RT @ArabdhaBiswas: @sandyskim and I will be at #cshldata24 !. Sandy’s giving a talk tomorrow on hierarchical modeling of CRISPR screens. I….

RT @YuchenCui1: 🚀 I am recruiting PhD students for Fall 2025 at the UCLA Robot Intelligence Lab! 🤖 If you are interested in robot learning….

RT @christinachanc: 1/n @uclanlp is researching how Black, LGBTQIA+, & women communities perceive and are affected by content moderation, a….

RT @KonsKallas: I am looking for 1-2 PhD students interested broadly in computer systems, compilers, and/or PL! If you would like to do you….

RT @liu_anji: [1/n] 🚀Diffusion models for discrete data excel at modeling text, but they need hundreds to thousands of diffusion steps to p….

RT @renatogeh: Where is the signal in LLM tokenization space?. Does it only come from the canonical (default) tokenization?. The answer is….

Check out our tool at and our PLDI paper at This work is a collaboration with @zengola, @guyvdb, and

HyBit uses the above theoretical results to bit-blast arbitrary discrete-continuous probabilistic programs. We scale with discrete structures in hybrid probabilistic programs better than state-of-the-art inference algorithms on a comprehensive suite of benchmarks.

We show that a large class of common distributions can be bit blasted succinctly without any loss of accuracy! We have also proved that these distributions exhibit efficient probabilistic inference.

HyBit discretizes continuous distributions in a probabilistic program to a specified bit-width and performs inference on the resulting discrete program. It does so via our newly developed approach to discretization called bit blasting💥.

Are you looking for an inference algorithm that supports your discrete-continuous probabilistic program? Look no further! We have developed a new probabilistic programming language (PPL) called HyBit that provides scalable support for discrete-continuous probabilistic programs.

RT @HonghuaZhang2: Proposing Ctrl-G, a neurosymbolic framework that enables arbitrary LLMs to follow logical constraints (length control, i….

RT @siyan_zhao: 🚨LLM RESEARCHERS🚨Want a free boost in speed and memory efficiency for your HuggingFace🤗LLM with ZERO degradation in generat….

RT @zengola: Very excited for LAFI@POPL coming up this Sunday: @Hong_Ge2 will be presenting our keynote! .18 accept….

RT @KareemYousrii: Neuro-Symbolic (NeSy) methods inject constraints into NNs, but do not support autoregressive models e.g. transformers. W….

RT @KareemYousrii: Featuring our chapter on semantic loss, neuro-symbolic entropy regularization and constrained adversarial networks! So g….

RT @zhezeng0908: Uncertainty quantification for neural networks via Bayesian model average is compelling, but uses just a few samples in pr….

Shoutout to @kaavya_sahay for her new paper on new year.

Great findings from @uclanlp on bias propagated via vision language models in their new paper, ENTIGEN.