Paper Citation Record · LEDGER

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts

As of 9 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 1 inbound Pith citation observation for arXiv:2605.24846.

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

pith.paper-citation-record.v1
2605.24846 v2

Coverage vector

measured 53 of 53 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T12:46:51.991939Z

measured 54 of 54 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:53:25.999733Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T19:53:26.497300Z

Reference resolution

53 of 53 outbound references displayed

verified exact1
verified fuzzy47
unresolved5
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 82c2b73a-692f-44da-a0c4-94fea3f00a1e · outbound

This paper cites write newline.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts write newline

Reference 1

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Observation 57feec5c-64d0-42fe-a9da-fce790c61cd4 · outbound

This paper cites an unresolved cited work.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Unresolved cited work

Reference 2

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Observation b195e5d7-945c-4458-9422-a5154b690f44 · outbound

This paper cites an unresolved cited work.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Unresolved cited work

Reference 3

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Observation 43d03204-2c29-4316-9867-1586ee118e46 · outbound

This paper cites Gpt-4 technical report.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Gpt-4 technical report

Reference 4

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Observation 23b9ee9f-9fbc-44db-af96-59e4a0eee412 · outbound

This paper cites Sparks of artificial general intelligence: Early experiments with gpt-4.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Sparks of artificial general intelligence: Early experiments with gpt-4

Reference 5

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Observation 1f683ad2-3635-4e8a-a1c2-0d5cef51725f · outbound

This paper cites Solving quantitative reasoning problems with language models.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Solving quantitative reasoning problems with language models

Reference 6

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Observation f7bc551d-953f-4ab4-9c2f-cc8724b1bd11 · outbound

This paper cites Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals

Reference 7

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Observation 98c45714-4c44-40a7-80d9-37b3b114a97a · outbound

This paper cites A survey of large language models.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts A survey of large language models

Reference 8

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Observation 705e14dc-115d-406e-8c1c-6075ab74daea · outbound

This paper cites Deepseek-r1 incentivizes reasoning in llms through reinforcement learning.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Deepseek-r1 incentivizes reasoning in llms through reinforcement learning

Reference 9

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Observation d451eb09-0326-49aa-87dc-e4588c911823 · outbound

This paper cites Longcat-flash-thinking-2601 technical report, 2026.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Longcat-flash-thinking-2601 technical report, 2026

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.055805Z

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Observation 592918a6-ea2f-4cad-8fb3-ec0d83ec6765 · outbound

This paper cites On generative agents in recommendation, 2024.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts On generative agents in recommendation, 2024

Reference 11

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Observation 978570f0-46b4-409d-bd5f-778a4c38b032 · outbound

This paper cites Internalizing safety understanding in large reasoning models via verification, 2026.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Internalizing safety understanding in large reasoning models via verification, 2026

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.085614Z

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Observation 74a96e56-5e28-428a-b137-b0dbefa9a750 · outbound

This paper cites L-mtp: Leap multi-token prediction beyond adjacent context for large language models, 2025.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts L-mtp: Leap multi-token prediction beyond adjacent context for large language models, 2025

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.078161Z

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Observation 79d6ed4c-5d76-4e70-9e2d-ea2cffe3c48e · outbound

This paper cites Unlocking emergent modularity in large language models, 2024.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Unlocking emergent modularity in large language models, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.064671Z

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Observation 1d73a9f5-b523-4053-9ebb-0a87fec4028a · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.035458Z

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Observation 39d2579e-f523-4403-bb20-cfbf5334e72b · outbound

This paper cites St-moe: Designing stable and transferable sparse expert models, 2022.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts St-moe: Designing stable and transferable sparse expert models, 2022

Reference 16

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Observation 85e60431-4c8b-4938-9eb7-37db33893f53 · outbound

This paper cites Towards monosemanticity: Decomposing language models with dictionary learning, 2023.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Towards monosemanticity: Decomposing language models with dictionary learning, 2023

Reference 17

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Observation 488b3947-803d-4bcb-a494-00ef78b1140e · outbound

This paper cites Llm modularity: The separability of capabilities in large language models, 2023.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Llm modularity: The separability of capabilities in large language models, 2023

Reference 18

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Observation 16c3560a-7e6a-46bf-a78d-71c4ba32a2d6 · outbound

This paper cites Language-specific neurons: The key to multilingual capabilities in large language models, 2024.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Language-specific neurons: The key to multilingual capabilities in large language models, 2024

Reference 19

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Observation 0aa2edba-a90b-4c6b-98ed-1d05dac90a1f · outbound

This paper cites The emergence of abstract thought in large language models beyond any language, 2025.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts The emergence of abstract thought in large language models beyond any language, 2025

Reference 20

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Observation 4cff422c-378d-41bf-b8fe-7d7515e613eb · outbound

This paper cites How do large language models handle multilingualism?, 2024.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts How do large language models handle multilingualism?, 2024

Reference 21

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This paper cites Mechanistic understanding of language models in syntactic code completion, 2025.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Mechanistic understanding of language models in syntactic code completion, 2025

Reference 22

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Observation 62ea827f-4a51-4f96-b670-56f11e09da76 · outbound

This paper cites Interpreting arithmetic mechanism in large language models through comparative neuron analysis, 2024.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Interpreting arithmetic mechanism in large language models through comparative neuron analysis, 2024

Reference 23

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Observation 9a19c99f-0ff2-4140-99c7-91b0a07e5b42 · outbound

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Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Unresolved cited work

Reference 24

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This paper cites Not all parameters are created equal: Smart isolation boosts fine-tuning performance, 2025 a.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Not all parameters are created equal: Smart isolation boosts fine-tuning performance, 2025 a

Reference 25

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This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks, 2019.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts The lottery ticket hypothesis: Finding sparse, trainable neural networks, 2019

Reference 26

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This paper cites Investigating pattern neurons in urban time series forecasting.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Investigating pattern neurons in urban time series forecasting

Reference 27

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This paper cites Qwen3 technical report, 2025.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Qwen3 technical report, 2025

Reference 28

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This paper cites Qwen2.5 technical report.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Qwen2.5 technical report

Reference 29

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Observation 7466bca9-a1d2-4786-8ee3-034747c9a0a0 · outbound

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Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Gemma 3 Technical Report

Reference 30

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This paper cites The llama 3 herd of models, 2024.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts The llama 3 herd of models, 2024

Reference 31

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Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Unresolved cited work

Reference 32

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This paper cites Measuring massive multitask language understanding, 2021 a.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Measuring massive multitask language understanding, 2021 a

Reference 33

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Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Measuring mathematical problem solving with the math dataset, 2021 b

Reference 34

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This paper cites Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Is your code generated by chatgpt really correct? rigorous evaluation of large language models for code generation

Reference 35

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This paper cites Language models are multilingual chain-of-thought reasoners.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Language models are multilingual chain-of-thought reasoners

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.097197Z

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Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Unresolved cited work

Reference 37

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This paper cites Documenting large webtext corpora: A case study on the colossal clean crawled corpus, 2021.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Documenting large webtext corpora: A case study on the colossal clean crawled corpus, 2021

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.149417Z

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This paper cites Pointer sentinel mixture models.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Pointer sentinel mixture models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.130747Z

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Observation 960586d7-02d0-4a7f-bf36-097217a53aac · outbound

This paper cites Openmathinstruct-2: Accelerating ai for math with massive open-source instruction data, 2024.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Openmathinstruct-2: Accelerating ai for math with massive open-source instruction data, 2024

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.133862Z

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Observation d139d622-ec34-4a37-8baf-21898a713674 · outbound

This paper cites Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of llms.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Wildguard: Open one-stop moderation tools for safety risks, jailbreaks, and refusals of llms

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.089104Z

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Observation 20f47fc5-5244-471a-945d-2c8cd29ee549 · outbound

This paper cites Training verifiers to solve math word problems.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Training verifiers to solve math word problems

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.145339Z

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Observation 55bac934-cf7b-4e8a-8ec6-7db6bad2308c · outbound

This paper cites O lympiad B ench: A challenging benchmark for promoting AGI with olympiad-level bilingual multimodal scientific problems.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts O lympiad B ench: A challenging benchmark for promoting AGI with olympiad-level bilingual multimodal scientific problems

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.139326Z

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Observation 80c9df97-2c9b-4e0c-b63b-86dbb3cb0eb9 · outbound

This paper cites AIME 2024 , 2024.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts AIME 2024 , 2024

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.135511Z

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Observation 93bbfd2e-3bc9-4bbc-8894-128828247fe0 · outbound

This paper cites Gshard: Scaling giant models with conditional computation and automatic sharding.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Gshard: Scaling giant models with conditional computation and automatic sharding

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.137636Z

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Observation f25d3773-c809-48d1-ad03-ee1a79194064 · outbound

This paper cites Emergent modularity in pre-trained transformers, 2023.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Emergent modularity in pre-trained transformers, 2023

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.079039Z

Source-reported events for the cited work

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Observation 56bf6599-c0ef-4341-a996-2edf9dcacea4 · outbound

This paper cites Smith, and Luke Zettlemoyer.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Smith, and Luke Zettlemoyer

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.160989Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 61edf59d-7fc2-47ca-ad2b-9a7f80f2c025 · outbound

This paper cites Are neural nets modular? inspecting functional modularity through differentiable weight masks, 2021.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Are neural nets modular? inspecting functional modularity through differentiable weight masks, 2021

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.039571Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c8cb8d38-4400-48c8-8206-78acf0e4f19b · outbound

This paper cites Recurrent independent mechanisms, 2020.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Recurrent independent mechanisms, 2020

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.124634Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c6f88196-3ceb-41ff-8196-3a1d0f59ac4c · outbound

This paper cites Knowledge neurons in pretrained transformers, 2022.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Knowledge neurons in pretrained transformers, 2022

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.125501Z

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Observation aa3d75b0-fc00-4de4-a839-2aa2e96568ba · outbound

This paper cites Transformer feed-forward layers are key-value memories.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Transformer feed-forward layers are key-value memories

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.083341Z

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Observation 5b1b23d5-526c-4087-8fe7-39c0629ee7b5 · outbound

This paper cites Locating and editing factual associations in gpt.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts Locating and editing factual associations in gpt

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.126423Z

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Observation f486b190-ec41-4ef5-9292-b5e6c573b2e0 · outbound

This paper cites The lottery ticket hypothesis for pre-trained bert networks, 2020.

Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts The lottery ticket hypothesis for pre-trained bert networks, 2020

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T04:45:59.062049Z

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Pith citing papers

Observation a1c9e453-598e-4be7-979c-de5c7e49473d · inbound

AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning cites this paper.

AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning Tiny Brains, Giant Impact: Uncovering the Keystone Neurons of LLM with Just a Few Prompts

Reference 73

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T19:53:26.507641Z

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