ERI

ERI

Research Thrusts

Models

How to adapt frontier methods and foundation models to science?

1. Topical fine-tuning
2. Tool-use
3. Advanced retrieval-augmented generation (RAG++)
   • Novel: Pre-generation: Agents continually add content to RAG corpus. ("Pre-thinking" across many vectors.)
4. Science-adapted tokenization/embedding (xVal, [IDK])
5. Specialized sampling
   • Entropy sampling: measure uncertainty of CoT trajectories
   • Novel: Handoff sampling:
     • Useful for:
       • text-to-text (specialization, creativity, etc.)
       • text-to-tool (e.g. math)
       • test-to-field (integrate non-textual FM)
     • Implementation:
       • MI-SAE on both spaces, find matches (or maybe just "analogies"?)

Challenge: Connect reasoning models to domain models.

1. Latent space reasoning
2. Establish mappings (analogies) between interpretability spaces
3. Cycling recaptioning/reframing
4. Tokenizer-for-science: learn right spectrum of representations (for text/image reasoning model)

Agents

How to make AI agents smarter?

1. Iteration schemes (loops, blocks)
   1. Thinking:
      • Blocky/neural: Define architecture, allow system to pick hyper-parameters
   2. Autonomous ideation:
      • Novel: Treat ideation as an AE problem in a semantic embedding space.
   3. Dynamic tree-of-thought: on-demand context generation, allows model to select among data representations (zoom, modality, etc.)
2. Encode Human Patterns
   1. Human scientist workflows (ideation, solving, etc.)
   2. Thought-templates, thought-flows
3. How to allow agents to run for long time-horizons coherently?
   1. Basket of Metrics: Need to define metrics of: (1) research success, (2) uncertainty (entropy sampling?)
   2. Tool-use to "call human" and request help/information
4. Memory
   1. Allow system to insert and retrieve from RAG at will.

Exocortex

What is the right architecture for AI swarms?

1. Interaction schemes
   1. Test options, identify match between science task and scheme
   2. Treat interaction graph as ML optimization problem
   3. Novel: Map-spatial: Use a map (e.g. of BNL) to localize docs/resources/etc.
   4. Novel: Pseudo-spatial: Use position in embedding space to localize everything. Evolving state (velocity/momentum) of agent carries information.
   5. Novel: Dynamic-pseudo-spatial: Allow the space to be learned and updated; directions in embedding space can dictate information flow
2. Establish benchmarks/challenges/validations

Infrastructure

Architecture

What software architecture is needed?

1. Code for scaffolding
2. Scheme for inter-agent messaging (plain English w/ pointers, etc.)
3. Data management

Hardware

How to implement inference-time compute for exocortex?

1. Heterogeneous hardware
2. Elastic (combine local & cloud)
3. Workflow management

Human-Computer Interaction (HCI)

What should the HCI be?

Resources

1. Need models, data, facilities, etc. all accessible as API endpoints.