Basis retweeted
For the past year @BasisOrg has been building a general framework for probabilistic reasoning in dynamical systems, called Dynestyx.
Blog: basis.ai/blog/dynestyx/
Dynamical systems are the workhorse of science and engineering: robotics, epidemiology, climate, biology, etc.
The basic thing you can do with a dynamical system is simulate it: run it forward to compute how it will evolve over time.
Dynestyx goes beyond this and lets you do inference and reasoning in dynamical systems. This includes:
- Forecasting: What will happen next, and how sure are we?
- State inference: Given noisy, partial measurements, where has the system actually been?
- System identification: How does the system work? What parameters and rules produced the data we see?
- Causal reasoning: Given the evidence, what would happen if we intervened on the system?
Dynestyx allows you to do probabilistic inference over all kinds of dynamical systems (ODEs, SDEs, PDEs). You (or an AI agent) write the model once, then swap in different inference methods with minimal changes.
Its generality allows us to tackle many different systems, as shown in the GIFS:
- recovering a high-res Navier–Stokes fluid flow from noisy low-res measurements
- steering particles through the wake behind a cylinder
- a robot hand turning a cube it can only see through a camera
- forecasting a 1978 flu outbreak at a boarding school as the data comes in
Also: it's open source. Please give it a try: github.com/BasisResearch/dyn…
Led by Matt Levine and @DanWaxmanML, with many contributions from our research staff
When learning dynamics from data, changing your models or inference methods shouldn’t mean rebuilding your analysis.
Meet Dynestyx, our probabilistic programming library for dynamical systems. Our latest blog explores why we built it and how it helps researchers access an unprecedented suite of state-of-the-art strategies for learning dynamics with quantified uncertainty.
Read more: basis.ai/blog/dynestyx/
Want to try Dynestyx?
Start with the tutorials: basisresearch.github.io/dyne…
Explore the code: github.com/BasisResearch/dyn…
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Next week, Basis researcher Nick Jourjine will present “Towards bioacoustic welfare indices for urban rats” at the Wild Animal Initiative and Scotland’s Rural College’s seminar series.
🗓️Nov. 10 | 12:30 p.m. GMT / 7:30 a.m. EST
📍Online & Edinburgh
To attend, contact WAI researcher Janire Castellano Bueno here: wildanimalinitiative.org/eve…
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What does the world sound like to a rat? 🐀
This Halloween, attendees of the City Creatures event in SF donned rat-ear headphones to hear ultrasonic calls from Basis’ urban rat research, made audible to humans.
Learn more: basis.ai/news/hidden-sounds-…
ALT Basis co-founder Emily Mackevicius and researcher Ralph Peterson at their "Hearing Rat Perspectives" exhibit at Pestec's City Creatures event.
What does the world sound like to a rat? 🐀
Try on rat ears at “Hearing Rat Perspectives,” our interactive sound art exhibit at @pestec's City Creatures event!
📍 Terra Gallery, SF · Oct 30
RSVP: luma.com/4zcbs4yk
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Heading to DCASE2026 this week? Catch our co-founder @e_mackevicius's keynote, “Who Is the Listener? Privacy-Aware, Behavior-Grounded Bioacoustics in Urban Rats,” at 9:30 a.m. Thursday!
dcase.community/workshop2026…
🐀🎙️Really excited to give a keynote at @DCASE_Challenge workshop next week 🎙️🐀
Talk title: Who Is the Listener? Privacy-Aware, Behavior-Grounded Bioacoustics in Urban Rats
@NJourjine is also presenting a related poster at BioDCASE tomorrow! biodcase.github.io/workshop2…
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Basis researcher @ralph_ep spoke at PestWorld 2026 this week about how AI and field neuroscience can help us understand urban rat behavior and inform rodent monitoring and mitigation.
Thank you to the pest management community for the warm welcome and thoughtful conversations about this work!
Photo credit: Bobbie Wilson, Pestec
ALT Basis researcher Ralph Peterson presents about how AI and field neuroscience can help us understand urban rat behavior and inform rodent monitoring and mitigation at PestWorld 2026.
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Most programming languages lack the precise rules software verification requires—a growing bottleneck as AI gets better at writing proofs. @kirancodes used LLMs to write those rules for a small language and proved in Lean that they match how the code runs. basis.ai/blog/ship-your-inte…
For a while now, @kirancodes has been excitedly telling everyone at @BasisOrg who would listen about a crazy idea she's been pursuing. Not everyone thought it was possible, but it's starting to work. See basis.ai/blog/ship-your-inte…
TL;DR: The bottleneck in verifying the world's software is shifting from theorem proving to specifications and semantics. We now have a way to verify semantics that can scale across all programming languages.
The basic idea:
To verify that a program is correct, you need the program, a specification you want to prove, and a semantics that defines what programs and specs mean.
Most programming languages (Python, Ruby, Lua…) have no formal semantics, because semantics are painful to write for complex languages.
You can ask an LLM to write a semantics for you, but it'll probably have mistakes.
But (i) programming languages do have interpreters/compilers, which ultimately are compiled down to machine code, and (ii) some instruction sets do have formal semantics. RISC-V has an official formal model.
So, crazy idea:
Step 1: Compile your interpreter (e.g. Python's) down to RISC-V. Now its behaviour is formally defined. It's exact, but unreadable: thousands of machine instructions.
Step 2: Ask your LLM to write a semantics for your language.
Step 3: Ask the LLM to prove, in @leanprover, that its semantics agrees with the compiled interpreter on every possible program. If the semantics is wrong, the proof gets stuck, and the LLM goes back to fix it.
Step 4: If the proof goes through, you have a semantics you don't need to trust, and you can verify programs in a language that never had one, with guarantees that reach the binary that actually runs.
Kiran has now done this end to end for a simple WHILE language, and we're scaling it up to Lua and OCaml, with Python as the north star.
How do urban rats and other animals move around and socialize in the wild? 🐀
At Basis, we’re developing tools to study these questions using thermal video, 3D reconstructions, and synthetic data.
Learn more here: basis.ai/blog/environment3dt…
Last week Basis researchers attended the National Urban Rat Summit, where @Ralph_Ep presented our work using acoustic monitoring to study rat behavior, while Nick Jourjine showcased monitoring devices on a nighttime “rat walk” 🐀.
Ralph and @e_Mackevicius also met @MayorWu. Ralph spoke with WBUR about the research: wbur.org/news/2026/09/29/bos…
We're hiring research scientists in PL and other areas. Join us! basis.ai/join-us/#careers
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Basis retweeted
New preprint on learning abstract world models for robotics planning. Paper + code below. 🤖🌐
Must an agent plan by simulating pixels frame by frame, or can it think in abstractions? Consider planning an international flight: we can reason about buying tickets, changing airplanes, and crossing borders without committing to the color of the airplane or the milliseconds before takeoff. Absent abstraction, planning over long time horizons would be intractable, because every minute detail of the world would need to be simulated.
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