Theory & Algorithms · Computer Science & AI
Computational Complexity
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343 papers, from 31 August 2026 to 5 November 2026: 282 arXiv preprints and 61 OpenAlex records.
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Newest papers, page 17
Bounded ratios for Lorentzian polynomials
We study multiplicative inequalities among the coefficients of Lorentzian polynomials through the notion of bounded ratios.Bridging simulation length scales with cellular automata
Multiscale simulation requires coupling physics models that operate at different characteristic length and time scales, because no single method spans the range needed for most problems of interest.Learning Parametric Monotone Games
We study the problem of learning from data a parametric Nash equilibrium (NE) problem that is monotone (or strongly monotone) for all parameter values.Fourteen and fifteen lonely runners
We prove the Lonely Runner Conjecture for fourteen and fifteen runners.Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization
Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance (ESG) mandates.Non-attacking rook placements on crossword grids
We introduce the notion of a non-attacking rook placement on a crossword grid.The $α$-Limit Problem: Convergence of a Linear Degenerate Interface Transmission Problem
We study the singular limit of a family of linear degenerate interface transmission problems arising from a regularization procedure in the newly proposed Two-Parameter Diffuse Domain Method (DDM2p).Text-guided flow matching enables sample-efficient crystal structure generation
Crystal generators can now propose periodic structures, but their control interfaces remain poorly matched to the mixed descriptors used in materials design.Efficiently Estimating Optimal Hyperparameter Scaling Laws through Power-Law Entropy Search
Optimal hyperparameter scaling laws describe how the best hyperparameters for large language model (LLM) training change with model and data scale, enabling practitioners to predict optimal…Upper and lower bounds on the OBDD-width of a special integer multiplication
We consider the Boolean function ${\rm SMul}_{n-1}^n(\boldsymbol{x},\boldsymbol{y})$, which computes the middle bit of the multiplication of two natural numbers represented as $n$-bit binary strings…Tight Maximin Share Guarantees of EFX and PMMS
In the fair division of indivisible goods among autonomous agents, allocations frequently satisfy strong pairwise fairness criteria while failing to guarantee an agent's full maximin share.Parameter-Robust Subspace Correction with Multiple Semidefinite Penalties
Independently weighted semidefinite penalties arise in augmented-Lagrangian and constrained formulations.An Agentic Retrobiosynthesis Framework with Learned Frontier Selection
Large language models are increasingly used as agents for multistep retrosynthesis, raising the question of how much their search policy contributes independently of the underlying reaction model.Rotational Equivariance in Machine Learning: A Comprehensive Tutorial
Rotational symmetry is one of the most important structural principles in machine learning on 3D data.Strengthening Recursive Constructions for Zero-Error Shannon Capacity
The exact Shannon capacity is unknown for every odd cycle beyond the five-cycle $C_5$, making odd cycles a central open problem in zero-error information theory.Learning Proportional Committees from Violation Feedback
We study violation-feedback learning of proportionally representative approval-based committees.Reaching Fairness by Reallocating Goods
Fair allocation of indivisible goods has largely been studied under the assumption that no prior allocation exists.Constrained Fair Allocations via Partition Matroid Reductions
We study fair allocation of indivisible goods under additive valuations and matroid constraints.Residual Maximin Share: Exact Finite-Agent Frontier, Sparse Extremizers, and Threshold Cuts
Residual maximin share (RMMS) is the largest share threshold that remains guaranteeable throughout dynamic allocation processes, even after previously allocated, lower-valued bundles are removed…Learning Materials Properties from Scarce Labels and Unlabeled Crystals
Learning materials properties from scarce labels and unlabeled crystals is a central challenge for data-driven materials discovery.