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The hungarian method for the assignment problem.Naval research logistics quarterly, 2(1-2):83–97

9 Pith papers cite this work. Polarity classification is still indexing.

9 Pith papers citing it

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2026 9

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UNVERDICTED 9

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representative citing papers

SymDrift: One-Shot Generative Modeling under Symmetries

cs.LG · 2026-05-07 · unverdicted · novelty 6.0

SymDrift makes drifting models produce symmetry-invariant samples in one step via symmetrized coordinate drifts or G-invariant embeddings, outperforming prior one-shot baselines on molecular benchmarks and cutting compute by up to 40x.

The Power of Order: Fooling LLMs with Adversarial Table Permutations

cs.LG · 2026-05-01 · unverdicted · novelty 6.0 · 2 refs

Semantically invariant row and column permutations in tables can cause LLMs to output incorrect answers, and a gradient-based attack called ATP efficiently finds such permutations that degrade performance across many models.

CogniFold: Always-On Proactive Memory via Cognitive Folding

cs.AI · 2026-05-13 · unverdicted · novelty 5.0

CogniFold extends Complementary Learning Systems theory to three layers with a prefrontal intent layer and uses graph self-organization to build proactive agent memory from continuous event streams.

RAM: Recover Any 3D Human Motion in-the-Wild

cs.CV · 2026-03-20 · unverdicted · novelty 4.0

RAM outperforms prior methods on PoseTrack and 3DPW for zero-shot multi-person 3D motion tracking and reconstruction by fusing semantic tracking, memory-augmented pose estimation, and predictive fusion.

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Showing 2 of 2 citing papers after filters.

  • SAM 3D Animal: Promptable Animal 3D Reconstruction from Images in the Wild cs.CV · 2026-05-08 · unverdicted · none · ref 19

    SAM 3D Animal is the first promptable framework for multi-animal 3D reconstruction from single images, built on SMAL+ and trained on the new Herd3D dataset, achieving SOTA results on Animal3D, APTv2, and Animal Kingdom benchmarks.

  • The Power of Order: Fooling LLMs with Adversarial Table Permutations cs.LG · 2026-05-01 · unverdicted · none · ref 24 · 2 links

    Semantically invariant row and column permutations in tables can cause LLMs to output incorrect answers, and a gradient-based attack called ATP efficiently finds such permutations that degrade performance across many models.