·
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/RetroChessBench: A Benchmark for Reconstructing and Proving Chess Histories
arxiv
PublishedOctober 31, 2026 at 4:00 AM

RetroChessBench: A Benchmark for Reconstructing and Proving Chess Histories

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

arXiv:2610.39341v1 Announce Type: cross Abstract: Frontier language models such as GPT 6 Astra have contributed to research mathematics, demonstrating substantial reasoning capabilities. Yet serious reasoning failures persist even in tasks governed by explicit rules. We demonstrate this in retrograd

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