Ensemble forecasting with IBM Granite time-series models
• 25
AI & ML interests
Enterprise AI and ML, Foundation Models, Responsible AI
Recent Activity
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Papers
DRACO: Fine-Grained Credit Assignment with Dynamic Rubrics for Long-Horizon Agent Training
Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents
Articles
Pretrained graph networks that predict QAOA parameters for max-cut.
Enterprise agent ecosystem featuring AssetOpsBench (industrial) and ITBench (SRE, FinOps, CISO), CUGA to accelerate AI Automation
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CUGA Apps
🚀60Explore and launch CUGA apps from a single dashboard
-
AssetOpsBench
🚀24Run a quick benchmark of web asset performance
-
CUGA Agent
🤖106Configurable Generalist Agent, leader in AppWorld Benchmark
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MCP-Cosmos: World Model-Augmented Agents for Complex Task Execution in MCP Environments
Paper • 2605.09131 • Published • 61
REAL-MM-RAG-Bench is a benchmark designed to evaluate multi-modal retrieval models under realistic and challenging conditions.
Datasets and models of the Otter-Knowledge project
GGUF-formatted versions of IBM Granite 3.2 models. Licensed under the Apache 2.0 license.
-
ibm-research/granite-3.2-2b-instruct-GGUF
Text Generation • 3B • Updated • 1.22k • 13 -
ibm-research/granite-3.2-8b-instruct-GGUF
Text Generation • 8B • Updated • 1.23k • 11 -
ibm-research/granite-vision-3.2-2b-GGUF
3B • Updated • 844 • 16 -
ibm-research/granite-guardian-3.2-3b-a800m-GGUF
Text Generation • 3B • Updated • 690 • 5
A collection of time series models trained by IBM
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ibm-research/ttm-research-r2
Time Series Forecasting • 855k • Updated • 9.9k • 9 -
ibm-research/ttm-r3
Time Series Forecasting • 1.41M • Updated • 1.84k • 9 -
ibm-research/flowstate
Time Series Forecasting • 9.07M • Updated • 43.5k • 13 -
ibm-research/patchtst-fm-r1
Time Series Forecasting • 0.3B • Updated • 26.8k • 10
An evaluation suite created for benchmarking of retrieval models on Table+Text retrieval datasets.
This category highlights the collective efforts of the AI Automation team in advancing Industry 4.0 applications and exploring innovations beyond it.
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AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance
Paper • 2506.03828 • Published • 20 -
FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes
Paper • 2506.03278 • Published • 7 -
ibm-research/AssetOpsBench
Viewer • Updated • 467 • 1.05k • 51 -
AssetOpsBench
📉4Evaluating Autonomous AI Agents for Industry 4.0 Tasks
REAL-MM-RAG-Bench is a benchmark designed to evaluate multi-modal retrieval models under realistic and challenging conditions.
Welcome to IBM’s multi-modal foundation model for materials, FM4M, designed to support and advance research in materials science and chemistry.
Pretrained graph networks that predict QAOA parameters for max-cut.
A collection of time series models trained by IBM
-
ibm-research/ttm-research-r2
Time Series Forecasting • 855k • Updated • 9.9k • 9 -
ibm-research/ttm-r3
Time Series Forecasting • 1.41M • Updated • 1.84k • 9 -
ibm-research/flowstate
Time Series Forecasting • 9.07M • Updated • 43.5k • 13 -
ibm-research/patchtst-fm-r1
Time Series Forecasting • 0.3B • Updated • 26.8k • 10
Enterprise agent ecosystem featuring AssetOpsBench (industrial) and ITBench (SRE, FinOps, CISO), CUGA to accelerate AI Automation
-
CUGA Apps
🚀60Explore and launch CUGA apps from a single dashboard
-
AssetOpsBench
🚀24Run a quick benchmark of web asset performance
-
CUGA Agent
🤖106Configurable Generalist Agent, leader in AppWorld Benchmark
-
MCP-Cosmos: World Model-Augmented Agents for Complex Task Execution in MCP Environments
Paper • 2605.09131 • Published • 61
An evaluation suite created for benchmarking of retrieval models on Table+Text retrieval datasets.
REAL-MM-RAG-Bench is a benchmark designed to evaluate multi-modal retrieval models under realistic and challenging conditions.
This category highlights the collective efforts of the AI Automation team in advancing Industry 4.0 applications and exploring innovations beyond it.
-
AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance
Paper • 2506.03828 • Published • 20 -
FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes
Paper • 2506.03278 • Published • 7 -
ibm-research/AssetOpsBench
Viewer • Updated • 467 • 1.05k • 51 -
AssetOpsBench
📉4Evaluating Autonomous AI Agents for Industry 4.0 Tasks
Datasets and models of the Otter-Knowledge project
REAL-MM-RAG-Bench is a benchmark designed to evaluate multi-modal retrieval models under realistic and challenging conditions.
GGUF-formatted versions of IBM Granite 3.2 models. Licensed under the Apache 2.0 license.
-
ibm-research/granite-3.2-2b-instruct-GGUF
Text Generation • 3B • Updated • 1.22k • 13 -
ibm-research/granite-3.2-8b-instruct-GGUF
Text Generation • 8B • Updated • 1.23k • 11 -
ibm-research/granite-vision-3.2-2b-GGUF
3B • Updated • 844 • 16 -
ibm-research/granite-guardian-3.2-3b-a800m-GGUF
Text Generation • 3B • Updated • 690 • 5
Welcome to IBM’s multi-modal foundation model for materials, FM4M, designed to support and advance research in materials science and chemistry.