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Paper Citation Record · LEDGER

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model

As of 14 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 2 inbound Pith citation observations for arXiv:2411.09012.

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

pith.paper-citation-record.v1
2411.09012 v3

Coverage vector

measured 31 of 31 reference resolution

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T05:26:14.301215Z

Reference resolution

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation 11a8fe72-4eff-44e7-8927-d08c25e43c11 · outbound

This paper cites Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Tiny Titans: Can Smaller Large Language Models Punch Above Their Weight in the Real World for Meeting Summarization?

Reference 1

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Observation eca3ac43-c4f2-47b4-8187-d818120db3e4 · outbound

This paper cites Training Compute-Optimal Large Language Models.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Training Compute-Optimal Large Language Models

Reference 2

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Observation 544bd0ed-7a28-4dcb-96d0-db80db3bb63f · outbound

This paper cites It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners

Reference 3

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Observation 14b264bb-98ba-4ede-ba56-69a7f5db6c7a · outbound

This paper cites Well-Read Students Learn Better: On the Importance of Pre-training Compact Models.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

Reference 4

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Observation 1141ab81-6868-4c74-8382-e22619dadbea · outbound

This paper cites AstroLLaMA: Towards Specialized Foundation Models in Astronomy.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model AstroLLaMA: Towards Specialized Foundation Models in Astronomy

Reference 5

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Observation 23830161-5b33-4062-95ef-8aa2f81827c3 · outbound

This paper cites Perkowski, R.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Perkowski, R

Reference 6

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Observation 7198b1a6-c6eb-4445-8263-a13079b78907 · outbound

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Unresolved cited work

Reference 7

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Observation 3c8829f4-bb3f-4ae9-9a59-f53b710ad8b2 · outbound

This paper cites de Haan, Astronomy and Computing 51, 100934 (2025).

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model de Haan, Astronomy and Computing 51, 100934 (2025)

Reference 8

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Observation bdba440c-6338-4c81-b797-ff95c990c689 · outbound

This paper cites Interpreting Multi-band Galaxy Observations with Large Language Model-Based Agents.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Interpreting Multi-band Galaxy Observations with Large Language Model-Based Agents

Reference 9

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Observation 3da5007e-8808-4fbd-ab2f-4c68e7fb6702 · outbound

This paper cites The Llama 3 Herd of Models.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model The Llama 3 Herd of Models

Reference 10

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Observation f781b0e4-e6e8-42fb-be41-c716279a650d · outbound

This paper cites AstroMLab 1: Who Wins Astronomy Jeopardy!?.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model AstroMLab 1: Who Wins Astronomy Jeopardy!?

Reference 11

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Observation 13ddd04b-2f62-4ed3-922e-cd5bf27881b0 · outbound

This paper cites Scaling Language Models: Methods, Analysis & Insights from Training Gopher.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Scaling Language Models: Methods, Analysis & Insights from Training Gopher

Reference 12

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Observation 326a607d-6371-4133-8226-016bedede1d8 · outbound

This paper cites Brown, B.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Brown, B

Reference 13

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This paper cites The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Reference 14

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Observation bc24689b-6e57-430a-b901-0e793876c763 · outbound

This paper cites ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model ScalingFilter: Assessing Data Quality through Inverse Utilization of Scaling Laws

Reference 15

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Observation 9e89c985-765d-40a9-92d3-18695ca0e4a2 · outbound

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Nougat: Neural Optical Understanding for Academic Documents

Reference 16

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Observation 2bfc25aa-87dc-4dea-9338-a038ec2fe55b · outbound

This paper cites tiktoken,.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model tiktoken,

Reference 17

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Optimizing Distributed Training on Frontier for Large Language Models

Reference 18

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Megatron-DeepSpeed,

Reference 19

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Observation 260754cc-48ac-4aaa-ae37-1a8b074efc96 · outbound

This paper cites Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model

Reference 20

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Observation f4d6b92f-4346-4fb0-adc4-1f669b04814f · outbound

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model BAAI/Infinity-Instruct · Datasets at Hugging Face,

Reference 21

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This paper cites What Matters for Model Merging at Scale?.

AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model What Matters for Model Merging at Scale?

Reference 22

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Dassanaike-Perera, S

Reference 23

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Arcee's MergeKit: A Toolkit for Merging Large Language Models

Reference 24

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Instruction-Following Evaluation for Large Language Models

Reference 25

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 26

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model Measuring Mathematical Problem Solving With the MATH Dataset

Reference 27

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model GPQA: A Graduate-Level Google-Proof Q&A Benchmark

Reference 28

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning

Reference 29

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark

Reference 30

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AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery

Reference 31

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Pith citing papers

Observation 11f879ff-9a9c-47c3-ae5a-073ac43523d2 · inbound

Multi-Agent System for Cosmological Parameter Analysis cites this paper.

Multi-Agent System for Cosmological Parameter Analysis AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model

Reference 32

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Observation 212da18c-def5-419c-82be-761b33a273c5 · inbound

Spectroscopic Binary Detection as Agent-Callable Tools: Detecting 40,000+ Main-Sequence Binary Candidates from SDSS DR19 APOGEE Spectra cites this paper.

Spectroscopic Binary Detection as Agent-Callable Tools: Detecting 40,000+ Main-Sequence Binary Candidates from SDSS DR19 APOGEE Spectra AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model

Reference 9

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