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If USDT gets to a frozen list - that means that Tether does not need to comply with requests to convert it into USD? I guess this is very useful for Tether and the other stable coin emitters.

Yeah - it depends on the frequency. I think the other guy is right that when it happens with low frequency then calling it 'context poisoning' makes sense - because cleaning the context mostly fixes it. I still would be very careful about using it - because it might be interpreted as if the poisoned context was the root cause - it might be a cause for the downstream processing - but it itself is caused by something else - so it will recur.

My model is about gaps - all ideas start with gaps. LLMs can fill in these gaps quickly - but some ideas are just bad - so the filling in does not improve them. Maybe it would be useful to treat that filling in as a kind of hallucination - the LLM trying to meed impossible requirements. But on other cases the ideas are good and the LLMs fill in the gaps in the right way. It is a kind of skill to see when LLMs are hallucinating in the gaps. I use LLMs very extensively and I am adding checks to that should catch when the filling in is weak - but it is still not fully autonomous.

I also try to keep the llm-enhanced (or even fully llm-generated) content separate from my human writing - because it is true that the failure modes are different. But think LLM writing might be useful even if it is not consumed directly - it can enhance LLM answering your questions.

My point is that calling it context poisoning is confusing because of two things. First - it is hard to establish how causal it is for the downstream. Second - cleaning the context would not fix it - because it would be poisoned on next round - you need active measures to fight it.

It is also a bit tongue in cheek - if you did not notice - because of course all of this can be explained if needed even if the name alone suggests something else.

"iced tea" in context was a symptom - it might influence the downstream - but it is hard to establish that. The root problem was in weights - naming it 'context poisoning' is poisoning our reasoning context :)

  > Several times, Gemini 2.5 became convinced it needed to get tea, even though it 
  > was playing the older game. The tea task for FireRed was in its weights, so it 
  > poisoned its context. It spent hours trying to find or give the guard tea. Once 
  > it had been added to its to-do list, the model could not get past it.

I don't understand why this is called context poisoning when the information was in the weights

I wish someone applied this as being ambitious about the quality of code.

'instructing a machine' is programming - how about this?

Product managers did not write instructions to a machine.

It is all about definitions. Writing instructions for llms is a new activity and we can call it as we wish - but I like to think about it as a new type of programming language instead of thinking about it as something completely new, because many analogies do transfer and you can use them to reason about larger systems composed of prompts. It is after all a way to instruct a machine.

There are differences and the article highlights some of them - but there are difference between programming in a functional language and an imperative one, there are differences between programming for a RISC machine and a CISC machine, there a lots of different semantics that programming languages support. Now we have a language with underspecified semantics.

IMHO instructing llms is programming - the analogy is not perfect but it is so close that it is quite useful. Programming is instructing a machine. We have now two types of programming - the traditional one that follows strict semantics and the new way of programming by prompting. The article still holds by showing the limitations of this new kind of programming.

I don't know - all of these cache hit rate advocacy is kind of goodhearting. Cache hit rates are less important than the accuracy of LLM answers and people seem to forget about it. For example I've seen articles how sub-agents poison the cache. Sure you can fill up your context with irrelevant data to have high cache hit rate - but that degrades the LLM work - sub-agents are about providing the LLM with a clean context - just the information needed to do its job, no distraction.

All of the main providers now offer cache management that can work with sub-agents - but it was kind of bolted on - and all of them have different apis - so nobody uses them anyway.