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#theoretical-computer-science News & Analysis

5 articles tagged with #theoretical-computer-science. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles
AIBullisharXiv – CS AI · Mar 97/10
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Accelerating Scientific Research with Gemini: Case Studies and Common Techniques

Google's Gemini-based AI models, particularly Gemini Deep Think, have demonstrated the ability to collaborate with researchers to solve open problems and generate new proofs across theoretical computer science, economics, optimization, and physics. The research identifies effective techniques for human-AI collaboration including iterative refinement, problem decomposition, and deploying AI as adversarial reviewers to detect flaws in existing proofs.

🧠 Gemini
AINeutralarXiv – CS AI · Jun 96/10
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On the Complexity of Offline Reinforcement Learning with $Q^\star$-Approximation and Partial Coverage

Researchers present a theoretical framework for offline reinforcement learning that answers a fundamental open question negatively: Q*-realizability and Bellman completeness alone are insufficient for sample-efficient learning under partial coverage. The work introduces a decision-estimation framework that improves sample complexity bounds for practical algorithms like Conservative Q-Learning and extends theoretical understanding to previously unexplored settings.

AINeutralarXiv – CS AI · May 285/10
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The Optimal Sample Complexity of Linear Contracts

Researchers have proven optimal sample complexity for learning linear contracts in offline settings, showing that Empirical Utility Maximization requires only O(ln(1/δ)/ε²) samples to approximate optimal contracts. This result matches theoretical lower bounds and establishes uniform convergence guarantees across all linear contracts.

AINeutralarXiv – CS AI · May 116/10
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Online Allocation with Unknown Shared Supply

Researchers introduce the Online Shared Supply Allocation (OSSA) problem, a theoretical framework for allocating limited resources across multiple locations before demand is known, common in humanitarian logistics and vaccine distribution. The proposed GPA algorithm achieves a 4/3-approximation ratio to optimal offline solutions, with proven tight bounds and a learning-augmented variant that incorporates forecasts.