GEOSTEER: Geodesic Optimization for Activation Steering in Large Language Models
Updated 34 min ago · first seen 11 Sept 2026
paper_01M294FNR3EX7ZFP8XA5MR6HEW
- Published
- 11 Sept 2026
- T1 · 34 min ago
- arXiv
- 2609.10658
- T1 · 34 min ago
- Category
- cs.LG
- T1 · 34 min ago
Abstract
Activation steering provides a lightweight way to control large language models (LLMs) by modifying their hidden activations at inference time. Among these approaches, norm-preserving steering aims to change model behavior without altering the activation norm, reducing the risk of representation collapse and degradation. However, existing norm-preserving methods are limited by predefined steering trajectories and by their reliance on one-step updates, which may fail to capture the complex structure of activation distributions. We propose GeoSteer, an optimization-based method for norm-preserving activation steering. GeoSteer formulates steering as a Riemannian optimization problem and updates activations through a sequence of small geodesic steps on the representation manifold. To avoid fixed steering directions, GeoSteer learns a nonlinear activation-space objective that distinguishes desired from undesired activations, and uses this function to adaptively guide each steering step. This multistep formulation yields smoother, more stable, and more consistent steering behavior while preserving the activation norm. Across TruthfulQA, RealToxicityPrompts, and UltraFeedback benchmarks, GeoSteer consistently improves over state-of-the-art activation steering baselines. These results suggest that norm-preserving steering can be made more effective by replacing predefined one-step edits with adaptive, geometry-aware optimization.
Authors 3
Xuan Cuong Ngo, Hao Vo, Ngan Le
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
- Arxiv announce type
- new
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
- arXiv id
- 2609.10658
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
- Categories
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
- Primary category
- cs.LG
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 34 min agohigh
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9
Source tiers
T19
Freshest observation
34 min ago
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- Authors
- Xuan Cuong Ngo, Hao Vo, Ngan Le
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| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| https://arxiv.org/pdf/2609.10658 | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
New paper: GEOSTEER: Geodesic Optimization for Activation Steering in Large Language Models
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 34 min ago | 1 |
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