·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation2h◆Counterfactual Model Testing Under Distribution Shift2h◆Centralized Critics for N-Agent Ad Hoc Teamwork2h◆Where and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning2h◆Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction2h◆Numina Fuse: Interactive Autoformalization in Lean 42h◆On the Limits of Sparse Linear Model with Shuffled Differential Privacy2h◆Video World Models Need Predictable Latents2h◆AutoPRL: Automated Path Representation Learning via a Learnable View Generator2h◆Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models2h◆Latent Goal Prediction from Language for Model-Based Planning2h◆Rectified Language Flow2h◆Trainable Boundary-Keeping Where No Formal Answer Exists: A Controlled Knock-In Study of Reward Training in Evidence-Sufficiency Judgment2h◆Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila2h◆Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights2h◆Intervention-Certified Amplification: A Validity Gate for $\sigma$-Weighted Post-Training2h◆Single-Cycle Battery Remaining Useful Life Prediction with Compressed Continuous Wavelet Features and Deep Neural Networks2h◆DoubleFree:Exploiting Exploration History for Rollout-Free Multi-Task LLMs Training2h◆Unbiased Alignment for Large Language Models with Noisy Preferences2h◆ExCal: Optimization-Free Multivariate Time Series Anomaly Detection via Excess Calibration2h◆MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation2h◆Counterfactual Model Testing Under Distribution Shift2h◆Centralized Critics for N-Agent Ad Hoc Teamwork2h◆Where and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning2h◆Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction2h◆Numina Fuse: Interactive Autoformalization in Lean 42h◆On the Limits of Sparse Linear Model with Shuffled Differential Privacy2h◆Video World Models Need Predictable Latents2h◆AutoPRL: Automated Path Representation Learning via a Learnable View Generator2h◆Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models2h◆Latent Goal Prediction from Language for Model-Based Planning2h◆Rectified Language Flow2h◆Trainable Boundary-Keeping Where No Formal Answer Exists: A Controlled Knock-In Study of Reward Training in Evidence-Sufficiency Judgment2h◆Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila2h◆Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights2h◆Intervention-Certified Amplification: A Validity Gate for $\sigma$-Weighted Post-Training2h◆Single-Cycle Battery Remaining Useful Life Prediction with Compressed Continuous Wavelet Features and Deep Neural Networks2h◆DoubleFree:Exploiting Exploration History for Rollout-Free Multi-Task LLMs Training2h◆Unbiased Alignment for Large Language Models with Noisy Preferences2h◆ExCal: Optimization-Free Multivariate Time Series Anomaly Detection via Excess Calibration2h◆
News/Strategic Scaling of Test-Time Compute: A Bandit Learning Approach
arxiv
PublishedApril 24, 2026 at 4:00 AM
▲bullish

Strategic Scaling of Test-Time Compute: A Bandit Learning Approach

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2506.12721v2 Announce Type: replace Abstract: Scaling test-time compute has emerged as an effective strategy for improving the performance of large language models. However, existing methods typically allocate compute uniformly across all queries, overlooking variation in query difficulty. To

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#large-language-models#compute-optimization#bandit-learning#benchmark

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#large-language-models#compute-optimization#bandit-learning#benchmark

Related coverage

More from ARXIV
arxivMAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation2harxivCounterfactual Model Testing Under Distribution Shift2harxivCentralized Critics for N-Agent Ad Hoc Teamwork2harxivWhere and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning2h
The Bubble Brief
MON · WED · FRI

Read large-language-models insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews