MNIST-PRO: MNIST is Back as a Partially Observable World for AI Agents
cs.AI, cs.CV
Submitted: 2026-08-31
Updated: 2026-08-31
Code: https://github.com/declare-lab/MNIST-PRO
Terminology
Sources
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- Don't Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination
- BLINK: Multimodal Large Language Models Can See but Not Perceive
- Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models
- The Context Gathering Decision Process: A POMDP Framework for Agentic Search
- Taming Overconfidence in LLMs: Reward Calibration in RLHF
- SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
- MMBench: Is Your Multi-modal Model an All-around Player?
- Habitat: A Platform for Embodied AI Research
- ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks
- GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
- ScrambleToolBench: Agents Search Exhaustively Even When Their Own Map Points to the Next Step
- ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models
- V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs
- OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
- SpatialBench: Benchmarking Multimodal Large Language Models for Spatial Cognition
- Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces
- MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
- MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
- An Exam for Active Observers
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