← Back to all news
arXiv AI By Haoran Liu, Guanyi Wang, Yu Yang

SAG-Sep: Sparse Augmented Graphs and Onion-Guided Search for Rounded Capacity Cut Separation

Read the original on arXiv AI →

SAG‑Sep is a new separator for rounded capacity cuts (RCCs) in capacitated vehicle‑routing problems that uses a single encoder pass to score node membership across vehicle‑count levels, avoiding iterative predict‑and‑coarsen steps. It encodes the LP solution as a Sparse Augmented Graph (SAG) with flow‑informed multi‑hop edges, and leverages the nested, onion‑like structure of high‑value RCCs through a training‑free Onion‑Sweep decoder that ranks customers and emits the most violated prefixes. On the NeuralSEP benchmark with 1,000‑customer instances, SAG‑Sep with Onion‑Sweep cuts the average root gap by 17.7% over the best NeuralSEP baseline and achieves a roughly 30‑fold per‑iteration speedup.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Oct 8

EDiS: Edge Disjoint Subgraph Sparsification Framework for Graph Neural Networks

The paper introduces EDiS, an Edge‑Disjoint Subgraph sparsification framework that separates a one‑time structural extraction of a graph into cacheable edge‑disjoint subgraphs from the per‑epoch graph composition. By recombining these cached subgraphs under edge‑budget constraints, EDiS avoids repeated sampling or recomputation while allowing varying training graphs across epochs. Experiments on 19 node‑classification benchmarks show that EDiS outperforms 17 baselines in accuracy/ROC‑AUC, achieving the lowest average rank and gap‑to‑best, especially at tight edge budgets.

By Sai Karthik Navuluru, Siddhartha Shankar Das, Franck Dernoncourt, S M Ferdous, Ryan A. Rossi, Nesreen K. Ahmed, Baris Coskunuzer, Alex Pothen, Lakshman Tamil, Mahantesh M Halappanavar
benchmarks
More like this →
arXiv Machine Learning
Sep 3

CRISP: Cliff-awaRe Input-adaptive Sparse Prefilling with Structural-Mass-Motivated Routing

CRISP (Cliff-awaRe Input-adaptive Sparse Prefilling) is a new method for long-context LLM inference that replaces costly quadratic attention prefilling with a dynamic, input-adaptive sparse routing scheme. It introduces a structural proxy, C_struct, to directly read routing decisions from the proxy attention map, eliminating the need for pooled matrix multiplication and KL divergence. Additionally, CRISP addresses the post-softmax mass cliff by using a sink-aware threshold based on the noise floor, theoretically reducing background noise accumulation to O(n). Empirical results on InfiniteBench, RULER, and LongBench show that CRISP outperforms existing sparse methods and can match or exceed exact dense attention, achieving up to a 5.30× speedup at 512k tokens and significant gains on retrieval-heavy tasks.

By Huu Huy Nguyen, Chien Van Nguyen, Franck Dernoncourt, Ryan A. Rossi, Linh Ngo Van, Jieyang Chen, Thien Huu Nguyen
llmsbenchmarks
More like this →