it’s my nature to keep posts rather impersonal, so i have to work extra hard to tweet this sort of personal testimony below from time to time as i think the reminder in it around AI and the “value of human intelligence” is worth it as of the end of Oct 2026, i have not (yet - you may insist) had the *slightest* moment of questioning the impact of AI on the meaning of any of my work/skills - or even feeling any unusual shift beyond having an amazing (and rather often infuriating) tool believe it or not, but my ability to produce a completed artifact* (paper, model, open source software, lecture, …) and the effort, expertise, or time one takes to produce has actually barely reduced - despite comprehensive productivity benefits from AI the reason is that i essentially NEVER choose to work for a pre-determined outcome or some field-wide well-specified problem; i only work on artifacts i aim to perfect according to *my* judgment and where my personalized ambition obviously has no set limit and isn’t somehow pre-loaded onto AI better tools simply change the costs of my choices - i quite literally work on moving goal-posts if you will! when the fixed constraint are only time and energy, AI simply allows me to re-explore scope and make fewer (or different!!) tradeoffs or compromises, which feels almost no different! i’m not saying every job or every endeavor - including very advanced and historically prized ones! - looks like this today, but i’m offering: (1) at least one voice in this moment (of so many annoying loud FUD voices!) that it’s totally possible and i suspect extremely scalable and i predict will remain so for the foreseeable future! (2) why “someone like me” is indeed choosing happily to be in academia and not elsewhere! (again lots of FUD out here!, but where else do you have the freedom to keep seeking to create value for deciding and solving ~arbitrary ambitious/useful problems, establish credit for it, and train superstars who do the same?) my fear and worry in this moment is a lot less about Al or "the value of human intelligence" and much more about how concentration of power has a very reliable history of being very dangerous to large numbers of affected people! a VERY different problem imo *i guess certain of those took way longer than i expected but do stay tuned for a half dozen things on the short horizon…

my days are like `N` hours of working with frontier models, babysitting them in frustration and disbelief at the frequent errors and slop interrupted by `M` min breaks of browsing X takes about how the same models (and _especially_ the next gen!) are superhuman at ~everything

After topping Korean MTEB without being trained on Korean ( mLateOn now tops another leaderboard that includes languages it has not been trained for and outperforms much larger models (and even rerankers) mLateOn benches results are strong, but what's not evaluated by current benches makes it even stronger

In-context continual learning requires models to accumulate experience and reuse it later in the same sequence. But an RNN compresses an ever-growing history into a fixed-size state, where each token gets a single write into memory. We study dynamic compression: letting the model revisit the past and reorganize its state as it discovers what needs to be reused.

one of the best minds in this space i thought i knew just how much @antoine_chaffin and @LightOnIO have done in such little time, but wow - the list gave me a pause. congrats @antoine_chaffin and SO excited for how this accelerates @perplexity_ai’s recent releases even further!

Intuition: Compaction is agentic recurrence (RNNs), whereas recursion (RLMs) is agentic attention. Recurrence maintains a constant-size state at all times by compressing the past. Attention keeps all context fully represented and then decides how to re-process it for each step.

@NoahZiems We are happy users at Weaviate! 💚 Between BRIGHT and OBLIQ-Bench I think the retrieval community is "benchmark-rich" for now 😆 *I should caveat this by clarifying that we have just tested on the Congress subset so far.

The amazingly fast (only 30M!) and extremely accurate @answerdotai ColBERT model is now supported in Sentence Transformers to make it easy to create and query embedding indexes locally directly in Python! 🥳

Sentence Transformers is the best option to train a multi-vector model from now on ☺️ We choose to build PyLate on top of ST to benefit from all the hard work from @tomaarsen Since our work has been very convincing and impactful, it ended up back in ST, which I'm very proud of

🚨I've just released Sentence Transformers v6.0! MultiVectorEncoder joins the family: ColBERT-style late interaction models are now a first-class model type, for training, inference & interpretation, alongside dense, sparse & reranker models. Big thread 🧵