Pith. sign in

Paper Citation Record · LEDGER

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference

As of 12 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2412.15750.

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

pith.paper-citation-record.v1
2412.15750 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:12:57.956319Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

  • verified exact1
  • verified fuzzy7
  • unresolved18
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ddad7123-beb5-4ad5-a409-fe0906be8c9a · outbound

This paper cites Frantar, E.; and Alistarh, D.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Frantar, E.; and Alistarh, D

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:12:58.264660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T11:12:57.871916Z digest=sha256:fc966a9a061c4adf1d89122f1cfc98d285f98d25e07d0aaf9d0dcbeb577e50e3

Observation 169fcbef-6b05-4749-acbf-4032fac2c4c6 · outbound

This paper cites Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Have Faith in Faithfulness: Going Beyond Circuit Overlap When Finding Model Mechanisms

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.875562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.875562Z digest=sha256:c257ea25dee004ec0ca4ff8de97f33c579630f799a4e04ff6ebaed7c662ba6d4

Observation 45df0b4a-33ca-4c9c-958a-3fa98f7bc5a5 · outbound

This paper cites In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.883145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.883145Z digest=sha256:c595971acdbeaf925658e789442031913b8592085c9213f4a528e42decc6d6b7

Observation 60ec9166-0ce8-4f13-b9ff-68134fd3d618 · outbound

This paper cites In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8046–8056.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 8046–8056

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:12:58.253991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T11:12:57.887908Z digest=sha256:31431fbe4378e947b1ba5b476e5e51b739b365c3cd448615ef9185a5901825cd

Observation 4651da23-7547-414d-8e37-7ec3e2c67bcf · outbound

This paper cites Scaling Laws for Neural Language Models.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Scaling Laws for Neural Language Models

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.891817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.891817Z digest=sha256:1ee5cf6d88921d855a3091b87d55c4b181b553fc7d1e009b79befb56b50781f0

Observation 3ac5574f-07ba-4730-b1b8-de3cf0bf1b3c · outbound

This paper cites LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.899535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.899535Z digest=sha256:73924f36417853657f20ba7df6add822aff18cdbfa78890a4fd086274a24ceda

Observation 3ab1cce8-5160-4710-b4e9-d7e339452e26 · outbound

This paper cites The Hydra Effect: Emergent Self-repair in Language Model Computations.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference The Hydra Effect: Emergent Self-repair in Language Model Computations

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.903041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.903041Z digest=sha256:101e5c312a0cbafe10eb2e26a1e8af31ae7bb70d75567750477f9e4fe045e0e7

Observation 72e91eb0-6257-4452-906c-18a85d60044c · outbound

This paper cites Advances in Neural Information Processing Systems, NeurIPS 2022 , 35: 17359–17372.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Advances in Neural Information Processing Systems, NeurIPS 2022 , 35: 17359–17372

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:12:58.242297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T11:12:57.906974Z digest=sha256:c350d086bd9d27645f7024866e58a77dbe74cbcaea7604cb0902fc608714c92c

Observation b6e721a2-b73f-46c6-ab9e-eeb311cc3ebc · outbound

This paper cites Https://transformer-circuits.pub/2022/in-context- learning-and-induction-heads/index.html.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Https://transformer-circuits.pub/2022/in-context- learning-and-induction-heads/index.html

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:12:58.229590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T11:12:57.910929Z digest=sha256:cdbdf53bb0b8852917aac6167ab1b022e0efb95b2e7fed0be1b5dd69c925e67e

Observation d55f039c-770e-4a77-89ff-9c17ddc0b075 · outbound

This paper cites LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.915322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.915322Z digest=sha256:51a5062ce8ba5f77b82732bfd0f6d99eebbd6ad74c305072ee70b7df70756670

Observation 09b759b7-c049-45ed-b124-21e351c3c0c9 · outbound

This paper cites What Matters In The Structured Pruning of Generative Language Models?.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference What Matters In The Structured Pruning of Generative Language Models?

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.923654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.923654Z digest=sha256:2d3576744d41ffbb95f9ff097e9882a14abe28f6d2f7e7701f40192b520779ff

Observation c7483f48-7b7c-400d-adac-bd1bc89fa158 · outbound

This paper cites In Rogers, A.; Boyd-Graber, J.; and Okazaki, N., eds., Find- ings of the Association for Computational Linguistics: ACL 2023, 7059–7073.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference In Rogers, A.; Boyd-Graber, J.; and Okazaki, N., eds., Find- ings of the Association for Computational Linguistics: ACL 2023, 7059–7073

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:12:58.205408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T11:12:57.928649Z digest=sha256:aefbae2c32c570feaba32a736e57d59588fe18cf432f3276f153bdc1c0d22dac

Observation 70a7926c-4bda-4bdc-9b7c-f379e1663fdb · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference A Simple and Effective Pruning Approach for Large Language Models

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.932676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.932676Z digest=sha256:b4353079ec06ebb1ad6065c87ef4fff0482cd321629f373de7e7ae7ba13e2e43

Observation 5471bffd-e177-4bf7-bffa-59f1c0a4fbe2 · outbound

This paper cites LaMDA: Language Models for Dialog Applications.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference LaMDA: Language Models for Dialog Applications

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.940954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.940954Z digest=sha256:204c16412b76d6ef0abdd5ecf5d91d9e9ca439e66b9576cefe2dacea1dff1324

Observation 1feb37e5-379d-44d0-b937-ca86ba6afdbc · outbound

This paper cites In Proceedings of the 2020 Confer- ence on Empirical Methods in Natural Language Process- ing: System Demonstrations, 38–45.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference In Proceedings of the 2020 Confer- ence on Empirical Methods in Natural Language Process- ing: System Demonstrations, 38–45

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:12:58.180697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T11:12:57.949315Z digest=sha256:7306e6186e7a570121437e7293bf1a2b49a1b29b0e6c8f081f060d976293f261

Observation 86e9b979-4933-4fda-8396-03b73a1673aa · outbound

This paper cites LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.952950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.952950Z digest=sha256:b0e1a9441ff4ec890816d482ce822b4aaa9404dfd4872aba5e8781829856760a

Observation 1587f2f9-a5d6-4e24-9ea5-e37b9dc581d4 · outbound

This paper cites A Survey on Model Compression for Large Language Models.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference A Survey on Model Compression for Large Language Models

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.956319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.956319Z digest=sha256:575578a1b8a595f1fd2228f5d86fa94c9d6955efac8cb0168bad7ebc60ec3b90

Observation 684f48ae-0f98-47cc-bc33-1835c2e87470 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Adam: A Method for Stochastic Optimization

Reference 2014

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.895877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.895877Z digest=sha256:426565b4d51a9a647f8ed4fd16a605c04c1fe717ea22329ad03e00c3ddb7170f

Observation 87063ed9-9559-4f93-9fa7-a1577da29aa9 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Distilling the Knowledge in a Neural Network

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.879496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.879496Z digest=sha256:5c60af1f7222245dc833fb72a590c90c4135ea33bb1cecd41e510c75e4d012ca

Observation 3d5e3cc3-a973-4bc7-acf8-76a7662710b9 · outbound

This paper cites an unresolved cited work.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Unresolved cited work

Reference 2017

Resolution
unresolved
raw_fallback, observed 2026-08-11T11:12:58.193208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T11:12:57.945435Z digest=sha256:1593020c3dd641a1b424b382940ea066daf50e88bd22fa43b279c3072c31e242

Observation fe72f192-da06-46d1-bef3-e84268b28754 · outbound

This paper cites an unresolved cited work.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Unresolved cited work

Reference 2018

Resolution
unresolved
raw_fallback, observed 2026-08-11T11:12:58.217338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T11:12:57.919349Z digest=sha256:55b7469c7add56ba505268e3fe56fd1a6390dba9e0148a6c8521dea0177ba10d

Observation 623c61a6-7a4c-4037-95d7-babf3dc0cf0d · outbound

This paper cites Distilling Task-Specific Knowledge from BERT into Simple Neural Networks.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Distilling Task-Specific Knowledge from BERT into Simple Neural Networks

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.936739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.936739Z digest=sha256:ab82cc010c8f20347b4a16b25fc7249de556b944d0adef1377637e1f12041b3c

Observation bc1a2462-150c-45b5-b790-efb8293bb271 · outbound

This paper cites Ad- vances in Neural Information Processing Systems, NeurIPS 2020, 33: 1877–1901.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Ad- vances in Neural Information Processing Systems, NeurIPS 2020, 33: 1877–1901

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T11:12:58.275830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T11:12:57.864079Z digest=sha256:07f692fe30327d07c50c17d6d7267a87f574808b73377c0a100780bd4042fc2d

Observation 224feb25-e902-4a3b-9169-ff53d94157e8 · outbound

This paper cites Toy Models of Superposition.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Toy Models of Superposition

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.868130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.868130Z digest=sha256:a107c9c8c0908376d2fa3aa2cc2d8dc51b99ab0ae87ebaece5731d82d24017d2

Observation c994f6d3-a967-47ea-82b0-c7969aa1c62c · outbound

This paper cites GPT-4 Technical Report.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference GPT-4 Technical Report

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-11T11:12:57.854841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:12:57.854841Z digest=sha256:448047074022093e64036232bb74fa8c7cd1a739c2b45ca745d410be68109138

Observation 0e75e75f-8280-42d0-af80-4d9fcdd79c3e · outbound

This paper cites Finding Transformer Circuits with Edge Pruning.

Extracting Interpretable Task-Specific Circuits from Large Language Models for Faster Inference Finding Transformer Circuits with Edge Pruning

Reference 2024

Resolution
verified exact
local_arxiv, observed 2026-08-11T11:12:58.155851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-08-11T11:12:57.859721Z digest=sha256:825a834935a17615b7dcc53a35e8d44a6550db7f50efa631263489dfff3065a0

Pith citing papers

No inbound Pith citation observations are available.