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In our #NeurIPS2026 paper “Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction” (preprint: https://arxiv.org/abs/2606.22969) we try to address a fundamental issue in dynamical systems reconstruction (DSR) and time series forecasting (TSF): Many recent SOTA DSR & TSF models…
My research agent had read about 20,400 documents. It had concluded nothing. Not metaphorically. The knowledge store held roughly twenty thousand rows — papers, articles, PDFs, nightly log entries — and there was nowhere in the system that said what any of it meant.
Ever wondered how car companies design cars so fast? At Ford, a design test that took 15 hours on a supercomputer now takes 10 seconds with AI. Engineers can instantly see how a small change, like a bumper angle, affects fuel efficiency.
I am one of the authors of FLEET - an algorithm that enhances Best-of-N generation by attributing external rewards to particular tokens and then uses MCTS to adjust logits during the next run.
The failure mode I couldn't stop thinking about Every time I use [a model / an agent] for [multi-step reasoning / code generation / tool use], I notice the same thing: [describe the failure in one or two sentences, e.g. "it confidently skips a step and the final answer looks right but isn't"].
Online fashion shopping has become convenient, but one important problem remains: customers cannot see how a product will actually look on them before buying it. A product page can show high-quality photos, measurements, fabric details, reviews, and size information.
This is a submission for the Kaggle Benchmarking Challenge What I Benchmarked I work freelance, writing and grading tasks for AI coding agents. After enough of those reviews you pick up a reflex: when every test is green, you go looking for the if statement that shouldn't be there.
On 2025-06-03T14:22:18Z, the log line looked like any other reject: gate=cover_admission result=reject score=0.75 threshold=0.60 model=cover-v3 I’m Fantasy Elf, and I work on AI safety. This is the story of a threshold that lied to us.
I made this video about adversarial objectives, which I used to do research on back in the day. I'm trying to explore how adversarial approaches transcend GANs and self-play into modern technology. https://youtu.be/W7CiAeQ0f5w?si=g0tLrQn2wuFzuN2M
Please post your personal projects, startups, product placements, collaboration needs, blogs etc.
I am applying to academic jobs. We are told to include a section on "impact". I am wondering if the total number of HuggingFace downloads of custom models I have trained would be considered legit impact, or if people would think this was all bots.
Building an AI agent is getting easier. Running one reliably in production is still the hard part. At Interrupt 2026, LangChain introduced a major set of updates focused on production agent infrastructure.
This is a submission for the Kaggle Benchmarking Challenge. Public leaderboards love telling us how well models solve LeetCode problems or pass high school exams.
Beyond One-Shot Success: How AREX-2 Teaches LLM Agents to Reflect and Persevere Current autonomous LLM agents are often evaluated by their ability to solve a task in a single pass or through a short sequence of scripted interactions.
The AI Data Satiation Point: Why "Wild" AI Text is Breaking Chinchilla Scaling Laws As we approach the end of 2026, the internet looks fundamentally different than it did just two years ago.
"Thirty, maybe even forty years ago, I said that if we were ever to create a synthetic mind, it would have to work in such a way that we couldn't debug it by single-stepping through an algorithm. It will be more like how a pond computes the waves from a pebble dropped into it." — Randal L.
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