Or is your definition of "open source" mean that a small indie dev should be able to reproduce the entire pipeline? Because that would disqualify more than just LLMs, but also hardware platforms like Arduino where you need to pay for manufacturing to get the underlying stuff built... is Arduino "open source?"

"The current behavior paper" -> The behavior in the running system that was previously described as papered over.

"Marker transport over-claim" -> The inaccurate review finding on the object's sentinel flag in the API response.

I suppose the cryptic/invented language problem is about token efficiency? But this sort of token efficiency is extremely difficult to deal with when it comes to conversation with a human about complex system. It might be efficient inside reasoning blocks, but when the model generates the final turn text, it should avoid this, as it's brutally inefficient due to the time spent wondering what each uniquely coined phrase means and having to ask for constant clarifications, which then you have to wait for another turn, eating up time and context while it burns more xhigh reasoning just thinking about how to explain its own awful language.

Codex is a harness, while GPT-5.5 is a model. The last codex-branded model was 5.3. Codex as a harness ships as a CLI, a desktop app, and a web product (and I'm not at all sure how similar the underlying harness is between them.)

Is the bug here supposed to be with the CLI harness, or the model? Does it also happen in pi, opencode, etc while running GPT-5.5?

To be fair I appreciate the effort of running and sharing the test. It will hopefully lead to better ones. But this is not a great test. Super interesting to think about what would constitute a better test.

For one, I think the agent would have to be expected to have productive interaction through the email channel, in a way the user depends on it generally working for some real world use case / value prop. In other words, needing emails to actually have the agent really do work, respond with results, etc. Also, most requests should be legit and the real attacks should be intelligently disguised, not pitiful/joke-level spam (although those would be arguably realistic to have in the stream, but, perhaps only as deflection so that the real attack is mischaracterized.)

> LongMemEval rewards recovering literal witnesses: exact dates, counts, preferences, and spans that often remain stable under tokenization.

Is this saying they chose a benchmark that is biased towards doing well against literal string matching, thus works well with grep, and then (gasp) showed that grep did well, finally declaring "grep is all you need"?

The examples in the benchmark's demo image(1) are all examples you could see grep doing well on. A conversation about bikes, then a query about bike(s) where "bike" is a common token hit. But not stuff like a conversation about a Beethoven sonata, then a question about classical music, where embedding based approach would shine.

(1) https://github.com/xiaowu0162/LongMemEval/blob/main/assets/l...

When I read that, I thought they must be using 'fork' wrong, and actually mean branch on the official repo, as that can't be right!?" Good lord.

My big takeaway from it was that breathing and neurological state are deeply connected and actually relaxed, natural, healthy breathing (and the corresponding state of the brain and nervous system) is something that most people have probably never even experienced unfortunately. We all think our state is normal, but I assure you, it is very far from the state where your control pause is 40s-60s or more, it's a radically different experience.

Also the nuance of what the control pause and how to measure it correctly is lost on I would say, even most people who attempt to learn Buteyko. The control pause is how long you can, under normal breathing, suspend breath with zero discomfort, and then prefectly resume normal breathing without any change from before. If you took a bigger breath to start, or when you start breathing again it's even slightly heavier, it's not a control pause measurement, it becomes an ego metric juiced to make you feel better about a number while avoiding the disappointing facts.

Buteyko claimed that healthy breathing had a 40+ second control pause. Which if you think about the real meaning and how to measure it, is a super long time. And I got there sometimes, it's a major learning experience about what deep alignment and relaxation of the brain/nerves can really feel like.

If you commit to nasal breathing for exercise as a constraint, it does force you to modulate your exertion while also increasing your CO2 and developing, according to the Buteyko folks, a new baseline for respiratory health and capability.

If you're running for your life from a tsunami, by all means mouth breathe. If your purpose is maximum exertion, of course mouth breathe. But that's not the only possible purpose of exercise. It can also be about respiratory training. Nasal breathing becomes a natural guideline/modulator for long term improvement in that regard.

It's like writing an article about "the dangers of PostgreSQL" ... when generating SQL from an LLM. It has nothing to do with Postgres specifically, it's that you're generating queries to run in a trusted context from an untrustable origin.

"including six dense models distilled from DeepSeek-R1 based on Llama and Qwen. "

people just don't read? not sure there's reason to criticize ollama here.