REVIEW 9 cited by
Foundation Models for Decision Making: Problems, Methods, and Opportunities
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. When such models are deployed in real world environments, they inevitably interface with other entities and agents. For example, language models are often used to interact with human beings through dialogue, and visual perception models are used to autonomously navigate neighborhood streets. In response to these developments, new paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. These paradigms leverage the existence of ever-larger datasets curated for multimodal, multitask, and generalist interaction. Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems that can interact effectively across a diverse range of applications such as dialogue, autonomous driving, healthcare, education, and robotics. In this manuscript, we examine the scope of foundation models for decision making, and provide conceptual tools and technical background for understanding the problem space and exploring new research directions. We review recent approaches that ground foundation models in practical decision making applications through a variety of methods such as prompting, conditional generative modeling, planning, optimal control, and reinforcement learning, and discuss common challenges and open problems in the field.
Forward citations
Cited by 9 Pith papers
-
Towards Efficient and Effective Alignment of Large Language Models
A thesis presenting Lion, WebR, LTE, BMC, and FollowBench, five empirical methods that together address LLM alignment data, training, and evaluation.
-
Beyond Generalist LLMs: Specialist Agentic Systems for Structured Code Workflow Execution
A specialist BPMN-to-agent pipeline beats general-purpose coding agents on tool-use accuracy, latency, and token cost for deterministic business workflows.
-
Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning
A 2M-parameter MAPF-GPT model fine-tuned with delta-driven data generation matches or exceeds much larger models on MAPF benchmarks and scales to over a million agents.
-
UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation
UAD distills affordance knowledge from vision-language models and DINOv2 features into a lightweight task-conditioned model that predicts pixel-level manipulation regions and improves few-shot imitation learning gener...
-
PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations
PDE-Transformer, a diffusion-transformer variant with shifted-window attention, multi-scale token processing, and per-channel tokens, outperforms leading transformer and operator baselines for PDE surrogate modeling a...
-
Robot Operation of Home Appliances by Reading User Manuals
A robot system that constructs a symbolic appliance model from a user manual and uses it to reliably execute natural language appliance operation tasks, outperforming direct VLM-based policies.
-
Embodied Spatial Intelligence: from Implicit Scene Modeling to Spatial Reasoning
The thesis demonstrates that combining implicit 3D scene representations with LLM-based reasoning, using text as an interface, yields strong performance on robotic perception and spatial language tasks.
-
A Review of Generative AI in Aquaculture: Foundations, Applications, and Future Directions for Smart and Sustainable Farming
A review that maps generative AI to aquaculture tasks, with a marine robotics case study, but the synthesis is weakened by overstated claims and weak citation support.
- CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity
Discussion (0). Continue with ORCID to comment.