Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-14T19:12:24.627033Z
Paper Citation Record · LEDGER
As of 29 July 2026, this Paper Citation Record lists 29 of 29 outbound references and 100 inbound Pith citation observations for arXiv:2601.08584.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-14T19:12:24.627033Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-14T22:25:04.080629Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-11T03:07:51.195601Z
29 of 29 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation b703829f-112a-44e4-9b1a-b285a58f1bb6 · outbound
Reference 1
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Ministral 3 GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Reference 2
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Reference 3
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Reference 4
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Ministral 3 Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
Reference 5
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Ministral 3 DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Reference 6
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Ministral 3 Distilled Pretraining: A modern lens of Data, In-Context Learning and Test-Time Scaling
Reference 7
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Ministral 3 Direct Language Model Alignment from Online AI Feedback
Reference 8
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Reference 9
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Ministral 3 LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
Reference 10
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Ministral 3 TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Reference 11
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Reference 13
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Ministral 3 From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline
Reference 14
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Ministral 3 WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild
Reference 15
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Ministral 3 PHYBench: Holistic Evaluation of Physical Perception and Reasoning in Large Language Models
Reference 16
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Ministral 3 Compact Language Models via Pruning and Knowledge Distillation
Reference 17
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Ministral 3 YaRN: Efficient Context Window Extension of Large Language Models
Reference 19
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Ministral 3 Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Reference 21
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Ministral 3 DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Reference 23
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Ministral 3 LLM Pruning and Distillation in Practice: The Minitron Approach
Reference 24
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Ministral 3 RoFormer: Enhanced Transformer with Rotary Position Embedding
Reference 25
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Ministral 3 A Simple and Effective Pruning Approach for Large Language Models
Reference 26
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Ministral 3 HellaSwag: Can a Machine Really Finish Your Sentence?
Reference 28
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Ministral 3 AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models
Reference 29
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Reference 26
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