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This paper demonstrates that linear probes decoding concepts from language model activations do not necessarily identify causally relevant features, and introduces a feature-level diagnostic using sparse autoencoders to separate probe alignment from behavioral drivers.
ARIA is a test-time unlearning method for large language models that uses sparse autoencoders to suppress unwanted knowledge during inference without modifying weights, improving the forget-retain trade-off and remaining robust to adversarial attacks.
This paper investigates emergent misalignment in fine-tuned language models, using SAE-based model diffing to identify persona features that control misalignment and attributing them to pre-training web documents.
This paper investigates the compressibility of latent-space communication between vision-language model agents by fitting a post-hoc sparse autoencoder to frozen Vision Wormhole activations. It demonstrates a 128x reduction in transmitted bytes with minimal accuracy loss, revealing that the dense communication channel is highly redundant.
CADENCE uses sparse autoencoders to decompose ECG foundation model representations into interpretable physiological concepts, significantly improving alignment with clinical phenotypes and waveform morphology.
This paper introduces Augmented Sparse Encoding Models to interpret brain responses to language using sparse features from language models, validated on high-field 7T fMRI data. It recovers known neural tuning properties and discovers a new voxel population tuned to people-related content.
This paper demonstrates that Whisper's hallucination failures on silence, noise, or music can be detected and mitigated purely from internal activations using sparse autoencoders, achieving large reductions in hallucination rate without fine-tuning.
This paper investigates whether interpretable features identified by sparse autoencoders in full-precision language models remain faithful after quantization, finding systematic degradation that behavioral metrics like perplexity can miss.
Introduces Latent Reward Steering (Lrs), an adaptive inference-time framework that uses sparse autoencoder latent states and a learned reward model to implicitly promote cognitive behaviors like verification and backtracking in reasoning LLMs, improving performance across multiple models and benchmarks.
A developer built AXON, a tool that visualizes GPT-2's internal concept activations as a live 3D force graph using Sparse Autoencoders, allowing users to see interpretable features firing before token generation.
This article introduces Qwen-Scope, a toolkit of Sparse Autoencoders (SAEs) trained on Qwen3 and Qwen3.5 models to enable mechanistic analysis and intervention. It releases 14 groups of SAE weights covering dense and MoE backbones, providing sparse representations for residual-stream activations.
WriteSAE introduces the first sparse autoencoder that decomposes matrix cache writes in state-space and hybrid recurrent language models, enabling superior token-level interventions compared to existing methods.