KumoRFM-2 (04/14/2026)

Highlights

KumoRFM-2 improves predictive quality, expands large-scale data connectivity, and adds new controls for temporal and developer workflows.

{"•"} Improved performance over KumoRFM-1 across benchmark groups, with gains measured directly against the previous release.

{"•"} More robust predictions in real-world data settings, including sparse features, missing links, and noisy columns.

{"•"} SQL-based execution for large relational datasets, with Snowflake and SQLite supported today.

{"•"} New SDK controls for lagged features, embedding outputs, and customizable inference behavior.

{"•"} No backward-incompatible interface changes.

Performance

Improved Higher accuracy across relational benchmarks

KumoRFM-2 delivers improved predictive performance across a wide range of relational tasks, especially classification and regression. The most important change from the previous release is the gain over KumoRFM-1: benchmark evaluations show consistent improvements across RelBench and SALT when measured directly against the previous release.

+2.89 AUROC points vs RFM-1
RelBenchV1 classification

9.5% lower normalized error vs RFM-1
RelBenchV1 regression

+3.74 AUROC points vs RFM-1
RelBenchV2 classification

15.6% lower normalized error vs RFM-1
RelBenchV2 regression

Improved More resilient to messy, incomplete data

KumoRFM-2 is designed for real-world relational data, where information is often sparse, incomplete, or noisy. In robustness evaluations, the model maintains strong performance under:

Feature scarcity: limited or missing feature coverage
Link scarcity: incomplete relational connections
Noisy columns: added irrelevant or uninformative columns

Scalability & Data Connectivity

New Run KumoRFM-2 directly on your data warehouse

KumoRFM-2 supports SQL-backed graph construction and execution for larger relational datasets. Snowflake-backed execution enables warehouse-native workflows, while SQLite support is available for local or lightweight SQL-backed development.

System	Status	Details
Snowflake	Supported	Supported when running inside Snowflake. Connector guide
SQLite	Supported	Available for local or lightweight SQL-backed workflows. Connector guide

                {`# Example: connect to a Snowflake-backed dataset
                                from kumoai import rfm

                                graph = rfm.Graph.from_snowflake(
                                  database="MY_DATABASE",
                                  schema="MY_SCHEMA",
                                )`}

New Features

New Temporal predictions with lagged features

KumoRFM-2 can include recent historical target values as lagged features during prediction. This is useful for time-dependent tasks such as churn prediction and demand modeling, where a user's or entity's recent behavior is often predictive of the next outcome. The lag\_timesteps option controls how many previous time steps are made available to the model as additional context during predict().

                {`# Example: use the last 10 time steps as lagged feature context
                                result = model.predict(
                                  query,
                                  indices=[0, 1, 2],
                                  lag_timesteps=10,
                                )`}

Tips: Use larger values when longer historical context is useful, and smaller values when only the most recent behavior should influence the prediction.

New Embeddings added as an optional prediction output

KumoRFM-2 can return embeddings alongside prediction results, enabling downstream use cases such as search, clustering, similarity analysis, and custom ML workflows.

                {`# Example: return embeddings with predictions
                                result = model.predict(
                                  query,
                                  indices=[0, 1, 2],
                                  return_embeddings=True,
                                )`}

New Customizable inference behavior

KumoRFM-2 adds inference\_config to the predict() API for controlling inference-time behavior, including ensembling. Defaults are selected automatically based on the task type, while advanced users can tune inference settings such as run\_mode and inference\_config to optimize stability, latency, and predictive performance once a query is production-ready.

num\_estimators: number of inference passes to ensemble for more stable predictions
column\_shuffle: vary column order across inference passes
category\_shuffle: vary categorical value ordering across inference passes
hop\_shuffle: vary subgraph depth across inference passes
class\_shuffle: vary class ordering across inference passes for classification tasks
Regression and forecasting options: configure target transforms and output summarization

                {`# Example: configure inference behavior
                                result = model.predict(
                                  query,
                                  indices=[0, 1, 2],
                                  inference_config=dict(
                                      num_estimators=4,
                                      column_shuffle=True,
                                      hop_shuffle=True,
                                      class_shuffle=True,
                                  ),
                                )`}

No backward-incompatible interface changes. Existing workflows continue to work as before. For more details, see the SDK inference configuration docs.

Preview Natural language to predictive query

Users can generate predictive queries from natural language input, helping teams move from a business question to a structured predictive query faster. This is a beta feature currently available in playground only.

Availability

Available Deployment models

Enterprise SaaS: available by request. Contact [kumorfm-support@kumo.ai](mailto:kumorfm-support@kumo.ai).
Snowflake Native App: available via private listing. Contact [kumorfm-support@kumo.ai](mailto:kumorfm-support@kumo.ai).
VPC deployment: available on AWS, GCP, and Azure. Contact [kumorfm-support@kumo.ai](mailto:kumorfm-support@kumo.ai).

Try it Get started with KumoRFM-2

Playground Python SDK Kumo MCP Kumo Coding Agent