·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Meta signs first AI data center deal in India with Reliance3h◆BiWM: Advancing Open-Source Interactive Video World Models with Bidirectional Autoregression6h◆Fisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning6h◆Integral Field Unit Spectroscopy with One Fiber6h◆AMEL: Accumulated Message Effects on LLM Judgments6h◆Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care6h◆From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG6h◆Operator Fusion for LLM Inference on the Tensix Architecture6h◆TRAPS: Therapeutic Response Analysis via Pathway-informed Stratification6h◆Population-Aware Physics-Informed Neural Particle Flow for Bayesian Update6h◆Domain Adapted Large Language Models for Additive Manufacturing6h◆Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring6h◆Finer is Better (with the Right Scaling)6h◆Deployment-Time Memorization in Foundation-Model Agents6h◆Business World Model6h◆Exploratory Responsiveness and Adaptive Rigidity under AI-Assisted Optimization6h◆Predictive Assistance and the Temporal Dynamics of Exploratory Compression6h◆From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs6h◆Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents6h◆Minimalist Genetic Programming6h◆Meta signs first AI data center deal in India with Reliance3h◆BiWM: Advancing Open-Source Interactive Video World Models with Bidirectional Autoregression6h◆Fisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning6h◆Integral Field Unit Spectroscopy with One Fiber6h◆AMEL: Accumulated Message Effects on LLM Judgments6h◆Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care6h◆From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG6h◆Operator Fusion for LLM Inference on the Tensix Architecture6h◆TRAPS: Therapeutic Response Analysis via Pathway-informed Stratification6h◆Population-Aware Physics-Informed Neural Particle Flow for Bayesian Update6h◆Domain Adapted Large Language Models for Additive Manufacturing6h◆Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring6h◆Finer is Better (with the Right Scaling)6h◆Deployment-Time Memorization in Foundation-Model Agents6h◆Business World Model6h◆Exploratory Responsiveness and Adaptive Rigidity under AI-Assisted Optimization6h◆Predictive Assistance and the Temporal Dynamics of Exploratory Compression6h◆From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs6h◆Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents6h◆Minimalist Genetic Programming6h◆
News/Beyond Static Evaluation: Co-Evolutionary Mechanisms for LLM-Driven Strategy Evolution in Adversarial Games
arxiv
PublishedJune 10, 2026 at 4:00 AM

Beyond Static Evaluation: Co-Evolutionary Mechanisms for LLM-Driven Strategy Evolution in Adversarial Games

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

arXiv:2606.10389v1 Announce Type: new Abstract: Recent advances in LLM-driven code evolution have enabled automated discovery by iteratively generating and improving programs. However, applying these methods to adversarial multi-agent games introduces a fundamental challenge: the evaluation landscap

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

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

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
arxivBiWM: Advancing Open-Source Interactive Video World Models with Bidirectional Autoregression6harxivFisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning6harxivIntegral Field Unit Spectroscopy with One Fiber6harxivAMEL: Accumulated Message Effects on LLM Judgments6h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

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

Originally published on arxiv ↗
HomeModelsNews