Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2408.00113.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T21:17:17.170614Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T14:23:30.734497Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation c42952d9-cc2e-4a03-864c-6542b831e299 · inbound
Evaluating Sparse Autoencoders on Targeted Concept Erasure Tasks cites this paper.
Evaluating Sparse Autoencoders on Targeted Concept Erasure Tasks Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T10:51:14.270695Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fe097f5d-d18e-4551-a47e-89f0af4336ac · inbound
Transformers Use Causal World Models in Maze-Solving Tasks cites this paper.
Transformers Use Causal World Models in Maze-Solving Tasks Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T14:35:02.265630Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a6d37708-16e0-4eaf-b85f-d88bc19d7689 · inbound
InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders cites this paper.
InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T21:17:17.170614Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d367ea90-d577-4649-a855-02e4d1549107 · inbound
Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework cites this paper.
Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T18:16:43.742914Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 44419a21-91e6-4772-9cd8-30de81f9d430 · inbound
Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations cites this paper.
Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
Reference 43
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T14:23:30.736363Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation fef5c1d4-da13-45a7-8185-3eb45d6f27b5 · inbound
Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations cites this paper.
Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T05:02:51.385870Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.