{"total":46,"rows":[{"id":"2406.19314","title":"LiveBench: A Challenging, Contamination-Limited LLM Benchmark","created":"2024-06-27","cited_by_count":262,"area":null},{"id":"2507.02833","title":"Generalizing Verifiable Instruction Following","created":"2025-07-03","cited_by_count":194,"area":null},{"id":"2408.16673","title":"Preserving Diversity in Supervised Fine-Tuning of Large Language Models","created":"2024-08-29","cited_by_count":70,"area":null},{"id":"2504.13161","title":"Nemotron-CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training","created":"2025-04-17","cited_by_count":66,"area":null},{"id":"2508.15884","title":"Jet-Nemotron: Efficient Language Model with Post Neural Architecture Search","created":"2025-08-21","cited_by_count":41,"area":null},{"id":"2409.11136","title":"Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models","created":"2024-09-17","cited_by_count":40,"area":null},{"id":"2410.19313","title":"COAT: Compressing Optimizer states and Activation for Memory-Efficient FP8 Training","created":"2024-10-25","cited_by_count":33,"area":null},{"id":"2507.06187","title":"The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains","created":"2025-07-08","cited_by_count":28,"area":null},{"id":"2407.10627","title":"Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated Chatbot Arena","created":"2024-07-15","cited_by_count":26,"area":null},{"id":"2412.14166","title":"MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data","created":"2024-12-18","cited_by_count":21,"area":null},{"id":"2510.06826","title":"Mid-Training of Large Language Models: A Survey","created":"2025-10-08","cited_by_count":21,"area":null},{"id":"2502.01637","title":"Scaling Embedding Layers in Language Models","created":"2025-02-03","cited_by_count":20,"area":null},{"id":"2411.10478","title":"Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Survey","created":"2024-11-11","cited_by_count":19,"area":null},{"id":"2411.19146","title":"Puzzle: Distillation-Based NAS for Inference-Optimized LLMs","created":"2024-11-28","cited_by_count":17,"area":null},{"id":"2407.01638","title":"LASSI: An LLM-based Automated Self-Correcting Pipeline for Translating Parallel Scientific Codes","created":"2024-06-30","cited_by_count":10,"area":null},{"id":"2503.16672","title":"Accelerating Transformer Inference and Training with 2:4 Activation Sparsity","created":"2025-03-20","cited_by_count":9,"area":null},{"id":"2512.00020","title":"Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead","created":"2025-10-29","cited_by_count":8,"area":null},{"id":"2407.18421","title":"Self-Directed Synthetic Dialogues and Revisions Technical Report","created":"2024-07-25","cited_by_count":7,"area":null},{"id":"2410.02749","title":"Training Language Models on Synthetic Edit Sequences Improves Code Synthesis","created":"2024-10-03","cited_by_count":7,"area":null},{"id":"2412.08587","title":"Advancing Single and Multi-task Text Classification through Large Language Model Fine-tuning","created":"2024-12-11","cited_by_count":7,"area":null},{"id":"2504.11409","title":"Minitron-SSM: Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning","created":"2025-04-15","cited_by_count":7,"area":null},{"id":"2506.14123","title":"Sampling from Your Language Model One Byte at a Time","created":"2025-06-17","cited_by_count":7,"area":null},{"id":"2510.14717","title":"Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling","created":"2025-10-16","cited_by_count":6,"area":null},{"id":"2602.14208","title":"Fast Catch-Up, Late Switching: Optimal Batch Size Scheduling via Functional Scaling Laws","created":"2026-02-15","cited_by_count":6,"area":null},{"id":"2606.14502","title":"From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI","created":"2026-06-12","cited_by_count":6,"area":null},{"id":"2505.19145","title":"Do Large Language Models (Really) Need Statistical Foundations?","created":"2025-05-25","cited_by_count":5,"area":null},{"id":"2506.06644","title":"Spark Transformer: Reactivating Sparsity in FFN and Attention","created":"2025-06-07","cited_by_count":5,"area":null},{"id":"2602.00398","title":"MemoryLLM: Plug-n-Play Interpretable Feed-Forward Memory for Transformers","created":"2026-01-30","cited_by_count":5,"area":null},{"id":"2505.18557","title":"TAG-INSTRUCT: Controlled Instruction Complexity Enhancement through Structure-based Augmentation","created":"2025-05-24","cited_by_count":3,"area":null},{"id":"2603.21606","title":"mSFT: Addressing Dataset Mixtures Overfitting Heterogeneously in Multi-task SFT","created":"2026-03-23","cited_by_count":3,"area":null},{"id":"2409.07136","title":"Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models","created":"2024-09-11","cited_by_count":2,"area":null},{"id":"2511.05811","title":"MOSS: Efficient and Accurate FP8 LLM Training with Microscaling and Automatic Scaling","created":"2025-11-08","cited_by_count":2,"area":null},{"id":"2602.02680","title":"FlexRank: Nested Low-Rank Knowledge Decomposition for Adaptive Model Deployment","created":"2026-02-02","cited_by_count":2,"area":null},{"id":"2602.03515","title":"Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation","created":"2026-02-03","cited_by_count":2,"area":null},{"id":"2604.07190","title":"The ATOM Report: Measuring the Open Language Model Ecosystem","created":"2026-04-08","cited_by_count":2,"area":null},{"id":"2504.06323","title":"Mosaic: Composite Projection Pruning for Resource-efficient LLMs","created":"2025-04-08","cited_by_count":1,"area":null},{"id":"2512.10545","title":"XDoGE: Multilingual Data Reweighting to Enhance Language Inclusivity in LLMs","created":"2025-12-11","cited_by_count":1,"area":null},{"id":"2503.02147","title":"Frankenstein Optimizer: Harnessing the Potential by Revisiting Optimization Tricks","created":"2025-03-04","cited_by_count":0,"area":null},{"id":"2507.18009","title":"GRR-CoCa: Leveraging LLM Mechanisms in Multimodal Model Architectures","created":"2025-07-24","cited_by_count":0,"area":null},{"id":"2507.22250","title":"Using Scaling Laws for Data Source Utility Estimation in Domain-Specific Pre-Training","created":"2025-07-29","cited_by_count":0,"area":null},{"id":"2512.13316","title":"ALIGN-FL: Architecture-independent Learning through Invariant Generative component sharing in Federated Learning","created":"2025-12-15","cited_by_count":0,"area":null},{"id":"2604.10627","title":"Computational Lesions in Multilingual Language Models Separate Shared and Language-specific Brain Alignment","created":"2026-04-12","cited_by_count":0,"area":null},{"id":"2606.14119","title":"FactoryLLM: A Safe and Open-Source AI Playground for Evaluating LLMs in Smart Factories","created":"2026-06-12","cited_by_count":0,"area":null},{"id":"2607.22662","title":"CuraWeb: Joint Optimization of Quality, Redundancy, and Diversity for Web-Scale Pretraining Data","created":"2026-06-29","cited_by_count":0,"area":null},{"id":"2609.18560","title":"The evolution of sex for artificial intelligence: a population-genetic framework for multigenerational model populations","created":"2026-09-16","cited_by_count":0,"area":null},{"id":"2609.38832","title":"Scaling Parameter and Context in Attention: Native Sparse Attention from Mixture-of-Head","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}