Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs | Paper Citation Record

Observation 5cbf2ce9-78e2-4ed4-882d-da2a992b4b83 · outbound

This paper cites Opencodereasoning: Advancing data distillation for compet- itive coding.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Opencodereasoning: Advancing data distillation for compet- itive coding

Reference 1

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This paper cites Towards tracing knowledge in language models back to the training data.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Towards tracing knowledge in language models back to the training data

Reference 2

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This paper cites Python-code-23k-sharegpt.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Python-code-23k-sharegpt

Reference 3

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This paper cites Syntactic clustering of the web.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Syntactic clustering of the web

Reference 4

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This paper cites Language models are few-shot learners.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Language models are few-shot learners

Reference 5

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Observation 1888ebc6-a048-4f6b-9e8d-42b3c0737177 · outbound

This paper cites Opendataarena: A fair and open arena for benchmarking post-training dataset value.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Opendataarena: A fair and open arena for benchmarking post-training dataset value

Reference 6

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This paper cites Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations

Reference 7

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Observation a3cc3f2f-3d40-466b-b279-3964411a8db7 · outbound

This paper cites Alpagasus: Training a better alpaca with fewer data.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Alpagasus: Training a better alpaca with fewer data

Reference 8

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This paper cites Theoremqa: A theorem-driven question answering dataset.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Theoremqa: A theorem-driven question answering dataset

Reference 9

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Observation af02e29f-a634-47b8-84a7-c4d188355351 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Training Verifiers to Solve Math Word Problems

Reference 10

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Observation bc04e0d7-6649-4dbd-93fb-b53a1ac1c078 · outbound

This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Reference 11

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This paper cites Smith, and Jesse Dodge.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Smith, and Jesse Dodge

Reference 12

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This paper cites MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning

Reference 13

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This paper cites The vendi score: A diversity evaluation metric for machine learning.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs The vendi score: A diversity evaluation metric for machine learning

Reference 14

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This paper cites Omni-MATH: A universal olympiad level 13 A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs mathematic benchmark for large language models.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Omni-MATH: A universal olympiad level 13 A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs mathematic benchmark for large language models

Reference 15

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Observation ccbbb9b6-35ab-4a86-94e3-4e36e7e8ee6d · outbound

This paper cites Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

Reference 16

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Observation 9889afaf-ab7a-4243-94b2-45fd96a8863f · outbound

This paper cites Know your data.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Know your data

Reference 17

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Observation 4dfa9d94-47c1-490c-8ca6-22c84e8cebb8 · outbound

This paper cites Openthoughts: Data recipes for reasoning models.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Openthoughts: Data recipes for reasoning models

Reference 18

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This paper cites Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Simfluence: Modeling the Influence of Individual Training Examples by Simulating Training Runs

Reference 19

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This paper cites Open instruct v1: A dataset for having llms follow instructions.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Open instruct v1: A dataset for having llms follow instructions

Reference 20

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This paper cites Measuring mathematical problem solving with the math dataset.NeurIPS.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Measuring mathematical problem solving with the math dataset.NeurIPS

Reference 21

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Observation 0de9f738-6e21-463f-9f6c-95bacaaf4e85 · outbound

This paper cites Opencoder: The open cookbook for top-tier code large language models.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Opencoder: The open cookbook for top-tier code large language models

Reference 22

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This paper cites Livecodebench: Holistic and contamination free evaluation of large language models for code.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Livecodebench: Holistic and contamination free evaluation of large language models for code

Reference 23

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This paper cites Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord, Christopher Wilhelm, Luca Soldaini, Noah A.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord, Christopher Wilhelm, Luca Soldaini, Noah A

Reference 24

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This paper cites MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via Context-Aware Multi-Stage Policy Optimization.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via Context-Aware Multi-Stage Policy Optimization

Reference 25

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Observation 868a42ff-7788-4660-89ec-71efa5ffe279 · outbound

This paper cites Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learning.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Can One Domain Help Others? A Data-Centric Study on Multi-Domain Reasoning via Reinforcement Learning

Reference 26

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This paper cites Cipherbank: Exploring the boundary of llm reasoning capabilities through cryptography challenge.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Cipherbank: Exploring the boundary of llm reasoning capabilities through cryptography challenge

Reference 27

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This paper cites An open-source data contamination report 14 A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs for large language models.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs An open-source data contamination report 14 A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs for large language models

Reference 28

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Observation 9eb7e3ec-ef58-4b49-ad79-b9d00bc6a94a · outbound

This paper cites Scaling code- assisted chain-of-thoughts and instructions for model reasoning.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Scaling code- assisted chain-of-thoughts and instructions for model reasoning

Reference 29

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This paper cites Truthfulqa: Measuring how models mimic human falsehoods.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Truthfulqa: Measuring how models mimic human falsehoods

Reference 30

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This paper cites What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs What makes good data for alignment? a comprehensive study of automatic data selection in instruction tuning

Reference 31

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This paper cites Hercules v1.0.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Hercules v1.0

Reference 32

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This paper cites The flan collection: Designing data and methods for effective instruction tuning.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs The flan collection: Designing data and methods for effective instruction tuning

Reference 33

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This paper cites A large-scale audit of dataset licensing and attribution in ai.Nature Machine Intelligence, 6(8):975–987.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs A large-scale audit of dataset licensing and attribution in ai.Nature Machine Intelligence, 6(8):975–987

Reference 34

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This paper cites #instag: Instruction tagging for analyzing supervised fine-tuning of large language models.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs #instag: Instruction tagging for analyzing supervised fine-tuning of large language models

Reference 35

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This paper cites Data measurements tool.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Data measurements tool

Reference 36

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This paper cites Mmevol: Empowering multimodal large language models with evol-instruct.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Mmevol: Empowering multimodal large language models with evol-instruct

Reference 37

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This paper cites Orca-math: Unlocking the potential of slms in grade school math.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Orca-math: Unlocking the potential of slms in grade school math

Reference 38

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This paper cites REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs REST: Stress Testing Large Reasoning Models by Asking Multiple Problems at Once

Reference 39

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This paper cites Large language model sourcing: A survey.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Large language model sourcing: A survey

Reference 40

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Observation c64160f4-1cea-4a4d-ac8c-caf470228b8d · outbound

This paper cites Mathfusion: Enhancing mathematical problem-solving of llm through instruction fusion.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Mathfusion: Enhancing mathematical problem-solving of llm through instruction fusion

Reference 41

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Observation 8d51bf0c-fa40-44c6-9641-75d429d2c694 · outbound

This paper cites Scalediff: Scaling difficult problems for advanced mathematical reasoning.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Scalediff: Scaling difficult problems for advanced mathematical reasoning

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.580825Z

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This paper cites Codeforces cots.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Codeforces cots

Reference 43

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Observation 3981cb91-8c05-4f1b-b69a-b1b49a977bfe · outbound

This paper cites Open-omega-forge-1m.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Open-omega-forge-1m

Reference 44

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Observation 38469e70-8342-4765-94df-b9b2d16d99f7 · outbound

This paper cites an unresolved cited work.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Unresolved cited work

Reference 45

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Observation 0ad919e3-14d9-43e7-b907-5dd6fce20f36 · outbound

This paper cites Nlp evaluation in trouble: On the need to measure llm data contamination for each benchmark.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Nlp evaluation in trouble: On the need to measure llm data contamination for each benchmark

Reference 46

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Observation 972b3991-9f00-4f94-b9d7-4404fe862ba5 · outbound

This paper cites A survey of large language models: evolution, architectures, adaptation, benchmarking, applications, challenges, and societal implications.Electronics, 14(18).

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs A survey of large language models: evolution, architectures, adaptation, benchmarking, applications, challenges, and societal implications.Electronics, 14(18)

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.559505Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 191bf84d-ecd9-4a3e-92cc-cfc52009a277 · outbound

This paper cites Randomness is all you need: Semantic traversal of problem-solution spaces with large language models.Available at SSRN 4721407.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Randomness is all you need: Semantic traversal of problem-solution spaces with large language models.Available at SSRN 4721407

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.595334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 11d09150-bfd2-4dc7-9487-c1ee2a267bfc · outbound

This paper cites EvolvedGRPO: Unlocking reasoning in LVLMs via progressive instruction evolution.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs EvolvedGRPO: Unlocking reasoning in LVLMs via progressive instruction evolution

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.603805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9c2be51f-3f77-45e0-97f7-d466bac7eb80 · outbound

This paper cites Analysis of euclidean distance and manhattan distance in the k-means algorithm for variations number of centroid k.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Analysis of euclidean distance and manhattan distance in the k-means algorithm for variations number of centroid k

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.546615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b068581e-54fe-4a89-afc3-4919604937b7 · outbound

This paper cites Openhermes 2.5: An open dataset of synthetic data for generalist llm assistants.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Openhermes 2.5: An open dataset of synthetic data for generalist llm assistants

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.560199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0e7b6068-5d73-4468-9c70-8c6795733b25 · outbound

This paper cites Not All Correct Answers Are Equal: Why Your Distillation Source Matters.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Not All Correct Answers Are Equal: Why Your Distillation Source Matters

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:06:00.597380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation aa0bdf87-3f9a-4d1d-aad9-324a09cdc361 · outbound

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

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Openmathinstruct-2: Accelerating AI for math with massive open-source instruction data

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.590228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0530fb43-9468-4510-88db-9f6cc1695812 · outbound

This paper cites Scibench: evaluating college-level scientific problem-solving abilities of large language models.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Scibench: evaluating college-level scientific problem-solving abilities of large language models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.600071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 734d5111-ac99-430f-8516-e9d08398949a · outbound

This paper cites an unresolved cited work.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-05-17T16:21:57.543055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 51faeaa3-49b7-412c-96ac-0d8416b64392 · outbound

This paper cites Light-r1: Curriculum sft, dpo and rl for long cot from scratch and beyond.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Light-r1: Curriculum sft, dpo and rl for long cot from scratch and beyond

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.606416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a01789ae-cb87-4744-9921-7c43b0da3855 · outbound

This paper cites Wizardlm: Empowering large pre-trained language models to follow complex instructions.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Wizardlm: Empowering large pre-trained language models to follow complex instructions

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.615184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e62d6095-46eb-441a-8470-96d9bdb1b8df · outbound

This paper cites Magpie: Alignment data synthesis from scratch by prompting aligned LLMs with nothing.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Magpie: Alignment data synthesis from scratch by prompting aligned LLMs with nothing

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.534624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fc6316b7-1875-4eb9-a57d-005a65d10b77 · outbound

This paper cites A practical two-stage recipe for mathematical LLMs: Maximizing accuracy with SFT and efficiency with reinforcement learning.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs A practical two-stage recipe for mathematical LLMs: Maximizing accuracy with SFT and efficiency with reinforcement learning

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.578607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1a661e6e-58e8-438e-b8c9-d5f44df38111 · outbound

This paper cites Anyedit: Mastering unified high-quality image editing for any idea.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Anyedit: Mastering unified high-quality image editing for any idea

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.577445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 19ec358e-0497-4530-8619-025ab9760f02 · outbound

This paper cites Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-05-11T09:06:00.579237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6e6eb058-86c4-41f4-8bf4-17f374b9a2e9 · outbound

This paper cites A Survey of Large Language Models.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs A Survey of Large Language Models

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-05-11T09:06:00.604928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation ed026522-b8e9-447f-965a-852f354a43eb · outbound

This paper cites Opencodeinterpreter: Integrating code generation with execution and refinement.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Opencodeinterpreter: Integrating code generation with execution and refinement

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.518888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d8d5adfa-0204-4ef9-8e58-a07e17e33050 · outbound

This paper cites Lima: Less is more for alignment.Advances in Neural Information Processing Systems, 36: 55006–55021.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs Lima: Less is more for alignment.Advances in Neural Information Processing Systems, 36: 55006–55021

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T16:21:57.583865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 96f9f9a2-f703-4337-8b4d-a2207f90ea75 · outbound

This paper cites A Survey of LLM $\times$ DATA.

Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs A Survey of LLM $\times$ DATA

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:06:00.528654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers