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Showing 51–100 of 2646 papers

TitleStatusHype
TabPFN: One Model to Rule Them All?Code0
One Model to Rank Them All: Unifying Online Advertising with End-to-End Learning—0
Reasoning Is Not All You Need: Examining LLMs for Multi-Turn Mental Health Conversations—0
One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIPCode1
Temperature is All You Need for Generalization in Langevin Dynamics and other Markov Processes—0
FP4 All the Way: Fully Quantized Training of LLMsCode1
Composable Cross-prompt Essay Scoring by Merging Models—0
Not All Tokens Are What You Need In ThinkingCode0
One RL to See Them All: Visual Triple Unified Reinforcement Learning—0
RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration—0
Is It Bad to Work All the Time? Cross-Cultural Evaluation of Social Norm Biases in GPT-4—0
One Model Transfer to All: On Robust Jailbreak Prompts Generation against LLMsCode0
Long-term impact of PM2.5 on mortality is exacerbated when wildfire events occurCode0
Action is All You Need: Dual-Flow Generative Ranking Network for Recommendation—0
CAIFormer: A Causal Informed Transformer for Multivariate Time Series Forecasting—0
TensorAR: Refinement is All You Need in Autoregressive Image Generation—0
All You Need is "Leet": Evading Hate-speech Detection AICode0
Not All Models Suit Expert Offloading: On Local Routing Consistency of Mixture-of-Expert ModelsCode0
Is (Selective) Round-To-Nearest Quantization All You Need?—0
Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning—0
General-Reasoner: Advancing LLM Reasoning Across All DomainsCode3
Not All Correct Answers Are Equal: Why Your Distillation Source Matters—0
Two Experts Are All You Need for Steering Thinking: Reinforcing Cognitive Effort in MoE Reasoning Models Without Additional Training—0
Half Search Space is All You Need—0
Augmenting Online RL with Offline Data is All You Need: A Unified Hybrid RL Algorithm Design and Analysis—0
Degradation-Aware Feature Perturbation for All-in-One Image RestorationCode2
Tianyi: A Traditional Chinese Medicine all-rounder language model and its Real-World Clinical Practice—0
Synthetic Data RL: Task Definition Is All You NeedCode2
One-for-All Pruning: A Universal Model for Customized Compression of Large Language Models—0
Not All Documents Are What You Need for Extracting Instruction Tuning Data—0
Not All Thoughts are Generated Equal: Efficient LLM Reasoning via Multi-Turn Reinforcement LearningCode0
Spectral Policy Optimization: Coloring your Incorrect Reasoning in GRPO—0
Addition is almost all you need: Compressing neural networks with double binary factorizationCode0
ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and LocalizationCode2
FP64 is All You Need: Rethinking Failure Modes in Physics-Informed Neural NetworksCode1
What Does Neuro Mean to Cardio? Investigating the Role of Clinical Specialty Data in Medical LLMs—0
All You Need Is Synthetic Task Augmentation—0
Beyond Pairwise Learning-To-Rank At Airbnb—0
Mirror Mirror on the Wall, Have I Forgotten it All? A New Framework for Evaluating Machine Unlearning—0
CVTree for 16S rRNA: Constructing Taxonomy-Compatible All-Species Living Tree Effectively and Efficiently—0
An Extra RMSNorm is All You Need for Fine Tuning to 1.58 Bits—0
Putting It All into Context: Simplifying Agents with LCLMs—0
Matrix Is All You Need—0
Attention Is Not All You Need: The Importance of Feedforward Networks in Transformer Models—0
AI-CDA4All: Democratizing Cooperative Autonomous Driving for All Drivers via Affordable Dash-cam Hardware and Open-source AI Software—0
Winning at All Cost: A Small Environment for Eliciting Specification Gaming Behaviors in Large Language Models—0
Cross-organ all-in-one parallel compressed sensing magnetic resonance imagingCode0
Sparsity is All You Need: Rethinking Biological Pathway-Informed Approaches in Deep LearningCode0
DFVO: Learning Darkness-free Visible and Infrared Image Disentanglement and Fusion All at OnceCode1
Study and improvement of search algorithms in two-players perfect information games—0
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