·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation3h◆Counterfactual Model Testing Under Distribution Shift3h◆Centralized Critics for N-Agent Ad Hoc Teamwork3h◆Where and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning3h◆Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction3h◆Numina Fuse: Interactive Autoformalization in Lean 43h◆On the Limits of Sparse Linear Model with Shuffled Differential Privacy3h◆Video World Models Need Predictable Latents3h◆AutoPRL: Automated Path Representation Learning via a Learnable View Generator3h◆Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models3h◆Latent Goal Prediction from Language for Model-Based Planning3h◆Rectified Language Flow3h◆Trainable Boundary-Keeping Where No Formal Answer Exists: A Controlled Knock-In Study of Reward Training in Evidence-Sufficiency Judgment3h◆Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila3h◆Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights3h◆Intervention-Certified Amplification: A Validity Gate for $\sigma$-Weighted Post-Training3h◆Single-Cycle Battery Remaining Useful Life Prediction with Compressed Continuous Wavelet Features and Deep Neural Networks3h◆DoubleFree:Exploiting Exploration History for Rollout-Free Multi-Task LLMs Training3h◆Unbiased Alignment for Large Language Models with Noisy Preferences3h◆ExCal: Optimization-Free Multivariate Time Series Anomaly Detection via Excess Calibration3h◆MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation3h◆Counterfactual Model Testing Under Distribution Shift3h◆Centralized Critics for N-Agent Ad Hoc Teamwork3h◆Where and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning3h◆Fast KVzip: Efficient and Accurate LLM Inference with Gated KV Eviction3h◆Numina Fuse: Interactive Autoformalization in Lean 43h◆On the Limits of Sparse Linear Model with Shuffled Differential Privacy3h◆Video World Models Need Predictable Latents3h◆AutoPRL: Automated Path Representation Learning via a Learnable View Generator3h◆Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models3h◆Latent Goal Prediction from Language for Model-Based Planning3h◆Rectified Language Flow3h◆Trainable Boundary-Keeping Where No Formal Answer Exists: A Controlled Knock-In Study of Reward Training in Evidence-Sufficiency Judgment3h◆Intrinsic Goals for Autonomous Agents: Hierarchical Exploration Captures Future Volitional Action in Drosophila3h◆Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights3h◆Intervention-Certified Amplification: A Validity Gate for $\sigma$-Weighted Post-Training3h◆Single-Cycle Battery Remaining Useful Life Prediction with Compressed Continuous Wavelet Features and Deep Neural Networks3h◆DoubleFree:Exploiting Exploration History for Rollout-Free Multi-Task LLMs Training3h◆Unbiased Alignment for Large Language Models with Noisy Preferences3h◆ExCal: Optimization-Free Multivariate Time Series Anomaly Detection via Excess Calibration3h◆
News/Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective
arxiv
PublishedMay 29, 2026 at 4:00 AM
▲bullish

Rethinking Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective

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

arXiv:2605.29319v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on table reasoning tasks but incur substantial inference cost due to long reasoning traces. Stepwise model routing mitigates this issue by dynamically assigning reasoning steps to smaller or larg

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
#table-reasoning#efficiency#routing#natural-language-processing

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#table-reasoning#efficiency#routing#natural-language-processing

Related coverage

More from ARXIV
arxivMAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation3harxivCounterfactual Model Testing Under Distribution Shift3harxivCentralized Critics for N-Agent Ad Hoc Teamwork3harxivWhere and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning3h
The Bubble Brief
MON · WED · FRI

Read table-reasoning 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