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This is very cool, though I don't understand exactly what they've done here. Is it some kind of LLM with convolutional layers added? The graph doesn't exactly
by andai 6mo ago
This is very cool, though I don't understand exactly what they've done here. Is it some kind of LLM with convolutional layers added?
The graph doesn't exactly make it clear but it describes a pipeline that goes beyond the LLM, so the CNN could be a separate model there.
- tomsyouruncle 6mo agoHere’s the academic paper behind it: https://arxiv.org/abs/2602.04101 https://arxiv.org/abs/2602.04101
- andai 6mo agoThanks. Well this is fascinating. >Instead of a single transformer, we combine (i) a stack of heterogeneous DNNs paired with small language models as perception modules It seems that we're reinventing the brain's organs one by one from first principles. (Though Transformer + Common Crawl unintentionally builds a whole bunch of them we don't even understand yet.) I found some broader context and the whole thing is indeed very harness-shaped: >Using Interfaze as a Tool Inside Your Agent https://interfaze.ai/blog/using-interfaze-as-a-tool-inside-your-agent https://interfaze.ai/blog/using-interfaze-as-a-tool-inside-y... Well, Harness is the wrong word here... "environment/tools the LLM interacts with" definitely fits though. Or "other organoid" to use the previous metaphor.
- yoeven 6mo agoYup does really depend on the use case. We see two types: workflows & agents. Workflows are the most common, there's a pipeline like processing loan documents before data gets loaded to the next step or translating user comments before being stored in the database. Agents are where you have a chat based system or a brain of sorts that calls many tools to achieve a user goal. The model doing this is a lot better at non deterministic task which then delegates to Interfaze for specific deterministic actions like OCR, Web extract then consumes that data. That's the article you referenced :)