·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
What Does an Ontology Catch? Curated, LLM-Generated and Data-Derived Ontologies as Checkers of LLM Agents over Knowledge Graphs3h◆GAUSS: A Workload-Aware Analytical and Monte-Carlo Simulator for LLM Inference Serving3h◆Algorithmic Bias from the Perspectives of Healthcare Professionals3h◆Evaluating Causal Reasoning in LLMs: A Case Study in the Challenges of Procedural Generation3h◆AI-Driven Digital Twin Framework for Warehouse Logistics Automation Optimization3h◆SkillCT: A Multidimensional Benchmark and Execution-Free Baseline for Agent Skill Assessment3h◆BridgeRouter: Gain-Aware Instance Routing over Cross-Modal Bridge Portfolios3h◆DecompGym: Benchmarking LLM Agents on Designing Decomposition Algorithms3h◆Inner-Learning: From Hand-Coded Beliefs to Declarative Knowledge in Trick-Taking Games3h◆Can LLMs Faithfully Solve Ontology Engineering Tasks? A Mechanistic Interpretability Study3h◆Investor's Last Exam: Can AI Agents Do Equity Investment Without Seeing the Future?3h◆Encode Once, Answer Many: Internalizing Context via Parametric Flow3h◆Masked Erosion: How Acceptance Gates Lose Redundant Safeguards in Self-Improving Agents3h◆Predict the Reward, Not the World: World Reward Models for ML Agents3h◆DES-JEPA: Learning a Shared Vocabulary for High-Dimensional Discrete Event Sequences3h◆Accelerating Nonlinear MPC by Behavior Cloning with Separated Timescales: Application to Nuclear Reactor Load-Following3h◆Value Probes: Learned Value Functions for Beam-Search Red Teaming3h◆From Leniency to Suspicion: How an Open Post-Training Recipe Changes Whom LLM Fact-Checkers Believe3h◆An Alignment Benchmark for Enterprise Agents on Era by Eon3h◆PT$^3$: Prototype-Guided Self-Supervised Pretraining for Generalizable mmWave Human Point Cloud Representation3h◆What Does an Ontology Catch? Curated, LLM-Generated and Data-Derived Ontologies as Checkers of LLM Agents over Knowledge Graphs3h◆GAUSS: A Workload-Aware Analytical and Monte-Carlo Simulator for LLM Inference Serving3h◆Algorithmic Bias from the Perspectives of Healthcare Professionals3h◆Evaluating Causal Reasoning in LLMs: A Case Study in the Challenges of Procedural Generation3h◆AI-Driven Digital Twin Framework for Warehouse Logistics Automation Optimization3h◆SkillCT: A Multidimensional Benchmark and Execution-Free Baseline for Agent Skill Assessment3h◆BridgeRouter: Gain-Aware Instance Routing over Cross-Modal Bridge Portfolios3h◆DecompGym: Benchmarking LLM Agents on Designing Decomposition Algorithms3h◆Inner-Learning: From Hand-Coded Beliefs to Declarative Knowledge in Trick-Taking Games3h◆Can LLMs Faithfully Solve Ontology Engineering Tasks? A Mechanistic Interpretability Study3h◆Investor's Last Exam: Can AI Agents Do Equity Investment Without Seeing the Future?3h◆Encode Once, Answer Many: Internalizing Context via Parametric Flow3h◆Masked Erosion: How Acceptance Gates Lose Redundant Safeguards in Self-Improving Agents3h◆Predict the Reward, Not the World: World Reward Models for ML Agents3h◆DES-JEPA: Learning a Shared Vocabulary for High-Dimensional Discrete Event Sequences3h◆Accelerating Nonlinear MPC by Behavior Cloning with Separated Timescales: Application to Nuclear Reactor Load-Following3h◆Value Probes: Learned Value Functions for Beam-Search Red Teaming3h◆From Leniency to Suspicion: How an Open Post-Training Recipe Changes Whom LLM Fact-Checkers Believe3h◆An Alignment Benchmark for Enterprise Agents on Era by Eon3h◆PT$^3$: Prototype-Guided Self-Supervised Pretraining for Generalizable mmWave Human Point Cloud Representation3h◆
News/Predict the Reward, Not the World: World Reward Models for ML Agents
arxiv
PublishedOctober 31, 2026 at 4:00 AM

Predict the Reward, Not the World: World Reward Models for ML Agents

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

arXiv:2610.38439v1 Announce Type: new Abstract: Search-based machine-learning (ML) agents improve a solution by writing candidate programs, executing them, and keeping the best. Execution gives the search reliable feedback, but a single run can take hours, and most runs end in programs the agent dis

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 →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivWhat Does an Ontology Catch? Curated, LLM-Generated and Data-Derived Ontologies as Checkers of LLM Agents over Knowledge Graphs3harxivGAUSS: A Workload-Aware Analytical and Monte-Carlo Simulator for LLM Inference Serving3harxivAlgorithmic Bias from the Perspectives of Healthcare Professionals3harxivEvaluating Causal Reasoning in LLMs: A Case Study in the Challenges of Procedural Generation3h
The Bubble Brief
MON · WED · FRI

Read AI 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