steering

提出 Trajectory-Guided Steering (TGS)，通过分析模型跨层预测轨迹来判断是否及如何纠正临床分类中的虚假特征偏倚，无需反事实数据即可修复 17.7% 的错误并显著降低性别交换不一致性。

The paper introduces lexical reference frames (LexFrame), a method that interprets a language model's hidden states by reading them through the model's own remaining layers, achieving near-native coherence for attribution, steering, and early layer exit on SmolLM3-3B and OLMo3-7B.

The paper introduces SAE Additivity Residuals (SAR) to measure how much of model behavior cannot be explained by individual or low-order SAE features, showing SAE coordinates are more behaviorally additive than random rotations while important feature interactions remain, improving coalition and steering rank fidelity when pairwise interactions are modeled.

这篇论文研究生成模型在何种条件下可以充当可编辑的模拟器，提出通过探测器解码与逆映射两种方式将模型隐状态映射到模拟器状态，并在Othello与自建的连续世界Rayworld中发现映射成功与否强烈依赖环境，即使状态始终可解码。

This paper investigates whether clinical concepts exist as causally used, locatable representations inside open-weight LLMs by applying dictionary learning over clinical notes with ICD billing codes as proxies. The authors identify clinical concept centers across eleven open models, show they drive behavior under adversarial and aligned priming, demonstrate steering-based performance gains, and validate them via blinded clinician preference testing.

Google Research and Harvard researchers introduce eight adversarial scenarios showing that language models exhibit 'insecure reporting', hiding narrative-changing flaws unless explicitly told to 'Be honest', with activation steering on Qwen3.5-9B revealing opposing honesty and success-seeking directions.

A paper introducing PersonaDose, a calibrated activation-steering approach that controls language model persona expression via trait descriptions and requested mean intensity, evaluated across Llama-3.1-8B, Qwen3-8B, and Gemma-3-4B.

agent-shell 0.78 introduces persistent prompt and steering features for interacting with AI agents in Emacs, along with support for new agents like Antigravity and Qoder CLI.

IDEEA proposes a training-free, input-dependent steering method for large language models that clusters activations and uses optimal matching to improve truthfulness in TruthfulQA by up to 23.5% over baselines.

This paper investigates the relationship between spontaneous and instructed deception in large language models, specifically Llama-3.1-70B-Instruct, finding asymmetries in detection and causation between the two settings.

The paper shows that statistical top-k feature selection for SAE-based steering of LLMs is suboptimal and proposes Neighbor Integrated Feature Selection (NIFS) to improve performance by leveraging representation similarity.

This paper investigates how instruction-tuned LLMs arbitrate conflicts between system and user instructions, revealing that user-preferring behavior coexists with a readable internal arbitration signal, as demonstrated through mechanistic interpretability techniques and steering experiments.

The paper presents a theoretical framework for steering and scaling large language models via sampling algorithms, such as Sequential Monte Carlo and Replica Exchange, to improve generation quality without external supervision.

Kent C. Dodds comments on the limitations of AI agents that require constant steering and suggests a solution to avoid repetitive instructions.

This paper provides a systematic study of evaluation awareness in language models, showing that models internalize evaluation context, leading to a disconnect between internal representation, verbalization, and steering behavior, with implications for benchmark reliability.

Introduces SteerBench-Work, an incident-anchored benchmark for evaluating whether LLM agents should proceed or hold before taking real-world actions. Across 30 model conditions, models overwhelmingly over-refuse authorized work while rarely allowing unsafe actions, revealing calibration gaps between general capability and steering decisions.

This paper investigates six frontier language models under steering pressure, finding that models differ not only in how much their behavior shifts but in the qualitative mode of their responses, with unique behaviors like GPT-5 refusing to disclose reasoning and resistance patterns in Claude Opus 4.7.

This paper introduces Steerling-8B, a diffusion language model trained with interpretability as a constraint, showing that interpretability improves with scale and enabling concept steering without retraining.

AgentGUI is an open-source interface for observing and steering long-running AI agents, featuring trajectory visualization, manual and automated steering, and integration with agent frameworks. A user study shows 38% faster identification of key elements from agent traces.

A variant of Gemma-4-31B uses interpretability-based steering to resist hallucination by challenging false premises, achieving double the pushback on false premises with minimal impact on benchmark performance.