Global Combinatorial Optimization
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Predicting Future Utility: Global Combinatorial Optimization for Task-Agnostic KV Cache Eviction
arXiv:2602.08585v2 Announce Type: replace Abstract: Given the quadratic complexity of attention, KV cache eviction is vital to accelerate model inference. Current KV cache eviction methods typically rely on instantaneous heuristic metrics, implicitly assuming that score magnitudes are consistent proxies for importance across all heads.
Regularized Large Neighborhood Search
Announce Type: new Abstract: Operations research practitioners typically tackle NP-hard combinatorial problems using large neighborhood search (LNS), a scalable heuristic that iteratively refines a current solution by locally re-optimizing subsets of its variables. In contrast, most existing approaches for integrating combinatorial optimization layers into neural networks still assume access to an exact global solution, which is computationally intractable. We bridge this gap by introducing...
Hybrid Metaheuristic Combining the Dragonfly Algorithm and Tabu Search for the Traveling Salesman Problem
Announce Type: new Abstract: The Traveling Salesman Problem (TSP) is a classical NP-hard combinatorial optimization problem that aims to find the shortest Hamiltonian cycle visiting each city exactly once and returning to the starting point. This paper proposes a hybrid metaheuristic for the TSP by combining the Dragonfly Algorithm (DA), a swarm-intelligence-based global search method, with Tabu Search (TS), a memory-based local search technique. The proposed method follows a High-Level...
Linear Ordering Problem: Time for a Change
arXiv:2605.31051v1 Announce Type: new Abstract: The Linear Ordering Problem (LOP) is a fundamental combinatorial optimization problem with important applications in areas such as economics, social choice, and machine learning. Its most prominent use is the triangulation of economic input-output tables, which helps identify critical industries in an economy.
Linear Ordering Problem: Time for a Change
arXiv:2605.31051v2 Announce Type: replace Abstract: The Linear Ordering Problem (LOP) is a fundamental combinatorial optimization problem with important applications in areas such as economics, social choice, and machine learning. Its most prominent use is the triangulation of economic input-output tables, which helps identify critical industries in an economy. Most existing algorithms have been evaluated on benchmarks derived from outdated macroeconomic data, which no longer reflect the...
Cosm: Collective Switched Motion for Fast and Accurate Sparse Ising Optimization
arXiv:2605.30355v1 Announce Type: new Abstract: We introduce Collective Switched Motion (Cosm), a heuristic algorithm for solving sparse Ising-type optimization problems. Cosm combines locally interacting continuous circular variables with global coordination rules that facilitate collective dynamics. Pairwise interactions occur sequentially over a set of conflict-free edge partitions, resulting in an interaction network that switches periodically.
Cosm: Collective Switched Motion for Fast and Accurate Sparse Ising Optimization
arXiv:2605.30355v1 Announce Type: cross Abstract: We introduce Collective Switched Motion (Cosm), a heuristic algorithm for solving sparse Ising-type optimization problems. Cosm combines locally interacting continuous circular variables with global coordination rules that facilitate collective dynamics. Pairwise interactions occur sequentially over a set of conflict-free edge partitions, resulting in an interaction network that switches periodically.
HiTokSR: A Coarse-to-Fine Tokenizer with Hierarchical Codebooks for High-Fidelity Real-World Image Super-Resolution
arXiv:2606.01157v1 Announce Type: new Abstract: Vector-quantized (VQ) generative models have shown promising results in real-world image super-resolution (Real-ISR). However, existing methods typically rely on a monolithic latent space that entangles low-frequency structures with high-frequency textures. This entanglement forces a single codebook to capture a combinatorially complex set of structure-texture pairings, which constrains representational capacity and limits codebook utilization.
COMPOSE: Hypergraph Cover Optimization for Multi-view 3D Human Pose Estimation
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Molecular glue degraders of HuR suppress BRAF-mutant colorectal cancer
Abstract BRAF gain-of-function mutations, particularly BRAF(V600E), affect roughly 10% of all patients with colorectal cancer (CRC), and portend poor prognosis with limited therapeutic interventions. BRAF inhibitors such as encorafenib are ineffective due to MAPK pathway reactivation driven by BRAF dimerization. Combined inhibition of BRAF and EGFR, although approved therapies, results in short survival benefits and frequent treatment resistance and relapse1,2,3.