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Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
InterpretabilityDictionary LearningMachine LearningLarge Language Models
Abstract:

Translating the internal representations and computations of models into concepts that humans can understand is a key goal of interpretability. While recent dictionary learning methods such as Sparse Autoencoders (SAEs) provide a promising route to discover human-interpretable features, they often only recover token-specific, noisy, or highly local concepts. We argue that this limitation stems from neglecting the temporal structure of language, where semantic content typically evolves smoothly over sequences. Building on this insight, we introduce Temporal Sparse Autoencoders (T-SAEs), which incorporate a novel contrastive loss encouraging consistent activations of high-level features over adjacent tokens. This simple yet powerful modification enables SAEs to disentangle semantic from syntactic features in a self-supervised manner. Across multiple datasets and models, T-SAEs recover smoother, more coherent semantic concepts without sacrificing reconstruction quality. Strikingly, they exhibit clear semantic structure despite being trained without explicit semantic signal, offering a new pathway for unsupervised interpretability in language models.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces Temporal Sparse Autoencoders (T-SAEs), which extend standard sparse autoencoders by incorporating a contrastive loss to encourage consistent feature activations across adjacent tokens. Within the taxonomy, it occupies the 'Temporal and Sequential Extensions' leaf under 'Sparse Dictionary Learning Methods', where it is currently the sole paper. This places it in a relatively sparse research direction, as the broader 'Sparse Dictionary Learning Methods' branch contains only seven papers across four leaves, with most work concentrated in 'Standard Sparse Autoencoder Approaches' (two papers) and domain-specific applications.

The taxonomy reveals that most interpretability work focuses on static feature extraction or geometric analysis of embedding spaces. The 'Embedding Space Analysis and Geometry' branch (eleven papers across four leaves) and 'Semantic Representation Learning Foundations' (ten papers across three leaves) represent more crowded areas. The paper's temporal approach connects to 'Layer-Wise Representation Dynamics', which examines how representations evolve across network depth, but diverges by focusing on sequential token-level evolution rather than layer-wise progression. The taxonomy's scope notes clarify that temporal modeling distinguishes this work from standard SAEs, which treat activations independently.

Among thirty candidates examined through semantic search, none were found to clearly refute any of the three core contributions. The data-generating process distinguishing semantic and syntactic variables, the T-SAE architecture with contrastive loss, and the empirical validation each examined ten candidates with zero refutable overlaps. This suggests that within the limited search scope, the specific combination of temporal contrastive learning applied to sparse autoencoders for semantic-syntactic disentanglement appears relatively unexplored. However, the search scale (thirty candidates, not hundreds) means substantial prior work outside this sample remains possible.

The analysis indicates the work occupies a genuinely sparse research direction within the taxonomy, with no direct siblings in its leaf and limited temporal extensions elsewhere in sparse dictionary learning. The absence of refutable candidates across thirty examined papers, combined with the taxonomy structure, suggests the temporal contrastive approach represents a distinct methodological contribution. However, the limited search scope and the paper's position in a young subfield mean this assessment reflects current visibility rather than exhaustive coverage of all potentially related work.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Discovering interpretable semantic features in language model representations. The field has organized itself around several complementary perspectives. Sparse Dictionary Learning Methods (including foundational work like Sparse Autoencoders Interpretability[2] and domain-specific extensions such as Protein SAE Features[5] and SAE RNA[29]) aim to decompose dense activations into sparse, human-interpretable feature sets. Embedding Space Analysis and Geometry explores the structural properties of learned representations, examining how semantic relationships manifest in vector spaces (e.g., Word Embedding Survey[22], Hyperbolic Word Embeddings[32]). Layer-Wise Representation Dynamics investigates how meaning evolves across network depth (Layer by Layer[3]), while Interpretability Evaluation and Benchmarking develops systematic ways to assess feature quality (Automatically Interpreting Features[49]). Additional branches address semantic representation learning foundations, implicit representations in neural models (Implicit Meaning Representations[10]), self-interpretation mechanisms (SelfIE[45]), representation manipulation and control (Word Embeddings Steers[6]), and specialized contexts ranging from emotion decoding (Decoding Emotion Patterns[27]) to sociopolitical frames (Sociopolitical Frames Interpretability[44]). A particularly active tension exists between static feature extraction and dynamic, context-sensitive approaches. Many studies focus on extracting fixed dictionaries of features, but a growing line of work examines how features evolve temporally or across contexts (Context Preserving Interpolation[4], Reasoning Memorization Direction[13]). Temporal Sparse Autoencoders[0] sits squarely within the Sparse Dictionary Learning branch but extends it to capture sequential dependencies, addressing a key limitation of standard sparse coding methods that treat each activation independently. This positions it alongside RAVEL[1] and other temporal extensions, contrasting with purely spatial decomposition approaches like Sparse Autoencoders Interpretability[2]. The work bridges static interpretability methods and dynamic representation analysis, offering a way to understand how semantic features unfold over processing steps rather than treating them as isolated snapshots.

Claimed Contributions

Data-generating process distinguishing semantic and syntactic variables

The authors formalize a framework modeling language production through latent variables that separate high-level semantic features (which remain consistent over sequences) from low-level syntactic features (which vary locally). This framework provides theoretical guidance for designing interpretability methods that can recover these distinct types of linguistic information.

10 retrieved papers
Temporal Sparse Autoencoders with contrastive loss

The authors propose Temporal SAEs (T-SAEs), a modification to standard Sparse Autoencoders that partitions features into high-level and low-level components and incorporates a contrastive loss encouraging high-level features to activate consistently over adjacent tokens. This enables self-supervised disentanglement of semantic from syntactic features without requiring explicit semantic labels.

10 retrieved papers
Empirical validation of semantic recovery and disentanglement

The authors demonstrate through extensive experiments that T-SAEs recover smoother and more coherent semantic concepts compared to baseline SAEs while maintaining reconstruction quality. They show improved recovery of semantic and contextual information, better disentanglement between feature types, and practical benefits for applications like safety monitoring and model steering.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Data-generating process distinguishing semantic and syntactic variables

The authors formalize a framework modeling language production through latent variables that separate high-level semantic features (which remain consistent over sequences) from low-level syntactic features (which vary locally). This framework provides theoretical guidance for designing interpretability methods that can recover these distinct types of linguistic information.

Contribution

Temporal Sparse Autoencoders with contrastive loss

The authors propose Temporal SAEs (T-SAEs), a modification to standard Sparse Autoencoders that partitions features into high-level and low-level components and incorporates a contrastive loss encouraging high-level features to activate consistently over adjacent tokens. This enables self-supervised disentanglement of semantic from syntactic features without requiring explicit semantic labels.

Contribution

Empirical validation of semantic recovery and disentanglement

The authors demonstrate through extensive experiments that T-SAEs recover smoother and more coherent semantic concepts compared to baseline SAEs while maintaining reconstruction quality. They show improved recovery of semantic and contextual information, better disentanglement between feature types, and practical benefits for applications like safety monitoring and model steering.

Table of Contents