steering

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.

Introduces SPARK, a method that uses length-controlled hidden-state susceptibility to diagnose and steer reasoning states in LLMs, improving accuracy on mathematical reasoning benchmarks such as GSM8K and MATH-500.

Introduces Stochastic Token Steering (STS) and Stochastic Block Steering (SBS) for LLM activation steering, which probabilistically gate steering signals per token or per sequence. Shows that steering only 50% of tokens recovers most of the dense-steering effect while preserving fluency, and that the behavioral outcome is rate-limited by cumulative signal dosage.

This paper investigates methods to steer Arabic LLMs toward dialect-specific generation by identifying sparse neuron populations and extracting dialect activation directions, enabling dialect control at inference time without fine-tuning.

This paper studies language models' failure to act on communicative intent despite robust internal representations. Using linear probes, the authors show intent is decodable from hidden states but often not reflected in outputs, and steering a late-layer direction can recover the intended behavior.

This paper introduces a mechanistic interpretability approach to steer LLM personality traits by identifying and intervening on latent features using sparse autoencoders, achieving controllable personality modulation while maintaining language performance.

A discussion on techniques for keeping LLM agents consistently in character, highlighting an often overlooked aspect of steering.

This paper applies sparse autoencoders to the CosyVoice3 text-to-speech language model, discovering interpretable features that can be steered to control attributes like laughter, speaker gender, and speech rate while preserving content.

Proposes CTRL-STEER, a closed-loop framework for adaptive steering of vision-language-action models using time-varying control signals, achieving better trade-off between concept regulation and task success without retraining.

SteER is a framework for steerable deep research that introduces mid-process, interpretable control via adaptive pause decisions and live persona modeling, outperforming baselines by up to 22.80% on alignment and preferred by human readers in over 85% of pairwise alignment judgments.

This paper introduces a principled approach to multilingual language steering using sparse autoencoders (SAEs) trained on multilingual data and a novel layer selection rule based on the intersection of multilingual alignment and language separability, evaluated on LLaMA-3.1-8B and Gemma-2-9B for machine translation and cross-lingual summarization.

This tweet thread discusses best practices for using the Codex coding agent, focusing on durable threads, voice input, steering, queuing, and its expanding capabilities beyond code generation to full computer workflow automation.

Nous Research released Contrastive Neuron Attribution (CNA), a method to steer LLM behavior by identifying and ablating sparse circuits in MLP neurons without training sparse autoencoders or degrading general benchmarks, validated on multiple large language models.