Questions we are working on.
This is a record of what we are investigating, not a publication list. Themes describe open questions and the reasoning behind our interest in them. Write-ups appear only once they exist and their claims have been checked.
Notes and write-ups
0 publishedNothing published in this area yet.
Write-ups appear here once they exist and their claims have been checked. Nothing is listed as research until it has been done — no placeholder papers, no benchmark figures we have not reproduced ourselves.
Open questions
8 themes- R-01Agentic AI
Long-horizon agentic reasoning
How does a system maintain a coherent objective across dozens of dependent steps without accumulating error?
Interest here is in externalised plan state, checkpointing, and mechanisms that let a system detect that its current approach has stopped working and revise it rather than repeating it.
- R-02Neuro-symbolic AI
Neuro-symbolic problem solving
Where is the right boundary between a learned model and an explicit solver for a given class of problem?
Practical interest in using models to interpret and formalise a problem, and deterministic search or constraint solving to answer it — so that the answer is checkable rather than merely persuasive.
- R-03Efficient Inference
Efficient model inference
What is the cheapest configuration that still meets a task’s quality bar on the target hardware?
Quantization and distillation trade quality for footprint unevenly across task types. The question is which capabilities degrade first, and how to detect that on a task-specific evaluation set rather than a general benchmark.
- R-04Agentic AI
Tool-using language models
How should tools be described, scoped and validated so that correct selection is the default rather than the exception?
Interest in schema design, argument validation, and error messages written for a model reader — treating the tool interface as an ergonomics problem rather than an API afterthought.
- R-05Evaluation
Evaluation of autonomous systems
How do you evaluate a process rather than a single output, when the same objective admits many valid trajectories?
Attention on trajectory-level scoring, partial credit, cost-aware metrics, and the reliability of model-based judges compared with human review on the same set.
- R-06Multimodal Systems
Multimodal understanding
How is grounding maintained when evidence for one answer is split across a document’s layout, an image and a database record?
Interest in cross-modal citation, resolving conflicts between sources of different reliability, and preserving spatial structure through extraction.
- R-07Edge AI
Small and specialised models
Which production steps are better served by a small task-specific model than a general one?
Classification, extraction, routing and reranking are often high-volume and narrow. The question is where a compact model matches a large one on the task while fitting a constrained target.
- R-08Neuro-symbolic AI
Reliable structured generation
How is output made schema-valid and semantically correct, not merely parseable?
Constrained decoding solves syntax. The open part is semantic validity — referential integrity, unit consistency, and satisfaction of domain constraints — and where that check belongs in the pipeline.