Morris Alper

Let’s make it speak Ithkuil.

I’m fascinated by the idea of a game like No Man’s Sky where infinite new civilizations could be generated dynamically with their own languages as the player explores the universe.

That’s definitely a valid concern, and I believe we need to rethink how LLMs and other generative models are deployed and used in practice. We do explicitly discuss some of these considerations.

As a conlanger myself, I was mainly curious to explore whether LLMs could be used as a creative assistant for humans, as well as procedural generation in games with unbounded worlds. I hope this gets more people interested in conlanging and experimenting themselves.

Thanks for the heads-up, fixing this.

ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline A fully automated system for constructing languages using large language models. Our multi-stage pipeline creates coherent, diverse artificial languages with their own phonology, grammar, lexicon, and...

Try out some of the newest languages on our project page:
conlangcrafter.github.io

ConlangCrafter could potentially be used in pedagogy, typological and NLP work, and many entertainment applications. Imagine a video game where aliens can speak countless new procedurally-generated languages.

To enhance consistency and diversity, our pipeline incorporates randomness injection and self-refinement mechanisms. This is measured by our novel evaluation framework, providing rigorous evaluation for the new task of computational conlanging.

The ConlangCrafter pipeline harnesses an LLM to generate a description of a constructed language and self refines it in the process. We decompose language creation into phonology, grammar, and lexicon, and then translate sentences while constructing new needed grammar points.

Conlangs (Constructed Languages), from Tolkien’s Elvish to Esperanto, have long been created for artistic, philosophical, or practical purposes.
As generative AI proves its creative power, we ask:
Can it also take on the laborious art of conlang creation?

The number of languages in the world just got a lot higher! At least constructed ones.
Meet ConlangCrafter - a pipeline for creating novel languages with LLMs.
A Japanese-Esperanto creole? An alien cephalopod color-based language?
Enter your idea and see a conlang emerge. 🧵👇

Now accepted to #NeurIPS2025!

WildCAT3D: Appearance-Aware Multi-View Diffusion in the Wild We present a framework for generating novel views of scenes learned from diverse 2D scene image data captured in the wild.

Check out our project page and paper for more info:
Project page: wildcat3d.github.io
Paper: arxiv.org/abs/2506.13030
(5/5)

At inference time, we inject the appearance of the observed view to get consistent novel views. This also enables cool applications like appearance-conditioned NVS! (4/5)

To learn from this data, we use a novel multi-view diffusion architecture adapted from CAT3D, modeling appearance variations with a bottleneck encoder applied to VAE latents and disambiguating scene scale via warping. (3/5)

Photos like the ones below differ in global appearance (day vs. night, lighting), aspect ratio, and even weather. But they give clues to how scenes are build in 3D. (2/5)

💥New preprint! WildCAT3D uses tourist photos in-the-wild as supervision to learn to generate novel, consistent views of scenes like the one shown below. h/t Tom Monnier and all collaborators (1/5)

Disappointing that arXiv doesn't allow XeLaTex/LuaLaTex submissions, which have the least broken multilingual support of LaTeX compilers. The web shouldn't be limited to English in 2025!

AI models make precise copies of cuneiform characters | Cornell Chronicle Researchers from Cornell and Tel Aviv University have developed an approach to use artificial intelligence for reading the ancient tablets.

More coverage of our work on AI for ancient cuneiform! news.cornell.edu/stories/2025...

ProtoSnap: Prototype Alignment for Cuneiform Signs The cuneiform writing system served as the medium for transmitting knowledge in the ancient Near East for a period of over three thousand years. Cuneiform signs have a complex internal structure which...

See our paper, project page, and GitHub for more details and a full implementation!
ArXiv: arxiv.org/abs/2502.00129
Project page: tau-vailab.github.io/ProtoSnap/
GitHub: github.com/TAU-VAILab/P...

Finally we show that ProtoSnap-aligned skeletons can be used as conditions for a ControlNet model to generate synthetic OCR training data. By controlling the shapes of signs in training, we can achieve SOTA on cuneiform sign recognition. (Bottom: synthetic generated sign images)

Our results show that ProtoSnap effectively aligns wedge-based skeletons to scans of real cuneiform signs, with global and local refinement steps. We provide a new expert-annotated test set to quantify these results.

ProtoSnap uses features from a fine-tuned diffusion model to optimize for the correct alignment between a skeleton matched with a prototype font image and a scanned sign. Perhaps surprising that image generation models can be applied to this sort of discriminative task!

We tackle this by directly measuring the internal configuration of characters. Our approach ProtoSnap "snaps" a prototype (font)-based skeleton onto a scanned cuneiform sign using a multi-stage pipeline with SOTA methods from computer vision and generative AI.

Some prior work has tried to classify scans of signs categorically, but signs' shapes differ drastically in different time periods and regions making this less effective. E.g. both signs below are AN, from different eras. (Top: font prototype; bottom: scan of sign real tablet)

Arguably the most ancient writing system in the world (since ~3300 BCE), cuneiform inscriptions in ancient languages (e.g. Sumerian, Akkadian) are numerous but hard to read due to the complex writing system, wide variation in sign shapes, and physical nature as imprints in clay.

Cuneiform at #ICLR2025! ProtoSnap finds the configuration of wedges in scanned cuneiform signs for downstream applications like OCR. A new tool for understanding the ancient world!
tau-vailab.github.io/ProtoSnap/
h/t Rachel Mikulinsky @ShGordin @ElorHadar and all collaborators.
🧵👇

Our results show that ProtoSnap effectively aligns wedge-based skeletons to scans of real cuneiform signs, with global and local refinement steps. We provide a new expert-annotated test set to quantify these results.

ProtoSnap uses features from a fine-tuned diffusion model to optimize for the correct alignment between a skeleton matched with a prototype font image and a scanned sign. Perhaps surprising that image generation models can be applied to this sort of discriminative task!

We tackle this by directly measuring the internal configuration of characters. Our approach ProtoSnap "snaps" a prototype (font)-based skeleton onto a scanned cuneiform sign using a multi-stage pipeline with SOTA methods from computer vision and generative AI.