotheses (Chu et al. 2025 Hua et al. 2025a).
RL guides exploration toward task-specific objectives, enabling creativity and risk-taking for poten- tially groundbrcaking hypothesos. Its challenges include reward over-optimization (model focuses on easily achievable rewards, limiting novelty), reward hacking (model exploits loopholes in the reward function to get high reward without truly solving the task), output instability (early train- ing can produce incoherent or repetitive outputs), sensitivity to reward design (poorly specified
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rewards may penalize creative or cross-domain ideas), and computational cost (multiple sampling and updates make largo-scale training expensive) for large-scale discovery (Mohammadi 2024 Kirk
et al. 2023 DeepSeek-AI et al. 2025
Methods (e.g., DPO, CRPO) balance reliability and creativity, im- proving quality and multi-objective control. Yet, they face system complexity (multi-stage pipelines are harder to debug, tune, and maintain), bias propagation (preferences from human- or AI- generated data can introduce systemic biases), cross-domain generulization (models optimizod on one domain may underperform on others), and feedback sensitivily (quality, consistency, and gran- ularity of preference signals directly affect creativity and exploration of unconventional ideas). that constrain exploration of unconventional ideas.
Empirical evidence supports this characterization. In the CycleResearcher (Weng et al. 2025a study, parameter-adapted models consistently outperform the AT Scientist baseline in peer- review review-style evaluation, achieving higher overall scores (5.15 5.38 vs. 4.31) and non-zero acceptance rates (24-35% vs. 0%). At criterion lovel, adapted models show improvements in soundness, pre- senlation, and contribution, with the latter-interpredd interpreted 2.15 to 2.53-2.60 approaching the performance of human-reviewed preprints across all three di- mensions. Complementary evidence from the CrPO (Ismayilzada et al. 2025al study, evaluated using NoveltyBench (Zhang et al. 2025c), further clarifies while SFT substantially improves quality- interpreted as valueness- (5.0 vs. 3.1-4.1 for base LLMs vs. 4.3-4.9 for CrPO) but yields more lim- ited gains in novelty (5.7 vs. 4.0 for base LLMs), remaining well below preference based approaches (e.g,, CrPO: 6.8 7.9). These results indicate that parameter adaptation primarily strengthens val- ueness, with stronger novelty emerging when optimization explicitly rewards exploration. Further comparisons can be found in Table 3
proxy for novelty- -improving from
In summary, SFT ensures consistency, RL drives exploration, and hybrid methods balance both. While these approaches face challenges such as dataset biases, reward mis-specification, overfitting, and limited generalization, they internalize crcativity and domain knowledge within the model itself. This reduces reliance on test-time computation or external resources and shifts the source of creativity from purely Process or Press signals toward the model's internal capabilities, aligning with the Person aspect of 4p framework (Rhodes) 1961). Addressing the limitations while leveraging these intrinsic strengths could be the key to unlocking LLMs' full potential for scientific hypothesis generation and discovery.
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Table 3: Method categories and their expected effects on novelty and valueness scientific ideation. Expected effects reflect empirically observed trends and theoretically motivated interpretations reported in prior work, rather than guaranteed outcomes.
Method
Primary Mechanism (4Ps)
Expected Effect on Expected Effect on Novelty
Representative
Category
Valueness
Works
Knowledge
External knowl- edge injection (Press)
Grounding, cor- rectness, and feasibil- ity; reduced halluci- nations Novelty by steer- ing generation toward lower-probabllity re- gions (constraints, role-play, reframing)
t/+ Combinatorial novelty via. recombi- nation of retrieved concepts; may overfit to retrieved context
Section et al. et al.
2.3 Gu
Retrieval
2025
Gao
Augmentation
2025b
Prompt-based Distributional Steering
Input-level guid- ance (Press Process)
t/4 Structured prompting can im- prove reasoning:
Section
3.5
[Zaao
et al.
2025 ct
misaligned straints may degrade value
con-
2024a
Inference-time Scaling Search
Systematic ex- & ploration beyond single-pass de- coding (Process)
1f Exploratory novelty via branching, refine- ment, and structured search debate, and critique (Pro- cess; proxy for Person)
t/4 Improves with evaluation or prun- ing: unconstrained search risks invalid ideas
Section (Sprueill et al.
4.4
2023
Feng
et al. 2025
Multi-agent & Deliberative Systems
Interaction,
1t1 Potential for trans- formational novelty via emergent interactions
Improved coher- ence, plausibility and even robustness through iterative critique
Section Gottweis et al. Johnson
5.3
2025
& Davis
2024
Parameter Adaptation & Learning
Internalization knowledge and strategies (Person)
+/t SFT, which rein- forces training pattorns; RL or PO via internal- ized exploration
Consistency, feasi- bility, and robustness through internalized constraints
Section
6.4
[een ct al.
2025a Ismay
ilzda
et al.
2025a
7 Evaluation of Scientific Idea Generation: Frameworks and Challenges
Evaluating L.L.-generate scientifie ideas is crucial yet difficult. Although evaluation mechanisms share techniques and sources with the feedback systems discussed in Section 4.2, they serve different purposes: feedback guides idea generation through search and refinement, while evaluation assesses final outputs to determine a model's effectiveness as a scientific ideator. In Rhodess 4Ps (Rhodes) useful, and correct outputs-serves serves as the key
1961; framework, the Product dimension-nowe endpoint of creativily, but assessing it challenging because scientific ideas are open-ended and their true impact unfolds over time. This makes evaluation major bottleneck: expert judgment is insightful but unscalable, automated metrics are efficient but reductive, and LLM-as-a-judge
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approaches promise a balance while still inheriting biases. In this section, we survey three major familics of evaluation frameworks: (1) Computational and Exccution-based metrics, which operationalize dimensions of creativity such novelty, diversity, feasibility and utility into quan- tifiable scores; (2) Expert review, which captures nuanced human intuition beyond what metrics alone can measure; and (3) LLM-as-a-judge models, which aim to synthesize the advantages of automation and human judgment. Table ! summarizes Evaluation paradigms, highlighting their core strategies, strengths, and limitations.
7.1 Computational and Execution- Metrics
Computational and Execution- Metrics translate creativity dimensions--ovelee valueness (diversity, feasibility, and impact) into measurable, reproducible signals; however, we emphasize that these metrics provide indirect proxies for creativity rather than direct mcasurements of creative insight, and should be interpreted as operational indicators rather than exhaustive characteriza- tions. Computational metrics derive these signals analytically through Model-based formula- tions, while execution based metrics validate them empirically through Simulation or real-world testing. Early work, such as SciMon (Wang et al. 2024a), evaluates the overall quality of gener- ated ideas using surface-level text similarity measures (e.g., rouge, bertscore, bartscore), without distinguishing between different aspects of creativity to compare generated ideas against reference texts. While such holistic NLP-based evaluations offer scalability and ease of automation, they fail to capture the conceptual depth and open-ended multidimensional nature of scientific creativity.
Recent research instead decomposes creativity into distinct dimensions, cach requiring specialized evaluation strategies: for instance, novelty and diversity are better assessed through semanlic distance or representational density, whereas feasibility and impact are more meaningfully estimated through ezecution-bass or experimentally grounded validation.
However, while computational and execution-based metrics enable systematic, high-throughput evaluation, they provide only partial view of creativity and often overlook the contextual insight and inluitive judgment that experts contribute. Together, they form a more fine-grained framework for assessing scientific idea generation, which we review in detail below.
7.1.1 Novelty and Originality
One of the most widely studied criteria in evaluating the scientific creativity of LLMs is novelty, often operationalized as the degree to which an idea departs from existing knowledge or occupies a sparse region the scientific landscape. Recent works have approached novelty measurement through a variety of perspectives, including semantic density and knowledge graph triplets.
Several recent studies have approached novelty assessment through semantic local density, assuming that truly novel ideas should lie in sparse regions of the scientific embedding space. (Su et al. (2024) introduced two such measures: Historical Dissimilarity (HD) the average Euclidean distance between generated idea's embedding and its five most similar historical abstracts-ant Overall Novelty (ON), which further incorporates the contemporaneity and impact of neighboring works:
ON = HD x CT
(1)
CD
where Cl denotes the average citation count of the five most similar contemporary papers and CD their semantic dissimilarity. But, such dessity-based scores are sensitive to domain-specific
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Table 4: Comparison of evaluation paradigms for scientific idea generation.
Evaluation
Representative
Strengths / limitations
Creativity Dimension
Example works
paradigm
methods
Human
Expert panel, real
Scidcator
(mmtth & Huang
Conceptual depth, captures nuanced aspects Time- consuming, costly, subjec- tive, unscalable
Feasibility, Impact
Judg-
peer-review
2025),
Nova Hu et al. 2024).
ment
submission
ATSeientist v2 Yamada et al.
Consensual As-
2025),
sessment Technique
CAT AAmabie 1982
Structured
A Adeation
Wang et al.
2025d
Likert-scale
LLM- as- Judge
Prompt-based
LiveldeaBench
Scalable, reproducible, Novelty, approximates Impact expert rea- soning and Zhu et al.
Feasibility,
judges
2024.
Fine-tuned
Reviewev
Weng el al.
judges
2025a
Reinforcement-
Deep
optimized
reward
2025b
HARPA
(Vasu et al.
intuition
models
2025).
Sensitive prompts, train- ing bias, and do- main drift
Hybrid
frame-
ReviewRL
Zeng et al. 2025
works
Comput- ation Based
Text similarity metrics: BLEU,
SciMon
Wang et al. 2024a et al.
Objective, in- terpretable, domain- agnostic, scal- able
Novelty, Diversity, Impact
CrPO
Ismayilzada
BERTScore, DSI
2025a
Semantic dis- tance: RND, HD, ON, APD, Vendi
VirSci
Su et al. 2024 2025ch
RND
Wang et al.
Vendi Score Friedman & Di-
Surface-level semantics; de- pends on corpus quality and em- bedding fidelity
Score
eng 2023
Graph centrality
SciND (uupta et al. 2024 Ke
Bayesian fusion
et al. 2023
HypoAgem
Duan et al.
2025
Execution Based
Simulators, pilers, autonomous labs
AI Scientist
Lu et al. 2024a
Grounded, caatcome--bssee validation
Feasibility, Impact
AI Co-Scientist
Gottweis
ct al.
2025
Behavioral exe- MindShift Wu et al.
2024
Expensive, domain-limited, low scalability
cution
LLM Speech (Podolak et al.
Counter
2024
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corpus variance ideas in dense fields (e.g., NLP) appear less novel than those in sparse ones (e.g., quantum biology).
Building on this intuition, Relative Neighbor Density (RND) evaluates novelty through the density of an idea's semantic neighborhood relative to its neighbors' own densities (Wang et al. 2025e]. Given a set of ideas I = idea;, each represented by an embedding G(idea;) obtained via a semantic encoder G()), RND computes for each idea i its P nearest neighbors in the literature corpus A:
U1, 02. ....... Up KNN(0,, P,A),
(2)
and estimates the local neighbor density:
Q Σ
ND(w:) = Q k=1
d( vk),
where d(.,-) is the cosine distance.
Novelty of o is quantified by comparing the candidate's ND with the average ND of its neighbors:
RND(,:) = [N(o:] EVENGE) ND(v)
1
ND(w:) +E
where (T) is the neighborhood set and & is a small constant for numerical stability. A higher score indicates that the candidate lies a sparser region relative to its peers. For instance, if an abstract has ND 0.12 while its neighbors average ND 0.36, the ratio RND 3.0 suggests strong novelty. Unlike HD and ON, which measure novelty in absolute terms, RND provides relative normalization mitigating cross-domain bias by comparing each idea's isolation to that of its immediate semantic neighbors. Empirical validation on large-scale datasets (PubMed and arXiv) showed that RND achieves AUROC > 0.80 across computer science and biomedical datasets, demonstrating robustness.
An alternative symbolic perspective adopted in the SciND benchmark (Gupta et al. 2024), which casts novelty detection as the identification of new showledge-graph. triplets. Here, scien- tific findings are reprosented as (subject, relation, object) triplcts extracted from the literature. A triplet is labeled novel if it does not appear in the historical scientific knowledge graph. For exam- ple, (*transformer architecture", "uses", "sparse attention routing") would be marked novel if prior graphs contained only ("transformer architecture", "uses", "softmax attention").. This approach of- fers interpretable, fine-grained novelty signals at the eutity-relation level, though it depends criti- cally on reliable relation extraction and graph coverage.
Taken together, these methods capture complementary aspects of novelty: ON measures absolute semantic distance, RND provides relative local density, and SciND adds symbolic, conceptually grounded interpretability. Combining these signals enables robust, multidimensional evaluation of originality in LLM-generated scientific idcas.
7.1.2 Diversity
While novelty measures how far single idea departs from prior knowledge, diversity evaluates the breadth of set of generated idcas. A model that outputs only narrow cluster of novel but similar
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hypotheses may underezplore the solution space. Diversity thus ensures that systems span multiple conceptual directions, not just isolated rare points. Empirical studics confirm that high novelty does not imply diversity- need to evaluate both dimensions jointly (Ruan et al. 2024
may generate unique yet Comain-concentrated ideas undesscorddngngngggggegoo the
common stralegy for measuring diversity is to compute the Average Pairwise Dissimilarity (APD) among generated responses in a shared embedding space. Let Y (B1....,yM. denote a set of M generated ideas, each represented by embedding ot- straightforward diversity score
2 Div(Y) = M(M -
Larger values indicate that the responses are semantically spread out. For example, if five candidate hypotheses include one in physics, one in computational biology, and three variations of the same NLP idea, the mean pairwise similarity would be skewed upward by the NLP cluster, leading to a lower overall diversity score.
Building on the same principle, instead of evaluating the whole set once, CrPO Ismayilzada et al, (2025a), computes per-response dispersion score. Responses with higher score-ias L... those further from their peers peers-receive stronger rewards, encouraging models to cover multiple directions rather than collapsing to a single theme.
Beyond cosine-based measures, Vendi Score Friedman & Dieng 2023 provides a more principled measure of set distinctness using Shannon entropy over the eigenvalues of the similarity matrix. Given similarity matrix K E MMM constructed from emboddings, let fAI...., A. denote its normalized eigenvalues (summing to 1). The Vendi Score is defined as:
Vendi(Y) exxp
Σ
bo log A4
This measure can be interpreted as the "effective number of distinct items" in the set. For ample, if three ideas are nearly identical, their embeddings produce a rank-1 similarity matrix, yielding Vendi(Y) se 1, despite M = 3. Conversely, if the three ideas are mutually orthogonal, the cigenvalues are uniform and Vendi(Y) = 3. This property makes the Vendi Score robust to redundancy.
In summary, APD provides simple and scalable measure of set-level spread, CrPO computes per- response dispersion score and Vendi Score adds redundancy sensitivity. Together, these approaches ensure that LLMs explore the scientific idea space broadly rather than converging prematurely on narrow regions.
7.1.3 Feasibility and Resource-Efficiency
While novelty and diversity measure idea exploration, feasibility and resource-effeienney concern execution-whette a generated idea can be realistically implemented given scientific or resource constraints. In some works, feasibility is indirectly estimated from prior knowledge by comparing new idcas to historically validated oncs. These rotrioval-based approaches roly on LLM critics to infer plausibility, as discussed in Section 7.3.1 However, growing class methods assess feasibility either computationally- by statistical models or more directly by executing or simulating them in real virtual environments, providing grounded, outcome-based validation.
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The Hypo.Agents framework (Duan al. 2025) proposes an N-R-F (Nov- clty-Roo
model that quantifies idea quality via Bayesian inference and information fusion. Using retrieved literature and semantic embeddings, it measures feasibility as the Bayesian probability of supporting wvidence-deriingg scores compulationally.
Execution-groumdeer evaluations appear most clearly in autonomous research pipelines. For instance, AI Scientist papers (Lu et al.) 2024a Yamada et al. 2025) or DeepScientist (Weng et al., 2025b)
integrates idea generation, code synthesis, and experimental validation closed loop. Hypotheses proposed by one agent are automatically implemented as machine-learning experiments, trained, and benchmarked; success metrics such performance gain reproducibility indicate feasibility. Similarly, HARPA (Vasu al., 2025) introduces a testability-driven ideation framework, where an LLM reward model prodicts fcasibility scores and selected hypotheses are subsequently verified through simulation or real-world trials, closing the loop between idea generation and empirical testing.
In materials and molocular science, exccution-based feasibility is operationalizod through simulation and experimental pipelines. [Brgg el al. (2023) introduce empirical measures such as Discovery Vield-the fraction of target materials discovered- and Discovery Probability the likelihood of identifying at least one viable candidate both derived from iterative experimental cycles. Similarly, Gallagher & Webb (2025) quantify efficiency as the proportion of candidate compounds passing synthesizability, stability, or toxicity constraints during virtual screening, effectively measuring the fraction of computationally or experimentally "non-wasted" recommendations. Systems like BenchML (Poelking et al.j b combine simulators and synthesis predictors to test chemical hypotheses, evaluating feasibility based on success rates such as molecular stability or docking performance.
Overall, these execution-grounded approaches provide the most direct signal of feasibility and resource-effiieency measuring not whether an idea 'sounds plausible", but whether it "works". However, exccution-base validation is limited to domains with accessible simulators or experimen- tal infrastruct ure, and many scientific fields lack fast or general-purpose implementation platforms. Consequently, scalable evaluation frameworks often combine them with llm-as-a-judge (Section 7.3 balancing empirical rigor with throughput.
7.1.4 Utility and Impact
Whereas feasibility measures whether an idea is plausible and resource-efficiet evaluation captures its cost-effeciveeness, impact aims to assess the signéficance
scientific idea once realized. An idea. may be technically feasible yet trivial in its contribution, or conversely, difficult to achieve but transformative in its outcomes. Impact evaluation is particularly challenging because it of- ten requires long-term or context-specific signals, yet several frameworks have emerged to provide measurable proxies.
computational line of work estimates the impact through Bibliometric and Scientometric indicators, treating citation counts, h-index contributions, and network centrality scores as a proxy for influence. Formally, given a knowledge graph G (V, E) of previous publications, the impact of new idea y can be approximated by its betweenness centrality once integrated into G, defined
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as:
Tx((y) Σ
CB(1) =
SPyZIE
where at is the total number of shortest paths between nodes & and t, and (v) is the number of such paths passing through y. High Cn(y) indicates that the idea bridges otherwise disconnected communities, a property correlated with transformative scientific discoveries (Ke et al.) [023). Such metries provide scalable, domain-agnostic way to estimate potential influence before real deployment, though they cannot capture context-speeific practical impact.
Beyond proxies, execution-base based approaches directly implement generated hypotheses, mirroring scientific experimentation. These methods differ from feasibility checks in scope: feasibility asks "can this be done?", while impact asks "does it matter?"
The AT Co-Scientist (Gottweis al. 2025) explicitly executes biomedical hypotheses to quan- tify real impact through end-to-end wet-lab experimentation. autonomously proposes and tests hypotheses in three increasingly complex domains-drne repurposing, novel target discovery, and microbial evolution. The system systematically measures downstream biological outcomes- such as inhibition efficacy or regenerative response- assess the practical significance of its discover- ies. This rigorous execution-driven evaluation exemplifies how impact in scientific ideation can be assessed through direct experimentation rather than proxy metrics.
Beyond laboratory science, behavioral exccution studics such as MindShift (Wu al. 2024 and LLM Counter-Speech (Podolak et al.) 2024) deploy AT-generated interventions in natural environ- ments digital health and social media to quantify real behavioral change speaii--pecific key performance indicator (KPI) measuring practical impact. Similarly, large-scale agent-based frameworks like AgentSociety (Piao et al. 2025) model how new social or policy ideas propagate through simulated societies, tracking collective outcomes as impact proxies. In summary, impact assessment through execution-driven tests, behavioral outcome studies or graph- based scientomet- ric, ensure that models are not merely producing ideas that are novel, diverse, or feasible, but ones that are also meaningful and capable of advancing science or society in a measurable way. However, such approaches
inherently limited: many scientific ideas not directly exccutable, domain simulators exist only for select fields, and measurable outcomes often capture only the practical success of an idea rather than its deeper creative or theoretical significance. Consequently, muanced dimensions of creativity and long-term scientific value still rely on human expertise and intuition, which remain the gold standard for assessing scientific ideas.
Table f summarizes the main evaluation paradigms- from human judgment to computational and exccution-based methods-ant their roles in assossing novelty, diversity, fcasibility and impact.
7.2 Human Expert Evaluation
Because computational metrics cannot fully capture the impact, value, nuanced creativity of sci- entific ideas, human experts remain the gold standard for evaluation in most scientific ideation studies. Expert reviewers can judge conceptual depth, originality, and methodological sound- ness qualities that automated metrics still struggle to approximate.
Typical setups follow a panel reciew format, modeled after peer review. For example, Aldeation (Wang et al.] 2025d) engaged professional concept designers to rate generated ideas
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using Likert scales (e.g., 1-5 ratings) for creativity, feasibility, and satisfaction, supplemented by qualitative interviews. Similar setups are found in Scideator (Smith & Huang 2025) and Nova (Hu et al. 2024h, where domain specialists score hypotheses for novelty, plausibility, and usefulness, pro- viding both quantitative and qualitative human feedback. More recently, large-scale human studies have emerged to improve robustness and generalizability of human evaluation. Notably, Si et al. (2024) evaluates LLM-generated research ideas using assessments from over 100 domain experts, explicitly separating judgments of novelty and usefulness.
To improve inter-rater reliability, several studies adopt the Consensual Assessment Technique (CAT) (Amabile) 1982), aggregating multiple expert scores into consensus ratings that reduce bias and align evaluation with established creativity research. At the most rigorous level, Al Scientist v2 (Yamada et al. 2025) clevates human evaluation to formal peer review by submitting autonomously generaled papers lo top-tier conferences, where acceptance serves as the strongest validation of impact and scientific merit.
While indispensable, human evaluation time-consuming, costly, and difficult to scale, with subjec- live variation among evaluators. This motivates the development of automated evaluators that can approximate expert ciniitionccpptriinn creativity, impact, and value scale. The next section explores these efforts under the paradigm of LLM-as-a-judge.
7.3 LLM-as-a-Judge Evaluation
The LIM-as-a-JJdde paradigm reframes large language models from generators to evaluators capa- ble of approximating aspects of human evaluative judgment. Early benchmarks such as MTT-Bench and Chatbot Arena (Zheng et al.) 2023) showed that LLMs can reliably proxy human preferences and judgments even in zero-shot settings, establishing their use as scalable evaluators across do- mains. In scientific discovery; LLM-judges are now applied to assess plausibility, novelty, and impact of hypotheses, occupying a practical middle ground between purely computational metrics (which often miss conceptual depth), execution-basse validation (which can be costly infeasible), and labor-intensive human review. However, these systems do not replicate domain expertise epis- temic reasoning and should be interpreted as preference-aligned evaluators rather than substitutes for expert judgment.
As shown Table 5 recent LLM--a--aJudde systems can be broadly grouped into four method- ological categories. The first involves Prompt-based or inference-time judges, where general- purpose LLMs are prompted or tool-called to evaluate idea quality along specifie dimensions. The second category, Fine-tuned judges, trains LLMs scientific peer-review corpora to better ap- proximate expert judgment. The third, Reinforcemen--ppimiieee judges, refines evaluation behavior using reward models or preference optimization to enhance consistency and reasoning depth. Finally, Hybrid frameworks integrate training-time alignment. with inference-time rea- soning strategies, combining the model's internalized intuition with up-to-date external knowledge to produce more stable, muanced, and aliane evaluations.
7.3.1 Prompt-based / Inference-time judges
LLMs' zero- and few-shot in-context reasoning enables them to act as first-line evaluators with min- imal supervision. In the zero-shot setting, models are guided by reviewing rubrics encoded directly
in prompts. For example, the GPT-4o-based AT Scientist (Lu et al. 2024a Yamada ct al. 2025) ap-
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Table 5: Comparison of LLM-as-a-Judge paradigms for evaluating scientific ideas, showing their mechanisms, representative works, and key advantages and drawbacks.
Judge
Core idea
Example works
Strengths
Limitations
category
Inference- Leverages struc- time Judges tured prompls, (Prompt- based)
AI
Scientist 2024a);
Lu Low-cost, inter- pretable, and reproducible; casy to gen-
Highly sensi- tive to prompl
et al.
retrieval engines, or multi-agent critique to approximate ex- pert review without fine-tuning.
Liveldea Ruar ct al.
phrasing and context; lacks consistent cal- ibration across runs.
2024
Chain-o Seain-ol Idea
eralize across
et al.
2024a
domains and
tasks.
Supervised Fine-tuned Judges
Trains models Revieer (Weng annolated peer- review corpora to internalize expert-like judg- ment and scoring patterns.
High correlation with expert judgments; improved con- sistency and interpretability.
Memorization instead of rea- soning; static preferences;
et al. OpenReviewer Ildah
2025a
& Ahmadi 2025
dependent on dataset cover- age and quality; may inherit bias.
RL
and Optimizes eval- uative behavior via reinforcement. ReviewRI learning preference align- ment for reasoning stability.
HARPA
(Vasu ct al.)
Generalization and adaptivity;
Expensive to train; vul- nerable reward mis- specification
Reward- Tuned Judges
2025
Zeng
high correlation with expert judgments; courages coher- ent, stable, and self-consistent evaluations.
al. 2025
REMOR
Tacchoy- 2025
otin & Acuna
and preference drift.
Hybrid (Train- ing + Infer- ence Integra- tion)
Combines fine- tuning with
DeepReview
Zhu
Merges the gen- eralization of training-bases quires maintain- ing multi-stage logic and exter- nal dependen-
Complex pipeline;
et al. 2025b
inference-time reasoning and external veri- fiers or factuality tools.
HARPA Vasu et al.
2025
methods with the factual grounding of inference scaling methods.
cies.
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plies NeurLPS-style reviewing criteria to generate structured assessments of generated mamuscripts. Bench (Ruan et al. 2024) employs multi-agent jury of LLM ovaluators- -cato assigned distinct system prompts and roles to rate generated ideas across originality, feasibility, fluency, flexibility, and significance. ResearchAgent (Back et al. 2024 coordinates multiple reviewer agents to iteratively critique and refine hypotheses. Similarly, Chain-of-ldes (Li et al., 2024a) introduces a comparative "idea arena," " evaluating pairs of hypotheses against each other rather than assigning absolute scores to reduce judgment drift and improve calibration.
Beyond pure prompting, a large body of work augments these evaluators with retrieval pipelines that ground LLM judgments in empirical evidence. Such retrieval- based inference enhances relia- bility in metrics like novelty, feasibility, and impact by contextualizing model reasoning with recent literature. For instance, the Idea Novelty Checker (Shahid et al. 2025) retrieves semantically re- lated papers via dense embeddinggs, filters and re-ranks them with LLM reasoning across facets such as purpose and mechanism, achieving over 13% higher agreement with expert judgments. Deep- Review (Zhu et al., 2025b) extends this approach with multi-step retricval and synthosis stages, generating structured "novelty verification" reports that summarize evidence gaps and method- ological contributions.
Retrieval grounding has similarly advanced feasibility assessment. Matter-of-Pact (Jansen et al. 2025) benchmarks literature-grount grounded reasoning in materials science, testing whether claims are feasible given pre-publication data. SciPIP (Wang et al. 2024ch integrates citation-graph reason- ing and semantic retrieval to estimate methodological plausibility and resource constraints, while Synlhh (Pu et al.] 2025)) decomposes ideas into problem method evaluation triplets and mea- sures proximity historically successful research, estimating both feasibility and experimental cost.
Finally, works like aiXiv (Zhang et al. 2025b) demonst.rate large-scale retrieval-augmented evalua- tion pipelines for impact prediction, combining literature grounding with structured LLM judgment.
Overall, prompt-based and retrioval-angmented judges offer fast, reproducible, and low-cost eval- uation with improved factual grounding. However, their reliability remains sensitive to prompt design, retrieval coverage, and base model reasoning quality.
7.3.2 Supervised fine-tuned judges
A more robust direction uses SFT on large review datasets, aligning LLMs to human scoring be- havior. Here, models are exposed to richly annotated review datasets containing both scores and rationales, enabling them to internalize reviewing patterns. CycleReviewer (Weng et al. 2025a), trained as a generative reward model on the Review-5K dataset, achieves a decision accuracy of 74.24%, complementing human expertise with consistent automated scoring. Building on this, Open OpenRevieeer Idahl & Ahmadi (025) applies full-parameter SFT to using the 80K reviews from top conferencos, producing structured reviews with strong reasoning quality.
Instrucct model
DeepReview (Zhu et al. 2025b) pushes Saa-ba evaluation further through a significantly larger and more structured training corpus- Deeppeerieu- integrates full rescarch papers, multi-stage reasoning traces, and final human-aligned assessmmntss. During inference, it employs retrieval-augmented review synthesis, self-verification, and reflection modules that enable iterative refinement of review reasoning. The resulting 14B-parameter model, despite its moderate size,
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surpasses larger models like CycleReviewer-70B, GPT-ol and Deepseek-R1 by dynamically scaling inference complexity, demonstrating that datasct design and structured supervision outweigh pure model scale.
These SF pipelines emphasize the importance of dataset curation and training objectives: the models learn scoring intuitions from reviewer' annotations, which yields high correlation with human judgments. However, recent studies highlight several limitations of SFT in capturing scientific novelty. (Shuich al. 2025) show that SFT often induces overreliance on spurious cues the data, reducing robustness when those features fail.
7.3.3 Reinforcement-optimizzee judges
The third regime advances beyond SFT memorization toward generalization via RL (Chu al., 2025). While SFT-based reviewers mimic human scoring behavior, they often overfit to annotation bias or linguistic patterns, limiting their ability to assess novel or out-of-distribution ideas. RL op- timization instead trains judges to reason through evaluation criteria, promoting deeper alignment, calibration, and consistency.
Recent frameworks such as J1 (Whitehouse et al. 2025 show that tailored RL objectives can internalize reasoning depth rather than surface agreement. J1 fine-tunes reviewers using reward signals from rubric adherence and multi-step self-consistency,, outperforming SFT and DPO base- lines in cross-domain generalization and inter-rater reliability. Similarly, ReviewRL (Zeng al., 2025 warm-starts from SFT and applies Reinforce+ optimization for review quality and stability, achieving superior coherence, factuality, and fairnoss across venues like NeurIPS and ICLR.
REMOR (Taechoyotin & Acuna 2025) exlends this direction with multi-objective RL that balances factual accuracy, reasoning depth, and creativity encouragement. By coupling review text with reasoning traces and composite rewards-covering alignment, novelty, and consistency- improves citation prediction accuracy and reduces verbosity compared to SFT--nly baselines.
Finally, HARPA (Vasu et al. 2025 adapts the reinforcement-optimnze paradigm to hypothesis generation. Its learned reward model scores idcas by testability and groundedness using prior experimental outcomes as feedback. HARPA-generaterr hypotheses outperform strong Al-researcher baselines in feasibility (+0.78) and groundedness (+0.85) on human ratings, achieving higher real- world execution success in the CodeScientist pipeline (20 vs. 11 successful runs). This illustrates how RL-trained evaluators can not only assess but also guide ideation toward scientifically meaningful discoveries. By directly optimizing for rating stability and agreement with human gold standards, RL-trained critics achieve more reliable judgments across domains.
In summary, RL introduces adaptive feedback loops that explicitly reward desirable evaluation be- haviors such as coherence, fairness, and consistency. Studies such as J1, ReviewRL, and REMOR demonstrate that RL-trained judges produce more stable, reasoning-aligneed, and domain-general evaluations offering more robust. foundation for scientific idea assessment. However, even these post-training approaches have intrinsic limits when evaluations depend on external scientific evi- dence knowledge-intennive reasoning.
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7.3.4 Hybrid Frameworks
The most effective designs often combine multiple strategics. Metrics such novelty, feasibil- ity, impact often require grounding in up-to-date literature, experiment outcomes, or citation graphs-information beyond what is encoded in model parameters. As result, SFT or pure RL post-trained reviewers may exhibit confident but uninformed judgments, especially in fast-evolving or specialized research areas.
To overcome these constraints, hybrid evaluators integrate learned judgment policies with test-time reasoning and external tool use. DeepReviewer (Zhu et al 2025b] exemplifies the fusion of training- based alignment and inference-time adaptability. It combines the structured evaluative intuition learned during supervised fine-tuning with external retrieval and self-verification mechanisms acti- vated at inference, allowing the model to ground its reasoning dynamically in relevant literature. Through its scalable inference modes (Fast, Standard, Best), DeepReviewer-141 adjusts delibera- tion depth to match computational budgets, maintaining both efficiency and analytical rigor. This synergy between training priors and adaptive test-time reasoning enables DeepReviewer to outper- form much larger models such as CycleReeviewer-70B, producing richer and more accurate reviews with fewer tokens and strong resilience to adversarial perturbations despie the absence of plicit robustness training. ReviewRL (Zeng et al. 2025) similarly augments its RL-trained critic with retricval-angmennee context (c.g-, ArXivMCP) to ground evaluations in verifiable scientific evidence. Such hybrid systems balance the elficiency of automation with the rigor of human-like review, highlighting a consensus that future evaluation frameworks will need to fuse training-baac based aligument with external grounding to achieve both reliability and transparency.
Recent work comparing human and LLM-based reviewers highlights complementary strengths but clear gaps. Zero-shot systems such Claude 3.5 Sonnet produce structured, comprehensive reviews yet lack the contextual depth and domain reasoning of humans (Renata & Lee 2025 Similarly, GPT-4o performs well in summarizing contributions but misses weaknesses and critical insights (Li et al. 2025b). Broader evaluations show that while GPT-based models reach over 60% accuracy in review prediction, they remain limited in long-context and analytical reasoning (Zhou et al. 2024 Other analyses further warn that LLMs, though reducing reviewer fatigue, risk amplifying bias and diminishing accountability (Hosseini & Horbach 2023). Overall, LLM-based judges provide scalable and consistent first-pass ovaluations but still require human calibration to ensure fairness, muance, and contextual rigor (Table 5).
7.4 Challenges in Evaluation
Among the four Ps of creativity, the Product dimension is uniquely challenging: represents the measurable outcome of creativity, whereas Person, Process, and Press serve as sources that shape how such products emerge (Kozbelt et al. 2010 Gruszka & Tang 2017). For scientific idea gen- cration, this means that evaluation is not just auxiliary step but the central bottleneck questions of novelly, feasibility, diversity, and impact must ultimately be resolved. Yet current. practices reveal deep limitations that hinder progress.
First, evaluation suffers from subjectivity and inconsistency. Human raters often diverge in their judgments depending on expertise, background, or expectations, and are prone to systematic bi- ases for instance, underrating Al-generated ideas or devaluing content once labeled as machine-
produced Glikson & Woolley 2020 Langham et al. 2022 Zhu et al. 2025c). Automated proxies
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Table 6:
Scientific idea generation benchmarks and how they operationalize creativity dimensions.
Key strengths limitations
Creativity
Benchmark
Task Input
Evaluation protocol
dimensions
Research- Bench Liu
Inspiration retrieval
Stage-wise wise evaluation
End-to-end modeling
discovery
Novelty,
Idea / hypothesis com-
Ranking accuracy
Ground-
et al.
position
Grounding
Explicit grounding
ing
Ranking stage
Human LLM judges
Implicit scores
IdeaBench
Research ideas from paper context
Expert or LLM judges
= Explicit ideation focus
Novelty, Feasibil- ity
Guo
Likert pairwise rank-
Scalable templates
et al.
Title abstract / rrf- erences
ing
Qualitative leasibility
2024
Judge sensitivity
LiveIdea-
Minimal
context.
Panel LLM judges
Low-context
creativity
Novelty,
Bench
prompts
Multi-axis scoring
probe
Diver- sity,
Ruan
Keyword / short input
Score aggregation
Highly scalable
et al.
Divergent ideation
Judge bias drift
Surprise
2024)
Lest
Weak feasibility grounding
HypoBench
Data-driven hypothe-
Ground-truth recov-
Strong validity signal Quantitative evaluation
Validity, Testabil-
Liu
ery
et al
Real + syuthetic tasks
Discovery rate metrics
Statistical focus
ity
2025c
Known structure
Human LLM judges
Limited ideation
MatDesign
Materials science do- main
Expert-aligned crite-
Rich feasibility chocks
Novelly,
Kumb
ria
Realistic hypotheses
Feasi-
har
Knowledge- constrained prompts Domain realism
Feasibility, plausibility Impact, testability
Narrow domain
bility,
et al.
High expert cost
Testabil-
2025)
ity
Research-
Code-level realization
Compile run tests
Objective feasibility sig-
Feasibility,
CodeBench
Implement recent
Functional correctness
nal
Correct-
Hua
hethods
Quality checks
Execution grounded No novelty measure
ness
al.
Execution-based tasks
2025b
Realization # ideation
LLM-
Equation discovery
Exact correctness
Formal validity
Validity, Testabil- ity
SRBench
Synthetic transforma-
Fit. accuracy
Strong testability
2025
tions
Robustness tests
Narrow discovery form
Contam--aware design
Limited ideation scope
Matter-
Literature-supported
Claim feasibility veri-
Explicit
Feasibilityy Validity
ofFact
claims
fication
grounding
Jansen
Materials science do-
Consistency checks
Not generative by itself Domain-specific
al.
main
Human LLM judges
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such as embedding similarity or LLM critics are not immune either, as they may replicate biases encoded in training data drift with prompting (Laskar ct al. 2024 Wang et al. 2025ch.
A second challenge is the lack of slandardizattin: most studies define their own evaluation met- rics, leading to fragmented practices and results that are not reproducible or comparable across benchmarks [Gehrmann et al. 2022 Laskar et al.) 2024). Table G illustrates this heterogeneity: benchmarks vary not only in task framing (idea vs. hypothesis generation vs. discovery vs. code realization), but also evaluation protocols (expert panels, L.M--s-juuge, ground-truth recovery, execution-based correctness) and in how common criteria like "novelty" or "feasibility" are oper- ationalized. Moreover, most existing benchmarks evaluate creativity primarily the level of the final artifact. Since the generative process is treated as black box, these benchmarks cannot di- rectly assess the source or mechanism of creativity, and instead focus on observablc outcomes such as novelty, plausibility, or executability. As a consequence, many evaluations rely on short-term or proxy-based metrics, leaving the dynamics of idea generation and longer-term scientific impact largely unexamined. (Ismayilzada al., 2025b). Fourth, there is the risk of bias amplification. Both human reviewers and LLM- judges can propagate historical inequities, undervaluing un- conventional or marginalized research directions unless carefully calibrated (Gehrmann et al.) 2022 Finally, persistent scalability gap remains unresolved: while expert review provides nuance and reliability, it is expensive and prone to fatigue effects, whereas automated metrics scale efficiently but fail to capture the depth of human judgment [Thllall] 2025).
Together, these challenges highlight evaluation as the most urgent bottleneck for LLM-driven sci- entific discovery. Overcoming them will require hybrid strategies that combine transparent and standardized metrics, scalable automated proxies, and carefully calibrated human oversight. Only by strengthening the evaluation of the Product dimension can ensure that generative models contribute not just more ideas, but ideas that are genuinely valuable for advancing science.
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8 Ethical Concerns
While the integration of LLMs into scientific ideation substantially lowers the time and cost of hypothesis generation, experimental design, and exploratory research, it simultaneously introduces a range of ethical risks. As idea generation becomes automated and scalable, evaluation and veri- fication must also be automated, since manual review cannot keep pace with the growing volume of outputs. This pressure is evident in contemporary peer review: NeurIPS 2025 received over 21,500 submissions, more than double its 2022 count (Foundation, 2025 far exceeding the ca- pacity of available expert reviewers and motivating the use of automated evaluation tools such as L.MM-s---jjude (Section 7.3 However, discussed in the previous section, reliable evaluation is fundamentally harder than gencration. Evaluation must verify correctness, fcasibility, evidential grounding, and must distinguish genuine creativity creativity-combinne combining novelty and scientific value from fabrication, hallucination, or superficially convincing but flawed ideas. This difficulty gives rise to the risk of plausible pseudo-science, where L.M-generated idcas appear coherent and innovative yet rest on incorrect assumptions or unsound reasoning that automated evaluators struggle to detect (Li et al. 2025a). In addition, L.L.-based ideation and evaluation systems may amplify existing biases in the scientific literature(Zhuu al.) 2025a), preferentially favoring dominant methods, paradigms, problem formulations while neglecting underexplored unconventional directions, thereby inforcing prevailing scientific dogmas. Beyond these modeling limilations, aulomation introduces security risks: recent work has demonstrated that
reviewers are vulnerable to prompt injection attacks (Collu et al.) 2025 Lin 2025 Zhu et al. 2025a Theocharopoulos et al. 2025 in
which adversarial text embedded in manuscripts manipulates automated reviews, highlighting how evaluation pipelines can be systematically exploited. More broadly, the use of L.Mss for scientific ideation raises dual-use concerns, particularly in domains such chemistry and biology, where generated ideas procedures could be misused if insufficient safeguards are in place(Urbina et al., 2022 While recent studies and guidelines have begun to outline responsible practices for using Al in scientific (escarch(Resni) & Hosscini 2024 Knöchel et al.] 2025),, substantial challenges remain for governance and policymaking to adequately address verification robustness, bias, adversarial manipulation, and usel-use risks automated scientific discovery.
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9 Future Works and Research Gaps
9.1 Embracing the Open-Ended Nature of Scientific Discovery
Unlike bounded reasoning tasks such as mathematics, scientific discovery is intrinsically open-ended: the space of possible hypotheses is unbounded, goals evolve as new evidence arises, and reframing questions often as important as solving them. Yet most current LLM-based methods whether retrieval-angmented generation, prompt-driven creativity, or inference-time sumn fixed objectives and closed task spaces. This mismatch suggests that the field has so far underappreciated the open-ended nature of scientific ideation, treating it as a well-posed reasoning benchmark rather than a contimually expanding search.
impliicillyyy as-
Concepts from open-ended learning offer useful inspiration for future directions. Novelty search demonstrated that progress can emerge by prioritizing behavioral novelty over explicit objectives (Lehman & Stanley 2011p. Quality-diversity methods such as MAP-EElits show how exploration can be structured to discover diverse, high-performing solutions across wide range of niches (Mouret& & Clune 2015 Co-evolutionary frameworks such POET (Wang et al. 2019) and hard-exploration strategies like Go-Erplore (Ecoffet et al. 2019 2021) illustrate how agents can sustain long-term innovation by gencrating now tasks alongside new solutions, or by systematically revisiting and extending prior stepping stones. At larger scales, environments like XLand demon- strate that procedurally generated, open-ended multi-task worlds can produce generally capable agents through continual adaptation (TTam et al., 2021). Earlier theoretical work such as POW- ERPLAY (Schmidhuber 2011 also emphasized continual self-invented tasks foundation for unbounded discovery.
Adapting these ideas to LLM-based pipelines could mark step change for scientific ideation. Instead of optimizing within static benchmarks, future systems should integrate mechanisms for continuous exploration, novelty seeking, and dynamic reframing, enabling them to generate not just candidate answers but entirely now scientific questions.
9.2 Establishing Standardized Benchmarks and Robust Evaluation Frameworks
Evaluation remains the most critical bottleneck for LLM-bbsed scientific ideation. At present, nearly every study introduces its own pipeline for assessing novelty, diversity, feasibility, which makes systematic comparison across approaches nearly impossible (Ismayilzada et al. 2022b Gupta et al. 2024 Ruan et al. 2024 In terms of creativity theory, this corresponds to the Product dimen- sion: creative outcomes are the dependent variables by which novelty, usefulness, and correctness are judged (Kozbelt et al. 2010). Yet current methods lack standardized and transparent met- rics for these criteria, leaving evaluations subjective and inconsistent. Automated metrics such as embedding-basee novelty scores (Wang et al. 2025c), diversity indices (Friedman & Dieng 2023 literature-rrounded overlap detection (Shahid et al. 2025) valuable but insufficient own. More importantly, human evaluation practices remain opaque: many papers fail to clearly report annotator expertise, evaluation protocols, or inter-rater reliability, leaving results subjective
their
and difficult to reproduce (Gehrmann et al. 2022 Laskar et al. 2024).
crucial step forward is the development of shared benchmarks and transparent evaluation pipelines. These should span multiple domains (e.g., biology, chemistry, mathematics, social sci- ences) and multiple levels of abstraction, from hypothesis plausibility to experimental fcasibility.
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Human evaluation in particular needs greater rigor: standardized reporting of evaluator back- grounds, annotation guidelines, and agreement metrics should become community norms. Beyond laboratory-style annotation, peer-review-styee evaluations hold particular promise. For instance, the AT Scientist project submitted an AT-generated paper to workshop as a form of external validation (Lu et al. 2024a), demonstrating how conference-style review could provide structured and credible human feedback. Similar efforts-whether via workshops, structured review panels, or crowd-sourced expert collectives could provide scalable, transparent, and standardized human-in- the-loop evaluation.
Beyond human assessment, future work must also address limitations of LLM-bosed automated evaluators. Supervised fine-tuned judges can suffer from memorization and overfitting, limiting their ability to generalize to genuincly novel idcas. Recent directions suggest augmenting evaluators with online relrieval and lest-lime reasoning, allowing judgments to be grounded in up-to-date literature and refined through iterative evaluation rather than static pattern matching [Zhu et al.) 2025b). More broadly, no single evaluator is sufficient for all criteria: novelty asscssment benefits from retrieval against current research, feasibility is best evaluated via domain-specific executors or simulators, and overall correctness coherence may be more reliably judged by fine-tuned expert models. This motivates modular, criterion-specific evaluation paradigm rather than monolithic scoring.
Finally, an important open challenge is spurious learning in trained evaluators, where models may rely on superficial cues (e.g., topic popularity or stylistic novelty) instead of core scientific qualities. Addressing this requires techniques inspired by the spurious correlation miligation literature, such as balanced or counterfactual sampling, re-weighting of evaluation data, and explicit control over correlated attributes during evaluator training Yang et al., 2023 Incorporating these strategies is essential for building reliable and fair automated reviewers for scientific ideation.
By establishing communty--accppter benchmarks, rigorous human evaluation practices, and robust automated ovaluators, the field can move beyond isolated proof-of-concopt systems toward results that are comparable, reproducible, and trusted by the broader scientific communily.
9.3 Building Domain-Rich Simulators for Feasible and Scalable Training
Direct interaction with real scientific enviromments- as wet labs, clinical trials, or field ex- periments- is often costly, slow, and in some cases unsafe. This poses a major barrier to RL and closed-loop discovery pipelines, which require frequent and structured feedback signals to improve. Developing high-fidelity, domain-rich simulators could therefore transform LLM-based scientific ideation by providing safe, rapid, and scalable environments for hypothesis testing.
The role of simulators in accelerating progress is well established in other areas of AI. In robotics and control, baysics-based simulators such as MuJoCo (Todorov et al. 2012) Isaac Gym (Makoviy- chuk et al. 2021) have enabled breakthroughs in RL by allowing agents to practice millions of interactions before real-world deployment. In scientific discovery, similar trends are emerging: AtomAgents integrates alloy design with materials simulators for itcrative hypothesis refinement [Ga & Buehler 2024), while Robin uses in silico biological assays and robotic web-labs to
close the loop between hypothesis generation and experimental validation (Ghareeb et al. 2025
More abstractly, multi-agent simulation environments such Casevo (Jiang et al. 2024 show how
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agent societies can serve as dynamic testbeds for evaluating ideation in complex social or political domains.
By building domain-specific simulators for materials, biology, physics, and socio-technical systems, the community can enable structured rewards, scalable pretraining, and safe exploration. These environments bridge symbolic reasoning and empirical science, accelerating the development of RL-based and hybrid discovery systems. Ultimately, simulators provide scaffolding for LLMs to generate, test, and refine hypotheses at scale- strennthhrngggnnn the Person and Press dimensions of creativity by offering feedback-rich environments that enhance agents' intrinsic scientific abilities.
9.4 Beyond Next- Token Prediction: Architectural Bottlenecks for Creativity
final challenge concerns the architectural limits of current LLMs, which are fundamentally trained on the next-token prediction objective. Although this paradigm produces fluent language and strong surface-level reasoning, it inherently biases models toward local coherence and incremental extrap- olation, discouraging bold conceptual leaps that scientific discovery often requires (Franceschelli
& Musolesi 2024 Bender et al. 2021 Marcus 2022). This tendency toward "safe" continuations
can stifle creativity and prevent models from venturing into genuinely novel scientific hypotheses. From the perspective the 4Ps framework, this reflects a limitation the Person dimonsion: the intrinsic capacilies of the idea-generating system are constrained by ils training objectives and inductive biases. Emerging research points to several promising alternatives Multi-token predic- tion frameworks, such as Roll the Dice and Look Before You Leap (Nagarajan et al., 2025), ), seek reduce the local myopia of auto-segressive decoding by predicting multiple tokens jointly, im- proving global coherence. State-space sequence models such Mamba demonstrate long-context stability and efficient memory handling, offering potential for sustained scientific reasoning (Gu &
Dao 2024 Halloran et al.) 2024). Meanwhile, Diffusion-bused language models (Nie el. al. 2025 Li & Liang 2022) depart entirely from the auto-regressive paradigm by framing text generation as itcrative denoising, which can encourage more flexible and globally optimized exploration. Hybrid approaches that combine these paradigms with transformers may provide the best path forward, balancing local fluency with global creativity.
Future rescarch should therefore go beyond next-token prediction to explore whether such archi- tectures or their integration into hybrid systems can unlock richer creative capacities and better reflect the open-ended, exploratory nature of scientific inquiry.
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10 Conclusion
Large language models are beginning to reshape the earliest stages of the scientifie discovery pipeline by serving powerful engines for idea generation. Building advances in reasoning paradigms such as inference-time scaling, reinforcement learning post-training, and multi-agent collaboration, researchers have shown that LLMs can retrieve, combine, and refine scientific concepts in ways that accelerate hypothesis formation. At the same time, frameworks of creativity highlight both the promise and the limitations current systems: while combinatorial and exploratory creativity are increasingly within reach, truly transformational discovery remains elusive.
Our survey organized this rapidly evolving field into complementary methodological dimensions: Knowledge augmentation, which grounds ideation in external evidence; Prompt-driven cre- ativity, which steers models toward novelty via careful role and constraint design; Inference- time search, which enables iterative refinement, branching exploration, and ensemble aggrega- tion; Collaborative multi-agent systems, which simulate the dynamics of research teams; and Parameter-level adaptations, which fine-tune models for domain-specific reasoning and aligu- ment. We also reviewed evaluation practices, showing how novelty, diversity, feasibility, and impact currently measured through patchwork of automated metrics and ad hoc human studies, with reproducibility and comparability remaining serious challenges.
Finally, viewed through the lens of creativity frameworks, several gaps become clear. Current meth- ods concentrate on the Process and Press dimensions structuring search and leveraging external knowledge- yet the Person and Product dimensions remain underdeveloped. Enhancing the person perspective may require rethinking training objectives, architectures, or moving from idea-level to agent-level search, as seen in open-ended paradigms. On the product side, the lack of standardized benchmarks and rigorous metric evaluations leaves creativity assessment vague and inconsis- tent. Addressing these blind spots by strengthening intrinsic generative capacities and grounding evaluation in transparent metrics-coule move LLMs beyond proof-of-concepe idcation toward gen- uine scientific contributions, evolving from research assistants into collaborators capable of pushing the frontiers of human knowledge.
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Jean-Daniel Zucker. grounded theory of ibstraction artificial intelligence. Philosophicul Trans- actions of the Royal Society of London. Series B: Biological Sciences, 358(1435):1293 1309, 2003. doi: 10.1098 1111999sttb... 2003.130.....
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Reviews:
[{"review_id": "z92bg2VoFp", "reviewer": "Reviewer_VBMt", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper presents a comprehensive survey of LLM based approaches for scientific idea generation. The problem of idea generation is framed as a dual objective problem, balancing novelty with scientific valueness.\n\nTo evaluate existing LLM based approaches, a methodical categorization is done as:\n1. External knowledge augmentation\n2. Prompt based distributional steering\n3. Inference time scaling\n4. Multi-agent collaboration\n5. Parameter level adaptation\nwith each section evaluating sub-strategies and their limitations.\n\nFor interpretation, the authors leverage Boden's taxonomy and Rhodes 4P framework to organize the field. The paper also reviews current evaluation practices for scientific ideation, while outlining open challenges and future research directions.\n\nKey strengths - the paper provides a clear, unifying framework to connect and evaluate diverse techniques for idea generation. It also highlights why evaluation remains difficult and why progress in benchmarks and validation frameworks is essential for the field.\n\nA key area where the paper can provide additional value is that it often relies on plausible but not always empirically validated assumptions; incorporating more detailed empirical evidences will help readers better assess the claims.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The survey is clear and generally careful in grounding its discussion in the literature. That being said, there are some higher-level interpretations - particularly around how certain techniques relate to deeper idea generation - that are based more on plausible reasoning than strong empirical evidence. Overall, the claims are credible, but they would benefit from clearer empirical support.", "audience_yn": "Yes", "audience_explanation": "Yes, researchers and practitioners working on LLM-based scientific discovery, automated research systems would find the paper's synthesis and framework useful.", "requested_changes": "I find the paper suitable for acceptance in its current form. The points below are suggestions for improvement that could further strengthen the presentation and sharpen some of the arguments:\n1. Section 2.3: The conclusion that knowledge augmentation primarily supports combinatorial creativity is reasonable but appears somewhat abrupt. Given the central role of Boden's taxonomy, it would help to make this mapping explicit earlier in the section by clearly indicating how semantic and relational retrieval align with creativity levels, rather than leaving the reader to infer it.\n2. Section 3.3: The claim that structured prompting constitutes a step towards transformational creativity appears overstated under Boden's definition. Transformational creativity requires altering conceptual space, whereas the prompt based methods discussed primarily challenge assumptions within a fixed, pretrained representation. It would strengthen the paper to either qualify this claim more carefully or clarify the specific sense in which such prompting is considered transformational.\n3. Section 4: The discussion suggested that tree-based search better preserves unconventional yet valuable hypotheses through backtracking. While plausible, this assumption is not directly supported by cited empirical studies. In practice, heuristic-guided tree search may still systematically deprioritize low-confidence or atypical ideas. Explicitly acknowledging this limitation would strengthen the analysis.\n4. Section 7.1: While the surveyed metrics effectively operationalize proxies such as novelty, diversity, feasibility, and impact, it would be helpful to more explicitly acknowledge that these measures remain indirect proxies for creativity, rather than direct measurements of creative insight or conceptual innovation.\n5. Section 7.3: The statement that LLM-as-a-Judge systems “approximate human reasoning” appears overstated. Existing benchmarks primarily demonstrate correlation with human preferences, not equivalence in reasoning or evaluative judgment-particularly in scientific contexts where domain intuition and epistemic uncertainty are central. Rephrasing this claim to avoid implying reasoning-level parity would improve rigor.", "broader_impact_concerns": "Not applicable. The paper is a survey and does not introduce new models or deployable systems, and thus poses no major concerns.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "AMUBcZ7Uye", "reviewer": "Reviewer_pN3c", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper provides a structured and comprehensive analysis of the capabilities and limitations of large language models (LLMs) in scientific ideation, with a particular focus on the dimensions of valueness and novelty. A key contribution of the work is the explicit grounding of scientific ideation in cognitive science theories of creativity, notably Rhodes’ 4Ps framework (1961) and Boden’s taxonomy of creativity (2004), which offer a conceptual lens for categorizing and interpreting LLM-generated ideas.\n\nBased on these frameworks, the paper organizes existing approaches to LLM-based scientific ideation into five categories: (1) knowledge and retrieval augmentation, (2) prompt-based distributional steering, (3) search and sampling expansions, (4) multi-agent and deliberative systems, and (5) parameter adaptation and learning. This taxonomy provides a clear and useful map of the current methodological landscape. In addition, the paper surveys existing metrics and evaluation methodologies for assessing generated scientific ideas, highlighting their strengths and limitations.\n\nOverall, the paper is well-structured, clearly written, and covers a broad range of relevant literature. Its main strengths lie in conceptual clarity, systematic organization, and breadth of coverage. Potential weaknesses include the reliance on relatively classical cognitive creativity frameworks, some ambiguity in the distinction between novelty and creativity, and limited empirical validation for several high-level claims.", "claims_and_evidence_yn": "No", "claims_explanation": "While many of the paper’s descriptive and organizational claims are well supported by prior literature, several stronger claims are not fully substantiated by concrete empirical evidence within the paper. For example, the paper argues that overcoming the limitation in scenario 4 (Figure 4) in scientific ideation “requires inference-time scaling or multi-agent interaction, which actively expand the search space and allow for the emergence of more novel, cross-disciplinary ideas.” However, this claim is largely supported by citations and conceptual reasoning rather than direct experimental validation or systematic comparison across methods.\n\nSimilarly, while the paper discusses novelty and valueness as central dimensions of scientific ideation, it does not consistently demonstrate them through quantitative or comparative evaluation, fore example, how specific method classes improve one or both dimensions. \n\nOverall, the evidence is sufficient for a high-level survey and conceptual analysis, but additional empirical grounding would be needed to convincingly support several of the paper’s broader claims.", "audience_yn": "Yes", "audience_explanation": "The topic of LLM-based scientific ideation is highly relevant to the TMLR audience, particularly researchers interested in large language models, scientific discovery, creativity, and evaluation methodologies. The paper’s synthesis of cognitive creativity theories with modern LLM techniques is likely to be of interest to readers seeking conceptual frameworks for understanding and positioning recent work in this rapidly evolving area.\n\nMoreover, the proposed taxonomy of methods and the discussion of evaluation practices provide a useful reference for both researchers developing new ideation systems and reviewers or practitioners assessing their outputs.", "requested_changes": "1. Clarify the distinction between novelty and creativity (Critical).\nThe paper would benefit from a more precise conceptual separation between novelty and creativity. While some methods may demonstrably increase novelty, it is less clear whether and how they enhance creativity in a stronger cognitive sense. Explicit definitions and clearer operationalization would improve rigor.\n\n2. Justify the choice of cognitive creativity frameworks (Non-critical).\nThe use of Rhodes’ 4Ps framework and Boden’s creativity taxonomy is conceptually helpful, but both frameworks are relatively classical. The authors could strengthen the paper by discussing why these frameworks remain appropriate today or by briefly acknowledging and situating more recent cognitive or computational creativity theories.\n\n3. Provide clearer criteria for the five-category taxonomy (Critical).\nThe basis for categorizing methods into the five proposed classes should be made more explicit. In particular, it would be useful to explain how these categories are created and whether these categories fully cover the current literature on LLM-based scientific ideation.\n\n4. Improve consistency between figure 3 and main text (Non-critical).\nFigure 3 appears to present a four-level taxonomy, while the main text describes five categories. It would be helpful to clarify this discrepancy and ensure consistency between the figure and the text.\n\n5. Strengthen empirical support for high-level claims (Critical).\nClaims regarding the effectiveness of five classes of methods would benefit from additional empirical evidence, ablation studies, or at least a more systematic synthesis of existing experimental results.\n\n6. Improve coherence between evaluation discussion and method taxonomy (Critical).\nIn Section 7, a direct comparison or evaluation of representative methods from each category using the discussed metrics would significantly strengthen the paper.\n\n7. Explicitly link each method class to novelty and valueness (Non-critical).\nA summary table mapping method categories to their expected or empirically observed impact on novelty and valueness would greatly enhance clarity and usability.", "broader_impact_concerns": "The paper is primarily a conceptual and methodological analysis and does not raise immediate ethical concerns. However, the authors may consider briefly discussing potential downstream risks of automated scientific ideation systems, such as the generation of misleading or low-quality hypotheses, over-reliance on model-generated ideas, or unequal access to advanced ideation tools. Addressing these points in a Broader Impact Statement would improve completeness but is not strictly required.", "additional_comments": "Overall, this is a well-written and timely paper that offers a valuable conceptual synthesis of LLM-based scientific ideation. With clearer definitions, stronger empirical grounding, and tighter integration between evaluation and method taxonomy, the paper has the potential to become a useful reference point for future work in this area.", "extra_scores": {}, "extra_text": {}}, {"review_id": "gMe7jIHWfA", "reviewer": "Reviewer_kzrQ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "Contributions: This survey applies cognitive science frameworks to understand LLM-based scientific discovery. Using Boden's creativity taxonomy and Rhodes' 4Ps model, the authors organize existing methods into five families and uncover an important pattern. Current research has made strong progress on two dimensions: Process through inference-time scaling and multi-agent systems, and Press through knowledge augmentation and prompting. But two other dimensions remain underexplored. The Person dimension, which concerns how to build intrinsically creative models, and the Product dimension, which involves rigorously evaluating creative outputs, have received far less attention. Consequently, today's systems achieve combinatorial and sometimes exploratory creativity, but transformational creativity remains out of reach. The paper also critiques current evaluation practices, examining computational metrics, human judgment, and LLM-as-a-judge approaches while documenting persistent problems with subjectivity, standardization, and measuring long-term impact.\n\nStrengths: The paper is very clear and well-organized. It brings in theoretical frameworks from cognitive science and notices that current work overemphasizes Process and Press while neglecting Person and Product, something that only becomes visible through the 4Ps lens. It uses Boden's creativity taxonomy together with Rhodes' 4Ps model for analysis and covers multiple scientific domains.\n\nWeaknesses: The classification methodology lacks clarity. The paper does not clearly explain how to determine which creativity level a particular method belongs to, nor does it demonstrate how existing literature was systematically analyzed to support these classifications. Moreover, the boundary between claims supported by existing evidence and those based on theoretical speculation remains ambiguous.", "claims_and_evidence_yn": "Yes", "claims_explanation": "This survey provides a clear theoretical framework for understanding the field by systematically aligning current LLM techniques with the 4Ps model and Boden's creativity taxonomy from cognitive science. The paper thoroughly examines the technical mechanisms underlying five major method families and uses Scenario Boxes to illustrate how different abstraction levels might operate in scientific research tasks. Additionally, the paper offers rigorous mathematical definitions for creativity evaluation metrics such as Relative Neighbor Density and the Vendi Score, and draws on extensive recent research from 2024-2025 to support its observation that current systems still face significant challenges in achieving transformational creativity. The paper's characterization of the current state of the field is well-documented and logically coherent.", "audience_yn": "Yes", "audience_explanation": "This paper addresses LLM-based scientific discovery, connecting core ML methods to fundamental questions about creativity and generative capabilities. The framework and evaluation insights transfer broadly to any AI application balancing novelty with reliability.", "requested_changes": "1. The authors should explicitly clarify how methods are categorized within Boden’s taxonomy . The paper needs to explain the criteria used to judge whether a method belongs to combinatorial, exploratory, or transformational creativity, demonstrate how existing literature was systematically analyzed to support these classifications , and consistently distinguish between evidence-based observations and theoretical speculations throughout the text .\n2. While the paper effectively identifies various problems in current evaluation practices, it would be significantly more valuable if the authors could propose constructive research directions to address these issues.", "broader_impact_concerns": "No specific ethical concerns identified.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "XPTRZtSKFi", "reviewer": "Reviewer_1hm4", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents a comprehensive survey of Large Language Models (LLMs) applied to the specific domain of Scientific Idea Generation. The authors propose a taxonomy that bridges machine learning methodologies with cognitive science frameworks, specifically organizing existing works through the lens of Rhodes’ 4Ps (Person, Process, Press, Product) and Boden’s three levels of creativity (Combinatorial, Exploratory, Transformational).\n\nThe survey categorizes current techniques into five distinct families: Knowledge Augmentation (Press), Prompt-based Steering (Press), Inference-time Search (Process), Multi-agent Collaboration (Process), and Parameter-level Adaptation (Person). Furthermore, it provides a detailed analysis of evaluation paradigms, distinguishing between computational metrics, execution-based validation, and human/LLM-based judgment.\n\nThe survey goes beyond reporting SOTA results to identify fundamental limitations. It powerfully argues that current methods are largely stuck at the \"Combinatorial\" or \"Exploratory\" levels of creativity. It identifies a critical research gap: that true \"Transformational\" creativity may be structurally limited by the next-token prediction paradigm itself , suggesting a necessary pivot toward open-ended learning and non-autoregressive architectures.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The authors abstracts complex technical methodologies into high-level concepts, supporting these abstractions with clear, concrete examples. For instance, Scenario 5 effectively illustrates the abstract mechanisms of search strategies, clearly contrasting Local Search, Beam Search, and Tree Search in the specific task of \"hallucination mitigation\".\n\nSome critiques due to lack of empirical grounding:\n\n- The categorization of methods into combinatorial, exploratory, and transformational creativity feels idealistic. There is little evidence provided on \"how to measure\" the transition between these levels or how to empirically distinguish them in current LLM outputs.\n\n- The mapping of technical methods to Rhodes' 4Ps framework (Person, Process, Press, Product) is interesting, but inherently interpretative and subjective. The \"accuracy\" of this taxonomy is argumentative rather than factual.\n\nFor example, the authors categorize inference-time scaling (search) under Process. However, it could be argued that since this involves self-correction and internal knowledge, it belongs to the Person dimension (internal capability). Similarly, knowledge augmentation is mapped to Press (environment), yet once data is retrieved, it functionally becomes part of the processing context. The mapping is not presented in a systematic or reproducible manner (e.g., a definitive matrix).\n\n- The evidence supporting the superiority of complex methods is weak due to the state of the field. While the paper claims to outline directions toward \"reliable\" and \"transformative\" applications, the evaluation section explicitly admits to a \"lack of standardized metrics\" and \"fragmented practices\".", "audience_yn": "Yes", "audience_explanation": "On the positive side, the paper provides a detailed discussion of Test-time Search, Reinforcement Learning (RL) Post-training, and Multi-Agent Debate. These are currently central technical pathways in the ML community for enhancing reasoning capabilities. By concretizing these general ML techniques within the specific scenario of scientific discovery, the survey offers valuable inspiration for researchers focused on algorithmic development.\n\nHowever, the submission dedicates significant space to cognitive science theories, specifically Boden’s taxonomy and Rhodes’ 4Ps. Technical ML researchers might find this framing unnecessary or distracting.", "requested_changes": "The current evaluation section admits to a \"lack of standardized metrics\" and mentions several benchmarks sporadically. To substantiate the claims regarding creativity and the 4Ps, I request the addition of a systematic summary table or matrix that organizes these key benchmarks (including other necessary benchmarks, e.g., ResearchBench, IdeaBench).\n\nExpand the discussion on ethical risks specific to scientific discovery. This should explicitly cover \"plausible pseudo-science\" (convincing but flawed ideas), dual-use risks (especially in chemical/biological generation), and bias amplification where models might reinforce existing scientific dogmas.\n\nMissing reference: Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers. ICLR 2025.", "broader_impact_concerns": "While the paper discusses \"Safety\" and \"Bias\" briefly in the evaluation section, critical ethical implications of generative scientific ideation warrant a more explicit and dedicated Broader Impact Statement.\n\nThere is a significant risk that LLMs optimized for \"novelty\" and \"plausibility\"  might generate highly convincing but scientifically flawed hypotheses. If produced at scale, this could flood the scientific community with noise, wasting experimental resources and eroding trust in the peer-review system.\n\nThe paper highlights methods for generating chemicals, proteins, and experimental protocols (e.g., AtomAgents, CodeScientist). The potential for these systems to lower the barrier for designing novel pathogens, toxins, or hazardous materials is a severe safety concern that requires robust guardrails beyond standard alignment training.\n\nAs noted in this paper, RAG and SFT rely on existing literature. There is a concern that these systems will reinforce existing scientific dogmas, marginalize non-English or non-mainstream research, and create feedback loops where AI cites only highly-cited papers, stifling truly transformational diversity.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)