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Paper Citation Record · LEDGER

Towards Automated Circuit Discovery for Mechanistic Interpretability

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 36 inbound Pith citation observations for arXiv:2304.14997.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2304.14997 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 36 of 36 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:36:43.804112Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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  • metadata mismatch0

External citation measurements

32
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation b618ee32-d664-46f8-9765-d3fc53dce4d8 · inbound

Sparse Autoencoders Find Highly Interpretable Features in Language Models cites this paper.

Sparse Autoencoders Find Highly Interpretable Features in Language Models Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 5

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arxiv_id, observed 2026-05-24T06:44:02.183406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-24T06:42:23.274826Z digest=sha256:e9b055f46a4a68addf8ae863feebd80028a21089b64b812d20301a4d5b3538c4

Observation 58bcce53-5c39-481b-9cde-cd4d88398c66 · inbound

How to use and interpret activation patching cites this paper.

How to use and interpret activation patching Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 2

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arxiv_id, observed 2026-05-16T20:34:06.914206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-16T20:34:06.888683Z digest=sha256:d698d7e0aed3a79636b72adaf205ebb60e3a3bf438fd5455ca0a874fe07fe795

Observation 82f206c9-eff3-4bc6-a355-f557798bbec4 · inbound

Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2 cites this paper.

Gemma Scope: Open Sparse Autoencoders Everywhere All At Once on Gemma 2 Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 1

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arxiv_id, observed 2026-05-15T05:47:20.036422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T05:47:19.953111Z digest=sha256:75c953ede0e1193e5bd8d18ee00ffd3c2191bae5171fd2c6407ee0a0140f9bde

Observation dbb44eab-a1fb-4e01-b41e-698aeab39382 · inbound

AI Governance through Markets cites this paper.

AI Governance through Markets Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 28

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no resolver link, observed 2026-08-10T04:36:43.804112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T04:36:43.804112Z digest=sha256:a71f635b16e779a686d2f4bdfb5817a4be67c1bf68eed67845e3fdc68f8eca0c

Observation 69061fed-e49d-4c2b-8b98-dd09645abde2 · inbound

Transcoders Beat Sparse Autoencoders for Interpretability cites this paper.

Transcoders Beat Sparse Autoencoders for Interpretability Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 16

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no resolver link, observed 2026-08-09T22:24:53.827765Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T22:24:53.827765Z digest=sha256:9e1f34059e11ade9221b5ae02cc56da906bbb5a123dda1ea38b98ca81fd9152c

Observation c689dd0f-0093-4237-bcae-33c9267541da · inbound

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning cites this paper.

Perspectives for Direct Interpretability in Multi-Agent Deep Reinforcement Learning Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 26

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T17:58:34.145737Z digest=sha256:7537837f5c3482ac1f135007b1df2e32d6fc24d510ceaff0d07a4fbad258985d

Observation ca0e0547-91ee-4238-a000-c40b0245b7fd · inbound

Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence cites this paper.

Beyond Induction Heads: In-Context Meta Learning Induces Multi-Phase Circuit Emergence Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 7

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:00:54.375524Z digest=sha256:39a007f9bceda2c0c03ec1d965db5b42ade4baec15414c379524c9c058b9d8d4

Observation 7a045aa9-53ee-46d0-948d-5305715a72bb · inbound

Circuit Stability Characterizes Language Model Generalization cites this paper.

Circuit Stability Characterizes Language Model Generalization Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 10

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no resolver link, observed 2026-08-07T12:35:36.560849Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:35:36.560849Z digest=sha256:90e992f75eea943e515eff0711308be11df579ce209927cb1397f91710a3bbe8

Observation e2bfb6fd-c972-4e1d-9227-d18828cdc1fd · inbound

From Indirect Object Identification to Syllogisms: Exploring Binary Mechanisms in Transformer Circuits cites this paper.

From Indirect Object Identification to Syllogisms: Exploring Binary Mechanisms in Transformer Circuits Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 23

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source=arxiv_source observed=2026-08-05T17:37:55.872665Z digest=sha256:1e096ac9f230e07055a5fd28987d1d373a5948c4a31fe35e97e4e92e184d83ff

Observation 7e2e582d-84cf-4b6f-af44-3536a0ba7fa2 · inbound

Towards Inclusive Toxic Content Moderation: Addressing Vulnerabilities to Adversarial Attacks in Toxicity Classifiers Tackling LLM-generated Content cites this paper.

Towards Inclusive Toxic Content Moderation: Addressing Vulnerabilities to Adversarial Attacks in Toxicity Classifiers Tackling LLM-generated Content Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 11

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no resolver link, observed 2026-08-04T16:40:31.149122Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T16:40:31.149122Z digest=sha256:5e54becb4671fde9f25eb26cdd9fca091b3b594f48ca68c05f953d15d1ca0eca

Observation b5b7cd1b-4e74-4a8a-89b1-e04ac683ef05 · inbound

Language Model Circuits Are Sparse in the Neuron Basis cites this paper.

Language Model Circuits Are Sparse in the Neuron Basis Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 3

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no resolver link, observed 2026-08-03T06:35:33.648793Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T06:35:33.648793Z digest=sha256:3b6db7b7a37a9fb7f47a648d85f2b03452b73d3479d7fa6746d048c1f826d8c2

Observation 935921d7-9ffd-4335-8944-20ba480364d2 · inbound

From Features to Actions: Explainability in Traditional and Agentic AI Systems cites this paper.

From Features to Actions: Explainability in Traditional and Agentic AI Systems Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 9

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:50:15.149847Z digest=sha256:7c81a5bfadb6d913aff9c64c9c924a65aef31a105e6a01c2da7c013433ea0394

Observation 2dc8261f-3111-4f91-8dbd-e8464cac9ab0 · inbound

Enhancing Multi-Robot Exploration Using Probabilistic Frontier Prioritization with Dirichlet Process Gaussian Mixtures cites this paper.

Enhancing Multi-Robot Exploration Using Probabilistic Frontier Prioritization with Dirichlet Process Gaussian Mixtures Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 9

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no resolver link, observed 2026-07-13T13:35:02.428552Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T13:35:02.428552Z digest=sha256:39c7e9c2174b89997e97df4451bfeceaa9de80acb2071e0cccdbfaae76377cee

Observation e34a12d1-2961-4a5c-9701-c9a274f3d1eb · inbound

STEAR: Layer-Aware Spatiotemporal Evidence Intervention for Hallucination Mitigation in Video Large Language Models cites this paper.

STEAR: Layer-Aware Spatiotemporal Evidence Intervention for Hallucination Mitigation in Video Large Language Models Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 9

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arxiv_id, observed 2026-05-13T20:18:13.386322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-13T20:16:04.999705Z digest=sha256:6d33431a036d152aa2c1440543bd1619587a3ef0a6a44e15352325ad84f9f9d6

Observation 614ee7ab-659c-4a58-9fb8-20b114024c09 · inbound

Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation cites this paper.

Co-Located Tests, Better AI Code: How Test Syntax Structure Affects Foundation Model Code Generation Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 9

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arxiv_id, observed 2026-05-11T12:01:05.319756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T04:21:39.637962Z digest=sha256:4b36ea2a89ffb66249a01fbf9120af785266888ea27ad7986714c749aedf5d57

Observation eed9b08f-35e9-45fa-8c7c-311d6d5efd39 · inbound

Dissociating Decodability and Causal Use in Bracket-Sequence Transformers cites this paper.

Dissociating Decodability and Causal Use in Bracket-Sequence Transformers Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 1

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verified exact
arxiv_id, observed 2026-05-08T21:39:24.472752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T12:12:32.768674Z digest=sha256:6e7f8c50534b16ae2c1d0edf9b223493c440822339781826ec0f1888ca3c9026

Observation a28a9de0-9ca5-4305-b7e9-5447e21d31af · inbound

Eliciting associations between clinical variables from LLMs via comparison questions across populations cites this paper.

Eliciting associations between clinical variables from LLMs via comparison questions across populations Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 6

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arxiv_id, observed 2026-05-08T21:34:13.339991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-08T12:59:49.837750Z digest=sha256:9a0fdc2e07eb8431988da88ed2a228d16edbf8e831fc08fdd226835de8eca7fb

Observation 9c2d2b64-9fae-4120-ac89-28b8c913dea6 · inbound

Dissecting Jet-Tagger Through Mechanistic Interpretability cites this paper.

Dissecting Jet-Tagger Through Mechanistic Interpretability Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 17

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arxiv_id, observed 2026-05-12T04:51:22.769645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T04:49:09.296991Z digest=sha256:79dfe9421fc9d368182fb3927a9b2a85a168d2b0ef1baf85d4962742f01a3e6d

Observation 647671f6-376d-4840-ad97-dd99c985d888 · inbound

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces cites this paper.

Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 77

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arxiv_id, observed 2026-05-14T20:17:53.992568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-14T20:17:01.224864Z digest=sha256:e6a49ff3598ad03be1a94b6ea5004dc56e48abaa2e5b3f35be99311ba7a752d2

Observation b9f33560-3e0f-4132-a279-c09b14bc4ff0 · inbound

Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy cites this paper.

Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 8

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arxiv_id, observed 2026-05-14T20:07:53.882972Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-14T20:04:57.638215Z digest=sha256:5e67d5aad92cf9f3ac4974d4eafcfb96a0d94f76132be1dd64fef2e113e8f4e5

Observation 3621a9d6-d6ff-4329-8ec4-5fb2336b0556 · inbound

Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy cites this paper.

Not Just RLHF: Why Alignment Alone Won't Fix Multi-Agent Sycophancy Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 8

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arxiv_id, observed 2026-05-20T21:33:46.486479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-20T21:30:30.384184Z digest=sha256:6fa817b0f763401a0ed4d66d43044b7cc889df13e4aec5643e67fdc97585d328

Observation bf50b9f1-1011-4798-89dc-6094a9ed5059 · inbound

How to Interpret Agent Behavior cites this paper.

How to Interpret Agent Behavior Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 11

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arxiv_id, observed 2026-05-14T18:27:35.897382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-14T18:23:25.269217Z digest=sha256:5baf60242abab5038328b73e6bf99a3bca43662bba3b01c9c5561240ea091218

Observation 2e7032fa-05a0-4eb4-99da-673ba84149d2 · inbound

When Are Two Networks the Same? Tensor Similarity for Mechanistic Interpretability cites this paper.

When Are Two Networks the Same? Tensor Similarity for Mechanistic Interpretability Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 6

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arxiv_id, observed 2026-05-15T03:19:43.704142Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T03:17:22.217041Z digest=sha256:e00d61db83072a78f83e12b6b932a1332fc332d02df4c4e34c5d90e1e8afc4f8

Observation 3928ba05-de27-4b63-89f3-4511d7010a7c · inbound

From Correlation to Cause: A Five-Stage Methodology for Feature Analysis in Transformer Language Models cites this paper.

From Correlation to Cause: A Five-Stage Methodology for Feature Analysis in Transformer Language Models Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 4

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arxiv_id, observed 2026-05-22T07:01:11.983212Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-22T06:56:18.332754Z digest=sha256:d60bd76258b138f7a83cf67a07ba038e7c3e8698cf290f376b477a98e8d32620

Observation a51b679d-5aa3-48e3-a009-2b59bdf01ae2 · inbound

MechELK: A Mechanistic Interpretability Framework for Eliciting Latent Knowledge in Large Language Models cites this paper.

MechELK: A Mechanistic Interpretability Framework for Eliciting Latent Knowledge in Large Language Models Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 1

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no resolver link, observed 2026-07-13T09:07:13.817929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T09:07:13.817929Z digest=sha256:15893cf8613c80c98af16506b36d422eb07f2766d98abd719c41a4d6138cd81b

Observation 5ef6806d-c57c-4110-a422-31cb39da382a · inbound

Explaining Attention with Program Synthesis cites this paper.

Explaining Attention with Program Synthesis Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 24

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arxiv_id, observed 2026-07-04T00:59:21.019640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-26T20:46:01.403927Z digest=sha256:30ad32899f67fd6dc61a862d0decfe5d6f9c3bae5306c16894a1b0ef17923e18

Observation 8176dc3b-dbf9-46e6-bfec-a7bdc664a3ce · inbound

Explaining Attention with Program Synthesis cites this paper.

Explaining Attention with Program Synthesis Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 24

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arxiv_id, observed 2026-06-30T11:54:39.156734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-30T10:20:46.759958Z digest=sha256:f4b42331f060dbeeddf658aa7c77b0585a7898343b82f1b09f9d96a8c9fc97b6

Observation b0da2d27-f41d-4260-9491-cc950260ffc4 · inbound

Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination cites this paper.

Graph-Native Reinforcement Learning Enables Traceable Scientific Hypothesis Generation through Conceptual Recombination Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 58

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metadata mismatch
arxiv_id, observed 2026-07-02T12:26:55.883819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-07-02T12:26:46.384850Z digest=sha256:218fc92f6011fbd170bdb32c963b1a47801708620e47d10335d02a6d870282cb

Observation 44d33f06-a2b5-4278-847a-5d8abde742ba · inbound

Mechanistic Interpretability for Neural Networks: Circuits, Sparse Features and Symbolic Reasoning cites this paper.

Mechanistic Interpretability for Neural Networks: Circuits, Sparse Features and Symbolic Reasoning Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 20

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local_arxiv, observed 2026-07-09T15:06:18.046309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-07-09T14:58:58.363330Z digest=sha256:9f4ee8867fb46a9db2238371b6b00d879bb8ef011ec811951d3e6e0183931540

Observation 625e0109-428b-4739-9faf-aba305a4a487 · inbound

Targeted Recovery of Weight-Space Mechanisms From Neural Networks cites this paper.

Targeted Recovery of Weight-Space Mechanisms From Neural Networks Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 25

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no resolver link, observed 2026-08-02T10:39:48.831071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-02T10:39:48.831071Z digest=sha256:21a690130407def9092e2f837dbb2e662292b908b2e65e964bc29cef999fb109

Observation a9d1c8ec-a8e5-478a-a2af-36ec007339b5 · inbound

Targeted Recovery of Weight-Space Mechanisms From Neural Networks cites this paper.

Targeted Recovery of Weight-Space Mechanisms From Neural Networks Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 154

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no resolver link, observed 2026-08-02T10:40:01.245170Z

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source=arxiv_source observed=2026-08-02T10:40:01.245170Z digest=sha256:f0f010edfdde8f866a8713e17432193983235cd4be8023a2b64dd7adb8d041bd

Observation 7bfc993f-2e67-4949-8221-d77f98e3a6f7 · inbound

Denoising Models Develop Human-Like Perceptual Illusion Representations Across Architectures cites this paper.

Denoising Models Develop Human-Like Perceptual Illusion Representations Across Architectures Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 2023

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unresolved
no resolver link, observed 2026-08-01T18:58:29.659933Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T18:58:29.659933Z digest=sha256:ac81db8cd54dd5aaf38f5d8c45128d171f6f42cc272f3cfc66f480133a606cd7

Observation 09a6bc1d-8b09-4c55-ae82-bcf4390311af · inbound

Routing Subspaces: Auditing Evaluation-to-Deployment Mismatch in Fine-Tuned Language Models cites this paper.

Routing Subspaces: Auditing Evaluation-to-Deployment Mismatch in Fine-Tuned Language Models Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 12

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no resolver link, observed 2026-08-02T14:26:49.070810Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T14:26:49.070810Z digest=sha256:b222c024815c2fb15b338cc2f6420ce3cdcb4416864b243f76454f769efcfab7

Observation c7406690-c367-4403-b650-4b17f2da9be3 · inbound

IFCLoRA: Topology-Aware Rank Allocation for Parameter-Efficient Fine-Tuning cites this paper.

IFCLoRA: Topology-Aware Rank Allocation for Parameter-Efficient Fine-Tuning Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 24

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no resolver link, observed 2026-08-01T05:25:22.684556Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T05:25:22.684556Z digest=sha256:ada68d1d3e73c55bdc8a6895e96b6b7cd696105b5a5a2c8ec3deac0bf0178cce

Observation 72e03d84-8e20-482a-970c-1563cb3fd42b · inbound

Reality Monitoring in Large Language Models: Self-Knowledge That Transforms with Conversation Memory cites this paper.

Reality Monitoring in Large Language Models: Self-Knowledge That Transforms with Conversation Memory Towards Automated Circuit Discovery for Mechanistic Interpretability

Reference 67

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no resolver link, observed 2026-07-31T23:35:49.065747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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LAWFUL: Law-Aligned Witness for Faithful Use of Latents cites this paper.

LAWFUL: Law-Aligned Witness for Faithful Use of Latents Towards Automated Circuit Discovery for Mechanistic Interpretability

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