Despite interdisciplinary research leading to larger and longer-term impact, most work remains confined to single-domain academic silos. Recent AI-based approaches to scientific discovery show promise for interdisciplinary research, but many prioritize rapidly designing experiments and solutions, bypassing the exploratory, collaborative reasoning processes that drive creative interdisciplinary breakthroughs. As a result, prior efforts largely prioritize automating scientific discovery rather than augmenting the reasoning processes that underlie scientific disruption. We present Idea-Catalyst, a novel framework that systematically identifies interdisciplinary insights to support creative reasoning in both humans and large language models. Starting from an abstract research goal, Idea-Catalyst is designed to assist the brainstorming stage, explicitly avoiding premature anchoring on specific solutions. The framework embodies key metacognitive features of interdisciplinary reasoning: (a) defining and assessing research goals, (b) awareness of a domain's opportunities and unresolved challenges, and (c) strategic exploration of interdisciplinary ideas based on impact potential. Concretely, Idea-Catalyst decomposes an abstract goal (e.g., improving human-AI collaboration) into core target-domain research questions that guide the analysis of progress and open challenges within that domain. These challenges are reformulated as domain-agnostic conceptual problems, enabling retrieval from external disciplines (e.g., Psychology, Sociology) that address analogous issues. By synthesizing and recontextualizing insights from these domains back into the target domain, Idea-Catalyst ranks source domains by their interdisciplinary potential. Empirically, this targeted integration improves average novelty by 21% and insightfulness by 16%, while remaining grounded in the original research problem.
Figures & tables
Figure 1. Interdisciplinary process of formalizing RL ( Sutton et al., 1998 ) .
Figure 2. Idea-Catalyst is a metacognition-driven framework that (a) analyzes target-domain progress, (b) identifies unresolved challenges, (c) explores source domains for analogous insights, and (d) integrates them into interdisciplinary idea fragments. The figure is illustrated with a real case study (summarized).
Domain-Specific Question ( qiD )
Domain-Agnostic Question ( q1′ )
How can models be trained to dynamically infer and adapt to user intent and task context in real-time collaborative scenarios?
How can understanding of intent and context be updated through continuous interaction?
How should a system decide when to take initiative vs. defer to human to maintain well-calibrated autonomy & control across contexts?
When should control be exercised versus withheld?
Table 1. Output examples of domain-specific & agnostic research question pairs for “human-AI collaboration”.
Domain-Specific Challenge ( q11,D )
Domain-Agnostic Challenge ( q1′1 )
How can a system adapt in real-time to high inter/intra-user variability?
How can behavior adapt to diverse collaborators & evolving goals/environments?
Table 2. Outputted dual representation of a remaining challenge qji in addressing qi , derived from analysis of Dtarget .
Source Domain Concept
How Does it Work?
Metacontrol State Model : Goal-directed behavior reflects a balance between persistence (maintaining current goals) and flexibility (switching goals when conditions change).
Dynamic regulation between persistence and flexibility allows adaptive behavior that remains focused while responding efficiently to changing goals and environments ( Castro et al., 2021 ) .
Cognitive Control : Control processes should be prospectively adjusted based on the expected frequency of goal switches.
Anticipatory adjustment of cognitive control reduces the cost of goal switching, enabling smoother transitions and lower cognitive load under frequent change ( Grahek et al., 2023 ) .
Dynamic Goal Pursuit : Goal pursuit can be supported through just-in-time adaptive interventions that monitor behavior and provide context-sensitive support.
Real-time monitoring and adaptive feedback help sustain goal pursuit under variability by aligning support with evolving goals and situational demands ( O’Driscoll et al., 2024 ) .
Table 3. Summarized conceptual takeaways t1,t2∈Tpsychology for challenge q′11 in Table 2 and their top identified paper.
Method
Insightfulness
Relevance
Overall
@1
@2
@3
@1
@2
@3
@1
@2
@3
Free-Form Source
18.25
23.06
27.35
47.75
51.45
51.57
44.25
48.06
50.14
Guided Dual
73.00
74.19
72.36
62.25
59.68
60.54
66.75
63.23
64.39
Idea-Catalyst
85.50
85.16
84.47 †
60.25
62.42
61.25
63.75
66.45 †
65.67
× Decompose
84.00 †
84.88 †
83.80
60.75 †
61.63
65.32
65.25 †
66.82
70.13
× Potential Ranking
84.71
84.28
84.83
59.40
59.97
61.13
64.16
64.18
65.03
Table 4. Takeaway-level average win rates at top- k ( k∈{1,2,3} ). Bold indicates best; † denotes second-highest.
Method
Novelty
Usefulness
Overall
@1
@2
@3
@1
@2
@3
@1
@2
@3
Free-Form Source
13.50
17.26
19.80
35.50
39.84
40.31
30.50
34.52
35.19
Guided Dual
68.00
70.32
67.95
70.25
65.48 †
64.39
72.25
68.39
66.10
Idea-Catalyst
83.25
84.03
83.05
65.75 †
66.13
66.38
71.25 †
70.65
70.09
× Decompose
82.00 †
81.94
78.48
61.00
63.43
65.06 †
63.75
65.91
67.85 †
× Potential Ranking
83.21 †
83.31 †
81.79 †
62.41
65.15 †
64.16
68.17
69.04 †
67.92
Table 5. Idea-level average win rates at top- k ( k∈{1,2,3} ). Bold indicates best; † denotes second-highest.
Method
Source Domain Framing
Applicability to Target Domain
Guided Dual
Theory of Mind (ToM) and its role in predicting others’ mental states
By equipping AI with ToM capabilities, the system can better understand and predict user intentions, leading to more natural and effective collaboration. This reduces cognitive load by aligning AI behaviors with user expectations and improving task efficiency.
Idea-Catalyst
Reciprocal information flow and role distribution enhance joint action coordination by allowing individuals to dynamically assign and shift roles based on task demands and the predictability of others’ actions.
When individuals engage in reciprocal information flow, they can dynamically assign roles (e.g., leader–follower) and adjust their strategies in real time based on the actions and predictability of their partner. This enables them to adapt to complex and undefined tasks without predefined boundaries.
Table 6. Qualitative Comparison of Source-Domain Takeaways. Exact outputs taken from Guided Dual and Idea-Catalyst .
Figure 3. Source-domain distributions (log-scale) for each method’s top three ideas.
Figure 4. Target-source flow of interdisciplinary inspiration.
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Primary Research Area
Target Domain
Research Problem Description
Natural Language Processing
Multilingual NLP
Are there tasks where LLM performance varies systematically across languages, particularly for culture-specific queries, and how can such performance disparities be mitigated?
Electrical Engineering
In-Memory Computing
How can the accuracy of in-memory computing systems be improved while preserving high energy efficiency, especially for Edge-AI applications?
Natural Language Processing
Multilingual Semantics
Why do language models change their answers across languages for the same query, even in high-resource settings, and does this reflect knowledge or semantic misalignment?
Natural Language Processing
Persuasion and Safety
How can we characterize and mitigate the susceptibility of LLMs to persuasion, including harmful or adversarial influence, while preserving beneficial adaptability?
Machine Learning
Model Interpretability
How can influence functions be made dynamic, such that the importance of data points adapts across models and training contexts rather than remaining static?
Natural Language Processing
User Simulation
How can LLM-based user simulators better maintain consistent personas, reflect diverse user behaviors, and be evaluated for realism in multi-turn interactions?
Appendix
Table 7. Participant backgrounds and research problems used in the human study. All participants are PhD researchers ranging from 3 to 5 years of research experience.
Figure 5. Screenshot of evaluation interface (reviewing questions/challenges).
Figure 6. Screenshot of evaluation interface (reviewing takeaways).
Figure 7. Screenshot of evaluation interface (reviewing ideas).
Research idea generation uses computational methods, especially language models, to propose and assess new questions, hypotheses, and research directions.