Systematic Comparison of Agentic AI Frameworks for Scholarly Literature Processing

1.1 Background and Motivation

The rapid growth of large language models (LLMs) has initiated the emergence of agentic artificial intelligence (AI), capable of reasoning, planning, and acting at its own dis- cretion. Gartner forecasts that by 2028, 33% of enterprise software applications will incorporate agentic AI, a staggering leap from 1% in 2024 [1]. The pace of this growth symbolizes the future potency of systems that can function autonomously while reaching human-level reasoning capabilities. Typical artificial intelligence applications follow determin- istic workflows with defined inputs and outputs. Agentic artificial intelligence systems exhibit emergent behavior by deciding on a course of action, planning, and responding to changing conditions. This is especially true for knowledge- intensive areas, such as academic research, where the informa- tion processing tasks we face are beyond our human capacity constraints. The academic world has its own specific challenges, in parallel to the potential of agentic AI. The amount of academic literature has exploded, with more than two million articles published in ArXiv alone and in hundreds of disciplines. Reviewing the entire literature or synthesizing knowledge is a considerable barrier to knowledge creation. The literature can take a long time to review by hand and is likely to be incomplete due to cognitive and resource limitations.

1.2 Problem Statement

Narrowly defined prevailing activities towards the act of processing academic literature have some important limita- tions. Manual literature reviews may take weeks to months to complete. During this period, several new publications were published. Consequently, the literature review may be outdated before it is finalized. When reviewing multiple documents, the cognitive burden of processing papers, interpreting findings, and synthesizing material results in incomplete synthesis or sometimes neglect of the literature entirely. The automated tools currently available often focus on the search or discovery phases, fail to provide a systematic method of synthesis and/or analysis needed by the researcher, and/or do not provide enough time savings to support a balanced approach to the synthesis of the literature. In addition, given the heterogeneous nature of academic content across disci- plines, an effective approach to processing reliant on manually reviewing academic publications must consider the need for adjusting processing based on the idiosyncrasies of domain- specific conventions, terminology, and sense-making activities. Existing automated tools fail to provide contextual adaptability to the processing of cross-domain academic content.

1.3 Research Contributions

This research brings forth several implications from both describing agentic AI frameworks in theory and applying them in practice within the domain of academic studies:

• Comprehensive Framework Analysis: We deliver an in- depth analysis of representative agentic AI frameworks vis-a-vis their respective architecting methods of imple- mentations and the resultant performance on a scalable measure.
• Domain-Specific Evaluation: We deliver a basically in- depth analysis about the adequacy of the framework for processing the academic literature, highlighting the major requirements and issues for the processing of scholarly content.
• Practical Implementation Study: We present an actual use of agentic AI, having fully developed an ArXiv paper summarization system using Microsoft AutoGen, with concrete implementation and performance results.
• Design Pattern Identification: We establish and catalog core design patterns within agentic AI frameworks to provide a theoretical understanding of multi-agent system implementations.
• Performance Benchmarking: We provide baseline per- formance results on academic literature processing tasks against which future comparative studies.

LITERATURE REVIEW AND BACKGROUND

2.1 Evolution of Agentic AI Systems

Autonomous agents have their roots in early artificial in- telligence work, where agents are defined as entities that can perceive their environments and take actions across states to reach goals. Now that LLMs are available, agents can access an entirely new level of reasoning and natural language under- standing through LLMs, allowing them to act autonomously without explicit human intervention. Today’s autonomous agent AI systems share some key characteristics: agents make decisions about what to do next with no explicit human involvement for each step; agents plan dynamically and change strategies based on progress within the middle of executing an agent plan; agents represent multimodal processing and can provide their own complete responses to actions across many formats of input and output; and agents create emergent behaviors based on responses from other agents and the environment. Although the theoretical framework on which multi-agent systems are based has its roots in distributed systems research, game theory, and cognitive science, the practical development of effective multi-agent systems was limited until LLMs made complex reasoning and natural language interaction elements available.

2.2 Framework Design Patterns

Analysis of current architectures of agentic AI reveals several distinct architectural patterns that fundamentally affect system capabilities and consequently application suitability:

2.2.1 Conversation-Based Architectures

In conversation-based architectures, agents interact in struc- tured dialogues, enabling natural communication and flexible workflow adaptation. Such systems are adequate in scenarios that need dynamic collaboration or even an adaptive approach to problem-solving. Microsoft AutoGen is representative of this family, allowing flexible dialogue patterns and emergent collaboration behaviors.

2.2.2 Role-Based Orchestration Systems

In role-based orchestration systems, agents have different roles and responsibilities, which make for structured work- flows with clearly demarked tasks. CrewAI operates as a role- based orchestration framework, being able to provide pre- dictable execution patterns and clear accountability structures.

2.2.3 Graph-Based Workflow Systems

Graph-based workflow systems represent agent interactions and task dependencies as directed graphs, enabling precise control over execution flow and state management. LangGraph and similar systems are well-suited to complex conditional logic and branching scenarios.

2.2.4 Modular Component Architectures

Modular component architectures emphasize flexible com- position of specialized components, enabling customizable agent capabilities and integration with external systems. This pattern supports extensibility and facilitates integration with existing software ecosystems, as exemplified by Microsoft Semantic Kernel.

2.3 Academic Literature Processing Systems

The automation of academic literature processing has trans- formed from basic keyword-based search and retrieval systems into a technology-rich tool that can synthesize content and extract knowledge from literature. The earliest systems or ex- amples provided improved opportunities to access information via indexing and retrieval when paired with user-executed manual processing of the retrieved content. Recently, some AI-enhanced summarization tools have en- abled researchers to synthesize single papers, but most do not seem capable of performing whole-of-literature synthesis processing, which is foundational for literature review gen- eration. The significant gap between the ability to retrieve information using advanced generative AI and the importance of synthesizing knowledge opens up various opportunities for agentic AI applications. Currently, new academic automation analytical processes are examining citation network analysis, topic modelling, and clustering content by similarity, to name a few; however, all of these tools involve user experience and knowledge of the domain’s field of study to use the tool as configured and to appropriately interpret and process the outputs. Without tech- nical expertise and knowledge-driven skills, many researchers may feel underserved by technology.

2.4 Framework Landscape Overview

The current agentic AI framework ecosystem includes sev- eral major platforms, each with distinct design philosophies and target applications:

• Microsoft AutoGen: Conversational multiagent systems with an emphasis on natural language interaction and autonomous code generation. [2] [10]
• Google Agent Development Kit (ADK): An enterprise- grade framework, supporting emerging protocols, deploy- ments irrespective of model, and compatible with open standards. [3]
• CrewAI: Role-based execution of tasks with workflow management and agent specialization. [4]
• LlamaIndex: Efficient data indexing and retrieval for knowledge-intensive applications. [5]
• LangGraph: Graph-based workflow management with fine control over agent interactions and state transitions. [6].
• Microsoft Semantic Kernel: Modular bridging capabili- ties between LLMs and conventional software paradigms. [7]

METHODOLOGY

3.1 Framework Selection Criteria

Our comparative study investigates six popular agentic AI frameworks, selected as follows: Market Share and Commu- nity support, Diverse technical architecture covering a range of design patterns, Technical Maturity and readiness for produc- tion, Openliness of the source code allowing for deeper anal- ysis, Quality of reported use case studies in the academic and knowledge-intensive sector. The chosen frameworks represent the state-of-the-art in agentic AI research and development, and together reflect the main architectural approaches that developers and researchers can presently access.

3.2 Evaluation Dimensions

We examine each structure using a variety of criteria meant to account for both technical expertise and practical factors relevant to scholarly literature processing applications:

• Architectural Foundation: Analysis of core design pat- terns, agent coordination mechanisms, and system exten- sibility features that influence long-term maintainability and customization capabilities.
• Implementation Complexity: Evaluation of the neces- sary technical knowledge, characteristics of the learning curve, and development effort requirements for the use of the efficient framework.
• Performance Characteristics: Assessment of resource utilization patterns, scalability characteristics, and com- putational efficiency under varied workload circum- stances.
• Integration Capabilities: Evaluation of interoperability with current academic tools and databases, support for API integration, and compatibility with external systems.
• Domain Adaptability: Examination of framework adapt- ability for processing academic content, including discipline-specific support, citation management, and scholarly conventions
• Community and Ecosystem: Evaluation of documenta- tion quality, availability of community support, and ma- turity of the ecosystem, including third-party extensions and integrations.

3.3 Academic Processing Requirements

Academic literature processing presents specific require- ments that influence framework suitability:

• Content Understanding: Ability to process scholarly writing conventions, technical terminology, and domain- specific notation systems across multiple disciplines.
• Citation Management: Support for academic citation standards, reference formatting, and bibliographic data handling in various formats and styles.
• Quality Assessment: Capabilities for evaluating source credibility, identifying peer-review status, and assessing publication venue quality and relevance.
• Synthesis Generation: Ability to integrate findings across multiple sources, identify research gaps, and gener- ate coherent analytical summaries that maintain academic rigor.
• Temporal Awareness: Understanding of research time- line dynamics, publication sequences, and evolutionary development of research topics over time.

3.4 Experimental Design

Our evaluation methodology combines qualitative analysis of framework documentation and architecture with quantitative assessment through practical implementation. We implement a standardized academic literature processing task using each framework where feasible, measuring performance across mul- tiple metrics including processing time, output quality, and implementation effort. For frameworks where complete implementation is not practical within the scope of this study, we provide detailed analysis based on documentation review, community feedback, and reported use cases from academic and industry applica- tions.

FRAMEWORK COMPARATIVE ANALYSIS

Table I Compact Comparison of Agentic Ai Frameworks

4.1 Microsoft AutoGen

4.1.1 Architecture and Design Philosophy

Microsoft AutoGen [2] represents the conversation-based approach to multi-agent systems wherein communication be- tween agents is in the form of structured dialogues that can change and adapt to task criteria. The premise of this framework is based on the norms of communication that allow for emergent behaviors via repeated conversations. The architecture allows for operation modes that include human- in-the-loop (HITL) and fully autonomous modes, allowing flexibility for applications that may require some level of human oversight. Agent definitions provide information about role descriptions and communication modalities rather than strict task definitions, which facilitates adaptive behavioral patterns. AutoGen’s conversation manager [2] maintains the con- text across multi-turn conversations and allows multiple deep branching points and conditional intentions to be managed. The framework provides built-in mechanisms to terminate con- versations, recover errors, and assess all quality of generated content.

4.1.2 Implementation Characteristics

In moderate technical settings, AutoGen requires some tech- nical knowledge to implement effectively and uses Python to configure and define agents in a standard manner. Significant documentation and examples are provided by the AutoGen framework so that developers with basic programming knowl- edge can become competent in using it. To customize agents, a person would define system prompts, define allowable actions, and define the communication con- structs. The framework can be adapted to work with different LLM providers, which provides more flexibility in model choices based on a user’s needs for cost, efficacy, or capa- bilities. One of the best features of AutoGen in particular is the code generation functions, with agents that self-correct, re- write, execute, or generate analytical code. These features are also useful in academic situations where processing structured data and generating analytical code can be highly important.

4.1.3 Academic Processing Suitability

AutoGen’s conversational model is a good fit for the pro- cessing needs of academic literature, particularly for tasks that can be undertaken iteratively and involve quality control. The flexibility of the framework allows it to accommodate various academic disciplines and citation requirements. The natural language interaction model can accommodate various forms of academic content, including norms pertaining to source credibility, limitations of research methodology, and combination of findings from multiple papers. However, the framework’s focus on emergent behavior may result in unpredictable processing times and resource consumption, which may limit the scalability of large-scale literature processing to a small number of cases.

4.2 Google Agent Development Kit (ADK)

4.2.1 Architecture and Design Philosophy

Google’s Agent Development Kit (ADK) [3] is a flexible and modular framework for the development and deployment of AI agents that emphasizes enterprise-grade and production readiness. Being the same toolkit that was developed and is used internally to create Google’s” Gemini” AI Apps, ADK first brings enterprise-grade agent functions to the open-source community. The design of the framework highlights compatibility and interoperability, considering that it is model-agnostic, deployment-agnostic, and compatible with other frameworks, thus allowing enterprise system integration and migration between different LLM providers. Bloom’s ADK support for the new Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol has it primed for future interoperability use cases, especially in enterprise sce- narios where agents need to be coordinated over organizational boundaries.

4.2.2 Implementation Characteristics

ADK requires a terminal and Python 3.10+ with full tooling to manage your development tasks. The ADK supports ad- vanced debugging and monitoring of production deployment. Within the context of ADK, a Tool signifies a specific capa- bility or function being supplied to your AI agent such that you could cause an action to take place and extend the AI agent’s operation beyond its native text generation capacity and reasoning. The modular system of this tool system allows for academic applications to customize and expose unique processing capabilities of the model. The framework supplies out-of-the-box integration with Google Cloud services, while offering an API that allows for nearly any other deployment configuration for the proper infrastructure requirements.

4.2.3 Academic Processing Suitability

The enterprise focus of ADK and the strong tooling make it ideal for institutional academic use cases that require high reliability and scalability. The modular architecture of the framework affords its compatibility and integration with ex- isting academic databases and systems. Protocol support and emphasis on interoperability enable coordination between agents between institutions and research groups, supporting collaborative research workflows and dis- tributed literature processing initiatives. However, the enterprise orientation of the framework may add unnecessary complexity overhead for uncomplicated aca- demic use cases, and it arguably represents more developer effort than simpler alternatives.

4.3 CrewAI

4.3.1 Architecture and Design Philosophy

CrewAI, a role-based task execution engine created for team-oriented agents, emphasizes a structured flow of work and clear specialization of agents and responsibilities. The framework models multi-agent systems in terms of teams of agents with structured hierarchies and communication proto- cols [4]. CrewAI [4] has a no-code experience to facilitate rapid prototyping and ready-made AI agent templates to facilitate deployment, even for users with minimal programming ex- periences. This democratizes agent development and supports sophisticated coordination. The role-based architecture supports predictable patterns of execution and easily identifiable accountability structures for agents to achieve reliable production results when consistent performance is required.

4.3.2 Implementation Characteristics

CrewAI is ideal when there are well-defined workflows with repeatable patterns and a preference for an easier entry point. The framework has a template-based structure to accelerate the development of complex multi-agent applications in an organized manner. CrewAI is also connected to several third-party applications and services. This also helps connect to services and tools used in academia. The no-code setup speeds up the process of iteration and prototyping, especially for education or research professionals who do not have much programming skill or simply want powerful automation.

4.3.3 Academic Processing Suitability

As a structured framework, CrewAI fits largely with the literature processing workflows described in academic liter- ature that incorporate the specialization of independent tasks and repetition of tasks. CrewAI’s role-based system allows for good coordination between the literature search, review, and synthesis processes. The expansive integration ecosystem provides more access to academic databases, reference managers, and publishing systems, with the potential to expand the automation of the entire workflow within the existing research toolchain. Nevertheless, the strict workflow framework emphasizes sets of defined workflows and may limit flexibility in ways that can be obtrusive to research situations in which solving a problem dynamically or searching and interpreting in patterns can be exploratory.

4.4 LlamaIndex

4.4.1 Architecture and Design Philosophy

LlamaIndex focuses on effective data indexing and retrieval methods, with an emphasis on optimizing knowledge-focused applications with quick information access needs for large document collections. LlamaIndex prioritizes search and in- formation extraction capabilities over multi-agent coordination capabilities. [5] The unified architecture function on the capabilities of document indexing, generation of embeddings, and optimized retrieval, which is foundational in that LlamaIndex works well in combination with, rather than totally replacing, multi- agent capabilities. LlamaIndex [5] is aimed at use cases where efficient access to information is the main friction point. The design philosophy of LlamaIndex focuses on simplicity and efficiency, prioritizing information retrieval capabilities over all agent coordination capabilities.

4.4.2 Implementation Characteristics

LlamaIndex has a straightforward implementation pattern that focuses exclusively on document processing and index- building. The LlamaIndex framework [5] can be adaptable to different content types and domain requirements because it supports a wide variety of document and embedding model types. Users can create integrations with vector databases and search systems that offer a scalable infrastructure for siz- able document collections by implementing abstraction with document embedding and index building procedures after ingesting the document content. This is especially relevant for comprehensive literature databases across disciplines and publication venues in the field. LlamaIndex is inherently less complex for implementation with its limited scope because the user is only invested in the implementation of information retrieval rather than a broader scope of multi-agent implementation. LlamaIndex is well- suited for applications where the main requirement is to access information.

4.4.3 Academic Processing Suitability

The information retrieval optimization of LlamaIndex makes it especially advantageous for academic applications that in- volve efficient searching and accessing documents. The frame- work is very strong when building a comprehensive literature database that provides advanced search capabilities. Nevertheless, the limited agent coordination available in the framework may necessitate a combination with other systems for the full range of literature processing workflows associated with synthesis, analysis, and report generation. The LlamaIndex framework is best suited as a component of larger academic processing systems and not as a stand-alone framework for a fully automated literature review.

4.5 LangGraph

4.5.1 Architecture and Design Philosophy

With graph-based representations of agent interactions and task dependencies, LangGraph excels at complex stateful workflows. LangGraph provides precise control over the exe- cution flow, state, and condition logic implementation. [6] Graph-based frameworks, such as LangGraph, provide pre- cise control of agent coordination patterns, allowing deter- ministic interactions and predictable resource consumption. A graph-based approach is optimal for applications that require reliable and repeatable processing patterns. The graph-based architecture allows for complex branching scenarios, conditional paths of execution, and complex state management, facilitating the implementation of highly com- plex academic processing workflows with multiple decision points and quality gates.

4.5.2 Implementation Characteristics

Implementing LangGraph can be technically challenging and will likely require advanced technical skills, especially in terms of defining a graph, managing states, and flow control, which are significant development tasks. LangGraph provides many powerful capabilities at the cost of development complexity. [6] The deterministic control capabilities of the framework allow optimization for particular performance targets and resource usage constraints, which is particularly useful for production applications with performance standards. Integration capabilities are analogous to programmatic inter- faces rather than no-code solutions, which require development skills but offer deep customization.

4.5.3 Academic Processing Suitability

LangGraph’s deterministic control and state management capabilities make it suitable for more complex workflows necessary for academic processing, particularly workflows that require rich quality gates, conditional processing paths, and deep auditing. LangGraph’s deterministic behavior patterns also allow ap- plications to rely on predictable results and more predictable resource consumption, which are particularly important in in- stitutionally funded deployments that process large collections of literature. The complexity of implementing LangGraph could limit adoption by academic researchers who do not have much pro-gramming experience and may ultimately suggest a sustained need for technical support or personnel to utilize it effectively.

4.6 Microsoft Semantic Kernel

4.6.1 Architecture and Design Philosophy

Microsoft Semantic Kernel [7] offers a powerful integration option for LLMs within the constraints and modalities of traditional software development. Its modular components allow developers to create various types of agents to complete specific tasks (e.g., web scraping, API interaction, and natural language processing). The framework is designed to fit within existing enterprise development workflows, allowing the benefits of familiar programming patterns and deployment models to work with traditional software engineers. The underlying principle of the Semantic Kernel is to create a comprehensive, extensible framework that is first and foremost interoperable with existing tools in the Mi- crosoft ecosystem, but still embraces open standards and cross- platform compatibility.

4.6.2 Implementation Characteristics

The framework provides strong integration with Microsoft development tools and cloud services while allowing alterna- tive platforms and deployment options. The traditional soft- ware development patterns used in the framework meant that there was less of a learning curve (for experienced developers). AI agents can function autonomously or semi- autonomously, which means they are more capable than standard software agents and can be a strong fit for several applications in the Semantic Kernel architecture. The modular nature of the framework supports a phased approach to adoption and integration into existing systems. The enterprise focus of this framework means strong secu- rity, monitoring, and management options suitable for insti- tutional academic use, an important consideration when more governance and oversight at higher education institutions is expected.

4.6.3 Academic Processing Suitability

Semantic Kernel’s enterprise features and capabilities for integration make it applicable for academic institutions, who want to create coordination between agentic AI systems and the existing infrastructure for research, such as library systems, research repositories, and publication platforms. Semantic Kernel has a modular architecture that enables the development of specialized agents for a particular task, while allowing for integration into the larger institution and their systems and workflows. [7] Nonetheless, there may be concerns about vendor lock-in, as the Microsoft ecosystem support for the framework implies to some institutions that value platform independence and open standards.

5. ARXIV SUMMARIZATION SYSTEM IMPLEMENTATION

5.1 System Design and Architecture

To illustrate the real-world application of agentic AI frame- works in academic situations, we delivered a complete ArXiv [8] paper summarization system based on Microsoft AutoGen [10]. The implemented system design tackles the particu- lar challenges associated with the processing of academic literature, while illustrating the capabilities and limitations associated with conversation-based multi-agent architectures.

5.1.1 Multi-Agent Architecture Design

Table II Specialized Agents in The Arxiv Summarization System

The system has a distributed architecture composed of two unique agents that work together to emulate and enhance the literature review behavior of human researchers.

Researcher Agent: This agent acts as an information re- trieval agent and repository, interacting through programmatic API access from the ArXiv [8] repository. The agent leverages a sophisticated use of query formulation for research databases to obtain articles and uses relevance filtering mechanisms for papers gathered for the research agenda defined by the researcher agent.

Summarizer Agent: This agent acts in all aspects of the analysis, interacting with the papers retrieved to create coher- ent literature reviews. The summarizer agent uses sophisticated natural language processing capabilities utilizing the language model API to produce high-quality output per discipline. The agents coordinate through AutoGen’s conversation ar- chitecture, which permits the agents to be flexibly sequenced and reset in accordance with intermediate results and quality assessment metrics.

5.1.2 Integration Architecture

The system coefficients multiple external services, using protocols that produce high integration engagement efficiency and reliability:

• ArXiv API Integration: A programmatic API to the ArXiv repository contains over two million scholarly articles, with effective query strategies to limit API calls and maximize the relevance of individual articles.
• Groq API Integration: High-performance LLM infer- ence for real-time academic text processing, with adaptive prompt engineering and model selection strategies. [9]
• AutoGen Orchestration: Multi-agent coordination through structured message passing, error recovery mechanisms, and workflow orchestration.

5.2 Implementation Details

5.2.1 Agent Configuration and Specialization

The architecture of the system involves utilizing two agents for the literature review process with complementary ca- pacities. The Researcher Agent assists in discovering and retrieving papers. It connects to the ArXiv API, determining optimal prompts and query approaches based on the domain of those papers. It has rate limiting and error handling to avoid occasional blocks in accessing key papers; its metadata extraction routines frame and organize outputs. The Summarizer Agent enables analysis and synthesis. It analyzes many papers simultaneously, extracts the overall themes, and identifies contradictions or gaps. In addition to summarizing, checks of coherence and rigor receive attention while maintaining conformity to the academic writing con- ventions of the disciplines and formatting the references to bibliographic standards. Together, these two agents automate the key elements of the literature review process.

5.2.2 Workflow Orchestration

To ensure coordinated execution, the system follows a structured multi-stage workflow (Table III). Each stage is aligned with specific quality gates, balancing efficiency with reliability.

Table III Workflow Orchestration of Summarization System

This workflow allows the system to progress from query definition to final summary output while ensuring intermediate validation at each step.

5.2.3 Quality Assurance Mechanisms

The pipeline has multiple layers of quality control. Quality control first uses input validation to ensure that we receive a well-formed query that does not contain ambiguous requests from users. The relevance assessment of each retrieved paper evaluates the quality of the retrieved paper in terms of the original research intention, excluding any irrelevant text or content. Iterative refinement through the final synthesized content allowed us to optimize the coherence of what we wrote and the academic rigor it contained. Finally, citation for- matting and integrity were checked for fitness with scholarly standards. All of this quality control improves the authenticity of the content produced by the site, if only through this pre- implementation process.

5.3 Technical Implementation

5.3.1 Technology Stack

The implementation relies on a combination of multi-agent coordination, large language models, and lightweight user- facing interfaces. Table IV summarizes the core components of the system.

Table IV Technology Stack of The Arxiv Summarization Prototype

5.3.2 Error Handling and Reliability

Reliability is enhanced by using multiple safeguards. API rate limiting is governed by a built-in automatic retry mecha- nism using exponential back-off, which minimizes the number of failed requests. Network interruptions initiate a graceful degradation of processing and notify users instead of outright killing the workflows. For processing failures, workflows can roll back execution to intermediate states, and tasks can be redirected through alternative workflows to maintain continu- ity.

5.3.3 User Interface Design

The front-end Streamlit-based UI prioritizes an accessible interface for academic users. Progress indicators that update in real-time provide transparency to users during the summariza- tion process, and users have configurable options to determine the scope, format, and details of the output of their literature. Export options also include common academic workflow steps, such as exporting in a LaTeX-compatible file and exporting a plain text output. Error reporting provides users with further transparency to use the system as a practical support tool for early stage literature exploration.

6. EXPERIMENTAL RESULTS AND ANALYSIS

6.1 Evaluation Methodology

The prototype system was tested using a representative set of queries from focused topic areas, interdisciplinary search areas, and trend analysis. Each query returned a limited number of ArXiv papers [8], which allowed us to assess feasibility rather than defining benchmarks on large-scale data. The evaluative analysis considered three foremost dimensions: (i) efficiency relating to processing time and consistency, (ii) quality of generated summaries, and (iii) usability for re- searchers. These align with established practices in evaluating language-model-based systems.

6.2 Efficiency Observations

Even for a small number of papers, a manual review of the literature often involves a multi-hour time commitment. Our prototype completed draft summaries in minutes, offering clear time savings for initial assessments. However, the variation in performance based on query difficulty and API responses means that it was not always faster for summarization built into the rest of the workflow. However, the system clearly reduced the labor required to assemble and organize relevant papers.

Table V Illustrative Comparison Between Manual and Automated Summarization