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The problem I see with this approach is threefold. First, from a technical standpoint the required context window would be massive if you're looking at a perso

The problem I see with this approach is threefold. First, from a technical standpoint the required context window would be massive if you're looking at a person's career/life holistically. Probably solvable, but definitely something to be aware of. Second, privacy goes completely out the window since you're sharing everything. You don't know what's relevant and what's not up front so you need to provide everything. Third, you would need a training dataset of all those input variables and their outcomes to be able to provide any sort of useful output. The first set of people to share everything wouldn't be able to derive any value from the tool, and I think you'd be hard pressed to convince enough people to do it to get a useful dataset.

Archonical 2mo ago
> First, from a technical standpoint the required context window would be massive if you're looking at a person's career/life holistically. Probably solvable, but definitely something to be aware of. Why would it be massive? The application layer typically compacts a profile of information about the users financial situation when offered. I doubt many of us have financial situations that would exceed the context window. > Third, you would need a training dataset of all those input variables and their outcomes to be able to provide any sort of useful output. The first set of people to share everything wouldn't be able to derive any value from the tool, and I think you'd be hard pressed to convince enough people to do it to get a useful dataset. Would you 'need' a training dataset of input variables and their outcomes for an LLM? Certainly for traditional ML, but the LLM toolcalling can simulate what an astute user should statistically do in their situation based on information on the internet and reason about the different constraints.
foxtrot8672 2mo ago
This is correct for out of the box AIs. This is why I built Roundtable, our domain aware persistence layer for AI context management. It's what powers Pendragon. Our protections aren't just "trust us", we show you how your data is controlled architecturally and secure. You don't necessarily need to provide everything. Arthur (our AI) is smart enough to see exactly which information it needs to answer a given question. but, yes, the more information you provide the easier of a time the AI will have in answering your question. Arthur doesn't guess. if there is crucial information it needs he will ask for it. it doesn't have to be a Plaid hook up, a csv or even a simple user response is a start. On your third point — you'd need an outcomes dataset — that's true for traditional ML, but it's not how this works. The normative layer is finance itself (life-cycle theory, tax rules, amortization) implemented as deterministic calculators, with the LLM doing explanation and elicitation. The paper under discussion is sort of the proof: the models already give theory-aligned advice with zero outcome training. The gap it found is input quality and statelessness, not a missing training set.

Archonical 2mo ago

> First, from a technical standpoint the required context window would be massive if you're looking at a person's career/life holistically. Probably solvable, but definitely something to be aware of. Why would it be massive? The application layer typically compacts a profile of information about the users financial situation when offered. I doubt many of us have financial situations that would exceed the context window. > Third, you would need a training dataset of all those input variables and their outcomes to be able to provide any sort of useful output. The first set of people to share everything wouldn't be able to derive any value from the tool, and I think you'd be hard pressed to convince enough people to do it to get a useful dataset. Would you 'need' a training dataset of input variables and their outcomes for an LLM? Certainly for traditional ML, but the LLM toolcalling can simulate what an astute user should statistically do in their situation based on information on the internet and reason about the different constraints.

foxtrot8672 2mo ago

This is correct for out of the box AIs. This is why I built Roundtable, our domain aware persistence layer for AI context management. It's what powers Pendragon. Our protections aren't just "trust us", we show you how your data is controlled architecturally and secure. You don't necessarily need to provide everything. Arthur (our AI) is smart enough to see exactly which information it needs to answer a given question. but, yes, the more information you provide the easier of a time the AI will have in answering your question. Arthur doesn't guess. if there is crucial information it needs he will ask for it. it doesn't have to be a Plaid hook up, a csv or even a simple user response is a start. On your third point — you'd need an outcomes dataset — that's true for traditional ML, but it's not how this works. The normative layer is finance itself (life-cycle theory, tax rules, amortization) implemented as deterministic calculators, with the LLM doing explanation and elicitation. The paper under discussion is sort of the proof: the models already give theory-aligned advice with zero outcome training. The gap it found is input quality and statelessness, not a missing training set.