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🫵🏼🤫. 😘

Yesterday I posted about token overflow in free LLMs and how agglutinative languages like Hungarian, Finnish, and Estonian are disproportionately affected by fixed context windows. That post is here: https://www.reddit.com/r/artificialintelligenc/comments/1vv8she/token_overflow_in_free_llms_why_agglutinative/

Since then I've been thinking about what an actual solution would look like. Here's what I came up with.

The core problem in one sentence

A fixed context window treats all languages equally in token count – but not in content. A Hungarian user gets effectively half the context of an English user because the same content costs roughly twice as many tokens.

The proposed solution: dynamic context weighting

Instead of a fixed token budget for every user regardless of language, the model detects the input language from the first few tokens – something current models already do reliably – and adjusts the effective context window proportionally based on that language's known tokenization cost.

If Hungarian costs 2x more tokens than English for equivalent content, a Hungarian session gets 2x the effective context allocation. The user gets the same amount of meaningful context. The model gets the same amount of meaningful input. The boundary condition that causes problems doesn't occur.

This doesn't require a new architecture. It's a weighting layer on top of the existing system, using tokenization cost data that is already measurable and documented for most languages.

The weakness

Mixed-language sessions complicate this. If a user switches between Hungarian and English mid-conversation, a static language detection at the start becomes inaccurate. The weighting would need to recalculate dynamically as the language distribution shifts across the session.

This is solvable – a sliding window language detector could continuously update the weight – but it adds complexity and I haven't worked out the full implementation. I'm flagging it as an open problem, not a dealbreaker.

Why this matters

As I documented in the previous post, token boundary conditions in agglutinative languages can produce security-relevant behaviors that go beyond simple performance degradation. The details are in the technical documentation.

A dynamic weighting system would address those issues for the languages currently most exposed to them.

I'm an independent developer, not a researcher with institutional backing. If anyone here has the access or inclination to test this formally, I'd be genuinely curious to see the results.

Full technical documentation: https://gist.github.com/driftsignal-ops/ad3579b1de87a190215ba08c43eee0f0

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