Paper Citation Record · LEDGER

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining

As of 5 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 2 inbound Pith citation observations for arXiv:2509.06806.

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

pith.paper-citation-record.v1
2509.06806 v6

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T11:50:26.030339Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-21T22:05:11.146329Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-21T22:05:41.503463Z

Reference resolution

34 of 34 outbound references displayed

verified exact13
verified fuzzy9
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch10

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ab98348c-a87d-454a-ae3c-869c35827f01 · outbound

This paper cites ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T18:11:42.722329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation afb554fe-504c-4e41-ad74-79dab74009f2 · outbound

This paper cites Cache Me If You Can: How Many KVs Do You Need for Effective Long-Context LMs?.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Cache Me If You Can: How Many KVs Do You Need for Effective Long-Context LMs?

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:11:42.728494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 93163b33-05ed-4713-821e-7ba7008a064a · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T18:11:43.872959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation bc36038c-3468-43e8-a1e8-9d5666dab71a · outbound

This paper cites MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T18:11:42.733862Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 69b8d4c9-f322-4782-b546-dfc99a16cb5c · outbound

This paper cites Batch prompting: Efficient inference with large language model apis.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Batch prompting: Efficient inference with large language model apis

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T18:11:43.875979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3c95f2c5-b5d7-4f8b-af76-30e1288bf36f · outbound

This paper cites Interpreting Tree Ensembles with inTrees.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Interpreting Tree Ensembles with inTrees

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-18T18:11:42.739577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation a8213d1a-d326-40fa-b742-2020188e8cf4 · outbound

This paper cites Spreadsheetllm: encoding spreadsheets for large language models.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Spreadsheetllm: encoding spreadsheets for large language models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T18:11:43.869248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 46cd683a-a937-4e25-92f8-67b717672abf · outbound

This paper cites Predictive learning via rule ensembles.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Predictive learning via rule ensembles

Reference 10

Resolution: metadata mismatch; doi, observed 2026-05-18T18:11:42.321072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1176b57b-4468-4344-9291-b161577c82c8 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T18:11:42.745379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c819cf13-6a85-4a96-97cf-2b048ca5260c · outbound

This paper cites Distilling the Knowledge in a Neural Network.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Distilling the Knowledge in a Neural Network

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-05-18T18:11:42.701511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation cc881240-6d74-4f52-8e25-d303c1946a62 · outbound

This paper cites Many-shot in-context learning in multimodal foundation models.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Many-shot in-context learning in multimodal foundation models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T18:11:43.885987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7b842f8f-3cfa-48fd-a388-1a545d74c4b1 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Adam: A Method for Stochastic Optimization

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-05-18T18:11:42.763004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7f075236-578c-48c6-8b59-c5a601a37f68 · outbound

This paper cites arXiv preprint arXiv:2508.02085 , year=.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining arXiv preprint arXiv:2508.02085 , year=

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T18:11:42.780659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5a48abb6-1eb7-4fa1-a85c-df75324efcb9 · outbound

This paper cites an unresolved cited work.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-05-18T18:11:43.882164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7a0852a9-b254-4617-bb6e-541b207dc3bd · outbound

This paper cites TALENT: A Tabular Analytics and Learning Toolbox.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining TALENT: A Tabular Analytics and Learning Toolbox

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T18:11:42.706606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b012f7ee-6320-41b1-9dfd-a18d7f7fd4f4 · outbound

This paper cites TabDPT: Scaling tabular foundation models on real data.arXiv preprint arXiv:2410.18164.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining TabDPT: Scaling tabular foundation models on real data.arXiv preprint arXiv:2410.18164

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:11:42.753307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation c42cbe8e-05cf-48e8-b1ad-c45d556cbb7d · outbound

This paper cites an unresolved cited work.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-05-18T18:11:43.866343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 7c66d859-976f-406d-a5a6-2662ab61302c · outbound

This paper cites 37 Wenyue Hua, Lizhou Fan, Lingyao Li, Kai Mei, Jianchao Ji, Yingqiang Ge, Libby Hemphill, and Yongfeng Zhang.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining 37 Wenyue Hua, Lizhou Fan, Lingyao Li, Kai Mei, Jianchao Ji, Yingqiang Ge, Libby Hemphill, and Yongfeng Zhang

Reference 20

Resolution: metadata mismatch; doi, observed 2026-05-18T18:11:42.344255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation fd440237-b0ac-479a-be60-0c128594d535 · outbound

This paper cites Tabicl: A tabular foun- dation model for in-context learning on large data.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Tabicl: A tabular foun- dation model for in-context learning on large data

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T18:11:43.879289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 5f1bdac3-3be8-4479-9033-c15bfc195d33 · outbound

This paper cites Benchmarking Multimodal AutoML for Tabular Data with Text Fields.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Benchmarking Multimodal AutoML for Tabular Data with Text Fields

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:11:42.769640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 9887c841-c659-443e-9963-3ee6a95ef692 · outbound

This paper cites Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Tokenization counts: the impact of tokenization on arithmetic in frontier LLMs

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:11:42.717241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2b3788f0-0793-4588-96fa-e3fc3d62a133 · outbound

This paper cites TableGPT2: A Large Multimodal Model with Tabular Data Integration.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining TableGPT2: A Large Multimodal Model with Tabular Data Integration

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:11:42.775039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 1773ca3b-948a-428c-9d1c-8789d5338f68 · outbound

This paper cites Table meets llm: Can large language models understand structured table data? a benchmark and empirical study.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Table meets llm: Can large language models understand structured table data? a benchmark and empirical study

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T18:11:42.340199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 904ef3b0-0524-40c8-aa89-adfe29a95e99 · outbound

This paper cites Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, and Matt Gardner.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Eric Wallace, Yizhong Wang, Sujian Li, Sameer Singh, and Matt Gardner

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T18:11:43.860032Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2dcc0688-5127-4cc1-aadd-32987b06b15a · outbound

This paper cites Voyager: An Open-Ended Embodied Agent with Large Language Models.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 27

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T18:11:42.328709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation e5c17c4b-f57a-4e75-9aea-5631a513aa88 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-18T18:11:42.686429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3a837242-e486-4566-839e-124b1db37896 · outbound

This paper cites Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Scalable In-Context Learning on Tabular Data via Retrieval-Augmented Large Language Models

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:11:42.691733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f70d0a6d-9364-4100-aa52-587760f53c9c · outbound

This paper cites Effective long-context scaling of foundation models.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Effective long-context scaling of foundation models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T18:11:43.889298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2eafef5d-91b8-4ca6-9ed2-91683aa3a8c3 · outbound

This paper cites R&d-agent: Automating data-driven ai solution building through llm-powered automated research, development, and evolution.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining R&d-agent: Automating data-driven ai solution building through llm-powered automated research, development, and evolution

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:11:42.711964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation b92d6718-0038-4efd-9e41-5bc655c0186f · outbound

This paper cites arXiv preprint arXiv:2407.00956 , year=.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining arXiv preprint arXiv:2407.00956 , year=

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-18T18:11:42.697236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation f8adb186-51a4-48a1-9bd1-deee20895f6d · outbound

This paper cites More is not always better? enhancing many-shot in-context learning with differ- entiated and reweighting objectives.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining More is not always better? enhancing many-shot in-context learning with differ- entiated and reweighting objectives

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T18:11:43.863565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 0436ef9e-474f-49ed-92fc-7d167740b6bd · outbound

This paper cites Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T18:11:43.856005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 3164e218-06d2-435f-8bb3-b53d531ee9cf · outbound

This paper cites LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models

Reference 35

Resolution: metadata mismatch; local_arxiv, observed 2026-05-18T18:11:42.758003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 20e1347b-a591-4c3c-a72f-e6dfcb07d950 · outbound

This paper cites ISBN 979-8-89176-251-0.

MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining ISBN 979-8-89176-251-0

Reference 36

Resolution: verified exact; doi, observed 2026-05-18T18:11:42.333901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Pith citing papers

Observation dbf4be86-5080-4aaf-ac33-f9f306b1869b · inbound

Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data cites this paper.

Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-21T22:05:41.506194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

Observation 2995f291-7801-4c4d-8206-b5eebdfbc137 · inbound

Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows cites this paper.

Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-16T22:48:38.311399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.