·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Structured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides6m◆Rethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation6m◆From Events to Reasoning: Hierarchical Rule Composition over Explicit States6m◆ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation6m◆Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model6m◆PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents6m◆From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems6m◆Null Controls Are Necessary but Not Sufficient: Evaluating Acceptance Guards for Scientific Discovery and Interpretability6m◆Learnable Splines for Unstructured Neural Operators6m◆Functional Welfare for AI Safety6m◆Author-in-the-loop Agentic Reproducibility Auditing for a Machine Learning Conference6m◆Harness-Delta Attribution: Dissecting Gains From Harness Evolution6m◆Testing the Planning Claims of LLM-Based Agents against Formal Semantics6m◆Are Large Reasoning Models Good Everyday Reasoners? A Comparison with Non-Reasoning Models on Logical Reasoning Tasks6m◆FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds6m◆Repairing LLM-Generated Specifications with Deterministic Diagnosis6m◆At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier6m◆From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents6m◆Not All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation6m◆FlexUMM: Flexible Representation Learning for Unified Multimodal Models6m◆Structured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides6m◆Rethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation6m◆From Events to Reasoning: Hierarchical Rule Composition over Explicit States6m◆ECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation6m◆Sensitivity Analysis of Decision Rules in an Agent-Based Agro-Ecological Model6m◆PRIME: Towards Verifiable Evidence-Grounded Reasoning in LLM Agents6m◆From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems6m◆Null Controls Are Necessary but Not Sufficient: Evaluating Acceptance Guards for Scientific Discovery and Interpretability6m◆Learnable Splines for Unstructured Neural Operators6m◆Functional Welfare for AI Safety6m◆Author-in-the-loop Agentic Reproducibility Auditing for a Machine Learning Conference6m◆Harness-Delta Attribution: Dissecting Gains From Harness Evolution6m◆Testing the Planning Claims of LLM-Based Agents against Formal Semantics6m◆Are Large Reasoning Models Good Everyday Reasoners? A Comparison with Non-Reasoning Models on Logical Reasoning Tasks6m◆FRACTAL: A Self-Similar Multi-Agent Orchestration Architecture with Formal Communication, Convergence, and Depth Bounds6m◆Repairing LLM-Generated Specifications with Deterministic Diagnosis6m◆At the Limits of Computability: Steps Towards Autonomous AI Discovery on the Busy Beaver Frontier6m◆From Relevant Memories to Sufficient Evidence: Rethinking Memory Utilization in LLM Agents6m◆Not All Graphs Are Maps: A Task-Agnostic Exploration Framework for Mobile GUI Navigation6m◆FlexUMM: Flexible Representation Learning for Unified Multimodal Models6m◆
News/Controlled-Minimax: Stabilizing Adversarial Optimization via Control Theory
arxiv
PublishedOctober 12, 2026 at 4:00 AM

Controlled-Minimax: Stabilizing Adversarial Optimization via Control Theory

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

arXiv:2610.12915v1 Announce Type: new Abstract: Minimax optimization is fundamental to adversarial learning yet suffers from severe stability challenges, often manifesting as persistent limit cycles or divergence. This paper investigates these instabilities through the lens of continuous-time dynami

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
arxivStructured Authority Injection: Persistent Human Control for LLM Agents via Typed MCP Overrides6marxivRethinking User Encoders: Routing Diffusion States for Scenario-Adaptive User Representation6marxivFrom Events to Reasoning: Hierarchical Rule Composition over Explicit States6marxivECHOES: Seeing Beyond the Current Image via Recursive History Compression and Privileged On-Policy Distillation6m
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