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Neil Rimer thinks the AI money is coming back out2h◆The Steering Budget: Examples beat Knobs2h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs2h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination2h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models2h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation2h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System2h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets2h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility2h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation2h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks2h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios2h◆When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration2h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents2h◆Inoculation Adapters: Improved Selective Generalization of Capabilities with Fewer Surprising Backdoors2h◆ANet Patu-1: The Value of Connection in the Agent Network2h◆Automatically Evolving Prompt Guidelines for Task-Specific Optimization2h◆MAPS: Modeling Co-Existing Subjective Perspectives and Shared Meaning in Multi-Agent Cognitive Dialogue2h◆Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation2h◆Tracing LLM Behavior to the Training Data with Empirical Next-Token Distributions2h◆Neil Rimer thinks the AI money is coming back out2h◆The Steering Budget: Examples beat Knobs2h◆Polestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs2h◆RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination2h◆When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models2h◆SAGA: Schema-Aware Grounding for Agentic Text-to-SPARQL Generation2h◆ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System2h◆LBA: Textual Hard-Label Adversarial Attack under Low Query Budgets2h◆Eta Given Delta: Defining LLM Tool Efficiency With Marginal Tool Utility2h◆Simplicity Paradox: Debunking myths about prompting and datasets for LLM evaluation2h◆LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks2h◆OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios2h◆When AI Blurs the Boundaries of Contribution: An Empirical Study of Authorship Calibration2h◆Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents2h◆Inoculation Adapters: Improved Selective Generalization of Capabilities with Fewer Surprising Backdoors2h◆ANet Patu-1: The Value of Connection in the Agent Network2h◆Automatically Evolving Prompt Guidelines for Task-Specific Optimization2h◆MAPS: Modeling Co-Existing Subjective Perspectives and Shared Meaning in Multi-Agent Cognitive Dialogue2h◆Automatic Hard Example Synthesis with Multi-Level Agentic Data Curation2h◆Tracing LLM Behavior to the Training Data with Empirical Next-Token Distributions2h◆
News/The DeepMind trio who built a poker AI are now making money for quant hedge funds
techcrunch
PublishedJune 30, 2026 at 8:33 PM
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The DeepMind trio who built a poker AI are now making money for quant hedge funds

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EquiLibre Technologies, a Prague-based AI lab founded by three ex-DeepMind researchers, is now valued at more than $500 million.

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