Experiments & Methodologies

We love poking at the edges of context constraints. A lot of our work orbits around the attention bottleneck—the fun, mathematical challenge of managing dense tokens in tight spatial windows without the model losing the plot. Truthfully, our initial drive was purely practical: trying to run massive, cascading agentic tasks while staying firmly within the free tier of API providers! Now, it's about scaling autonomous capabilities intelligently.

1. Context Compression

This involves pure algorithmic, lossless compression. We're experimenting with ways to structurally reorganize input vectors to bump up informational density without losing any critical context dependencies. It's about knowing exactly how much you can pack in before an agent forgets its premise.

2. Context Optimization

This stage is all about lossy compression. By leveraging semantic filters and stripping away syntactic noise, we're trying to aggressively slice localized redundancy out of prompts and memory streams. The goal? Creating ultra-fast, cheap inference traversals so agents can run longer loops.

3. Context and Skill Distillation

We're investigating enhancement and abstraction techniques for context constraints and native skills. By distilling these properties, you can drop the token usage drastically while getting the model to generate practically identical, highly useful agentic outputs.

4. Context and Skill Refinement

To actually prove any of this works, we spend a lot of time building evaluation tools. We write the frameworks to evaluate, A/B test natively, and compare how efficient different context injections and skill executions really are for long-running processes. We rely heavily on these loops to refine our compression theories.