Tag
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 introduces a concept-based interpretability framework for end-to-end autonomous driving models, using unsupervised dictionary learning via sparse autoencoders to decompose driving behavior into human-interpretable concepts. The framework enables analysis and targeted correction of model decisions, improving overall driving performance.
Proposes using Top-k Sparse Autoencoders to disentangle dense sentence embeddings into human-interpretable concepts, enabling steering of retrieval results without retraining.
This paper introduces sparse autoencoders to resolve superposition in neural networks, improving interpretability and geometric fidelity of latent spaces, and presents GW-map for cross-modal alignment between image representations and single-cell RNA sequencing data.
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.
This paper introduces turn-averaged sparse autoencoders (SAEs) that operate on average activations across conversational turns, enabling efficient feature discovery and attribution graphs for long contexts. It also proposes a nested architecture for joint training with per-token features.
The paper introduces Vocabulary-Aligned Sparse Autoencoder (VASAE), a method that trains SAE features under vocabulary-aligned anchoring, assigning each feature an intrinsic token name based on nearest token embedding, achieving high alignment in early layers without reducing reconstruction quality.
Introduces PairSAE, a method that adapts sparse autoencoders to interpret pairwise representations in protein co-folding models, enabling the discovery of interpretable features that align with biological annotations and predict binding affinities.
This paper proposes a continual learning method for LLMs that uses pretrained sparse autoencoders (SAEs) to regularize in activation space instead of weight space, achieving better memory efficiency and stronger performance on benchmarks while avoiding catastrophic forgetting without storing previous data.
This paper introduces Qwen3-Instruct SAE, a suite of sparse autoencoders trained on Qwen3 instruction-tuned models, enabling the discovery of millions of interpretable features and demonstrating refusal steering capabilities.
This paper uses Dedicated Feature Crosscoders to localize RL-induced tool-use capability in Qwen2.5-3B to a single steerable feature, achieving +65pp tool-correctness via feature steering and demonstrating capability spillover to frozen base models.
This paper proposes using sparse autoencoders to detect out-of-distribution inputs for transformers, including typos and jailbreak prompts, by analyzing spurious concept activations. The method enables a mechanistically grounded fine-tuning strategy to improve LLM robustness.
This paper proposes a post-hoc certification framework for sparse autoencoder (SAE) based interpretability, deriving an upper bound on the frozen language model's risk using measurable quantities. The framework is validated on GPT-2 Small, Gemma-2B, and Llama-3-8B, showing non-vacuous bounds and revealing depth-dependent behavior.
This paper demonstrates that interventions on Sparse Autoencoder (SAE) features can be unreliable because suppressed behavior can recover through residual-space optimization, even while the intervention remains active. It reveals a critical gap between feature-level control and actual behavioral completeness in language models.
This paper proposes replacing the inner product scoring in sparse autoencoders with a learned combination of cosine similarity and input magnitude, showing that the resulting features are more interpretable and concept-aligned, with the optimizer consistently preferring cosine over inner product.
Introduces Rational Sparse Autoencoder (RSAE), which replaces fixed encoder activations with trainable rational functions, improving reconstruction and sparsity trade-offs on residual-stream activations of open-weight language models across multiple baseline families.
The paper hypothesizes that language model activations contain a low-rank dense component that is inefficiently represented by sparse autoencoders (SAEs). By adding a linear bottleneck to absorb dense structure, the authors reduce dense latents and improve sparse probing performance on Gemma-2-2B.
This paper studies seed dependence in sparse autoencoders, finding that stable features carry most predictive signal while unstable features reflect reproducible low-dimensional subspaces.
This paper identifies a shared latent mechanism across diverse backdoor behaviors in LLMs, using sparse autoencoders to detect and causally suppress these features, enabling unified backdoor detection and mitigation across models and attack types.
Query Lens extends Logit Lens to interpret sparse autoencoder features by jointly considering encoder-side key features and decoder-side value features, and accounting for indirect effects from downstream modules. The paper also introduces the Subspace Channel Hypothesis, suggesting downstream modules read features through layer-specific subspaces.