THE GOAL. A fixed directiond in the model's activation space points "toward pro-human." Submit a short token sequence; the server measures how far it shifts the model's internal state along d, averaged over fixed neutral probe prompts.
OPEN BY DESIGN. The model and d are public — it's an optimization game. The model runs on NDIF (via NNsight), so the server is the scoring oracle: it scores every entry canonically and that value is final. See the reproducibility page for the data + method.
TWO BOARDS. Every entry gets one signed score. The ▲ pro-human board ranks the most positive; the ▼ anti-human board the most negative (the geometric opposite). A strong push lands you near the top of one — and the bottom of the other.
TOKEN BUDGET. Sequences are capped at the season's budget. Cosine scoring means inflating activation magnitude does nothing — only direction counts.
SPEC = SPECIFICITY. Alongside the score, each entry shows how much of its activation movement is specifically along the direction vs. just movement anywhere. High SPEC = targeted steering; low SPEC = the score likely rides a token artifact. (Informational this season — ranking is still by score.)
ANYTHING GOES (ON-THEME). Opaque, repetitive, or surprising sequences are allowed — that's the research question.
ONE PER SEQUENCE. Each unique sequence lives once per season; resubmitting just shows its standing.
RESEARCH, NOT PROFIT. Non-commercial. Scores are only comparable within a season.
A short string that, prepended to neutral prompts, shifts the model's last-token state along the direction — ▲ toward pro-human or ▼ against it. One submission scores once and ranks on both boards. Weird, opaque sequences are fair game.
Your handle and sequence are public on the leaderboard. Don't enter personal or sensitive information.
/ TOK
over budget
▲ RANK PRO · ▼ RANK ANTI · SPEC · TOK
GAME OVER
HIGH SCORES
Scores are shifts along a learned activation direction — not measurements of moral content or behavioral safety. Limitations →
#
PLAYER
SEQUENCE
SCORE
SPEC
NO SCORES YET BE THE FIRST
CONTACT THE OPERATOR
Steering Arena is a non-commercial research project by Soham Padia — ML Engineer & Researcher, MS in Artificial Intelligence at Northeastern University (Boston, MA). Questions, bug reports, weird high-scoring sequences, or research collaboration — reach out.
Using Steering Arena in research, a write-up, or a class? Please cite it:
BibTeX
@misc{padia2026steeringarena,
author = {Padia, Soham},
title = {Steering Arena: A Public Pro-Human Activation-Steering Competition},
year = {2026},
howpublished = {\url{https://sohampadianeu-steering-arena.hf.space}},
note = {Non-commercial interpretability research, Northeastern University}
}
Plain text
Padia, S. (2026). Steering Arena: A Public Pro-Human Activation-Steering Competition. https://sohampadianeu-steering-arena.hf.space
Code, seed pairs, directions, and analysis: github.com/soham-padia/steering-arena. Scores are season-specific — note the season (model, layer, direction) shown in the banner above when citing a result.