aseg

Previous WR's for longest flight were mostly in the Andes or Alps. You want to be in a mountain range to utilize either the [ridge lift](https://en.wikipedia.org/wiki/Orographic_lift) of a mountain face or [mountain wave](https://en.wikipedia.org/wiki/Lee_wave), ideally in a polar region during the summer to maximize the daylight hours so you can fly under VFR for longer.

However, while the Sierra Nevada's have great mountain wave and ridge lift, the number of daylight hours is not ``competitive''. Their main innovation was in acclimatizing themselves with using night vision goggles for long duration in a glider. There's an article on this [here](https://magazine.weglide.org/gliding-at-night-breaking-the-3...) which describes the acclimatizing flights and the 3k km flight in great detail. It doesn't get official recognition because the FAI requires the flight to be done in daylight, but still an extremely cool flight!

Here are the reviews for this paper btw: https://openreview.net/forum?id=xNsIfzlefG

And here's a list of all the rejected papers: https://openreview.net/group?id=ICLR.cc/2025/Conference#tab-...

Oh btw, it needs to be able to react in milliseconds (so no AI, unfortunately). Here's an example of what an early release looks like btw: https://youtu.be/Gu0mZC2mLEg?si=dzVMxG-rW5624T_m

notice how he's always on the stick. Also notice how fast it goes from stable to unstable positions.

> even a crash

Recklessness is never the answer in aviation (or coding matter of fact). Practically, good luck convincing insurance to cover a 100 ton (any appreciable cargo load) plane that might fall out of the sky on any property in the general vicinity.

This is my biggest concern with this concept as well. Towing things is challenging because the tow plane's center of gravity can change drastically depending on the forces on the glider it is towing -- if the glider deploys its spoilers / crabs in a crosswind / gets in your wake turbulence you're not going to be able to predict how it changes your CG (and your control authority) without training or experience. Also, with gliders, the tow plane is traveling at around 60MPH to 90MPH, with a decision window of 2-3 seconds. Commercial planes travel at ~500 MPH... The concept seems like a hard sell to the pilot unions. I bet they've thought about this though.

3. I have a couple of graphics motivating, conceptually, what an island/population looks like: https://trishullab.github.io/lasr-web/ . This whitesheet might also be useful: https://arxiv.org/abs/2305.01582

4. This is an interesting question. I believe so. However, my observations were derived from a non turing complete language (mathematical equations). There might be other ways of enforcing a succinctness pressure.

- One lesson DeepMind drew from AlphaCode, AlphaTensor, and AlphaChip is that large‑scale pre‑training, combined with carefully chosen inductive biases, enables models to solve specialized problems at—or above—human performance.

- These systems still require curated datasets and experts who can hand‑design task‑specific pipelines.

- Conceptually, this work is an improved version of FunSearch (https://github.com/google-deepmind/funsearch/).

- In broad terms, FunSearch (and AlphaEvolve) follow three core design principles:

    - Off‑the‑shelf LLMs can both generate code and recall domain knowledge. The “knowledge retrieval” stage may hallucinate, but—because the knowledge is expressed as code—we can execute it and validate the result against a custom evaluation function.

    - Gradient descent is not an option for discrete code; a zeroth‑order optimizer—specifically evolutionary search—is required.

    - During evolution we bias toward (1) _succinct_ programs and (2) _novel_ programs. Succinctness is approximated by program length; novelty is encouraged via a MAP‑Elites–style “novelty bias,” yielding a three‑dimensional Pareto frontier whose axes are _performance, simplicity,_ and _novelty_ (see e.g. OE‑Dreamer: (https://claireaoi.github.io/OE-Dreamer/).

- Any general‑purpose foundation model can be coupled with evolutionary search.

- A domain expert merely supplies a Python evaluation function (with a docstring explaining domain‑specific details). Most scientists I've talked with - astronomers, seismologists, neuroscientists, etc. - already maintain such evaluation functions for their own code.

- The output is an interpretable program; even if it overfits or ignores a corner case, it often provides valuable insight into the regimes where it succeeds.

Cons

- Evolutionary search is compute‑heavy and LLM calls are slow unless heavily optimized. In my projects we need ≈ 60 k LLM calls per iteration to support a reasonable number of islands and populations. In equation discovery we offset cost by making ~99 % of mutations purely random; every extra 1 % of LLM‑generated mutations yields roughly a 10 % increase in high‑performing programs across the population.

- Evaluation functions typically undergo many refinement cycles; without careful curation the search may converge to a useless program that exploits loopholes in the metric.

Additional heuristics make the search practical. If your evaluator is slow, overlap it with LLM calls. To foster diversity, try dissimilar training: run models trained on different data subsets and let them compete. Interestingly, a smaller model (e.g., Llama-3 8 B) often outperforms a larger one (Llama‑3 70 B) simply because it emits shorter programs.

  Location: Pasadena, CA
  Remote: No/Yes
  Willing to relocate: Yes
  Technologies: Computer Vision, Code generation, Program Synthesis, Coq, Lean, PyTorch, Julia
  Résumé/CV: https://atharvas.net/cv/
  Email: [email protected]

- Symbolic Regression @ NeurIPS24 (https://trishullab.github.io/lasr-web/)

- Visual Programming @ CVPR25 (https://trishullab.github.io/escher-web/)