Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:40:27.825037Z
Paper Citation Record · LEDGER
Efficient Pretraining Length Scaling
As of 17 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 4 inbound Pith citation observations for arXiv:2504.14992.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T07:31:30.334937Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T07:43:11.766620Z
Reference resolution
63 of 63 outbound references displayed
- verified exact0
- verified fuzzy12
- unresolved50
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 2a495a46-b68e-4c72-b08b-8ed75d565a47 · outbound
This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.
Efficient Pretraining Length Scaling Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.558018Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 37f726f6-5bd4-452b-b1ee-51c5db3e5e8e · outbound
This paper cites Gqa: Training generalized multi-query transformer models from multi-head checkpoints.
Efficient Pretraining Length Scaling Gqa: Training generalized multi-query transformer models from multi-head checkpoints
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.563405Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 15b54ffb-3f76-4735-b1f1-a3cc8cfe9928 · outbound
This paper cites Longformer: The Long-Document Transformer.
Efficient Pretraining Length Scaling Longformer: The Long-Document Transformer
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.567873Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 36e29e45-f8e2-4088-83a9-6c5861deb028 · outbound
This paper cites Piqa: Reasoning about physical commonsense in natural language.
Efficient Pretraining Length Scaling Piqa: Reasoning about physical commonsense in natural language
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.572503Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 091c1abf-aee0-4067-8db9-777408e2aedd · outbound
This paper cites Striped Attention: Faster Ring Attention for Causal Transformers.
Efficient Pretraining Length Scaling Striped Attention: Faster Ring Attention for Causal Transformers
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.576790Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f0f1e1f3-92a8-43a5-b4ed-aa5dc82d590b · outbound
This paper cites Step-level Value Preference Optimization for Mathematical Reasoning.
Efficient Pretraining Length Scaling Step-level Value Preference Optimization for Mathematical Reasoning
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.581679Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9ecf90b-d3f6-4c55-ae68-2d61dd38abc3 · outbound
This paper cites Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking.
Efficient Pretraining Length Scaling Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.586615Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d98a2357-b202-48e5-ad30-4a0bf0bd91ea · outbound
This paper cites Generating Long Sequences with Sparse Transformers.
Efficient Pretraining Length Scaling Generating Long Sequences with Sparse Transformers
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.591194Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b9e293a4-de22-4678-9395-6af6dcc67457 · outbound
This paper cites Unified scaling laws for routed language models.
Efficient Pretraining Length Scaling Unified scaling laws for routed language models
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.595724Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 80048c91-2a95-4320-867c-e50c8cc87163 · outbound
This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.
Efficient Pretraining Length Scaling Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.599886Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 665c3ae4-19f4-4b9f-bc13-659ac7dbb42b · outbound
This paper cites Training Verifiers to Solve Math Word Problems.
Efficient Pretraining Length Scaling Training Verifiers to Solve Math Word Problems
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.604174Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8a8c855b-09c9-45d8-a140-9ace5b336d3f · outbound
This paper cites Flashattention-2: Faster attention with better parallelism and work partitioning.
Efficient Pretraining Length Scaling Flashattention-2: Faster attention with better parallelism and work partitioning
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:40:28.816535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c858dc2e-bee8-45ca-b2d5-c2fbc0795e09 · outbound
This paper cites Fu, Stefano Ermon, Atri Rudra, and Christopher Ré.
Efficient Pretraining Length Scaling Fu, Stefano Ermon, Atri Rudra, and Christopher Ré
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:40:28.803359Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a35e5cf4-34aa-4230-be4f-26896c2c47d0 · outbound
This paper cites Flash-decoding for long-context inference, October.
Efficient Pretraining Length Scaling Flash-decoding for long-context inference, October
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:40:28.789928Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0fcddfd9-b261-4dee-aa43-098683fe5ded · outbound
This paper cites LongNet: Scaling Transformers to 1,000,000,000 Tokens.
Efficient Pretraining Length Scaling LongNet: Scaling Transformers to 1,000,000,000 Tokens
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.624969Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 90fe73a9-05d6-4945-bf02-11a2bcb339ef · outbound
This paper cites Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach.
Efficient Pretraining Length Scaling Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.629386Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c26e4082-807b-40ae-a037-b99d19b14464 · outbound
This paper cites Think before you speak: Training language models with pause tokens.
Efficient Pretraining Length Scaling Think before you speak: Training language models with pause tokens
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:40:28.763606Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 92849502-2711-40bf-ae22-62a82284aab2 · outbound
This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.
Efficient Pretraining Length Scaling DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.638548Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3f7478eb-756e-4cf2-aecd-d02af900b175 · outbound
This paper cites PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models.
Efficient Pretraining Length Scaling PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.642741Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 80155363-9798-4441-97c7-6b2949a45db3 · outbound
This paper cites Training Large Language Models to Reason in a Continuous Latent Space.
Efficient Pretraining Length Scaling Training Large Language Models to Reason in a Continuous Latent Space
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.646890Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7afb1830-a59a-4eaf-b75e-33d7f4ffe78f · outbound
This paper cites Measuring Massive Multitask Language Understanding.
Efficient Pretraining Length Scaling Measuring Massive Multitask Language Understanding
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.651372Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 209e70b9-66b9-4053-bb77-0f80cd91d6c9 · outbound
This paper cites Scaling Laws for Transfer.
Efficient Pretraining Length Scaling Scaling Laws for Transfer
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.655454Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f1b9f26b-e458-4b6f-9015-6ba6d282e343 · outbound
This paper cites FlashDecoding++: Faster Large Language Model Inference on GPUs.
Efficient Pretraining Length Scaling FlashDecoding++: Faster Large Language Model Inference on GPUs
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.659632Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 163c4d11-ee79-453a-af08-6709b56dad43 · outbound
This paper cites Mistral 7B.
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.664264Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ca625383-aefd-4784-9ef3-633c5428580e · outbound
This paper cites Minference 1.0: Accelerating pre-filling for long-context llms via dynamic sparse attention.Advances in Neural Information Processing Systems, 37:52481–52515, 2024.
Efficient Pretraining Length Scaling Minference 1.0: Accelerating pre-filling for long-context llms via dynamic sparse attention.Advances in Neural Information Processing Systems, 37:52481–52515, 2024
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.669416Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a06f4371-7320-412d-91cd-c08c3daec77f · outbound
This paper cites Longllmlingua: Accelerating and enhancing llms in long context scenarios via prompt compression.
Efficient Pretraining Length Scaling Longllmlingua: Accelerating and enhancing llms in long context scenarios via prompt compression
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.673545Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 497d90ed-2ef1-463c-818a-3a55a1e04edd · outbound
This paper cites SWE-bench: Can Language Models Resolve Real-World GitHub Issues?.
Efficient Pretraining Length Scaling SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.677625Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7e232f65-c2fc-48fb-afb6-a44fe20f03a4 · outbound
This paper cites Scaling Laws for Neural Language Models.
Efficient Pretraining Length Scaling Scaling Laws for Neural Language Models
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.681973Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1a5ddb20-51a2-40f5-a640-73dd082d3a0f · outbound
This paper cites Natural questions: a benchmark for question answering research.
Efficient Pretraining Length Scaling Natural questions: a benchmark for question answering research
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.686173Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3437b068-4646-4092-950c-17ae22082461 · outbound
This paper cites Efficient memory management for large language model serving with pagedattention.
Efficient Pretraining Length Scaling Efficient memory management for large language model serving with pagedattention
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.690438Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9cee53b-3d09-4738-9c19-20f8869de968 · outbound
This paper cites MiniMax-01: Scaling Foundation Models with Lightning Attention.
Efficient Pretraining Length Scaling MiniMax-01: Scaling Foundation Models with Lightning Attention
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.694729Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 31d8bb76-a3fe-4582-a07b-bc8dbe2f1d63 · outbound
This paper cites SnapKV: LLM Knows What You are Looking for Before Generation.
Efficient Pretraining Length Scaling SnapKV: LLM Knows What You are Looking for Before Generation
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.698916Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 689cae58-4f74-4dae-b1f9-6d8c08e0d3ea · outbound
This paper cites DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model.
Efficient Pretraining Length Scaling DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.708789Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e171f76d-316f-4c7e-ac7e-db93ce48ec98 · outbound
This paper cites DeepSeek-V3 Technical Report.
Efficient Pretraining Length Scaling DeepSeek-V3 Technical Report
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.713440Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b540b48a-e3b9-4079-bcf0-63075bde9b1f · outbound
This paper cites Ring Attention with Blockwise Transformers for Near-Infinite Context.
Efficient Pretraining Length Scaling Ring Attention with Blockwise Transformers for Near-Infinite Context
Reference 35
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.717780Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6970b460-588e-45a6-a150-d711cd57ba0a · outbound
This paper cites Cotformer: More tokens with attention make up for less depth.
Efficient Pretraining Length Scaling Cotformer: More tokens with attention make up for less depth
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:40:28.733667Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 10601342-b229-4e83-bc93-b1f013364f00 · outbound
This paper cites 2 OLMo 2 Furious.
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.726822Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cf7b5a91-4723-42d9-911b-62c308fbc876 · outbound
This paper cites Learning to reason with llms, 2024.
Efficient Pretraining Length Scaling Learning to reason with llms, 2024
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.730983Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e15b3320-6f79-4d74-be8f-d37b62ddbe0a · outbound
This paper cites Learning to reason with llms, 2025.
Efficient Pretraining Length Scaling Learning to reason with llms, 2025
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.735013Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9e485649-1b75-42d9-b5bf-ab6eba06eb3f · outbound
This paper cites Vicky Zhao, Lili Qiu, and Dongmei Zhang.
Efficient Pretraining Length Scaling Vicky Zhao, Lili Qiu, and Dongmei Zhang
Reference 40
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-16T11:40:27.738918Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a340972b-df2c-4a84-a40c-c1ea645abdda · outbound
This paper cites Gpqa: A graduate-level google-proof q&a benchmark.
Efficient Pretraining Length Scaling Gpqa: A graduate-level google-proof q&a benchmark
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.742943Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e4498814-6522-4633-b142-e43347dd63db · outbound
This paper cites SparQ Attention: Bandwidth-Efficient LLM Inference.
Efficient Pretraining Length Scaling SparQ Attention: Bandwidth-Efficient LLM Inference
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.747393Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 91e7e568-39a5-4459-84a7-f28fa8143952 · outbound
This paper cites Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021.
Efficient Pretraining Length Scaling Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.751446Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 942dcaf6-5bca-4dae-a22d-3cede4caf60c · outbound
This paper cites Proximal Policy Optimization Algorithms.
Efficient Pretraining Length Scaling Proximal Policy Optimization Algorithms
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.755193Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5ae0e7c7-7d5d-4826-8039-6c42aebce566 · outbound
This paper cites FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision.
Efficient Pretraining Length Scaling FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.759136Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a1ac3a2b-439b-4ba0-b529-fe90cf13ed15 · outbound
This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
Efficient Pretraining Length Scaling DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.763275Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cdf1eecf-8b62-4f5e-81d5-ed42dee80707 · outbound
This paper cites Sparsebert: Rethinking the importance analysis in self-attention.
Efficient Pretraining Length Scaling Sparsebert: Rethinking the importance analysis in self-attention
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:40:28.676571Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ffa160b7-62b5-4451-9cee-ba8cc6e5f20b · outbound
This paper cites LLM Pretraining with Continuous Concepts.
Efficient Pretraining Length Scaling LLM Pretraining with Continuous Concepts
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.771507Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9651b31e-bf8a-4581-8fe1-d93d9bb3bc07 · outbound
This paper cites CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge.
Efficient Pretraining Length Scaling CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.775667Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 62ff530d-9287-492f-8f52-d4e11f830858 · outbound
This paper cites Quest: Query-aware sparsity for efficient long-context llm inference.
Efficient Pretraining Length Scaling Quest: Query-aware sparsity for efficient long-context llm inference
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:40:28.663361Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9cf96c16-855e-49a5-a497-93f7bf9a3a39 · outbound
This paper cites Gemini: A Family of Highly Capable Multimodal Models.
Efficient Pretraining Length Scaling Gemini: A Family of Highly Capable Multimodal Models
Reference 51
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.784477Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f878e569-4991-4ae0-9492-22124a6c8e85 · outbound
This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.
Efficient Pretraining Length Scaling Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.788641Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d7008f2f-ee9d-4ac4-906b-d724213ddca8 · outbound
This paper cites Spatten: Efficient sparse attention architecture with cascade token and head pruning.
Efficient Pretraining Length Scaling Spatten: Efficient sparse attention architecture with cascade token and head pruning
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.792728Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 087072a8-55d2-4a7f-9dfd-b557ca3f5767 · outbound
This paper cites Openhands: An open platform for ai software developers as generalist agents.
Efficient Pretraining Length Scaling Openhands: An open platform for ai software developers as generalist agents
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.796755Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 506aa59f-7d7a-4a29-8cb5-61cd03e2d6d7 · outbound
This paper cites Chain-of-thought prompting elicits reasoning in large language models.Advancesin neural information processing systems, 35:24824–24837, 2022.
Efficient Pretraining Length Scaling Chain-of-thought prompting elicits reasoning in large language models.Advancesin neural information processing systems, 35:24824–24837, 2022
Reference 55
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.800794Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b9b29f29-a0e1-4ad3-af00-201c0d2f4ad6 · outbound
This paper cites Efficient streaming language models with attention sinks.
Efficient Pretraining Length Scaling Efficient streaming language models with attention sinks
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:40:28.633681Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 742911d0-84db-4836-a71b-037ca41da3bb · outbound
This paper cites Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention.
Efficient Pretraining Length Scaling Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.808920Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2cb9823b-4fc8-4f12-bf00-ca70efdd8d24 · outbound
This paper cites Big bird: Transformers for longer sequences.
Efficient Pretraining Length Scaling Big bird: Transformers for longer sequences
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:40:28.620016Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fccd7f9e-4885-43b9-83c7-359895c4e452 · outbound
This paper cites Quiet-star: Language models can teach themselves to think before speaking.
Efficient Pretraining Length Scaling Quiet-star: Language models can teach themselves to think before speaking
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:40:28.606051Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d34e39bc-7abd-4d54-8354-81e1d36388c6 · outbound
This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.
Efficient Pretraining Length Scaling HellaSwag: Can a Machine Really Finish Your Sentence?
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.820980Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 41be79ca-507e-42dd-9121-b032ca2660bb · outbound
This paper cites H2o: Heavy-hitter oracle for efficient generative inference of large language models.
Efficient Pretraining Length Scaling H2o: Heavy-hitter oracle for efficient generative inference of large language models
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:40:28.592405Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 100ce4b3-c7de-4b4b-953b-e3426844ab92 · outbound
This paper cites Accessed: 2024-9-29.
Efficient Pretraining Length Scaling Accessed: 2024-9-29
Reference 2023
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-16T11:40:28.776898Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2cbdfd7b-e0f9-45b7-95e4-990a0b58189b · outbound
This paper cites SnapKV: LLM Knows What You are Looking for Before Generation.
Efficient Pretraining Length Scaling SnapKV: LLM Knows What You are Looking for Before Generation
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:40:27.704329Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 9e028773-dd76-486a-a7fa-06f751cc643f · inbound
Scaling Latent Reasoning via Looped Language Models cites this paper.
Scaling Latent Reasoning via Looped Language Models Efficient Pretraining Length Scaling
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T07:43:11.770485Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b3c40fe4-0676-45ed-83ed-00fe7703e31f · inbound
Scaling Latent Reasoning via Looped Language Models cites this paper.
Scaling Latent Reasoning via Looped Language Models Efficient Pretraining Length Scaling
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T07:31:30.334937Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7003648e-fb23-457e-9306-e412ae4fdc60 · inbound
The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook cites this paper.
The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlook Efficient Pretraining Length Scaling
Reference 235
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T14:03:01.974171Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f683faf2-3189-4eec-9029-366a612255d5 · inbound
Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models cites this paper.
Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models Efficient Pretraining Length Scaling
Reference 92
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T06:36:29.285778Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.