Datasetpublic

vibrantlabsai/tau2-infinity-wg

tau2-infinity-wg Airline customer-service tasks mined by world-gen adversarial failure-search — each task is an artifact on which a target model diverges from an oracle model. Companion to vibrantlabsai/tau2-infinity; consumed by the tau2_infinity_wg Prime Intellect RL environment. Overview Unlike tau2-infinity (adaptively generated toward a target difficulty band), tasks here are discovered by hypothesis-disproof: generate a hypothesized failure mode, confirm the… See the full description on the dataset page: https://huggingface.co/datasets/vibrantlabsai/tau2-infinity-wg.

sourceHugging Faceapache-2.0updated 5mo agoView on Hugging Face

1likes10downloads

Dataset Card

tau2-infinity-wg

Airline customer-service tasks mined by world-gen adversarial failure-search — each task is an artifact on which a target model diverges from an oracle model. Companion to vibrantlabsai/tau2-infinity; consumed by the tau2_infinity_wg Prime Intellect RL environment.

Overview

Unlike tau2-infinity (adaptively generated toward a target difficulty band), tasks here are discovered by hypothesis-disproof: generate a hypothesized failure mode, confirm the oracle resolves it cleanly while the target fails, then keep only cases where both hold. This biases the set toward borderline/adversarial scenarios rather than uniformly hard ones.

Each task pins an initial airline world (users, flights, reservations) and a set of objectives the agent must satisfy while handling a call from an LLM-simulated customer with a fixed personality and some hidden information.

Property	Value
Number of tasks	10
Failure categories	booking (7), policy (3)
Objectives per task	3–4 (mean 3.6)
Max turns	10–20 (median 15)
Customer simulator	LLM-driven, per-task personality + hidden info
Source	world-gen adversarial hypothesis-disproof

Dataset Schema

Column	Type	Description
`task_id`	string	Task identifier (e.g. `failure-0189abb3`)
`task_description`	string	Natural-language scenario the customer is calling about
`customer_personality`	string	Freeform persona for the customer simulator
`customer_hidden_info`	JSON string	Facts the customer knows but may not disclose unprompted
`max_turns`	int	Episode turn budget
`objectives`	JSON string	List of `{id, description, type, verification_context}` — judged at end of episode
`n_objectives`	int	Objective count
`world_state`	JSON string	`world_export.world` — entities, relationships, timeline
`tools`	JSON string	Tool specifications available to the agent (identical across tasks)
`failure_category`	string	e.g. `booking`, `policy`
`failure_notes`	string	Human-readable failure-mode notes from the mining run
`raw_artifact`	JSON string	Full original world-gen artifact (superset of above)

Tasks

task_id	failure_category	n_objectives	max_turns
`failure-0189abb3`	booking	4	16
`failure-04c75bdf`	booking	4	15
`failure-2782cbce`	booking	4	12
`failure-2c501ab3`	policy	4	16
`failure-46b8621c`	booking	4	15
`failure-46e0e882`	booking	3	12
`failure-5214cbeb`	policy	3	10
`failure-86b0108a`	policy	3	12
`failure-96b8509e`	booking	4	20
`failure-dde59d2e`	booking	3	15

Usage

python

from datasets import load_dataset
import json

ds = load_dataset("vibrantlabsai/tau2-infinity-wg", split="test")

for task in ds:
    print(task["task_id"], task["failure_category"])
    objectives = json.loads(task["objectives"])
    world = json.loads(task["world_state"])

License

Apache 2.0