They explicitly are only doing lexical matching. I actually don’t mind this if it lowers the cost; LLMs can turn lexical search into a very powerful tool by iterating on the queries. Just look at the success of coding agents using grep and similar code search tools.
yeah, but the bigger the corpus the more you are relying on the LLM guessing the right keyword, if there is no reranking in place, you will just end up burning through a lot of tokens.
coding agents is a very "niche" use-case where lexical matching works very well. code is a graph, by looking at few lines of code you can guess naming conventions etc. codebases are small compared to knowledge bases.
the bigger your index, the more you will need way better tools such as search.
I think it is a potentially good camera format, good medical & scientific imaging format, good RAW compression format (Apple uses it in some newer iPhones for this), good media interchange formats for tools like Photoshop (think about storing all of your layers inside of one JPEG XL that's fully compatible with .psd files), and more. It is incredibly expressive and versatile, which is what makes it so risky on the Web.
The ARC-AGI-3 harness was throwing away reasoning tokens between turns. This is very bad harness design.
The models are designed to keep the reasoning tokens separate from the output and only publicly emit tool calls and the sometimes a summary of the reasoning tokens. The models are trained to depend on those private reasoning tokens. You can’t just delete them.
Another perspective on this is that coordination is the hardest problem to solve in deploying technology that depends on cooperation between different parties. It takes a few of the big centralized players to agree to put their weight behind it.
It’s a very soft skill that changes as the capabilities of AI change.
But one thing that doesn’t change is the need to specify an end goal correctly and precisely. I think the emphasis of knowledge/information-processing work is going to increasingly be placed on verification mechanisms. This is practically equivalent to precisely defining an end goal.
Spend time deeply thinking about what it means for a solution to be correct. What properties will a correct solution have? What of those properties are testable? Write those things down and tell the agent.
As models get better, agents will be able to target more and more difficult end goals. The strategy just becomes more useful. (It’s useful for people as well.)
For example, if you want an agent to write a photo editor, think about what end properties the editor should have. There are reference images for color space and rendering transformations. That’s a good start.
Sometimes the goal will be fuzzier. “I want a feature like CaptureOne where I provide a reference image and it makes my image look like that.” Well, time to think really hard about what that means. Iterate with AI on how to test for that precisely. Come up with some good metrics/heuristics. Maybe it means local contrast should match. Maybe the overall distribution of colors. Maybe something more complicated.
Then you have a target and you can let an implementation agent work against that. If it fails, it’s either because the agent is bad or your target was incorrect or incomplete. As models get better, the limiting factor becomes your ability to correctly define a problem.
But it does seem like their index isn’t great.