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Over the course of the internship, the intern will contribute to the development of meta-cognitive methods and internal mechanisms that improve the adaptability, efficiency, and reliability of multimodal foundation model-based agents. The research will explore how internal model representations and inference-time mechanisms can be understood and leveraged to enable more capable, self-aware, and adaptive agentic AI systems. Potential research directions include (but are not limited to):
- Developing methods to analyze, probe, interpret, and manipulate internal representations of multimodal foundation models, including hidden states, neural activations, latent features, and computational circuits, to enable meta-cognitive capabilities such as self-assessment, uncertainty estimation, error detection, and adaptive intervention.
- Investigating inference-time and test-time scaling strategies for multimodal foundation models, including vision–language and vision–language–action models, to improve reasoning, planning, adaptation, and self-correction.
- Developing meta-cognitive mechanisms that leverage internal model signals to dynamically assess model behavior, identify limitations or failures, and adapt reasoning or decision-making strategies at inference time.
- Designing and curating benchmarks and evaluation methodologies for assessing meta-cognitive capabilities, internal-state awareness, robustness, and adaptation in multimodal agentic systems across complex and long-horizon tasks.
Minimum Qualifications
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- Currently enrolled as a Ph.D. student in Computer Science, Machine Learning, or a related field at a reputed university (exceptional M.S. candidates with a minimum of 1 year of research experience may also be considered.
- Strong familiarity with agentic AI systems, modern foundation models, and their internal representations (e.g., hidden states, neural activations, embeddings, and latent structures).
- Experience in open-source deep learning frameworks
Bonus Qualifications
- Experience with representation learning, mechanistic interpretability, or analysis of neural activations in foundation models.
- Familiarity with techniques for probing, interpreting, or steering internal model behavior (e.g., activation-level analysis, feature attribution, or circuit-level analysis).
- Experience with robot learning, learning from demonstrations, imitation learning, reinforcement learning, multimodal foundation models, or vision-language-action models.
- Familiarity with policy representations, skill learning, action representations, hierarchical decision-making, or grounded multimodal reasoning.
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| Years of Work Experience Required |
0 |
| Desired Start Date |
1/11/2027 |
| Internship Duration |
3 Months |
| Position Keywords |
Agentic AI, Meta-Cognitive AI |
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