Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-09T02:52:28.852922Z
Paper Citation Record · LEDGER
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization
As of 7 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2607.07678.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
50 of 50 outbound references displayed
- verified exact7
- verified fuzzy39
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch2
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1694792b-e549-4aba-b9d3-5d02733aaaad · outbound
This paper cites Alabdulmohsin, Vinh Quoc Tran, and Mostafa Dehghani.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Alabdulmohsin, Vinh Quoc Tran, and Mostafa Dehghani
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.892857Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0ce85a18-55d6-4b94-b8ff-326507bd9a14 · outbound
This paper cites Neural machine translation by jointly learning to align and translate.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Neural machine translation by jointly learning to align and translate
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.832797Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 7063f56a-3c4d-4bd3-9053-34bb55f5ba5e · outbound
This paper cites Round and round we go! what makes rotary positional encodings useful? InICLR, 2025.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Round and round we go! what makes rotary positional encodings useful? InICLR, 2025
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.856586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation da561eac-8b08-41aa-8b20-b3d4756f7c61 · outbound
This paper cites Relational inductive biases, deep learning, and graph networks.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Relational inductive biases, deep learning, and graph networks
Reference 4
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-09T02:55:53.505422Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 04ff8185-920b-4cf7-9be6-17d86a28ebbe · outbound
This paper cites by Parts.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization by Parts
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.846895Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ece55931-732c-4909-96b3-f24412798e09 · outbound
This paper cites NTK-Aware Scaled RoPE Allows LLaMA Models to Have Extended (8k+) Context Size Without Any Fine-Tuning and Minimal Perplexity Degradation.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization NTK-Aware Scaled RoPE Allows LLaMA Models to Have Extended (8k+) Context Size Without Any Fine-Tuning and Minimal Perplexity Degradation
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.845011Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 29d6936a-fe67-4fc3-a44e-5fab0b4cf375 · outbound
This paper cites Extending Context Window of Large Language Models via Positional Interpolation.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Extending Context Window of Large Language Models via Positional Interpolation
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T02:55:53.504805Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 841f58c6-d6fb-49c7-96e8-417e11025cc0 · outbound
This paper cites Training Verifiers to Solve Math Word Problems.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Training Verifiers to Solve Math Word Problems
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T02:55:53.520293Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 90fbd041-b348-4dd2-8ec7-42a983133588 · outbound
This paper cites Nemotron-CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Nemotron-CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T02:55:53.507969Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ba26b5e8-bba0-4e5a-8247-860db6e21584 · outbound
This paper cites Dynamically Scaled RoPE Further Increases Performance of Long Context LLaMA with Zero Fine-Tuning.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Dynamically Scaled RoPE Further Increases Performance of Long Context LLaMA with Zero Fine-Tuning
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.850561Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8dd77c53-abfc-460f-8cfb-1a8a671423b9 · outbound
This paper cites What is Wrong with Perplexity for Long-context Language Modeling?.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization What is Wrong with Perplexity for Long-context Language Modeling?
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T02:55:53.525674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 7ac5d788-9e4a-46dc-9fc9-228b099904e7 · outbound
This paper cites Rethinking invariance in in-context learning.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Rethinking invariance in in-context learning
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.887524Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 32be4c5b-cf09-41e6-941a-34772186d023 · outbound
This paper cites When attention sink emerges in language models: An empirical view.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization When attention sink emerges in language models: An empirical view
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.885655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 5ec052e8-682c-46ed-a97e-02913556d7f2 · outbound
This paper cites Serial position effects of large language models.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Serial position effects of large language models
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.840929Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 728d0568-2d93-421b-a4de-01ed6c854ef5 · outbound
This paper cites Large language models are zero-shot rankers for recommender systems.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Large language models are zero-shot rankers for recommender systems
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.834878Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 25934c14-3821-4bbf-bade-07ff0e251fd3 · outbound
This paper cites Fourier position embedding: Enhancing attention’s periodic extension for length generalization.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Fourier position embedding: Enhancing attention’s periodic extension for length generalization
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.839036Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ace7169e-fb47-49d3-a124-20a099e71fc9 · outbound
This paper cites Massive values in self-attention modules are the key to contextual knowledge understanding.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Massive values in self-attention modules are the key to contextual knowledge understanding
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.852489Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 638eb450-fd9a-4808-a077-c2924b0cd87b · outbound
This paper cites nanochat: The best chatgpt that $100 can buy, 2025.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization nanochat: The best chatgpt that $100 can buy, 2025
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.854554Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 77ad794b-56ea-4d7d-9f77-dcfce9301e61 · outbound
This paper cites The impact of positional encoding on length generalization in transformers.Advances in Neural Information Processing Systems, 36:24892–24928.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization The impact of positional encoding on length generalization in transformers.Advances in Neural Information Processing Systems, 36:24892–24928
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.883743Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e227fe76-e5bf-46a5-acf9-8619a88bdcce · outbound
This paper cites an unresolved cited work.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-07-09T02:55:53.835870Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 23e0c57e-3b2a-41a8-b1fb-ace95c02c96b · outbound
This paper cites Mutual information functions of natural language texts.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Mutual information functions of natural language texts
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.881592Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 857c26ca-5e7d-4140-8c44-9d094a04b7d2 · outbound
This paper cites Lost in the middle: How language models use long contexts.Transactions of the Association for Computational Linguistics, 2024.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Lost in the middle: How language models use long contexts.Transactions of the Association for Computational Linguistics, 2024
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.842923Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 39746906-d9dd-4804-b07d-49803647a185 · outbound
This paper cites Decoupled weight decay regularization.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Decoupled weight decay regularization
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.860391Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation db667a91-1eed-4dd4-8553-6631b722052a · outbound
This paper cites Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.900093Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 77336fae-c8fc-4f5f-b32e-6f2089c1964c · outbound
This paper cites Base of rope bounds context length, 2024.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Base of rope bounds context length, 2024
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.879676Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3bfab21b-c9c0-4a61-a6f0-62e1913da9ab · outbound
This paper cites Note on the bias of information estimates.Information theory in psychology: Problems and methods, 1955.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Note on the bias of information estimates.Information theory in psychology: Problems and methods, 1955
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.870307Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 17271e25-aa14-4e9f-8ed3-e09180f598ee · outbound
This paper cites Rethinking the role of demonstrations: What makes in-context learning work? InEMNLP, 2022.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Rethinking the role of demonstrations: What makes in-context learning work? InEMNLP, 2022
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.864263Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c401083d-0136-4980-9599-db737b126952 · outbound
This paper cites Mitchell.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Mitchell
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.889207Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8e626207-c5b8-4f20-ab68-ecde5be4822a · outbound
This paper cites Frequency bands in roPE: Base frequency and context length shape the interpolation–extrapolation trade-off.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Frequency bands in roPE: Base frequency and context length shape the interpolation–extrapolation trade-off
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.866397Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 780a16b0-ecb2-4ff2-aa74-996b8603f274 · outbound
This paper cites In-context Learning and Induction Heads.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization In-context Learning and Induction Heads
Reference 30
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-09T02:55:53.510704Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 6290b305-bd55-4ba2-9b21-8af38392101e · outbound
This paper cites Yarn: Efficient context window extension of large language models.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Yarn: Efficient context window extension of large language models
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.868302Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e0125542-9c0d-40c7-bde6-5ddda5db775d · outbound
This paper cites The mechanistic basis of data dependence and abrupt learning in an in-context classification task.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization The mechanistic basis of data dependence and abrupt learning in an in-context classification task
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.830788Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 91a0ecef-7c38-4d91-972a-6051c2c9b561 · outbound
This paper cites Roformer: Enhanced transformer with rotary position embedding.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Roformer: Enhanced transformer with rotary position embedding
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.877800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e200f095-8e72-49de-bc06-c7ddd1d4131a · outbound
This paper cites Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.874127Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0736f869-6dc4-4a67-91a8-b90208155d68 · outbound
This paper cites Hashimoto.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Hashimoto
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.828734Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 9c32bdf4-720b-4310-8de1-84a059ce7b72 · outbound
This paper cites Qwen2.5: A party of foundation models, September 2024.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Qwen2.5: A party of foundation models, September 2024
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.872324Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 21bfe6b3-c7f1-45a9-a4c9-7b154b3b257d · outbound
This paper cites LLaMA: Open and Efficient Foundation Language Models.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization LLaMA: Open and Efficient Foundation Language Models
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T02:55:53.517990Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation bfadd9d9-c3e1-4886-97dc-fa7d42b95913 · outbound
This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Llama 2: Open Foundation and Fine-Tuned Chat Models
Reference 38
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T02:55:53.523024Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2574888a-590e-444c-b904-b02ce971fbeb · outbound
This paper cites Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.875850Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation b5514cb7-9d29-4b4a-b2de-397ce211dfba · outbound
This paper cites Kakade, Hao Peng, and Heng Ji.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Kakade, Hao Peng, and Heng Ji
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.891012Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation a300dc53-d92f-4f2e-a137-b76537a25550 · outbound
This paper cites an unresolved cited work.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Unresolved cited work
Reference 41
- Resolution
- unresolved
- raw_fallback, observed 2026-07-09T02:55:53.858476Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 53e2db8a-040b-402a-b8e8-499e3f8ff9b5 · outbound
This paper cites On the role of attention masks and layernorm in transformers.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization On the role of attention masks and layernorm in transformers
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.862252Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 1a3f7d3d-68b2-4bcd-a516-6d78c2c04165 · outbound
This paper cites On the emergence of position bias in transformers.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization On the emergence of position bias in transformers
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.894668Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 81277e6e-bdb1-44e9-a6a3-cd7a0993c5e3 · outbound
This paper cites Efficient streaming language models with attention sinks.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Efficient streaming language models with attention sinks
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.839852Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation e22f475f-a47b-4caa-b7fa-645bad8cc22d · outbound
This paper cites Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process
Reference 45
- Resolution
- verified exact
- local_arxiv, observed 2026-07-09T02:55:53.514975Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 38e1eb26-8f65-4542-a5da-16adccc536ac · outbound
This paper cites Reddi, and Sanjiv Kumar.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Reddi, and Sanjiv Kumar
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.848655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 3ca05476-8c3f-469d-8cf2-615542bc6cc8 · outbound
This paper cites Found in the middle: How language models use long contexts better via plug-and-play positional encoding, 2024.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Found in the middle: How language models use long contexts better via plug-and-play positional encoding, 2024
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.779142Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 50d61d94-f5bd-4018-9844-a1beaa0b7eea · outbound
This paper cites Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.897922Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation bc3321f1-1aeb-477d-885b-acf01a44c0e6 · outbound
This paper cites Xing, Haotong Zhang, Joseph E.
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization Xing, Haotong Zhang, Joseph E
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.836812Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation c119a7cc-620f-4da8-a434-21d4cd5b6195 · outbound
This paper cites [43] show the multi-layer effects of masks and positional encodings [43].
How Data Shapes RoPE Frequency Usage: From Positional Scale Matching to Length Generalization [43] show the multi-layer effects of masks and positional encodings [43]
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-09T02:55:53.820066Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Pith citing papers
No inbound Pith citation observations are available.