It’s probably deepseek r1, which is a “reasoning” model so basically it has sub-models doing things like running computation while the “supervisor” part of the model “talks to them” and relays back the approach. Trying to imitate the way humans think. That being said, models are getting “agentic” meaning they have the ability to run software tools against what you send them, and while it’s obviously being super hyped up by all the tech bro accellerationists, it is likely where LLMs and the like are headed, for better or for worse.
Still, this does not quite address the issue of tokenization making it difficult for most models to accurately distinguish between the hexadecimals here.
Having the model write code to solve an issue and then ask it to execute it is an established technique to circumvent this issue, but all of the model interfaces I know of with this capability are very explicit about when they are making use of this tool.
It’s not out of the question that we get emergent behaviour where the model can connect non-optimally mapped tokens and still translate them correctly, yeah.
The concern is that the model doesn’t actually see the world in terms of distinct hexadecimals, but instead as tokens of variable size - you can see this using the tiktokenizer-webapp: enter some text and it will split it into the series of tokens the model actually will process.
It’s not impossible for the model to work it out anyway, but it is a reason for this type of task to be a bit harder on LLMs.
I understand how base models tokenize language. What I’m curious about you’re basing your response off a horrendously screenshotted meme image of someone interacting with deepseek. Is your concern that deepseek isn’t showing the code used to approach a hex string? Because that’s certainly a valid concern, though you can ask the model to output the code it is running. That’s definitely an ethics improvement that should be made in the UI, but it’s very clear what the model is doing under the hood
It’s probably deepseek r1, which is a “reasoning” model so basically it has sub-models doing things like running computation while the “supervisor” part of the model “talks to them” and relays back the approach. Trying to imitate the way humans think. That being said, models are getting “agentic” meaning they have the ability to run software tools against what you send them, and while it’s obviously being super hyped up by all the tech bro accellerationists, it is likely where LLMs and the like are headed, for better or for worse.
Still, this does not quite address the issue of tokenization making it difficult for most models to accurately distinguish between the hexadecimals here.
Having the model write code to solve an issue and then ask it to execute it is an established technique to circumvent this issue, but all of the model interfaces I know of with this capability are very explicit about when they are making use of this tool.
Not really a concern. It’s basically translation, which language models excel at. It just needs a mapping of the hex to byte
It is a concern.
Check out https://tiktokenizer.vercel.app/?model=deepseek-ai%2FDeepSeek-R1 and try entering some freeform hexadecimal data - you’ll notice that it does not cleanly segment the hexadecimal numbers into individual tokens.
I’m well aware, but you don’t need to necessarily see each character to translate to bytes
It’s not out of the question that we get emergent behaviour where the model can connect non-optimally mapped tokens and still translate them correctly, yeah.
I’m confused, is the concern when the model doesn’t properly identify when it is using software to identify something like a hex pattern?
The concern is that the model doesn’t actually see the world in terms of distinct hexadecimals, but instead as tokens of variable size - you can see this using the tiktokenizer-webapp: enter some text and it will split it into the series of tokens the model actually will process.
It’s not impossible for the model to work it out anyway, but it is a reason for this type of task to be a bit harder on LLMs.
I understand how base models tokenize language. What I’m curious about you’re basing your response off a horrendously screenshotted meme image of someone interacting with deepseek. Is your concern that deepseek isn’t showing the code used to approach a hex string? Because that’s certainly a valid concern, though you can ask the model to output the code it is running. That’s definitely an ethics improvement that should be made in the UI, but it’s very clear what the model is doing under the hood