OpenNovelty.org

Agentic Analogical Reasoning for Large Language Models

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.5Download Report PDF
Analogical reasoningLarge Language Models
Abstract:

Analogical reasoning helps humans grasp new concepts by relating them to familiar ones. Recent work seeks to improve LLM reasoning by prompting analogical cor- respondences with semantically related scenarios. However, existing approaches are single-turn reasoning and may generate unreliable analogical instances, which restricts their effectiveness in complex reasoning tasks. To address these limita- tions, we propose a novel Agentic Analogical Reasoning (AAR) paradigm for LLM reasoning. This paradigm treats the LLM as an agentic reasoner to integrate multi-turn insights along the reasoning trajectory of iteratively generating analogi- cal queries to trigger internal or external knowledge for analogical exemplification, and selectively identifying appropriate analogies to conduct further reasoning. To equip LLMs with AAR capability, we design an analogical trajectory optimization algorithm including analogical trajectory generation and re-weighted trajectory training. Furthermore, a mixed training strategy is devised to progressively inter- nalize agentic analogical reasoning as an intrinsic capability of LLMs. Finally, we conduct extensive experiments on seven reasoning-intensive datasets and achieve significant performance improvements over prior state-of-the-art (SOTA) methods. The code is available at https://anonymous.4open.science/r/ICLR-8381.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces an Agentic Analogical Reasoning (AAR) paradigm that treats the LLM as an iterative agent performing multi-turn analogical reasoning. According to the taxonomy, this work resides in the 'Agentic and Multi-Turn Analogical Reasoning' leaf under 'Prompting and Reasoning Enhancement Methods'. This leaf contains only two papers total, indicating a relatively sparse research direction within the broader field of 50 papers. The sibling paper, Thought Propagation, explores general reasoning propagation mechanisms, suggesting that agentic multi-turn approaches represent an emerging but not yet crowded subfield.

The taxonomy reveals that neighboring leaves include 'Analogical Prompting and In-Context Learning' (four papers on single-turn methods), 'Retrieval-Augmented Analogical Reasoning' (three papers integrating external knowledge), and 'Self-Supervised and Self-Consistent Learning' (two papers on training mechanisms). The scope note for the paper's leaf explicitly excludes single-turn prompting and retrieval methods, positioning AAR as distinct from static prompt engineering. The broader 'Prompting and Reasoning Enhancement Methods' branch contains six leaves with varying densities, suggesting that while prompting research is active, the specific agentic multi-turn angle remains less explored.

Among 30 candidates examined, each of the three contributions shows at least one refutable candidate. Contribution A (AAR paradigm) examined 10 papers with 1 refutable match, Contribution B (trajectory optimization) examined 10 with 1 refutable, and Contribution C (mixed training strategy) examined 10 with 1 refutable. The statistics suggest that within this limited search scope, some prior work overlaps with each contribution, though the majority of examined candidates (27 out of 30 total) do not clearly refute the claims. The uniform distribution across contributions indicates that the novelty concerns are spread rather than concentrated in one area.

Based on the top-30 semantic matches examined, the work appears to build on an emerging but sparse research direction. The taxonomy structure shows that agentic multi-turn analogical reasoning is less populated than single-turn prompting or retrieval-augmented methods. However, the presence of refutable candidates for all three contributions suggests that the specific technical mechanisms may have precedents in the examined literature. The analysis does not cover exhaustive citation networks or domain-specific venues beyond the semantic search scope.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: analogical reasoning for large language models. The field organizes around four main branches that capture different facets of how LLMs perform and improve analogical reasoning. The first branch, Analogical Reasoning Capabilities and Evaluation, focuses on benchmarking and measuring emergent abilities, with works like Emergent Analogical Reasoning[1] and ANALOGICAL Benchmark[34] establishing testbeds for assessing model performance on tasks ranging from word analogies to story-based mappings. The second branch, Prompting and Reasoning Enhancement Methods, explores techniques to elicit or amplify analogical thinking through prompt engineering, multi-turn interactions, and agentic frameworks. The third branch, Reasoning Foundations and Theoretical Analysis, investigates the underlying mechanisms and limitations of analogical processes in neural architectures, examining questions about concept representation and generalization. The fourth branch, Applications and Domain-Specific Implementations, applies analogical reasoning to specialized domains such as scientific discovery, creative ideation, and legal reasoning, demonstrating practical utility across diverse contexts. Within the Prompting and Reasoning Enhancement Methods branch, a particularly active line of work centers on agentic and multi-turn strategies that decompose analogical tasks into iterative steps. Agentic Analogical Reasoning[0] exemplifies this direction by framing analogy-making as an interactive agent process, contrasting with simpler single-shot prompting approaches. This emphasis on iterative refinement aligns closely with Thought Propagation[3], which explores how reasoning chains can be expanded and propagated across multiple turns to improve coherence and depth. While Thought Propagation[3] focuses on general reasoning propagation mechanisms, Agentic Analogical Reasoning[0] tailors these ideas specifically to the structure-mapping demands of analogical tasks. Together, these works highlight an emerging theme: moving beyond static prompts toward dynamic, feedback-driven reasoning loops that better capture the exploratory nature of human analogical thinking.

Claimed Contributions

Agentic Analogical Reasoning (AAR) paradigm

A new reasoning paradigm that treats LLMs as agentic reasoners performing iterative multi-turn analogical reasoning. The paradigm consists of three core actions (thinking, analogizing, contextualizing) executed in cycles to progressively build reasoning trajectories by generating analogical queries, triggering internal or external knowledge, and selectively identifying appropriate analogies.

10 retrieved papers
Can Refute
Analogical trajectory optimization algorithm

A training algorithm that generates analogical reasoning trajectories using external knowledge retrieval, assigns importance weights to trajectories based on their support for correct answers, and integrates trajectory reweighting into the ELBO objective function to encourage generation of more supportive trajectories.

10 retrieved papers
Can Refute
Mixed training strategy for capability internalization

A training strategy that progressively enhances the intrinsic analogical capabilities of LLMs by leveraging both self-generated and externally retrieved analogical trajectories, gradually transitioning from external retrieval to autonomous internal analogy generation.

10 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Agentic Analogical Reasoning (AAR) paradigm

A new reasoning paradigm that treats LLMs as agentic reasoners performing iterative multi-turn analogical reasoning. The paradigm consists of three core actions (thinking, analogizing, contextualizing) executed in cycles to progressively build reasoning trajectories by generating analogical queries, triggering internal or external knowledge, and selectively identifying appropriate analogies.

Contribution

Analogical trajectory optimization algorithm

A training algorithm that generates analogical reasoning trajectories using external knowledge retrieval, assigns importance weights to trajectories based on their support for correct answers, and integrates trajectory reweighting into the ELBO objective function to encourage generation of more supportive trajectories.

Contribution

Mixed training strategy for capability internalization

A training strategy that progressively enhances the intrinsic analogical capabilities of LLMs by leveraging both self-generated and externally retrieved analogical trajectories, gradually transitioning from external retrieval to autonomous internal analogy generation.

Table of Contents