Seth Karten
@sethkarten.ai
160 followers 270 following 140 posts
Autonomous Agents | PhD @ Princeton | World Gen @ Waymo | Prev: CMU, Amazon | NSF GRFP Fellow
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sethkarten.ai
🚀 New preprint!
🤔 Can one agent “nudge” a synthetic civilization of Census‑grounded agents toward higher social welfare—all by optimizing utilities in‑context? Meet the LLM Economist ↓
Diagram of LLM Economist: left—grid of persona‑conditioned worker agents; center—planner LLM sends tax schedule; right—social‑welfare ‘hill‑climb’.
sethkarten.ai
You probably aren’t reading enough papers.
You probably didn’t cite the 10 closest papers to your work
Thus, LLMs probably have a better understanding of where your paper sits in the literature ¯\_(ツ)_/¯
sethkarten.ai
We should have the highest standards for the most influential research companies
sethkarten.ai
In the future people will play games for the mind similar to going to the gym for the body
sethkarten.ai
If you arent paying attention, we are in a rapidly shifting period of ML paper culture. ICLR/ICML/NeurIPS are being treated as random, out of touch processes with more and more unnecessary work to submit
Most people are saying TMLR is the only good alternative, but are skeptical
sethkarten.ai
The most interesting papers arent being published at the “prestigious” venues anymore. Where are you publishing and what do you work on?
sethkarten.ai
🚨 Hackathon Weekend! 🚨

Jumpstart your PokéAgent Challenge submission ahead of NeurIPS!

📅 Sept 13–14
✅ Leaderboards reset Sat 10AM EDT
🎙️ Lightning talks in LLMs, RL, and Pokemon
💬 Live Office hours
🏆 $2k in prizes
PokéAgent Challenge @ NeurIPS 2025 Hackathon Weekend Schedule. Saturday, Sept 13th: 10 AM leaderboards reset; 12–1:30 PM livestream talks (overview, Aaron Traylor on Pokémon as an AI Problem, Seth Karten on Pokéchamp, Jake Grigsby on Metamon, plus more). Sunday, Sept 14th: 1–3:30 PM organizer office hours; 11:59 PM top teams earn up to $2k in GCP credits. Sponsored by Google DeepMind and AIJ.
Reposted by Seth Karten
sethkarten.ai
The NeurIPS 2025 PokéAgent Challenge is offering compute credits, courtesy of our sponsor Google DeepMind, to help you train bigger models & run more experiments.

📌 To apply:
1️⃣ Make a submission to Track 1 or 2 at pokeagent.github.io
2️⃣ Fill out the compute credit form on the site
PokéAgent Challenge - NeurIPS 2025
pokeagent.github.io
sethkarten.ai
We need an AI sports league and US robotics olympics
sethkarten.ai
Does this include survey papers?
sethkarten.ai
Eventually you could train this, but currently context switching at this general level is far too difficult. And I think the modular agentic system approach makes the most sense as this could scale to parallel running components
sethkarten.ai
A foundation agent could be equally as modular with a harness to facilitate context switching. You could even wrap the harness in an llm harness to for routing which module to use
sethkarten.ai
If you were deploying a robot irl, you would have
- a perception harness for visual understanding
- a planning harness for long horizon reasoning
- a control harness to make sure the actions are executed properly
sethkarten.ai
The solution would generalize to another two player partially observable turn-based text game. The most bespoke items are tools, but there has been work recently that shows that you can make these tools modular LLM calls, further increasing generality
sethkarten.ai
Interesting study. Though, are you in the model purist camp? (Give raw state. Let model do all the work.) Alternatively scaffolding/harness can do wonders improving the planning capabilities of LLMs: arxiv.org/abs/2503.04094
sethkarten.ai
Mad about data centers? Call your reps to build more nuclear
sethkarten.ai
Hey #academics

Why are neurips workshop deadlines due a month before main track acceptances? Seems counterintuitive to have the two tracks compete with each other

#machinelearning
sethkarten.ai
The NeurIPS 2025 PokéAgent Challenge is offering compute credits, courtesy of our sponsor Google DeepMind, to help you train bigger models & run more experiments.

📌 To apply:
1️⃣ Make a submission to Track 1 or 2 at pokeagent.github.io
2️⃣ Fill out the compute credit form on the site
PokéAgent Challenge - NeurIPS 2025
pokeagent.github.io
sethkarten.ai
While the foundation model was not explicitly trained on the task, it can use its budget to adapt to the task distribution — meta-learning
sethkarten.ai
Ok this is helpful to understand where people may get confused. Rather than giving a foundation model examples, one can have to foundation model explore, exploit, and improve all in-context (eg ICRL). This continuous improvement allows the foundation model to act as meta-learners
sethkarten.ai
Whats your definition of each?
sethkarten.ai
If your final product doesnt reason in-context, how is it supposed to meta-learn and address distribution shifts and environment changes?
sethkarten.ai
Papers are dead. Maybe it is time to start the youtube channel…