Saved in:
| Main Authors: | Geuter, Jonathan, Kornhardt, Gregor |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.05542 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Universal Neural Optimal Transport
by: Geuter, Jonathan, et al.
Published: (2022)
by: Geuter, Jonathan, et al.
Published: (2022)
CarBoN: Calibrated Best-of-N Sampling Improves Test-time Reasoning
by: Tang, Yung-Chen, et al.
Published: (2025)
by: Tang, Yung-Chen, et al.
Published: (2025)
AdaBoN: Adaptive Best-of-N Alignment
by: Raman, Vinod, et al.
Published: (2025)
by: Raman, Vinod, et al.
Published: (2025)
TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling
by: Qiu, Jiahao, et al.
Published: (2024)
by: Qiu, Jiahao, et al.
Published: (2024)
Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment
by: Huang, Audrey, et al.
Published: (2025)
by: Huang, Audrey, et al.
Published: (2025)
BoTTA: Benchmarking on-device Test Time Adaptation
by: Danilowski, Michal, et al.
Published: (2025)
by: Danilowski, Michal, et al.
Published: (2025)
Guided Speculative Inference for Efficient Test-Time Alignment of LLMs
by: Geuter, Jonathan, et al.
Published: (2025)
by: Geuter, Jonathan, et al.
Published: (2025)
BoSS: A Best-of-Strategies Selector as an Oracle for Deep Active Learning
by: Huseljic, Denis, et al.
Published: (2026)
by: Huseljic, Denis, et al.
Published: (2026)
Near-Optimal Online Deployment and Routing for Streaming LLMs
by: Li, Shaoang, et al.
Published: (2025)
by: Li, Shaoang, et al.
Published: (2025)
BOND: Aligning LLMs with Best-of-N Distillation
by: Sessa, Pier Giuseppe, et al.
Published: (2024)
by: Sessa, Pier Giuseppe, et al.
Published: (2024)
A Regret Perspective on Online Multiple Testing
by: Hao, Qingyang, et al.
Published: (2026)
by: Hao, Qingyang, et al.
Published: (2026)
Best-of-$\infty$ -- Asymptotic Performance of Test-Time LLM Ensembling
by: Komiyama, Junpei, et al.
Published: (2025)
by: Komiyama, Junpei, et al.
Published: (2025)
Test-Time Scaling in Diffusion LLMs via Hidden Semi-Autoregressive Experts
by: Lee, Jihoon, et al.
Published: (2025)
by: Lee, Jihoon, et al.
Published: (2025)
Steering Frozen LLMs: Adaptive Social Alignment via Online Prompt Routing
by: Zhang, Zeyu, et al.
Published: (2026)
by: Zhang, Zeyu, et al.
Published: (2026)
Revisiting the (Sub)Optimality of Best-of-N for Inference-Time Alignment
by: Sriraman, Ved, et al.
Published: (2026)
by: Sriraman, Ved, et al.
Published: (2026)
RoS-Guard: Robust and Scalable Online Change Detection with Delay-Optimal Guarantees
by: Zhu, Zelin, et al.
Published: (2025)
by: Zhu, Zelin, et al.
Published: (2025)
When Fewer Layers Break More Chains: Layer Pruning Harms Test-Time Scaling in LLMs
by: Wang, Keyu, et al.
Published: (2025)
by: Wang, Keyu, et al.
Published: (2025)
S*: Test Time Scaling for Code Generation
by: Li, Dacheng, et al.
Published: (2025)
by: Li, Dacheng, et al.
Published: (2025)
BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute
by: Ding, Dujian, et al.
Published: (2025)
by: Ding, Dujian, et al.
Published: (2025)
Constrained Best Arm Identification with Tests for Feasibility
by: Cai, Ting, et al.
Published: (2025)
by: Cai, Ting, et al.
Published: (2025)
T-POP: Test-Time Personalization with Online Preference Feedback
by: Qu, Zikun, et al.
Published: (2025)
by: Qu, Zikun, et al.
Published: (2025)
RouteHijack: Routing-Aware Attack on Mixture-of-Experts LLMs
by: Xu, Zhiyuan, et al.
Published: (2026)
by: Xu, Zhiyuan, et al.
Published: (2026)
Rethinking RoPE: A Mathematical Blueprint for N-dimensional Positional Embedding
by: Liu, Haiping, et al.
Published: (2025)
by: Liu, Haiping, et al.
Published: (2025)
Best-of-N Jailbreaking
by: Hughes, John, et al.
Published: (2024)
by: Hughes, John, et al.
Published: (2024)
Aligning Tree-Search Policies with Fixed Token Budgets in Test-Time Scaling of LLMs
by: Miyamoto, Sora, et al.
Published: (2026)
by: Miyamoto, Sora, et al.
Published: (2026)
Q-ROAR: Outlier-Aware Rescaling for RoPE Position Interpolation in Quantized Long-Context LLMs
by: Qiao, Ye, et al.
Published: (2025)
by: Qiao, Ye, et al.
Published: (2025)
Test-Time Training on Graphs with Large Language Models (LLMs)
by: Zhang, Jiaxin, et al.
Published: (2024)
by: Zhang, Jiaxin, et al.
Published: (2024)
Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
by: Hübotter, Jonas, et al.
Published: (2024)
by: Hübotter, Jonas, et al.
Published: (2024)
Understanding the Role of Training Data in Test-Time Scaling
by: Javanmard, Adel, et al.
Published: (2025)
by: Javanmard, Adel, et al.
Published: (2025)
Variational Best-of-N Alignment
by: Amini, Afra, et al.
Published: (2024)
by: Amini, Afra, et al.
Published: (2024)
Majority of the Bests: Improving Best-of-N via Bootstrapping
by: Rakhsha, Amin, et al.
Published: (2025)
by: Rakhsha, Amin, et al.
Published: (2025)
RouteLLM: Learning to Route LLMs with Preference Data
by: Ong, Isaac, et al.
Published: (2024)
by: Ong, Isaac, et al.
Published: (2024)
Best of mini-N in-loop Sampling: A Contextual Quality Reward Model for Reliable and Efficient Best-of-N Sampling
by: Rho, Hyung Gyu, et al.
Published: (2025)
by: Rho, Hyung Gyu, et al.
Published: (2025)
Rethinking RoPE Scaling in Quantized LLM: Theory, Outlier, and Channel-Band Analysis with Weight Rescaling
by: Qiao, Ye, et al.
Published: (2025)
by: Qiao, Ye, et al.
Published: (2025)
Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling
by: Tran, Dao, et al.
Published: (2026)
by: Tran, Dao, et al.
Published: (2026)
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data
by: Mou, Xudong, et al.
Published: (2025)
by: Mou, Xudong, et al.
Published: (2025)
Learning Generative Selection for Best-of-N
by: Toshniwal, Shubham, et al.
Published: (2026)
by: Toshniwal, Shubham, et al.
Published: (2026)
Rethinking Optimal Verification Granularity for Compute-Efficient Test-Time Scaling
by: Chen, Hao Mark, et al.
Published: (2025)
by: Chen, Hao Mark, et al.
Published: (2025)
Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction
by: Shen, Junhong, et al.
Published: (2025)
by: Shen, Junhong, et al.
Published: (2025)
Effective Test-Time Scaling of Discrete Diffusion through Iterative Refinement
by: Lee, Sanghyun, et al.
Published: (2025)
by: Lee, Sanghyun, et al.
Published: (2025)
Similar Items
-
Universal Neural Optimal Transport
by: Geuter, Jonathan, et al.
Published: (2022) -
CarBoN: Calibrated Best-of-N Sampling Improves Test-time Reasoning
by: Tang, Yung-Chen, et al.
Published: (2025) -
AdaBoN: Adaptive Best-of-N Alignment
by: Raman, Vinod, et al.
Published: (2025) -
TreeBoN: Enhancing Inference-Time Alignment with Speculative Tree-Search and Best-of-N Sampling
by: Qiu, Jiahao, et al.
Published: (2024) -
Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment
by: Huang, Audrey, et al.
Published: (2025)