Related papers: AgentLens: Visual Analysis for Agent Behaviors in LLM-based Autonomous Systems

Related papers

ATLAS: Discovering Agent Strategies through LLM-Guided Abstraction and Automata Learning [6.410715142296986]
ATLAS (Automata Learning for Agent Trajectory Analysis and Strategy Discovery) is an approach for recovering interpretable behavioral models from agent trajectories.<n>As a proof of concept, we apply ATLAS to trajectories generated by an LLM-based penetration-testing agent.<n>We discuss how learned behavioral models can support explainability, model-guided exploration, auditing, and analysis of agentic systems.
arXiv Detail & Related papers (2026-08-14T14:51:48Z)
From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents [56.187590188698955]
Large Language Models have enabled increasingly capable autonomous agents, yet personalization remains critical for making such agents practically useful.<n>Recent benchmarks have begun evaluating personalization in agents, but they largely rely on static preference snapshots, fixed interaction logs, or question answering over predefined user profiles.<n>We introduce IBA-Bench, a benchmark for implicit behavioral alignment constructed from longitudinal interaction histories.<n>We propose IBA-Agent, an agent framework that reconciles conflicting priorities through broad retrieval and trajectory-level alignment.
arXiv Detail & Related papers (2026-08-03T12:52:04Z)
From Question Answering to Task Completion: A Survey on Agent System and Harness Design [67.8241530947325]
This survey examines LLM-based agents through a model-harness lens.<n>We first clarify the functional definition of agents and the implementation view of an LLM-based agent as a foundation model and an execution harness.<n>We then analyze the limits of model-centric scaling, trace four paradigms of agent engineering, and decompose the execution harness into six coupled runtime responsibilities.
arXiv Detail & Related papers (2026-06-14T05:40:16Z)
How to Interpret Agent Behavior [56.59836196946289]
We introduce ACT*ONOMY, a taxonomy for describing and analyzing agent behavior at runtime.<n>By providing a shared vocabulary, ACT*ONOMY helps researchers, agent designers, and end users interpret agent behavior more consistently.
arXiv Detail & Related papers (2026-05-13T14:52:40Z)
Exploring Interaction Paradigms for LLM Agents in Scientific Visualization [7.22431217973039]
This paper examines how different types of large language model (LLM) agents perform on scientific visualization (SciVis) tasks.<n>We compare three primary interaction paradigms, including domain-specific agents with structured tool use, computer-use agents, and general-purpose coding agents.
arXiv Detail & Related papers (2026-04-30T15:22:28Z)
Learning to Retrieve from Agent Trajectories [72.8923565916533]
We argue that retrieval models for agentic search should be trained directly from agent interaction data.<n>We introduce learning to retrieve from agent trajectories as a new training paradigm, where supervision is derived from multi-step agent interactions.<n>Our results highlight agent trajectories as a practical and scalable supervision source, pointing to a promising direction for retrieval in the era of agentic search.
arXiv Detail & Related papers (2026-03-30T17:59:02Z)
See, Think, Act: Online Shopper Behavior Simulation with VLM Agents [58.92444959954643]
This paper investigates the integration of visual information, specifically webpage screenshots, into behavior simulation via VLMs.<n>We employ SFT for joint action prediction and rationale generation, conditioning on the full interaction context.<n>To further enhance reasoning capabilities, we integrate RL with a hierarchical reward structure, scaled by a difficulty-aware factor.
arXiv Detail & Related papers (2025-10-22T05:07:14Z)
Beyond Pipelines: A Survey of the Paradigm Shift toward Model-Native Agentic AI [27.209787026732972]
The rapid evolution of agentic AI marks a new phase in artificial intelligence.<n>This survey traces the paradigm shift in building agentic AI.<n>It examines how each capability has evolved from externally scripted modules to end-to-end learned behaviors.
arXiv Detail & Related papers (2025-10-19T05:23:43Z)
RAGCap-Bench: Benchmarking Capabilities of LLMs in Agentic Retrieval Augmented Generation Systems [31.4909149697414]
Retrieval-Augmented Generation (RAG) mitigates key limitations of Large Language Models (LLMs)<n>Recent work extends this paradigm through agentic RAG systems, where LLMs act as agents to iteratively plan, retrieve, and reason over complex queries.<n>We propose RAGCap-Bench, a capability-oriented benchmark for fine-grained evaluation of intermediate tasks in agentic RAG.
arXiv Detail & Related papers (2025-10-15T04:13:00Z)
A Survey on Agentic Multimodal Large Language Models [84.18778056010629]
We present a comprehensive survey on Agentic Multimodal Large Language Models (Agentic MLLMs)<n>We explore the emerging paradigm of agentic MLLMs, delineating their conceptual foundations and distinguishing characteristics from conventional MLLM-based agents.<n>To further accelerate research in this area for the community, we compile open-source training frameworks, training and evaluation datasets for developing agentic MLLMs.
arXiv Detail & Related papers (2025-10-13T04:07:01Z)
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey [103.32591749156416]
The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL)<n>This survey formalizes this conceptual shift by contrasting the degenerate single-step Markov Decision Processes (MDPs) of LLM-RL with the temporally extended, partially observable Markov decision processes (POMDPs) that define Agentic RL.
arXiv Detail & Related papers (2025-09-02T17:46:26Z)
Agentic Reinforced Policy Optimization [66.96989268893932]
Large-scale reinforcement learning with verifiable rewards (RLVR) has demonstrated its effectiveness in harnessing the potential of large language models (LLMs) for single-turn reasoning tasks.<n>Current RL algorithms inadequately balance the models' intrinsic long-horizon reasoning capabilities and their proficiency in multi-turn tool interactions.<n>We propose Agentic Reinforced Policy Optimization (ARPO), a novel agentic RL algorithm tailored for training multi-turn LLM-based agents.
arXiv Detail & Related papers (2025-07-26T07:53:11Z)
Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance [1.2749527861829046]
Our framework integrates Knowledge Graphs (KGs) and Large Language Model (LLM)-based agents.<n>It transforms raw DES data into a semantically rich KG, capturing relationships between simulation events and entities.<n>An LLM-based agent uses iterative reasoning, generating interdependent sub-questions. For each sub-question, it creates Cypher queries for KG interaction, extracts information, and self-reflects to correct errors.
arXiv Detail & Related papers (2025-07-23T07:18:55Z)
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)
AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction [0.3749861135832073]
This paper introduces LUCID-MA, an innovative AI powered framework where multiple AI agents collaboratively analyze and understand crime data.<n>Our system that consists of three core components: an analysis assistant highlights crime patterns; a feedback component that reviews and refines analytical results; and a prediction component that forecasts future crime trends.<n>A scoring function is incorporated to evaluate agent performance, providing visual plots to track learning progress.
arXiv Detail & Related papers (2025-06-13T05:39:28Z)
Multi-Agent Collaboration via Evolving Orchestration [61.93162413517026]
Large language models (LLMs) have achieved remarkable results across diverse downstream tasks, but their monolithic nature restricts scalability and efficiency in complex problem-solving.<n>We propose a puppeteer-style paradigm for LLM-based multi-agent collaboration, where a central orchestrator dynamically directs agents in response to evolving task states.<n> Experiments on closed- and open-domain scenarios show that this method achieves superior performance with reduced computational costs.
arXiv Detail & Related papers (2025-05-26T07:02:17Z)
Large Language Model Agent: A Survey on Methodology, Applications and Challenges [88.3032929492409]
Large Language Model (LLM) agents, with goal-driven behaviors and dynamic adaptation capabilities, potentially represent a critical pathway toward artificial general intelligence. This survey systematically deconstructs LLM agent systems through a methodology-centered taxonomy. Our work provides a unified architectural perspective, examining how agents are constructed, how they collaborate, and how they evolve over time.
arXiv Detail & Related papers (2025-03-27T12:50:17Z)
Research on the Application of Spark Streaming Real-Time Data Analysis System and large language model Intelligent Agents [1.4582633500696451]
This study explores the integration of Agent AI with LangGraph to enhance real-time data analysis systems in big data environments. The proposed framework overcomes limitations of static, inefficient stateful computations, and lack of human intervention. System architecture incorporates Apache Spark Streaming, Kafka, and LangGraph to create a high-performance sentiment analysis system.
arXiv Detail & Related papers (2024-12-10T05:51:11Z)
Understanding Ranking LLMs: A Mechanistic Analysis for Information Retrieval [20.353393773305672]
We employ a probing-based analysis to examine neuron activations in ranking LLMs. Our study spans a broad range of feature categories, including lexical signals, document structure, query-document interactions, and complex semantic representations. Our findings offer crucial insights for developing more transparent and reliable retrieval systems.
arXiv Detail & Related papers (2024-10-24T08:20:10Z)
Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [117.94654815220404]
G"odel Agent is a self-evolving framework inspired by the G"odel machine. G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
arXiv Detail & Related papers (2024-10-06T10:49:40Z)
Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation [49.27250832754313]
We present AgentCOT, a llm-based autonomous agent framework. At each step, AgentCOT selects an action and executes it to yield an intermediate result with supporting evidence. We introduce two new strategies to enhance the performance of AgentCOT.
arXiv Detail & Related papers (2024-09-19T02:20:06Z)
RAG-Modulo: Solving Sequential Tasks using Experience, Critics, and Language Models [5.0741409008225755]
Large language models (LLMs) have emerged as promising tools for solving challenging robotic tasks. Most existing LLM-based agents lack the ability to retain and learn from past interactions. We propose RAG-Modulo, a framework that enhances LLM-based agents with a memory of past interactions and incorporates critics to evaluate the agents' decisions.
arXiv Detail & Related papers (2024-09-18T20:03:32Z)
GenAgent: Build Collaborative AI Systems with Automated Workflow Generation -- Case Studies on ComfyUI [64.57616646552869]
This paper explores collaborative AI systems that use to enhance performance to integrate models, data sources, and pipelines to solve complex and diverse tasks. We introduce GenAgent, an LLM-based framework that automatically generates complex, offering greater flexibility and scalability compared to monolithic models. The results demonstrate that GenAgent outperforms baseline approaches in both run-level and task-level evaluations.
arXiv Detail & Related papers (2024-09-02T17:44:10Z)
Scaling Large Language Model-based Multi-Agent Collaboration [72.8998796426346]
Recent breakthroughs in large language model-driven autonomous agents have revealed that multi-agent collaboration often surpasses each individual through collective reasoning. This study explores whether the continuous addition of collaborative agents can yield similar benefits.
arXiv Detail & Related papers (2024-06-11T11:02:04Z)
Rethinking ChatGPT's Success: Usability and Cognitive Behaviors Enabled by Auto-regressive LLMs' Prompting [5.344199202349884]
We analyze the structure of modalities within both two types of Large Language Models and six task-specific channels during deployment. We examine the stimulation of diverse cognitive behaviors in LLMs through the adoption of free-form text and verbal contexts.
arXiv Detail & Related papers (2024-05-17T00:19:41Z)
Understanding Large Language Model Behaviors through Interactive Counterfactual Generation and Analysis [22.755345889167934]
We present an interactive visualization system that enables exploration of large language models (LLMs) through counterfactual analysis.<n>Our system features a novel algorithm that generates fluent and semantically meaningful counterfactuals.<n>A user study with LLM practitioners and interviews with experts demonstrate the system's usability and effectiveness.
arXiv Detail & Related papers (2024-04-23T19:57:03Z)
KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents [54.09074527006576]
Large Language Models (LLMs) have demonstrated great potential in complex reasoning tasks, yet they fall short when tackling more sophisticated challenges. This inadequacy primarily stems from the lack of built-in action knowledge in language agents. We introduce KnowAgent, a novel approach designed to enhance the planning capabilities of LLMs by incorporating explicit action knowledge.
arXiv Detail & Related papers (2024-03-05T16:39:12Z)
AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems [112.76941157194544]
We propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering. We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimize both kinds of agents together. Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions.
arXiv Detail & Related papers (2023-10-13T16:37:14Z)
A Framework for Understanding and Visualizing Strategies of RL Agents [0.0]
We present a framework for learning comprehensible models of sequential decision tasks in which agent strategies are characterized using temporal logic formulas. We evaluate our framework on combat scenarios in StarCraft II (SC2) using traces from a handcrafted expert policy and a trained reinforcement learning agent.
arXiv Detail & Related papers (2022-08-17T21:58:19Z)
Learning Causal Models of Autonomous Agents using Interventions [11.351235628684252]
We extend the analysis of an agent assessment module that lets an AI system execute high-level instruction sequences in simulators. We show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable causal model of the system.
arXiv Detail & Related papers (2021-08-21T21:33:26Z)
Multi-Agent Imitation Learning with Copulas [102.27052968901894]
Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
arXiv Detail & Related papers (2021-07-10T03:49:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.