I2I-STRADA -- Information to Insights via Structured Reasoning Agent for Data Analysis
- URL: http://arxiv.org/abs/2507.17874v1
- Date: Wed, 23 Jul 2025 18:58:42 GMT
- Title: I2I-STRADA -- Information to Insights via Structured Reasoning Agent for Data Analysis
- Authors: SaiBarath Sundar, Pranav Satheesan, Udayaadithya Avadhanam,
- Abstract summary: Real-world data analysis requires a consistent cognitive workflow.<n>We introduce I2I-STRADA, an agentic architecture designed to formalize this reasoning process.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in agentic systems for data analysis have emphasized automation of insight generation through multi-agent frameworks, and orchestration layers. While these systems effectively manage tasks like query translation, data transformation, and visualization, they often overlook the structured reasoning process underlying analytical thinking. Reasoning large language models (LLMs) used for multi-step problem solving are trained as general-purpose problem solvers. As a result, their reasoning or thinking steps do not adhere to fixed processes for specific tasks. Real-world data analysis requires a consistent cognitive workflow: interpreting vague goals, grounding them in contextual knowledge, constructing abstract plans, and adapting execution based on intermediate outcomes. We introduce I2I-STRADA (Information-to-Insight via Structured Reasoning Agent for Data Analysis), an agentic architecture designed to formalize this reasoning process. I2I-STRADA focuses on modeling how analysis unfolds via modular sub-tasks that reflect the cognitive steps of analytical reasoning. Evaluations on the DABstep and DABench benchmarks show that I2I-STRADA outperforms prior systems in planning coherence and insight alignment, highlighting the importance of structured cognitive workflows in agent design for data analysis.
Related papers
- AgenticData: An Agentic Data Analytics System for Heterogeneous Data [12.67277567222908]
AgenticData is an agentic data analytics system that allows users to pose natural language (NL) questions while autonomously analyzing data sources across multiple domains.<n>We propose a multi-agent collaboration strategy by utilizing a data profiling agent for discovering relevant data, a semantic cross-validation agent for iterative optimization based on feedback, and a smart memory agent for maintaining short-term context.
arXiv Detail & Related papers (2025-08-07T03:33:59Z) - A Pre-training Framework for Relational Data with Information-theoretic Principles [57.93973948947743]
We introduce Task Vector Estimation (TVE), a novel pre-training framework that constructs supervisory signals via set-based aggregation over relational graphs.<n>TVE consistently outperforms traditional pre-training baselines.<n>Our findings advocate for pre-training objectives that encode task heterogeneity and temporal structure as design principles for predictive modeling on relational databases.
arXiv Detail & Related papers (2025-07-14T00:17:21Z) - Understanding Software Engineering Agents: A Study of Thought-Action-Result Trajectories [18.129031749321058]
Large Language Model (LLM)-based agents are increasingly employed to automate complex software engineering tasks.<n>Despite their widespread adoption, the internal decision-making processes of these agents remain largely unexplored.<n>We present a large-scale empirical study of the thought-action-result trajectories of three state-of-the-art LLM-based agents.
arXiv Detail & Related papers (2025-06-23T16:34:52Z) - Deep Research Agents: A Systematic Examination And Roadmap [79.04813794804377]
Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
arXiv Detail & Related papers (2025-06-22T16:52:48Z) - Structured Prompting and Feedback-Guided Reasoning with LLMs for Data Interpretation [0.0]
Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and task generalization.<n>This paper introduces the STROT Framework, a method for structured prompting and feedback-driven transformation logic generation.
arXiv Detail & Related papers (2025-05-03T00:05:01Z) - A Survey on (M)LLM-Based GUI Agents [62.57899977018417]
Graphical User Interface (GUI) Agents have emerged as a transformative paradigm in human-computer interaction.<n>Recent advances in large language models and multimodal learning have revolutionized GUI automation across desktop, mobile, and web platforms.<n>This survey identifies key technical challenges, including accurate element localization, effective knowledge retrieval, long-horizon planning, and safety-aware execution control.
arXiv Detail & Related papers (2025-03-27T17:58:31Z) - A Survey of Model Architectures in Information Retrieval [64.75808744228067]
We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation.<n>We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)<n>We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
arXiv Detail & Related papers (2025-02-20T18:42:58Z) - Mechanistic Unveiling of Transformer Circuits: Self-Influence as a Key to Model Reasoning [9.795934690403374]
It is still unclear which multi-step reasoning mechanisms are used by language models to solve such tasks.<n>We employ circuit analysis and self-influence functions to evaluate the changing importance of each token throughout the reasoning process.<n>We demonstrate that the underlying circuits reveal a human-interpretable reasoning process used by the model.
arXiv Detail & Related papers (2025-02-13T07:19:05Z) - Advancing Agentic Systems: Dynamic Task Decomposition, Tool Integration and Evaluation using Novel Metrics and Dataset [1.904851064759821]
Advanced Agentic Framework: A system that handles multi-hop queries, generates and executes task graphs, selects appropriate tools, and adapts to real-time changes.
New Novel Evaluation Metrics: Introduction of Node F1 Score, Structural Similarity Index (SSI), and Tool F1 Score to comprehensively assess agentic systems.
AsyncHow-based dataset for analyzing agent behavior across different task complexities.
arXiv Detail & Related papers (2024-10-29T18:45:13Z) - Data Analysis in the Era of Generative AI [56.44807642944589]
This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges.
We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow.
We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
arXiv Detail & Related papers (2024-09-27T06:31:03Z) - Instruction Tuning for Large Language Models: A Survey [52.86322823501338]
We make a systematic review of the literature, including the general methodology of supervised fine-tuning (SFT)<n>We also review the potential pitfalls of SFT along with criticism against it, along with efforts pointing out current deficiencies of existing strategies.
arXiv Detail & Related papers (2023-08-21T15:35:16Z) - A Mechanistic Interpretation of Arithmetic Reasoning in Language Models
using Causal Mediation Analysis [128.0532113800092]
We present a mechanistic interpretation of Transformer-based LMs on arithmetic questions.
This provides insights into how information related to arithmetic is processed by LMs.
arXiv Detail & Related papers (2023-05-24T11:43:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.