All evaluations

šœĀ²-Bench Telecom Benchmark Leaderboard

A dual-control conversational AI benchmark simulating technical support scenarios where both agent and user must coordinate actions to resolve telecom service issues.

šœĀ²-Bench (Tau-2 Bench) introduces a new paradigm for evaluating conversational AI by simulating both the agent and user to actively modify a shared world state.
The telecom domain tests agents' abilities to guide users through technical troubleshooting to test problem-solving and effective communication skills.
Developed by Sierra Research, this benchmark addresses gaps between other benchmarks and real-world customer service scenarios where users are active participants in problem resolution.

All evaluations are conducted independently by Artificial Analysis. More information can be found on our Intelligence Benchmarking Methodology page.

Publication

View on arXiv

šœĀ²-Bench: Evaluating Conversational Agents in a Dual-Control Environment

Victor Barres, Honghua Dong, Soham Ray, Xujie Si, Karthik Narasimhan.

Existing benchmarks for conversational AI agents simulate single-control environments, where only the AI agent can use tools to interact with the world, while the user remains a passive information provider. This differs from real-world scenarios like technical support, where users need to actively participate in modifying the state of the (shared) world. In order to address this gap, we introduce šœĀ²-bench, with four key contributions: 1) A novel Telecom dual-control domain modeled as a Dec-POMDP, where both agent and user make use of tools to act in a shared, dynamic environment that tests both agent coordination and communication, 2) A compositional task generator that programmatically creates diverse, verifiable tasks from atomic components, ensuring domain coverage and controlled complexity, 3) A reliable user simulator tightly coupled with the environment, whose behavior is constrained by tools and observable states, improving simulation fidelity, 4) Fine-grained analysis of agent performance through multiple ablations including separating errors arising from reasoning vs communication/coordination. In particular, our experiments show significant performance drops when agents shift from no-user to dual-control, highlighting the challenges of guiding users. Overall, šœĀ²-bench provides a controlled testbed for agents that must both reason effectively and guide user actions.

šœĀ²-Bench Telecom

GLM-4.7-Flash (Reasoning) scores the highest on šœĀ²-Bench Telecom with a score of 98.8%, followed by GLM-5-Turbo with a score of 98.5%, and GLM 5V Turbo (Reasoning) with a score of 98.5%

šœĀ²-Bench Telecom Benchmark Leaderboard: Results

Independently benchmarked by Artificial Analysis

šœĀ²-Bench Telecom Benchmark Leaderboard: Token Usage

Tokens used to run the evaluation
Input tokens
Reasoning tokens
Answer tokens

The total number of tokens used to run the evaluation, including input tokens (prompt), reasoning tokens (for reasoning models), and answer tokens (final response).

šœĀ²-Bench Telecom Benchmark Leaderboard: Cost Breakdown

Cost (USD) to run the evaluation
Input cost
Reasoning cost
Answer cost

The cost to run the evaluation, calculated using the model's input and output token pricing and the number of tokens used.

šœĀ²-Bench Telecom Benchmark Leaderboard: Score vs. Release Date

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