Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T05:10:44.518306Z
Paper Citation Record · LEDGER
As of 17 August 2026, this Paper Citation Record lists 100 of 146 outbound references and 16 inbound Pith citation observations for arXiv:2412.00714.
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 2026-08-12T05:10:44.518306Z
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 2026-08-16T11:08:29.907770Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T08:29:41.993245Z
100 of 146 outbound references displayed
External citation measurements
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Scaling New Frontiers: Insights into Large Recommendation Models GPT-4 Technical Report
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Scaling New Frontiers: Insights into Large Recommendation Models Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve
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Scaling New Frontiers: Insights into Large Recommendation Models Unresolved cited work
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Scaling New Frontiers: Insights into Large Recommendation Models Unresolved cited work
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Scaling New Frontiers: Insights into Large Recommendation Models Unresolved cited work
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Scaling New Frontiers: Insights into Large Recommendation Models 2018.{TVM}: An automated{End-to-End} optimizing compiler for deep learning
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Scaling New Frontiers: Insights into Large Recommendation Models FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
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Scaling New Frontiers: Insights into Large Recommendation Models Unresolved cited work
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Scaling New Frontiers: Insights into Large Recommendation Models DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
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Scaling New Frontiers: Insights into Large Recommendation Models In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval
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Scaling New Frontiers: Insights into Large Recommendation Models Unresolved cited work
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Scaling New Frontiers: Insights into Large Recommendation Models Unresolved cited work
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Scaling New Frontiers: Insights into Large Recommendation Models On the Embedding Collapse when Scaling up Recommendation Models
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Scaling New Frontiers: Insights into Large Recommendation Models Unresolved cited work
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Scaling New Frontiers: Insights into Large Recommendation Models A Survey on User Behavior Modeling in Recommender Systems
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Scaling New Frontiers: Insights into Large Recommendation Models KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
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Scaling New Frontiers: Insights into Large Recommendation Models Unresolved cited work
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Scaling New Frontiers: Insights into Large Recommendation Models In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models
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Scaling New Frontiers: Insights into Large Recommendation Models Unresolved cited work
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Scaling New Frontiers: Insights into Large Recommendation Models Scaling Laws for Neural Language Models
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Scaling New Frontiers: Insights into Large Recommendation Models Kang and J
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Scaling New Frontiers: Insights into Large Recommendation Models Variational Graph Auto-Encoders
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Scaling New Frontiers: Insights into Large Recommendation Models Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates
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Scaling New Frontiers: Insights into Large Recommendation Models Unresolved cited work
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Observation bd6fdd50-76ce-4130-83e9-4fb054b96c1a · inbound
Number it: Temporal Grounding Videos like Flipping Manga Scaling New Frontiers: Insights into Large Recommendation Models
Reference 19
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Observation 7b1bf9fb-3522-4e5b-b591-226cffa10548 · inbound
Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens Scaling New Frontiers: Insights into Large Recommendation Models
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Observation 1161de12-98cc-4a67-b7c2-e817332045a5 · inbound
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation Scaling New Frontiers: Insights into Large Recommendation Models
Reference 10
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FuXi-$\alpha$: Scaling Recommendation Model with Feature Interaction Enhanced Transformer Scaling New Frontiers: Insights into Large Recommendation Models
Reference 14
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Climber: Toward Efficient Scaling Laws for Large Recommendation Models Scaling New Frontiers: Insights into Large Recommendation Models
Reference 10
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Killing Two Birds with One Stone: Unifying Retrieval and Ranking with a Single Generative Recommendation Model Scaling New Frontiers: Insights into Large Recommendation Models
Reference 11
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Towards Large-scale Generative Ranking Scaling New Frontiers: Insights into Large Recommendation Models
Reference 5
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DLF: Enhancing Explicit-Implicit Interaction via Dynamic Low-Order-Aware Fusion for CTR Prediction Scaling New Frontiers: Insights into Large Recommendation Models
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Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent Scaling New Frontiers: Insights into Large Recommendation Models
Reference 12
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RankMixer: Scaling Up Ranking Models in Industrial Recommenders Scaling New Frontiers: Insights into Large Recommendation Models
Reference 12
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FuXi-\beta: Towards a Lightweight and Fast Large-Scale Generative Recommendation Model Scaling New Frontiers: Insights into Large Recommendation Models
Reference 14
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Observation f97647af-0a33-4888-afa2-86c99bad6b8c · inbound
Faster and Memory-Efficient Training of Sequential Recommendation Models for Large Catalogs Scaling New Frontiers: Insights into Large Recommendation Models
Reference 9
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From Scaling to Structured Expressivity: Rethinking Transformers for CTR Prediction Scaling New Frontiers: Insights into Large Recommendation Models
Reference 5
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Observation 64699409-d1aa-4f29-9fda-1e699931b9c8 · inbound
Why Thinking Hurts: Diagnosing and Rectifying Linguistic Inertia in Large Language Models for Recommendation Scaling New Frontiers: Insights into Large Recommendation Models
Reference 9
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Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Scaling New Frontiers: Insights into Large Recommendation Models
Reference 8
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The Pitfall of Scaling Up: Uncovering and Mitigating Popularity Bias Amplification in Scaling Transformer-based Recommenders Scaling New Frontiers: Insights into Large Recommendation Models
Reference 26
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