Datasets:

ulab-ai
/

xRouteBench

configs:
  - config_name: llmrouter_generic
    data_files:
      - split: train
        path: llmrouter_generic/train.parquet
      - split: test
        path: llmrouter_generic/test.parquet
  - config_name: memory_locomo
    data_files:
      - split: train
        path: memory_locomo/train.parquet
      - split: test
        path: memory_locomo/test.parquet
  - config_name: memory_longmemeval
    data_files:
      - split: train
        path: memory_longmemeval/train.parquet
      - split: test
        path: memory_longmemeval/test.parquet
  - config_name: timeseries
    data_files:
      - split: train
        path: timeseries/train.parquet
      - split: test
        path: timeseries/test.parquet
  - config_name: video
    data_files:
      - split: train
        path: video/train.parquet
      - split: test
        path: video/test.parquet
  - config_name: multimodal_geometry3k
    data_files:
      - split: train
        path: multimodal_geometry3k/train.parquet
      - split: test
        path: multimodal_geometry3k/test.parquet
  - config_name: multimodal_mathvista
    data_files:
      - split: train
        path: multimodal_mathvista/train.parquet
      - split: test
        path: multimodal_mathvista/test.parquet
  - config_name: personalized
    data_files:
      - split: train
        path: personalized/train.parquet
      - split: test
        path: personalized/test.parquet
  - config_name: llm_candidates
    data_files:
      - split: train
        path: llm_candidates/train.parquet
  - config_name: llmrouter_generic_queries
    data_files:
      - split: train
        path: llmrouter_generic_queries/train.parquet
      - split: valid
        path: llmrouter_generic_queries/valid.parquet
      - split: test
        path: llmrouter_generic_queries/test.parquet
  - config_name: memory_locomo_queries
    data_files:
      - split: train
        path: memory_locomo_queries/train.parquet
      - split: valid
        path: memory_locomo_queries/valid.parquet
      - split: test
        path: memory_locomo_queries/test.parquet
  - config_name: memory_longmemeval_queries
    data_files:
      - split: train
        path: memory_longmemeval_queries/train.parquet
      - split: valid
        path: memory_longmemeval_queries/valid.parquet
      - split: test
        path: memory_longmemeval_queries/test.parquet
  - config_name: timeseries_queries
    data_files:
      - split: train
        path: timeseries_queries/train.parquet
      - split: valid
        path: timeseries_queries/valid.parquet
      - split: test
        path: timeseries_queries/test.parquet
  - config_name: video_queries
    data_files:
      - split: train
        path: video_queries/train.parquet
      - split: valid
        path: video_queries/valid.parquet
      - split: test
        path: video_queries/test.parquet
  - config_name: multimodal_geometry3k_queries
    data_files:
      - split: train
        path: multimodal_geometry3k_queries/train.parquet
      - split: valid
        path: multimodal_geometry3k_queries/valid.parquet
      - split: test
        path: multimodal_geometry3k_queries/test.parquet
  - config_name: multimodal_mathvista_queries
    data_files:
      - split: train
        path: multimodal_mathvista_queries/train.parquet
      - split: valid
        path: multimodal_mathvista_queries/valid.parquet
      - split: test
        path: multimodal_mathvista_queries/test.parquet
  - config_name: personalized_queries
    data_files:
      - split: train
        path: personalized_queries/train.parquet
      - split: valid
        path: personalized_queries/valid.parquet
      - split: test
        path: personalized_queries/test.parquet
language:
  - en
task_categories:
  - text-generation
tags:
  - llm-routing
  - model-selection
  - benchmark
pretty_name: xRouteBench

xRouteBench — LLM Routing Benchmark

xRouteBench is a benchmark for training and evaluating LLM routers — systems that pick the best LLM from a candidate pool for each incoming query, trading off performance vs. price cost.

Every query in each scenario was executed against all 18 candidate LLMs, recording each model's response, task performance, token usage, and latency. A router learns from the train split which model to pick, and is evaluated on test.

Scenarios (configs)

Config	Domain	Train rows / queries	Test rows / queries	Metric
`llmrouter_generic`	13 classic NLP benchmarks (MMLU, GSM8K, MATH, MBPP, ARC, …)	80,802 / 4,487	67,122 / 3,729	em_mc, GSM8K, MATH, code_eval, f1
`memory_locomo`	Long-conversation memory QA (RAG top-k=5)	15,930 / 885	5,652 / 314	f1
`memory_longmemeval`	Long-term memory eval (RAG top-k=5)	4,986 / 277	1,818 / 101	f1
`timeseries`	Time-series understanding (7 sub-tasks)	17,568 / 976	2,286 / 127	mc
`video`	Egocentric video QA (Charades-Ego)	3,618 / 201	486 / 27	em
`multimodal_geometry3k`	Geometry math (multimodal)	8,640 / 480	1,098 / 61	em
`multimodal_mathvista`	Visual math reasoning	14,400 / 800	1,800 / 100	em, em_mc
`personalized`	Personalized preference (LLM-judge; chat-format queries)	2,464 / 2,235	308 / 303	llm_judge
`llm_candidates`	The 18-model candidate pool with pricing	18 models	—	—

Schema (routing data)

Each row = one (query, candidate model) pair:

Field	Type	Description
`task_name`	str	Sub-task the query belongs to (e.g. `gsm8k`, `mbpp`)
`query`	str	Full input prompt (personalized: JSON-encoded chat messages)
`ground_truth`	list/str	Reference answer(s)
`metric`	str	Scoring metric for this row
`choices`	str	Options for multiple-choice items (JSON-encoded)
`task_id`	str	ID within the sub-task
`model_name`	str	Candidate LLM this row was executed with
`response`	str	The model's actual response
`token_num`	int	Total tokens
`input_tokens` / `output_tokens`	int	Token breakdown (for price computation)
`response_time`	float	Latency in seconds
`performance`	float	Task score of this model on this query (0–1)
`embedding_id`	int	Index into precomputed query-embedding files (not included here)

Raw query configs (`*_queries`)

Each scenario also ships its raw query set (no model executions) as a <scenario>_queries config with train / valid / test splits — use these to run your own candidate models from scratch. Fields: task_name, query, ground_truth, metric, choices, task_id (+ conversation_id/category for the memory scenarios). The memory queries are the RAG top-k=5 turn-pair variant used in the published experiments. Note: the valid split exists only here; the routing-data configs have train/test.

Candidate pool & pricing (`llm_candidates` config)

18 models spanning $0.05–$1.25 per 1M input tokens (25× spread) served via Together AI / NVIDIA NIM. Row cost = input_tokens × input_price/1e6 + output_tokens × output_price/1e6.

Usage

from datasets import load_dataset

ds = load_dataset("ulab-ai/xRouteBench", "llmrouter_generic")   # any config name above
train, test = ds["train"], ds["test"]

pricing = load_dataset("ulab-ai/xRouteBench", "llm_candidates")["train"]

Composite reward for cost-aware routing (GraphRouter-style):

reward = α · norm(performance) − β · norm(price_cost)

Notes

Query embeddings (.pt) are not included; they can be regenerated from the query field with any sentence encoder.
The memory scenarios use the RAG top-k=5 turn-pair context variant.