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Showing 4626–4650 of 14182 papers

TitleStatusHype
Fine-grained Preference Optimization Improves Zero-shot Text-to-Speech—0
Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2—0
Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training—0
Large Language Model Guided Self-Debugging Code Generation—0
Control Search Rankings, Control the World: What is a Good Search Engine?—0
A Contemporary Survey of Large Language Model Assisted Program Analysis—0
Large Language Model as Universal Retriever in Industrial-Scale Recommender System—0
Efficient Vision Language Model Fine-tuning for Text-based Person Anomaly Search—0
Entropy Adaptive Decoding: Dynamic Model Switching for Efficient Inference—0
GenSE: Generative Speech Enhancement via Language Models using Hierarchical Modeling—0
FinBloom: Knowledge Grounding Large Language Model with Real-time Financial Data—0
LLM-USO: Large Language Model-based Universal Sizing Optimizer—0
Analyzing Similarity Metrics for Data Selection for Language Model Pretraining—0
ComplexDec: A Domain-robust High-fidelity Neural Audio Codec with Complex Spectrum Modeling—0
Flatten Graphs as Sequences: Transformers are Scalable Graph Generators—0
JingFang: A Traditional Chinese Medicine Large Language Model of Expert-Level Medical Diagnosis and Syndrome Differentiation-Based Treatment—0
Connections between Schedule-Free Optimizers, AdEMAMix, and Accelerated SGD VariantsCode0
Automating Mathematical Proof Generation Using Large Language Model Agents and Knowledge Graphs—0
EditIQ: Automated Cinematic Editing of Static Wide-Angle Videos via Dialogue Interpretation and Saliency Cues—0
MPIC: Position-Independent Multimodal Context Caching System for Efficient MLLM Serving—0
Unlocking Efficient Large Inference Models: One-Bit Unrolling Tips the Scales—0
Position: Stop Acting Like Language Model Agents Are Normal Agents—0
Prompt-based Depth Pruning of Large Language Models—0
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model—0
Rethinking Homogeneity of Vision and Text Tokens in Large Vision-and-Language Models—0
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