1. Bibliographic Information

1.1. Title

The central topic of the paper is VitaBench: Benchmarking LLM Agents with Versatile Interactive Tasks in Real-world Applications. It focuses on creating a new benchmark to evaluate the capabilities of Large Language Model (LLM)-based agents in complex, real-world interactive scenarios.

1.2. Authors

The paper is authored by the Meituan LongCat Team. While a full author list is mentioned as being in "Contributions," the core contact authors specified are Wei He (Fudan University, intern at Meituan LongCat Team) and Qi Gu (Meituan). The team structure suggests a collaborative effort from a corporate research group, likely focusing on applications relevant to Meituan's services (food delivery, in-store consumption, online travel).

1.3. Journal/Conference

The paper is published at arXiv, a preprint server. While not a peer-reviewed journal or conference in its current state, arXiv is a widely used platform for disseminating research in fields like AI and computer science. The presence of a $v2$ in the PDF link indicates it has undergone at least one revision since its initial submission.

1.4. Publication Year

The paper was published at (UTC): 2025-09-30T16:33:49.000Z, indicating a future publication date. This suggests it is a preprint that will be officially released or is planned for release around that time.

1.5. Abstract

The paper introduces VitaBench, a novel and challenging benchmark designed to evaluate LLM-based agents in versatile interactive tasks grounded in real-world settings. It addresses the limitations of existing benchmarks which fail to capture the complexity of handling extensive information, leveraging diverse resources, and managing dynamic user interactions. VitaBench simulates real-world scenarios from food delivery, in-store consumption, and online travel services, incorporating 66 tools. A framework that eliminates domain-specific policies allows for flexible composition of scenarios and tools, yielding 100 cross-scenario tasks and 300 single-scenario tasks. Each task is derived from real user requests and demands agents to reason across temporal and spatial dimensions, utilize complex tool sets, proactively clarify ambiguous instructions, and track shifting user intent in multi-turn conversations. The authors propose a rubric-based sliding window evaluator for robust assessment of diverse solution pathways. Comprehensive evaluation reveals that even advanced models achieve only a 30% success rate on cross-scenario tasks and less than 50% on others, highlighting significant challenges. The authors believe VitaBench will accelerate the development of AI agents for practical real-world applications.

1.6. Original Source Link

The original source link is: https://arxiv.org/abs/2509.26490. The PDF link is: https://arxiv.org/pdf/2509.26490v2.pdf. This is a preprint published on arXiv.

2. Executive Summary

2.1. Background & Motivation

The core problem VitaBench aims to solve is the inadequate evaluation of LLM-based agents in complex, real-world interactive scenarios by existing benchmarks. As LLM-based agents are increasingly deployed in real-life applications, the benchmarks used to assess their capabilities often fall short in reflecting the true complexity of these environments.

The problem is important because current benchmarks frequently:

• Fail to capture inherent complexity: They often simplify the challenges of handling extensive information, leveraging diverse resources, and managing dynamic user interactions.

• Overemphasize predefined policies: Many benchmarks rely on domain-specific policies and constrained action spaces, which limit an agent's autonomous exploration and decision-making capabilities in open-ended environments.

• Inadequately consider user-centric aspects: They often overlook the complexities arising from diverse user behavioral attributes and conversational patterns throughout multi-turn interactions.

  The paper's entry point or innovative idea is to define task complexity along three crucial dimensions—reasoning, tool usage, and interaction—and then construct a benchmark, VitaBench, that rigorously tests agents across these dimensions using versatile interactive tasks inspired by real-world applications like food delivery, in-store consumption, and online travel services. By modeling tools as interconnected graphs and implementing a sophisticated user simulator and rubric-based evaluator, VitaBench aims to provide a more realistic and challenging assessment environment.

2.2. Main Contributions / Findings

The paper makes several primary contributions and findings:

• Novel Benchmark Design (VitaBench): Introduction of VitaBench, the most complex life-serving simulation environment to date, featuring 66 tools across three real-world domains (food delivery, in-store consumption, online travel). It moves beyond domain-specific policies by modeling inter-tool dependencies as a directed graph, enabling flexible composition of scenarios.
• Comprehensive Task Generation: VitaBench includes 100 cross-scenario tasks and 300 single-scenario tasks, derived from real user requests. These tasks require agents to handle temporal and spatial reasoning, complex tool sets, proactive clarification of ambiguous instructions, and tracking shifting user intent in multi-turn conversations.
• Sophisticated Evaluation Framework: Proposal of a rubric-based sliding window evaluator that robustly assesses diverse solution pathways in complex and stochastic environments. This evaluator is designed to handle the nuances of multi-step, interactive tasks.
• Three-Dimensional Task Complexity Framework: Formalization of task complexity for agents in real-world applications along three dimensions: reasoning complexity ($\mathcal{C}_{reason}$), tool complexity ($\mathcal{C}_{tool}$), and interaction complexity ($\mathcal{C}_{interact}$). This framework provides systematic guidance for benchmark design and evaluation.
• Empirical Evaluation and Insights: Comprehensive evaluation of advanced LLMs on VitaBench reveals significant limitations:

  • Even the best models achieve only a 30.0% success rate on cross-scenario tasks and less than 50% on single-scenario tasks, underscoring the challenge of real-world scenarios.

  • Reasoning errors dominate (61.8%), followed by tool usage errors (21.1%) and interaction management failures (7.9%).

  • Agents exhibit poor self-awareness and limited error recovery capabilities.

  • Thinking mechanisms (e.g., chain-of-thought) generally improve both effectiveness and efficiency.

  • The benchmark also highlights stability issues with models, as indicated by $Pass^k$ metrics.

    These findings solve the problem of accurately measuring LLM agent capabilities in complex, dynamic, and user-centric environments, providing clear directions for future research and development in AI agents.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand VitaBench and its contributions, a reader needs familiarity with several core concepts in AI agents and Large Language Models (LLMs):

• Large Language Models (LLMs): These are advanced neural network models trained on massive datasets of text and code, capable of understanding, generating, and processing human language. They form the "brain" of the LLM agents. Examples include GPT-4, Claude, Gemini.
• LLM Agents: An LLM agent is an LLM augmented with additional capabilities, allowing it to interact with an environment, use tools, plan, and execute actions to achieve a goal. Unlike a simple LLM that just generates text, an agent can observe, think, and act.
• Benchmarking: In machine learning, a benchmark is a standardized set of tasks and metrics used to evaluate and compare the performance of different models or systems. VitaBench is specifically a benchmark for LLM agents.
• Partially Observable Markov Decision Process (POMDP): This is a mathematical framework for modeling decision-making in environments where the state is not fully observable to the agent.
  • Markov Decision Process (MDP): A discrete-time stochastic control process where decisions are made. It consists of a set of states, a set of actions, a transition function (probability of moving to a new state given current state and action), and a reward function. The "Markov" property means that the future state depends only on the current state and action, not on the entire history.
  • Partial Observability: In a POMDP, the agent does not directly observe the true state of the environment. Instead, it receives observations that are probabilistically related to the underlying state. The agent must maintain a belief distribution over the possible states and make decisions based on this belief.
• Tool Use (Tool-Augmented LLMs): This refers to the ability of LLMs to invoke external functions, APIs, or databases to gather information or perform actions that go beyond their intrinsic text generation capabilities. Tools allow agents to interact with the real world, perform calculations, search the web, or manage applications.
• Multi-turn Conversation/Dialogue: An interaction between a user and an agent that spans multiple exchanges, where the agent needs to maintain context, track user intent, and respond coherently over time.
• User Simulation: The process of creating an artificial user that interacts with an agent, mimicking human behavior, preferences, and conversational patterns. This is crucial for evaluating agents in controlled environments without needing real human users for every test.
• Rubric-based Evaluation: A method of assessment where performance is judged against a set of predefined criteria (a rubric), often with different levels of achievement. In VitaBench, this is used to track an agent's progress towards task completion.
• Cross-scenario Tasks: Tasks that require an agent to integrate information and perform actions across multiple distinct domains or types of services (e.g., ordering food and booking travel). This is more complex than single-scenario tasks that stay within one domain.
• Reasoning Errors: Failures where an LLM agent makes incorrect logical deductions, misinterprets instructions, or fails to synthesize information correctly, leading to wrong decisions or actions.
• Tool Usage Errors: Failures where an LLM agent either chooses the wrong tool, provides incorrect parameters to a tool, or fails to recover from a tool invocation error.
• Interaction Management Errors: Failures related to handling the conversational flow, such as not clarifying ambiguous instructions, losing track of user intent, or responding inappropriately in a multi-turn dialogue.
• Thinking Mechanisms (e.g., Chain-of-Thought): Strategies used to prompt LLMs to perform explicit reasoning steps before generating an answer or action. This often involves asking the LLM to "think step by step" or generate a "plan," which can improve performance on complex tasks.

3.2. Previous Works

The paper contextualizes VitaBench by comparing it against prominent existing agent-user interaction benchmarks. These benchmarks typically aim to evaluate various aspects of LLM agents, but VitaBench argues they do not simultaneously challenge agents across all three dimensions of complexity (reasoning, tool, interaction).

Here's a summary of the related benchmarks and their characteristics as presented in the paper's Table 1:

• ToolTalk [Farn and Shin, 2023]: Focuses on tool usage in a conversational setting.

• IN3 [Qian et al., 2024]: Emphasizes implicit user intention understanding.

• MINT [Wang et al., 2024a]: Focuses on multi-turn interactions with tool and language feedback.

• ToolSandbox [Lu et al., 2025]: A stateful, conversational, interactive evaluation benchmark for LLM tool use capabilities.

• DialogTool [Wang et al., 2025]: Explores role-playing users but is critiqued for not enabling agents to proactively seek missing information.

• UserBench [Qian et al., 2025]: Emphasizes user-centric agents and proactively seeking missing information.

• $\tau$-Bench [Yao et al., 2024]: A benchmark for tool-agent-user interaction in real-world domains. VitaBench builds on its ideas but aims to overcome limitations like reliance on predefined policy documents.

• $\tau^2$-Bench [Barres et al., 2025]: An evolution of $\tau$-Bench, evaluating conversational agents in a dual-control environment.

  The paper provides a comparative table to illustrate the gaps VitaBench fills. The traits used for comparison are:

• Reasoning Complexity:

  • Multifaceted Composite Information: Ability to integrate diverse information (temporal, spatial, common-sense).
  • Goal Objective Ambiguity: Agent's need to clarify ambiguous user goals.

• Tool Complexity:

  • # Tools: Number of available tools.
  • Inter-tool Dependency Scenarios: Presence of dependencies between tools.
  • Cross-scenario: Tasks spanning multiple domains.

• Interaction Complexity:

  • # Turns approx.: Approximate number of conversational turns.

  • User Profile Attributes: Modeling diverse user characteristics.

  • User Behavior: Dynamic user states and behavioral patterns.

    The following are the results from Table 1 of the original paper:

    Benchmark	Reasoning Complexity			Tool Complexity			Interaction Complexity
    	Multifaceted Composite Information	Goal Objective Ambiguity	# Tools	Inter-tool Dependency Scenarios	Cross	# Turns approx.	User Profile Attributes	User Behavior
    	ToolTalk [Farn and Shin, 2023]	X	X	28		X	[2, 10]	X	X
    IN3 [Qian et al., 2024]	X		0	-	-	[2, 10]	×
    MINT [Wang et al., 2024a]	X		8	X	X	[2, 10]
    ToolSandbox [Lu et al., 2025]			34	✓	×	[10, 30]
    DialogTool [Wang et al., 2025]	X	xxxxxxx	31		✓	[10, 30]	xx×>
    UserBench [Qian et al., 2025]			5	X	X	[10, 30]		xxxxx
    τ-Bench [Yao et al., 2024]	}	xxx	28		X	[30, 50]		X
    τ2-Bench [Barres et al., 2025]			X	38		X	[30, 80]
    VitaBench (ours)	✓	✓	66	✓	✓	[50, 100]	✓	√

Legend for Table:

• ✓: Fully addressed
• X: Partially addressed
• · · ·: Not addressed

3.3. Technological Evolution

The field of LLM agents has rapidly evolved from basic LLM capabilities to sophisticated systems that can interact with complex environments. Initially, LLMs focused on text generation and understanding. The evolution then moved towards tool augmentation, where LLMs were given access to external APIs to retrieve real-time information or perform actions. Early tool use benchmarks, like ToolTalk, focused on agents using a limited set of tools within predefined dialogue trajectories.

Subsequent advancements introduced the need for multi-turn interaction (e.g., MINT, DialogTool), allowing agents to engage in more natural conversations and handle evolving user needs. The recognition of user-centric aspects led to benchmarks like UserBench, which emphasize proactive information seeking and handling ambiguous instructions. Simultaneously, the complexity of the simulated environments increased, leading to frameworks like ToolSandbox and the

\tau`-Bench` family, which provide larger toolsets and more realistic domain simulations.

`VitaBench` fits into this evolution by pushing the boundaries of all three complexity dimensions simultaneously. It acknowledges the progress in tool use and interaction but identifies a gap in fully integrating these with complex `reasoning` and `cross-domain capabilities` in a truly realistic, user-driven setting. It builds upon the idea of `multi-turn, tool-augmented agents` in `real-world domains` (like $\tau$-Bench) but significantly expands the tool complexity, interaction nuances (user profiles, dynamic behavior), and reasoning demands (multifaceted composite information, goal ambiguity) to create a more holistic and challenging evaluation.

## 3.4. Differentiation Analysis
Compared to the main methods in related work, `VitaBench` introduces several core innovations and differentiators:

*   **Holistic Complexity:** Unlike previous benchmarks that often focus on one or two aspects of complexity, `VitaBench` simultaneously challenges agents across `reasoning`, `tool use`, and `interaction` dimensions. This is evident from Table 1, where `VitaBench` is marked with '✓' across all traits, a distinction not shared by any other benchmark.
*   **Expanded Tool Ecosystem:** `VitaBench` presents the largest toolset to date (66 tools) within a single benchmark, significantly more than its predecessors (e.g., `ToolSandbox` with 34,

\tau^2-Bench with 38). This large and interconnected toolset increases tool complexity and necessitates advanced planning.

• Elimination of Domain-Specific Policies: VitaBench models inter-tool dependencies using a directed graph with pre-conditions and post-conditions instead of relying on explicit policy documents. This design encourages more autonomous exploration and reasoning from agents, pushing them beyond rote adherence to rules.

• Robust Cross-Scenario Tasks: VitaBench introduces cross-scenario tasks that require agents to seamlessly navigate and coordinate actions across multiple distinct real-world domains (delivery, in-store, travel). This ability to switch contexts and integrate services is a key challenge not fully addressed by others.

• Dynamic User Simulation: It features a sophisticated user simulator that captures diverse behavioral attributes and conversational patterns, including dynamic states that evolve throughout the interaction. This forces agents to proactively clarify ambiguous instructions and track shifting user intent, reflecting realistic human-agent interaction.

• Rubric-based Sliding Window Evaluation: The proposed rubric-based sliding window evaluator is a key innovation for robustly assessing diverse solution pathways in complex and stochastic environments. This moves beyond simpler database state comparisons by allowing evaluation of intermediate steps and complex behaviors like recommendations.

  In essence, VitaBench differentiates itself by creating a benchmark that not only scales up the number of tools and turns but also deeply integrates multi-domain reasoning, dynamic user interaction, and flexible tool orchestration in a way that closely mirrors the challenges of real-world AI agent deployments.

4. Methodology

4.1. Principles

The core idea behind VitaBench is to create a benchmark that mirrors the complexity of real-world LLM agent applications by focusing on versatile interactive tasks. The theoretical basis is rooted in the Partially Observable Markov Decision Process (POMDP) framework, which naturally models agent decision-making under uncertainty and incomplete information, a hallmark of real-world interactions. The intuition is that for LLM agents to be truly effective in practical settings, they must be able to reason, use tools, and interact with users in a highly integrated and adaptive manner, without being constrained by oversimplified environments or explicit domain-specific policies. VitaBench quantifies task complexity along three dimensions: reasoning, tool, and interaction, providing a structured approach to benchmark design and evaluation.

4.2. Core Methodology In-depth (Layer by Layer)

4.2.1. The POMDP Formalism

The paper formalizes the agent task using a Partially Observable Markov Decision Process (POMDP). This framework is chosen because it accurately represents situations where an agent needs to make decisions in an environment whose state is not fully known or observable.

For a specific environment $e \in \mathcal{E}$, where $\mathcal{E}$ is the set of distinct environments, the POMDP is defined as a tuple: $ ( \mathcal{U}, \bar{S}, \mathcal{A}, \mathcal{O}, \mathcal{T}, r )_e $

Let's break down each component for a beginner:

• $\mathcal{U}$: This is the instruction space. It represents the set of all possible initial goals or requests given to the agent by the user. For instance, "book a flight" or "order food."
• $\bar{S}$: This is the state space. It encompasses all possible configurations of the environment. In VitaBench, the state is composed of two main parts:
  • $\mathcal{S}_{\mathrm{db}}$: The state of the databases (e.g., current inventory, user accounts, order statuses).
  • $\mathcal{S}_{\mathrm{user}}$: The user state, which includes the user's preferences, current emotional state, and any implicit or explicit information they have provided. So, the overall state space is a combination of these: $\mathcal{S} = \mathcal{S}_{\mathrm{db}} \otimes \mathcal{S}_{\mathrm{user}}$. The $\otimes$ symbol here denotes a composition or Cartesian product, meaning the full state is a combination of database and user states.
• $\mathcal{A}$: This is the action space. It defines all possible actions the agent can take. In VitaBench, actions are categorized into two types:
  • Tool invocation: Calling API tools to interact with databases or external services.
  • Interactive dialogue: Generating conversational responses to interact with the simulated user.
• $\mathcal{O}$: This is the observation space. Since it's a POMDP, the agent doesn't see the full state $\mathcal{S}$, but receives observations. These observations are the agent's perception of the environment. In VitaBench, observations include:
  • $\mathcal{O}_{\mathrm{db}}$: Feedback received after tool calls (e.g., results from a database query).
  • $\mathcal{O}_{\mathrm{user}}$: The conversation history with the user (e.g., user's last message). So, the observation space is $\mathcal{O} = \mathcal{O}_{\mathrm{db}} \otimes \mathcal{O}_{\mathrm{user}}$.
• $\mathcal{T}$: This is the state transition function. It describes how the environment's state changes based on the agent's actions.
  • API calls (tool invocations) are modeled with deterministic transitions $\mathcal{T}_{\mathrm{db}}$, meaning if an agent calls an API with certain parameters, the database state changes predictably. These are implemented as Python functions.
  • User interactions involve stochastic transitions $\mathcal{T}_{\mathrm{user}}$, meaning the user's state might change unpredictably (e.g., they might become impatient) or their response might vary, even to the same query. These are implemented using a language model as a user simulator.
• $r$: This is the reward function. It assigns a numerical value (reward) to the agent's actions and state transitions, indicating how well the agent is performing. For VitaBench, the reward $r(e, u, \tau) \in [0, 1]$ is computed after the entire interaction ends, reflecting the success of the task.

Agent Interaction Flow: Given an initial instruction $u \in \mathcal{U}$, the agent starts in an initial state $s_0$. This $s_0$ includes the prompt (initial instruction) and the initial state of the databases. The agent receives an initial observation $o_0$, which typically includes the first user request and the available tools.

The LLM-based agent, parameterized by $\theta$ (representing the model's learned parameters), then generates an action $a_1$ based on its policy $\pi_\theta$: $ a_1 \sim \pi_\theta ( \cdot | o_0 ) $ This means the agent's first action is sampled from its policy given the initial observation.

After this action, the environment transitions to a new state $s_1$, and the agent receives a new observation $o_1$. This process continues iteratively. At each step $t$, the agent uses the history of observations and actions up to t-1 to generate its next action $a_t$: $ a_t \sim \pi_\theta ( \cdot | o_0, a_1, o_1, \dots, a_{t-1}, o_{t-1} ) $ The agent continues interacting until the task is completed, generating a trajectory $\tau$: $ \tau = ( s_0, a_1, s_1, a_2, s_2, \ldots, a_T, s_T ) \sim \pi_\theta ( \tau | e, u ) $ Here, $T$ is the total number of interaction rounds. It's important to note that the trajectory $\tau$ captures the complete state transitions, but the agent only has access to the partial observations $o_t$ derived from these states $s_t$. Finally, the reward $r(e, u, \tau)$ is calculated based on the completed trajectory.

4.2.2. Agentic Task Complexity Framework

Building on the POMDP formalism, VitaBench formalizes task complexity along three dimensions: reasoning, tool, and interaction. This framework helps in systematically designing and evaluating benchmarks.

The overall task complexity $\mathcal{C}_{task}$ is defined as: $ \mathcal{C}{\mathrm{task}} = \langle \mathcal{C}{\mathrm{reason}}, \mathcal{C}{\mathrm{tool}}, \mathcal{C}{\mathrm{interact}} \rangle $ where each component represents a different facet of complexity.

• Reasoning Complexity ($\mathcal{C}_{\mathrm{reason}}$): This quantifies the cognitive demands placed on the agent to process extensive environmental information under partial observability. It's about how much "thinking" the agent needs to do. It is characterized by:

  • Entropy of the observation space $H(\mathcal{O})$: A higher entropy means more uncertainty or variability in what the agent observes, requiring more complex inference.
  • Degree of partial observability $\eta = 1 - \frac{|\mathcal{O}|}{|\mathcal{S}|}$: This measures how much of the true state $\mathcal{S}$ is hidden from the agent (not directly reflected in observations $\mathcal{O}$). A value closer to 1 indicates higher partial observability, meaning the agent has to infer more about the hidden state.
  • Number of reasoning points: These are specific instances in a task where the agent needs to perform complex inference or connect disparate pieces of information.

• Tool Complexity ($\mathcal{C}_{\mathrm{tool}}$): This captures the structural intricacy of navigating the tool-augmented action spaces. It's about how difficult it is to use the available tools effectively.

  • Tools are modeled as a directed graph $G = (V, E)$.
    • $V$: Vertices (nodes) representing individual tools.
    • $E$: Edges representing inter-tool dependencies. An edge from tool A to tool B means tool A must be executed before tool B, or its output is a prerequisite for tool B.
  • Graph density: This measures how interconnected the tools are. A denser graph means more dependencies and a more complex planning problem.
  • Coverage ratio of task-relevant subgraph: For any given task, only a subset of all available tools might be relevant. The complexity increases with the size and intricacy of this relevant subgraph.
  • Cross-scenario settings: These further amplify tool complexity by expanding the action space $\mathcal{A}$ across multiple domains (e.g., needing tools from both food delivery and travel services).

• Interaction Complexity ($\mathcal{C}_{\mathrm{interact}}$): This reflects the challenges of managing dynamic multi-turn conversations with users. It's about how difficult it is to interact effectively with the human user.

  • Uncertainty of user intent $H(q_t|q_{t-1})$: This measures how unpredictable the user's next query $q_t$ is, given their previous query $q_{t-1}$. High uncertainty means the agent needs to be more flexible and adaptive.

  • Dynamic user states $\mathcal{S}_{\mathrm{user}}$: Real-world users have states (e.g., preferences, patience, emotional status) that evolve throughout the interaction. The agent must continuously adapt its strategy based on these shifting states.

  • Number of turns: More turns imply longer context windows and greater potential for shifts in user intent or ambiguity.

  • User profile attributes: Diverse user personas (e.g., cold, dependent, logical) introduce varied communication styles and expectations, requiring the agent to adapt its dialogue strategy.

  • User behavioral patterns: Users might exhibit specific patterns like impatience if instructions are repeated, or a reduced willingness to respond if an agent provides irrelevant information.

    These dimensions collectively define task complexity in VitaBench, guiding the design of tasks and providing a framework for analyzing agent failures.

4.2.3. Benchmark Construction

The construction of VitaBench is divided into two stages: Framework Design and Task Generation.

4.2.3.1. Stage I: Framework Design

This stage involves building the foundational components of the benchmark.

• Abstraction of Real-World Services: The authors systematically abstract real-world life-serving applications (food delivery, in-store consumption, online travel services) into a set of API tools and underlying databases. This means they identify the essential functionalities and data structures required for these services.
• Tool Modeling and Dependencies:
  • Tools are modeled as a directed graph $G=(V,E)$, where $V$ are tools and $E$ are dependencies.
  • Each tool's description is augmented with pre-conditions and post-conditions:
    • Pre-conditions: States or information required before a tool can be executed (e.g., modify_order requires the order_id obtained from get_order_detail).
    • Post-conditions: The changes or outcomes after a tool's execution.
  • This "contract-based" encoding of domain rules directly into the tool structure eliminates the need for verbose policy documents, which traditionally constrain agent autonomy. This also increases reasoning complexity because the agent must infer the correct sequence of tool calls based on their pre/post-conditions.
• User Simulator Implementation:

  • To capture the inherent uncertainty of real-world interactions, a user simulator is implemented, following principles from Yao et al. [2024].

  • This simulator is designed to mimic realistic user behavior by applying prompt-based constraints to maintain persona consistency and behavioral attributes.

  • Crucially, the simulator's knowledge boundaries reflect realistic scenarios; for example, the agent cannot directly access dietary restrictions but must infer them from order history or user responses, contributing to partial observability.

    The following figure (Figure 3 from the original paper) shows the overall construction pipeline:

    该图像是图3，是VitaBench构建流程的示意图，展示了工具定义、数据库、任务环境信息、用户画像及评分标准等组件如何结合形成复杂的跨场景交互任务。

4.2.3.2. Stage II: Task Generation

This stage focuses on creating the actual tasks for the benchmark.

• Task Generation Components: The tasks are generated by combining user profiles, initial user instructions, environmental information, and evaluation rubrics.
• User Profiles:
  • Derived from authentic platform data, anonymized and enriched.
  • Create distinct personas with varied personal attributes (e.g., cold, dependent, logical) and communication styles.
  • These attributes influence conversational dynamics, requiring adaptive agent behavior.
• Task Instructions:
  • Synthesized from multiple real user requests to form composite objectives.
  • Manually reviewed and refined to ensure clarity and domain coverage.
  • Include cross-scenario settings where agents must navigate between distinct contexts (e.g., booking a restaurant and a train ticket in the same conversation).
• Environmental Information:
  • Combines service provider and product information from real-world life-serving platforms with model-generated entities and user preferences.
  • This ensures a rich and extensive dataset for agents to interact with.
• Rubrics:
  • Comprehensive evaluation rubrics are created for each task.
  • These rubrics capture multiple solution pathways and intermediate evaluation points, allowing for nuanced assessment.
• Data Statistics: The benchmark includes:

  • Databases: Containing data for service providers, products, and transactions.

  • API Tools: 66 tools in total, categorized into write, read, and general operations.

  • Tasks: 100 cross-scenario and 300 single-scenario tasks (100 for each domain: Delivery, In-store, OTA).

    The following are the results from Table 2 of the original paper:

    	Cross-Scen.	Delivery	In-store	OTA
    Databases
    Service Providers	1, 324	410	611	1, 437
    Products	6, 946	788	3, 277	9, 693
    Transactions	447	48	28	154
    API Tools	66	20	24	38
    Write	27	4	9	14
    Read	33	10	10	19
    General	6	6	5	5
    Tasks	100	100	100	100

4.2.4. Rubric-based Sliding Window Evaluator

Evaluating agent performance in VitaBench is challenging due to the extensive number of solution paths and stochastic interactions. The authors propose a rubric-based sliding window evaluator to address this.

• Manual Rubric Design: For each task, rubrics $\mathcal{R} = \{r_1, \ldots, r_k\}$ are manually designed. These are atomic criteria derived from the task information (e.g., "restaurant within 500m", "user only eats vegetarian food").

• Sliding Window Mechanism:

  • Each agent trajectory (the sequence of interactions) is divided into overlapping windows $W_i$.
  • Each window $W_i$ consists of $w$ consecutive turns (e.g., 10 turns).
  • Adjacent windows share $\delta$ turns (e.g., 2 turns) to ensure information coherence and account for context. This overlap helps the evaluator maintain context across window boundaries.

• LLM-as-a-Judge: An LLM (specifically, Claude-2.1) acts as the judge for each window. It evaluates the agent's progress within that window.

• Persistent Rubric State Tracking:

  • The evaluator maintains a state vector $\mathfrak{s} \in \{0, 1\}^k$.
  • Each element $s_j$ in the vector corresponds to a rubric item $r_j$.
  • Initially, all $s_j$ are 0 (not satisfied).
  • When an LLM-as-a-Judge processes a window $W_i$, it makes judgments for that window. If a rubric item $r_j$ is satisfied in any window, its corresponding $s_j$ in the state vector is permanently marked as 1. This ensures that once a requirement is met, it stays met, regardless of subsequent interactions.

• Task Success Condition: For the benchmark evaluation, a strict all-or-nothing criterion is adopted. A task is considered successful only if all rubric items are satisfied: $ \text{Success} = \mathbb{1} \left[ \sum_{j} s_j = k \right] $ where $\mathbb{1}[\cdot]$ is the indicator function (1 if the condition is true, 0 otherwise), and $k$ is the total number of rubric items. This means the agent must fulfill every single requirement to pass the task.

• Reliability Validation: The reliability of this method is validated against human judgments, achieving a Cohen's\kappa \geq 0.81$ (a statistical measure of inter-rater agreement), confirming its robustness.

  This rubric-based sliding window evaluator provides fine-grained supervision over intermediate transitions and accounts for the multiple valid solution paths in complex environments, which traditional database state comparisons might miss.

5. Experimental Setup

5.1. Datasets

The VitaBench benchmark itself serves as the primary dataset for the experiments. It is designed to simulate real-world scenarios from three daily applications: food delivery, in-store consumption, and online travel services (OTA).

Dataset Characteristics: The dataset comprises a rich environment with a large number of interconnected components:

• Domains:

  • Cross-Scenarios: Tasks that span across multiple domains.
  • Delivery: Tasks related to food or item delivery.
  • In-store: Tasks involving services within a physical store or restaurant (e.g., booking, ordering).
  • OTA (Online Travel Agency): Tasks related to travel services (e.g., train tickets, hotels).

• Scale of Data: The benchmark features extensive databases and toolsets as detailed in Table 2 (already presented in Section 4.2.3.2).

  • Service Providers: Thousands of entities across domains (e.g., 1,324 for cross-scenario, 410 for delivery, 611 for in-store, 1,437 for OTA).
  • Products: Tens of thousands of unique products (e.g., 6,946 for cross-scenario, 788 for delivery, 3,277 for in-store, 9,693 for OTA).
  • Transactions: Hundreds of example transactions across domains.
  • API Tools: A total of 66 API tools are available in cross-scenario settings, broken down into write (modifying data), read (retrieving data), and general (utility) operations. Single domains use subsets of these tools (e.g., 20 for Delivery, 24 for In-store, 38 for OTA).

• Tasks:

  • 100 cross-scenario tasks (main results).
  • 300 single-scenario tasks (100 for each of the three domains).

• Task Derivation: Each task is derived from multiple real user requests, which are then synthesized into composite objectives. This ensures realistic and complex task definitions.

• User Profiles: The benchmark includes anonymized and enriched user profiles derived from authentic platform data. These profiles define distinct personas with varied personal attributes and communication styles, introducing interaction complexity.

• Environmental Information: Combines real-world service provider and product information with model-generated entities and user preferences.

  The choice of these datasets and their realistic generation is crucial for validating the method's performance because they directly address the identified gaps in existing benchmarks by providing extensive information, diverse resources, and dynamic user interactions in real-world contexts.

Example of a Data Sample (from Appendix C): The paper provides a detailed example of a conversation trajectory, which implicitly defines a data sample. A user profile and an instruction for the agent form the core of a task data sample.

User Profile Example:

User ID: U010038
Profession: Blue-collar worker
Gender: Male
Age Range: 30-35
Residence: Harbin
Home Address: Room 502, Building 3, Jiangpan Jiayuan, No. 89 Dongzhi Road, Daowai District, Harbin, Heilongjiang Province
Work Address: Harbin New Area Equipment Manufacturing Industrial Park Zone C, No. 1299 Chuangxin First Road, Songbei District, Harbin
Dietary Restrictions: Avoid high purine foods (organ meats/seafood soup), avoid fried foods
Relationship Status: Married with children
Personality: Cold and concise in expression, lacks emotional communication and patience

Instruction Example:

Instruction:
T PM, ticket for that day. She wants to sit in first class and preferably arrive in Dalian before 11 AM.
(Note: The instruction in the original paper is truncated due to a formatting error, but implies a multi-part task involving family travel, potentially booking trains, restaurants, and other services.)

The conversation trajectory (provided in Appendix C of the paper) then shows a multi-turn interaction based on this profile and instruction, involving tool calls for restaurant booking, delivery order creation, and train ticket booking, demonstrating the cross-scenario and multi-faceted nature of the tasks.

5.2. Evaluation Metrics

The paper uses three metrics to evaluate model performance, specifically for results from four runs ($k=4$).

5.2.1. Avg@k

Conceptual Definition: Avg@k represents the average success rate of an agent over $k$ independent task trials. It gives a direct measure of the model's typical performance when faced with a task multiple times.

Mathematical Formula: Let $S_i$ be an indicator variable for the success of the $i$-th trial, where $S_i = 1$ if the trial is successful and $S_i = 0$ otherwise. $ \text{Avg@k} = \frac{1}{k} \sum_{i=1}^{k} S_i $

Symbol Explanation:

• $k$: The total number of independent trials for a given task (in this paper, $k=4$).
• $S_i$: An indicator variable that is 1 if the $i$-th trial is successful, and 0 if it fails. Success is determined by the rubric-based sliding window evaluator.

5.2.2. Pass@k

Conceptual Definition: Pass@k represents the probability that at least one out of $k$ independent task trials is successful. This metric is useful for understanding the model's capability to achieve success even if it's not consistently successful, often indicating potential if multiple attempts are allowed (e.g., through rerunning or sampling).

Mathematical Formula: Assuming that each trial has an independent success probability $p$ (which can be estimated by Avg@k), the probability of at least one success in $k$ trials is: $ \text{Pass@k} = 1 - (1 - p)^k $ Where $p$ is the success probability of a single trial. In the context of this paper's reported Avg@4, if Avg@4 is used as an estimate of $p$, then the formula would be: $ \text{Pass@k} = 1 - (1 - \text{Avg@4})^k $

Symbol Explanation:

• $p$: The success probability of a single task trial. In practice, this is often estimated by the average success rate (Avg@k from multiple runs).
• $k$: The total number of independent trials (in this paper, $k=4$ for main results, and also tested for other $k$ values in analysis).

5.2.3. Pass^k

Conceptual Definition: $Pass^k$ represents the probability that all $k$ independent task trials are successful. This metric is crucial for assessing the stability and consistency of a model, as it indicates how reliably an agent can achieve success repeatedly without failure. A high $Pass^k$ implies robust performance.

Mathematical Formula: Assuming each trial has an independent success probability $p$ (estimated by Avg@k), the probability that all $k$ trials are successful is: $ \text{Pass^k} = p^k $ Again, if Avg@4 is used as an estimate of $p$: $ \text{Pass^k} = (\text{Avg@4})^k $

Symbol Explanation:

• $p$: The success probability of a single task trial, estimated by the average success rate (Avg@k).
• $k$: The total number of independent trials (in this paper, $k=4$ for main results, and also tested for other $k$ values in analysis).

5.3. Baselines

The paper conducts a comprehensive evaluation against a wide array of advanced Large Language Models (LLMs). These models represent the state-of-the-art in LLM capabilities from various major developers. The baselines are categorized into "Non-thinking Models" and "Thinking Models" to analyze the impact of explicit reasoning mechanisms.

Evaluated LLM Baselines:

• OpenAI Series:
  • 03 (likely an alias for GPT-3.5 or a similar model)
  • 04-mini (likely an alias for GPT-4o-mini or a similar model)
  • GPT-5 (a hypothetical or internal alias for a very advanced model, potentially GPT-4.1 as mentioned in the text or a future iteration)
  • GPT-4.1 (also potentially an alias for a specific GPT-4 variant)
• Anthropic Claude Series:
  • Claude-4-Sonnet
  • Claude-4.1-Opus
• Google Gemini Series:
  • Gemini-2.5-Flash
  • Gemini-2.5-Pro
• DeepSeek Series:
  • DeepSeek-V3-324
  • DeepSeek-R1-0528
  • DeepSeek-V3.1
  • DeepSeek-V3.23 (or DeepSeek-V3.2-Exp in the table)
• Qwen3 Series:
  • Qwen3-32B
  • Qwen3-235B-A22B-2507 (or Qwen3-235B-A22B-Instruct-2507 / Qwen3-235B-A22B-Thinking-2507 variants)
  • Qwen3-Max
• Other Recent Language Models:
  • Kimi2 [Bai et al., 2025] (Kimi-K2-0905 in table)
  • Doubao-Seed-1.6 (Doubao-Seed-1.6 GPT-4.1 or Doubao-Seed-1.6-Thinking variants)
  • GLM-4.5 [Zeng et al., 2025]
  • LongCat-Flash [Li et al., 2025] (LongCat-Flash-Chat or LongCat-Flash-Thinking variants)

Why these baselines are representative: These models represent a broad spectrum of the most powerful and widely recognized LLMs from leading AI research institutions and companies. They include models with varying architectures, training scales (e.g., 32B, 235B parameters mentioned for Qwen3), and capabilities, allowing for a comprehensive assessment of current LLM agent performance limitations. The inclusion of "thinking" and "non-thinking" versions for some models (e.g., Gemini-2.5-Flash, GLM-4.5, Claude-4-Sonnet) is particularly insightful, as it allows for an ablation study on the impact of explicit reasoning strategies within these LLMs when acting as agents. The paper specifically excludes small models (<32B parameters) due to the benchmark's difficulty, suggesting a focus on high-capacity models.

Experimental Setup Details:

• Agent Implementation: LLMs are provided with tool descriptions (OpenAPI schema) and allowed to decide when and how to use them.
• Interaction Limits: No explicit limit on interaction rounds for the agent model. Tasks terminate if a task is completed or if a predefined number of consecutive no-content actions (#ToP##) or failures occur.
• User Simulator: Implemented using GPT-4-0504.
• Evaluator: Implemented using Claude-2.1 to avoid over-powering with the evaluated agent models (meaning, to prevent the evaluator from having similar or superior capabilities to the agents being tested, which could bias results).
• Temperature: All results are based on a temperature of 0 for the LLM (meaning deterministic output, reducing randomness for more consistent evaluation) unless otherwise specified.

5.4. Metrics

The metrics for the main experiments are calculated based on 4 runs ($k=4$) for each task.

• Avg@4: Average success rate across 4 runs.
• Pass@4: Probability that at least one of the 4 runs is successful.
• $Pass^4$: Probability that all 4 runs are successful.

6. Results & Analysis

6.1. Core Results Analysis

The comprehensive evaluation results on VitaBench reveal significant challenges for current LLM agents, especially in cross-scenario tasks.

The following are the results from Table 3 of the original paper:

The best performance for each category and domain is in bold in the original table, but HTML tables do not support this styling directly.

Key Observations:

• Significant Challenge of Real-World Tasks: VitaBench poses a substantial challenge to current LLM agents. Even the top-performing model, 03 (high), achieves an Avg@4 of only 30.0% on cross-scenario tasks. This highlights fundamental limitations in handling the combined complexities of reasoning, tool use, and interaction across different domains.

• Performance Drop in Cross-Scenarios: There is a dramatic drop in performance when moving from single-scenario tasks to cross-scenario tasks. For example, 03 (high) achieves Avg@4 scores of 53.5% (Delivery), 53.5% (In-store), and 37.8% (OTA), but only 30.0% in cross-scenarios. This suggests that LLM agents struggle with navigating expanded action spaces and coordinating across disparate domains.

• Single-Scenario Performance: Even in single-scenario settings, no model achieves a 50% Avg@4 success rate in all domains. The In-store domain generally sees slightly higher performance than Delivery and OTA, but still below 60% for the best models. This implies that even specialized domain knowledge is not perfectly handled.

• Impact of Thinking Mechanisms: Models employing thinking mechanisms generally outperform their non-thinking counterparts. For instance, Claude-4.1-Opus (w/o thinking) achieves 21.8% Avg@4 on cross-scenarios, while its (w/ thinking) version reaches 29.0%. Similarly, GLM-4.5 improves from 20.0% to 22.8%. This indicates that explicit reasoning steps (e.g., chain-of-thought) are beneficial for complex agentic tasks.

• Variability Across Models: There's a wide range of performance across different LLMs. Some models like DeepSeek-V3-0324 and Qwen3-32B (non-thinking) perform very poorly (e.g., $3.8$ on cross-scenarios), while 03 (high) and Claude-4.1-Opus (w/ thinking) lead the pack. This suggests differences in underlying LLM capabilities significantly impact agent performance.

  The following figure (Figure 1 from the original paper) shows the overall performances on VitaBench:

  该图像是图1，展示了VitaBench基准测试中各模型在多场景和单场景任务上的整体表现，按主结果排序。柱状图使用不同颜色区分思考模型和非思考模型，条纹部分代表跨场景任务成绩，实色部分代表单场景平均成绩。

6.1.1. Exploration vs. Stability

The paper analyzes Pass@k and $Pass^k$ metrics to understand the trade-offs between exploration (multiple attempts) and stability.

• Pass@k (Increased Sampling Improves Completion): The Pass@4 results show that allowing multiple attempts substantially improves the completion rate. For example, 03 (high) achieves 30.0% Avg@4 but 61.0% Pass@4 on cross-scenarios. This means that even if a model isn't consistently successful, it has a good chance of succeeding if given a few tries.

• Pass^k (Concerning Instability): Conversely, $Pass^k$ metrics (especially $Pass^4$) reveal concerning instability. Even for the best models, $Pass^4$ scores are very low (e.g., 03 (high) achieves only 6.0% on cross-scenarios). This means it's highly unlikely for an agent to successfully complete a complex task four times in a row, indicating a lack of robustness and consistent reliability.

  The following figure (Figure 4 from the original paper) shows the comparison of Pass@k and Pass^k performance:

  该图像是图表，展示了不同模型在多尝试次数下的 Pass@k 和 Pass^k 性能对比，具体为 Claude-4-S 和 GPT-4.1 两个模型的表现，横轴为尝试次数 $k$，纵轴分别对应 Pass^k 和 Pass@k。

Figure 4 (Pass@k vs. Pass^k performance) further illustrates this. While Pass@k (represented by the upward-sloping lines) generally increases with more attempts ($k$), $Pass^k$ (represented by the downward-sloping lines) plummets rapidly, underscoring the severe stability challenges.

6.1.2. Thinking Mechanisms and Efficiency

The analysis confirms that thinking mechanisms not only improve effectiveness but also efficiency.

• Effectiveness Improvement: As noted previously, models with thinking capabilities generally show higher Avg@4 scores across scenarios compared to their non-thinking counterparts (e.g., Claude-4.1-Opus improves from 21.8% to 29.0%).

• Efficiency Improvement: Figure 5 shows that thinking models tend to achieve better performance with fewer turns on average. This suggests that explicit reasoning allows agents to formulate more effective plans, leading to more targeted user interactions and precise clarifying questions, thus reaching the goal in fewer steps.

  The following figure (Figure 5 from the original paper) shows model performance vs. Turns:

  该图像是一个展示模型性能与对话轮数关系的散点图，区分了‘思考型’(Thinking)和‘非思考型’(Non-Thinking)两类模型。图中横轴为对话轮数，纵轴为性能指标（Avg@4），不同颜色表示不同模型类别，具体模型名称标注在对应点旁。

The plot indicates that thinking models (darker points) often achieve higher Avg@4 scores at comparable or even fewer turns than some non-thinking models.

6.2. Reliability Analysis of VitaBench Components

Given that VitaBench incorporates model-based components (user simulator and evaluator), the paper conducts reliability analyses.

6.2.1. User Simulator Reliability

The user simulator is evaluated for information fidelity and persona consistency.

• Information Fidelity: Two human annotators assessed 100 conversations. The simulator achieves high fidelity (average $9.48/10$) in adhering to task instructions, user profiles, absence of hallucinations, and contextual relevance. Minor deviations were observed but did not compromise task requirements.

• Persona Consistency: Persona-behavior alignment averaged $9.34/10$ across 300 conversations with distinct personality types. Cooperative personas showed the highest consistency, aligning with LLMs' inherent collaborative tendencies. Scattered personas showed lower controllability, which is expected for more challenging behaviors.

  The following figure (Figure 6 from the original paper) shows user simulator reliability evaluation:

  该图像是论文中图6的柱状图，展示了用户模拟器在信息保真度（Info Fidelity）和人格一致性（Persona Consistency）两方面的评估结果。图中不同场景下的评分接近满分，误差条表示数据波动范围。

This chart confirms the high reliability of the user simulator in mimicking realistic user interactions.

6.2.2. Evaluator Reliability

The rubric-based sliding window evaluator is validated for its agreement with human judgments and its stability.

• Human Agreement: An ablation study (Table 4) shows that the proposed method (Baseline) achieves the highest agreement with human judgments (Cohen's\kappa = 0.828

). This is significantly higher than methods without a `rubric structure` (

\kappa < 0.07

).
*   **Sliding Window vs. Full Trajectory:** While a full trajectory evaluation with rubrics yielded similar final scores (`19%` vs. `20%`), the `evaluation model's limited long-context capability` hindered accurate assessment of all rubrics in the very long context of a full trajectory. The `sliding window design` effectively handles this by breaking down the trajectory into manageable segments, maintaining `95% task-level accuracy`.

    The following are the results from Table 4 of the original paper:

    <div class="table-wrapper"><table><thead><tr><td>Method</td><td>Score</td><td>Task Acc.</td><td>Rubric Acc.</td><td>Cohen's κ</td></tr></thead><tbody><tr><td>Baseline</td><td>20.0</td><td>95.0</td><td>88.5</td><td>0.828</td></tr><tr><td>w/o Sliding Window</td><td>19.0</td><td>90.0</td><td>87.6</td><td>0.604</td></tr><tr><td>w/o Rubric Checklist</td><td>91.0</td><td>22.0</td><td>-</td><td>0.018</td></tr><tr><td>w/o Both</td><td>82.0</td><td>32.0</td><td>-</td><td>0.067</td></tr></tbody></table></div>

*   **Explanation of `Cohen's`\kappa

:** Cohen's\kappa

is a statistic that measures inter-rater agreement for qualitative items. It is generally thought to be a more robust measure than simple percent agreement calculation, as it takes into account the possibility of the agreement occurring by chance. A `kappa` value of 0.81-1.00 indicates "almost perfect" agreement.

### 6.2.3. Statistical Stability
The paper determines the optimal number of evaluation runs for statistical precision. By analyzing the `Mean Squared Error (MSE)` of average estimates across different run counts (from 1 to 20), it's found that $k=4$ runs strike an optimal balance between precision and computational cost. Using $k=4$ reduces `MSE` by `77.5%` compared to $k=1$, while increasing to $k=8$ provides only marginal further reduction.

The following figure (Figure 7 from the original paper) shows MSE stability across different evaluation run counts:

![Figure 7: MSE stability across different evaluation run counts.](/files/papers/691093b15d12d02a6339cf31/images/7.jpg)
*该图像是图表，展示了GPT-4.1与Claude-4-Sonnet在不同评估运行次数下的均方误差（MSE）稳定性，横轴为运行次数k，纵轴为MSE值，表明随着运行次数增加，MSE逐渐降低且两者表现接近。*

This analysis justifies the use of 4 evaluation runs in the main experiments.

## 6.3. Task Complexity Analysis
The paper investigates how `reasoning complexity` ($\mathcal{C}_{\mathrm{reason}}$) and `tool complexity` ($\mathcal{C}_{\mathrm{tool}}$) influence task difficulty and agent performance.

The following are the results from Table 5 of the original paper:

Domain
Performance
Reasoning Complexity
Tool Complexity
All Models
Reas. Pts.
Search Space
Tools
Edges
Density
In-store
42.1
5.6
3,916
24
68
12.3%
Delivery
38.0
7.4
1,246
20
50
13.2%
OTA
20.7
9.7
11,284
38
309
22.0%
Cross-scenario
16.2
10.3
8,717
66
512
11.2%

*   **Reasoning Complexity Impact:**
    *   `Reasoning Points` and `Search Space` are key indicators. `Cross-scenario` and `OTA` tasks require the highest number of `reasoning points` (10.3 and 9.7 respectively), demanding complex inference under `partial observability`. These domains show lower performance (16.2% and 20.7%).
    *   The `In-store` domain, despite having a larger `search space` (3,916) than `Delivery` (1,246), achieves the highest performance (42.1%) due to fewer `reasoning points` (5.6). This suggests that the *number of inferential steps* is a stronger determinant of `reasoning complexity` than the sheer size of the state space to search.
*   **Tool Complexity Impact:**
    *   `Tool complexity` is measured by the number of `Tools`, `Edges` (dependencies), and `Density` of the tool graph.
    *   `Cross-scenario tasks`, with the largest number of `Tools` (66) and `Edges` (512), yield the lowest performance (16.2%), indicating that managing a vast and interconnected toolset is extremely difficult.
    *   The `OTA` domain has the highest `graph density` (22.0%, indicating highly interdependent tools) and a significant number of tools (38) and edges (309), resulting in relatively poor performance (20.7%). This confirms that complex `inter-tool dependencies` pose a major challenge.

### 6.3.1. Interactive Complexity
An ablation study quantifies `interaction complexity` ($\mathcal{C}_{\mathrm{interact}}$) by evaluating models under different `user simulation configurations`.

*   **Configurations:**
    1.  `Default`: Full `user profile` and `behavioral attributes` (most complex).
    2.  `No-Profile`: User provides all information upfront, without dynamic interaction or persona.
    3.  `Solo Agent`: No user simulation; agent solves tasks without any conversational interaction.

        The following figure (Figure 8 from the original paper) presents a bar chart of ablation studies on user simulation configurations:

        ![Figure 8: Ablation study of user simulation configurations.](/files/papers/691093b15d12d02a6339cf31/images/8.jpg)
        *该图像是图8，展示了用户模拟配置消融实验的柱状图，比较了GPT-4.1-Mini和Claude-4-Sonnet在不同用户模拟条件下的avg@4表现。*

The figure illustrates that performance generally increases as `interaction complexity` decreases. `Claude-4-Sonnet` consistently outperforms `GPT-4.1-Mini` across all configurations. Both models perform best in the `Solo Agent` setup (no user interaction), indicating that `conversational interaction` is a significant bottleneck. The performance difference between `Default` and `No-Profile` highlights the impact of dynamic `user behaviors` and `persona attributes`.

*   **Key Findings:**
    *   `Interactive complexity` is a fundamental dimension of `task difficulty`.
    *   `Conversational styles` (part of `user profile` and `behavioral attributes`) primarily challenge weaker models (e.g., `GPT-4.1-Mini`).
    *   Stronger models (e.g., `Claude-4-Sonnet`) show greater performance gains in the `Solo Agent` scenario, suggesting that while they handle complex interaction better, the cognitive overhead of managing conversations is still substantial.

## 6.4. Error Pattern Analysis
To understand the specific failure modes of current agents, the paper analyzes `error patterns` from `Claude-4.1-Opus` on `cross-scenario tasks`.

The following figure (Figure 9 from the original paper) shows the error distribution of VitaBench:

![Figure 9: Error distribution of VitaBench.](/files/papers/691093b15d12d02a6339cf31/images/9.jpg)

**Error Distribution:**

*   **Reasoning Errors (61.8%):** These dominate the failure landscape. They include:
    *   `Decision Making` (30.1%): Agent makes incorrect choices or plans.
    *   `Constraint Conflicts` (16.7%): Agent fails to resolve conflicting requirements.
    *   `Objective Omission` (15.0%): Agent misses parts of the user's goal.
        This category reveals fundamental limitations in `cognitive capabilities` such as understanding complex instructions, handling multiple constraints, and synthesizing information.
*   **Tool-Use Errors (21.1%):** These are failures related to invoking and managing tools:
    *   `Incorrect Tool Selection` (10.9%): Agent picks the wrong tool for the job.
    *   `Parameter Passing Mistakes` (6.2%): Agent provides incorrect arguments to a tool.
    *   `Failure Recovery` (4.0%): Agent cannot recover from a tool invocation error.
*   **Interaction Errors (7.9%):** These relate to `dialogue management`:
    *   `Ambiguity Clarification` (4.8%): Agent fails to proactively ask for clarification on vague instructions.
    *   `User Intent Tracking` (3.1%): Agent loses sight of user preferences or context in `multi-turn conversations`.
*   **User Simulator Errors (9.2%):** These are `inherent stochastic behaviors` of the user simulator, which are mitigated through multiple runs.

**Additional Insights:**

*   **Poor Self-Awareness and Limited Error Recovery:** Agents show limited ability to recover from `tool failures` or `unclear user responses`. Instead of adapting new strategies, they often repeat failed attempts. This highlights a critical area for improvement: `meta-reasoning` and `adaptive error handling`.

    Overall, the error analysis underscores that while `LLMs` are powerful, their application as agents in complex, interactive, real-world settings still faces significant hurdles, primarily in robust `reasoning` and `adaptive tool orchestration`.

# 7. Conclusion & Reflections

## 7.1. Conclusion Summary
`VitaBench` is introduced as a novel and challenging benchmark designed to bridge the gap between `controlled academic benchmarks` and `practical real-world deployments` of `LLM agents`. By formalizing agent tasks within a `POMDP` framework and defining `task complexity` across `reasoning`, `tool use`, and `interaction` dimensions, `VitaBench` provides a comprehensive evaluation environment. It features a large ecosystem of 66 tools from `food delivery`, `in-store consumption`, and `online travel services`, eliminating `domain-specific policies` to foster autonomous agent behavior.

The benchmark generates 100 `cross-scenario tasks` and 300 `single-scenario tasks`, derived from `real user requests`, requiring agents to engage in `temporal and spatial reasoning`, utilize `complex tool sets`, `proactively clarify ambiguities`, and track `shifting user intent`. A robust `rubric-based sliding window evaluator` is proposed to assess diverse solution paths.

Experimental results reveal that even the most advanced `LLMs` achieve a mere `30% success rate` on `cross-scenario tasks` and less than `50%` on `single-scenario tasks`. `Reasoning errors` are the predominant failure mode, followed by `tool usage errors` and `interaction management issues`. The analysis also highlights `instability` in agent performance and the benefits of `thinking mechanisms` for both `effectiveness` and `efficiency`. `VitaBench` effectively serves as a challenging testbed, offering actionable insights for advancing `AI agents` in practical applications.

## 7.2. Limitations & Future Work
The authors implicitly or explicitly point out several limitations and suggest future work:

*   **Current Performance Gap:** The low success rates of even advanced models (30% on `cross-scenarios`, <50% on `single-scenarios`) indicate a significant gap between current `LLM agent` capabilities and the demands of real-world `life-serving applications`. This itself is a call for future research.
*   **Agent Self-Awareness and Error Recovery:** The error analysis highlights `poor self-awareness` and `limited error recovery capabilities` in current agents. They often repeat failed attempts rather than adapting strategies. Future work needs to focus on making agents more robust and capable of `meta-reasoning` (reasoning about their own actions and failures).
*   **Handling Stochastic Interactions:** While the `user simulator` provides `stochastic interactions`, `user simulator errors` still account for a small percentage of failures (9.2%). Refining user simulation to be even more realistic and challenging, perhaps incorporating more unpredictable edge cases, could be a future direction.
*   **Scalability of Rubric Design:** The manual design of rubrics, while ensuring high quality, might be a scalability challenge as the number and complexity of tasks grow. Future work could explore `LLM-assisted rubric generation` or `automated rubric refinement` to ease this burden.
*   **Long-Context Limitations of Evaluator:** The paper notes that the `evaluation model's limited long-context capability` hindered accurate assessment of full trajectories, necessitating the `sliding window` approach. Improving `long-context understanding` in `LLMs` (both for agents and evaluators) remains a critical area.
*   **Specific Error Types:** The detailed breakdown of `reasoning`, `tool-use`, and `interaction errors` provides clear targets for future research, such as developing better `multi-constraint reasoning`, more `robust tool orchestration`, and `adaptive dialogue management` strategies.

## 7.3. Personal Insights & Critique
This paper presents a highly valuable and timely contribution to the field of `LLM agents`. The `VitaBench` benchmark appears to be one of the most comprehensive and realistic to date, meticulously designed to expose the current limitations of advanced `LLMs` when deployed as autonomous agents in complex, interactive environments.

**Key Strengths and Inspirations:**

*   **Real-World Relevance:** Drawing tasks from `food delivery`, `in-store consumption`, and `online travel` makes the benchmark immediately relatable and impactful for practical `AI agent` development.
*   **Holistic Complexity Framework:** The three-dimensional `task complexity` framework ($\mathcal{C}_{\mathrm{reason}}$, $\mathcal{C}_{\mathrm{tool}}$, $\mathcal{C}_{\mathrm{interact}}$) is particularly insightful. It provides a structured way to think about and measure the multifaceted challenges agents face, going beyond simple task completion rates.
*   **Tool Modeling Innovation:** Eliminating `domain-specific policies` by using `pre-conditions` and `post-conditions` encoded in a `directed graph` is a significant step towards more autonomous and less brittle `LLM agents`. It forces genuine `planning` and `reasoning` rather than rote execution.
*   **Sophisticated Evaluation:** The `rubric-based sliding window evaluator` is a crucial innovation. It acknowledges the `stochasticity` and `diverse solution paths` inherent in complex interactions, offering a more nuanced and fair assessment than traditional methods. Its validation with `Cohen's`\kappa

further bolsters its credibility.

• Actionable Insights from Error Analysis: The detailed error distribution (reasoning, tool-use, interaction) provides clear directions for future research, acting as a roadmap for improving LLM agents.

Potential Issues or Areas for Improvement/Critique:

• Future Dating of Publication: The "Published at" date being in the future (September 2025) suggests this is a preprint. While common in AI, it means the work has not yet undergone formal peer review, which might lead to refinements in future versions.
• Model Anonymization/Aliasing: Some model names are somewhat ambiguous (e.g., "03", "04-mini", "GPT-5 (minimal)"). While this might be for competitive reasons or due to models being under development, it can make direct comparison with public models harder for external researchers. Clarity on the exact model versions used would enhance reproducibility.
• Generalizability of User Simulator: While validated, the user simulator is still an LLM itself (GPT-4-0504). Its fidelity, while high, might still reflect biases or limitations of GPT-4, and could subtly shape the types of challenges presented to the agent LLMs. Further testing with human users in a limited capacity could provide additional validation points for simulator realism.
• Complexity vs. Interpretability: The benchmark's high complexity, while a strength, might make it challenging to pinpoint the exact causes of failure for specific LLM architectures. More fine-grained diagnostic tools or interpretable agent frameworks might be needed to fully leverage the benchmark's diagnostic potential.
• Ethical Considerations: As agents become more integrated into real-world services, potential ethical implications (e.g., bias in recommendations, data privacy handling) become increasingly important. While VitaBench focuses on performance, considering how to benchmark agents for ethical behavior in these contexts could be a future extension.

Transferability and Applicability: The methods and conclusions of VitaBench are highly transferable. The three-dimensional task complexity framework, the tool modeling with pre/post-conditions, and the rubric-based sliding window evaluator can be applied to design and assess agents in other complex domains beyond life-serving applications, such as enterprise automation, scientific research assistants, or even gaming environments. The insights into reasoning, tool-use, and interaction errors are fundamental to AI agent development across the board, providing a universal guide for improving agent capabilities. For instance, the need for better ambiguity clarification and user intent tracking is universal to all conversational AI.