{"total":24,"rows":[{"id":"2107.00910","title":"Learned Token Pruning for Transformers","created":"2021-07-02","cited_by_count":139,"area":"inference"},{"id":"2312.04916","title":"EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism","created":"2023-12-08","cited_by_count":69,"area":"inference"},{"id":"2408.14690","title":"Training-Free Activation Sparsity in Large Language Models","created":"2024-08-26","cited_by_count":55,"area":"inference"},{"id":"2106.05409","title":"Zero Time Waste: Recycling Predictions in Early Exit Neural Networks","created":"2021-06-09","cited_by_count":39,"area":"inference"},{"id":"2410.20771","title":"MrT5: Dynamic Token Merging for Efficient Byte-level Language Models","created":"2024-10-28","cited_by_count":28,"area":"inference"},{"id":"2307.10780","title":"Learned Thresholds Token Merging and Pruning for Vision Transformers","created":"2023-07-20","cited_by_count":21,"area":"inference"},{"id":"2410.15252","title":"Lossless KV Cache Compression to 2%","created":"2024-10-20","cited_by_count":7,"area":"inference"},{"id":"2512.10324","title":"EchoingPixels: Aliasing-Resistant Joint Token Reduction for Audio-Visual LLMs","created":"2025-12-11","cited_by_count":6,"area":"inference"},{"id":"2212.01562","title":"Understanding the Robustness of Multi-Exit Models under Common Corruptions","created":"2022-12-03","cited_by_count":4,"area":"inference"},{"id":"2503.20282","title":"Faster Parameter-Efficient Tuning with Token Redundancy Reduction","created":"2025-03-26","cited_by_count":4,"area":"inference"},{"id":"2604.19398","title":"GRASPrune: Global Gating for Budgeted Structured Pruning of Large Language Models","created":"2026-04-21","cited_by_count":2,"area":"inference"},{"id":"2609.15160","title":"T-LoopFormer: Token-Level Elastic-Depth Looped Transformers for Latent Reasoning with Dynamic Routing","created":"2026-09-14","cited_by_count":2,"area":"inference"},{"id":"2510.23766","title":"BitSkip: An Empirical Analysis of Quantization and Early Exit Composition in Transformers","created":"2025-10-27","cited_by_count":1,"area":"inference"},{"id":"2603.21365","title":"TIDE: Token-Informed Depth Execution for Per-Token Early Exit in LLM Inference","created":"2026-03-22","cited_by_count":0,"area":"inference"},{"id":"2606.31145","title":"SeKV: Resolution-Adaptive KV Cache with Hierarchical Semantic Memory for Long-Context LLM Inference","created":"2026-06-30","cited_by_count":0,"area":"inference"},{"id":"2607.02922","title":"STAC: Selective Spatiotemporal Aggregation and Compression for Video Reasoning Segmentation","created":"2026-07-03","cited_by_count":0,"area":"inference"},{"id":"2607.04244","title":"Quantize the Target, Quantize the Drafter: Efficient Inference with Qwen3.5-4B","created":"2026-07-05","cited_by_count":0,"area":"inference"},{"id":"2608.01638","title":"Dynamic Resolution Routing for Efficient Egocentric Grounding","created":"2026-08-03","cited_by_count":0,"area":"inference"},{"id":"2608.22110","title":"How Weight Encoding Affects Language Model Placement and Performance on the Apple Neural Engine","created":"2026-08-22","cited_by_count":0,"area":"inference"},{"id":"2608.28846","title":"A rigor-matched audit of periodic-step layer skipping for efficient llm inference: conflayers versus swift, with a supplemental analysis of trained routing alternatives","created":"2026-08-28","cited_by_count":0,"area":"inference"},{"id":"2609.12337","title":"Theoretical Guarantees for One-Shot Magnitude Pruning and Compute-Adaptive Early Exit","created":"2026-09-11","cited_by_count":0,"area":"inference"},{"id":"2609.13682","title":"LayerRoute: Adaptive Layer-Skipping with LoRA-Preserved Quality for Efficient LLM Inference","created":"2026-09-12","cited_by_count":0,"area":"inference"},{"id":"2609.32663","title":"Distance-KV: Exploiting Relative Distance for Efficient Long-Context Inference","created":"2026-09-26","cited_by_count":0,"area":"inference"},{"id":"2610.18690","title":"Just Respect the Semantics: Image-Token Compression via Pre-LLM Semantic Variable-Resolution Tokenization and In-LLM Relevance Pruning","created":"2026-10-13","cited_by_count":0,"area":"inference"}],"next_cursor":null}