Pointer Networks

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10 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Causal Edit Serialization
Oct 16, 2026Louis RobinsonPointer Networks Language Model Editing
Independent Researcher
Hierarchical Possession-Aware Graph Pointer Network for Pass Receiver Selection
Sep 4, 2026Jingyi Wang, Da Li, Kaixin Wang +1Pointer Networks Graph Neural Networks
Beijing Engineering Research Center for IoT Software and Systems, Beijing University of Technology, Beijing, China · Beijing Key Laboratory of Interdisciplinary Intelligent Technologies in Sports Medicine and Engineering, Beijing University of Technology, Beijing, China
PLATO: Pointer Learner for Agent and Task Openness
Jul 27, 2026Alireza Saleh Abadi, Leen-Kiat Soh, Daniel Alan Redder +2Pointer Networks Multi-Agent Systems
School of Computing University of Nebraska-Lincoln · School of Computing University of Georgia · Oberlin College
Pointer-Augmented Autoregressive Generation of Patent Claims with Joint Topology and Content Decoding
Jul 27, 2026Yongmin Yoo, Zhangkai Wu, Longbing CaoPointer Networks Autoregressive Generation
Frontier AI Research Centre, Macquarie University · School of Computing, FSE, Macquarie University
Pointer-CAD v2: Plan-Then-Construct CAD Generation with Dimension-Aware Parametric Precision
Jun 28, 2026Dacheng Qi, Chenyu Wang, Jingwei Xu +2Pointer Networks Parametric CAD Modeling
The University of Hong Kong, Hong Kong SAR · Shenzhen Loop Area Institute, Shenzhen, China · TranscEngram, Shenzhen, China +2
Rethinking the Pointer Loss in Table Structure Recognition: Geometry-Aware Pointer Loss for Spatial Locality
Jun 17, 2026Hong-Jun Choi, Jongho Lee, Jaeyoung KimPointer Networks Table Structure Recognition
Teamreboott Inc., South Korea
Critic-Free Deep Reinforcement Learning for Maritime Coverage Path Planning on Irregular Hexagonal Grids
Mar 30, 2026Carlos S. Sepúlveda, Gonzalo A. RuzPointer Networks Coverage Path Planning
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Av. Diagonal las Torres 2640, Santiago, Chile · Dirección de Programas, Investigación y Desarrollo, Armada de Chile, General del Canto 398, Valparaíso, Chile · Millennium Nucleus for Social Data Science (SODAS), Santiago, Chile +1
MeshTailor: Cutting Seams via Generative Mesh Traversal
Mar 28, 2026Xueqi Ma, Xingguang Yan, Congyue Zhang +1Pointer Networks 3D Mesh Generation
Shenzhen University · Simon Fraser University
Offline Decision Transformers for Neural Combinatorial Optimization: Surpassing Heuristics on the Traveling Salesman Problem
Mar 26, 2026Hironori Ohigashi, Shinichiro HamadaPointer Networks Neural Combinatorial Optimization
Panasonic Connect Co., Ltd., Japan
Pointer-CAD: Unifying B-Rep and Command Sequences via Pointer-based Edges & Faces Selection
Mar 4, 2026Dacheng Qi, Chenyu Wang, Jingwei Xu +6Pointer Networks Boundary Representation Modeling
Transcengram · Beihang University · The University of Hong Kong +4

Oct 16, 2026J/K move · Enter open · S save

Causal Edit Serialization

Louis Robinson

Independent Researcher

Pointer Networks Language Model Editing Autoregressive Language Modeling

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Standard autoregressive language models generate text sequentially, but revision requires referring back to tokens in the input context or those already generated. Causal Edit Serialization (CES) is introduced as a framework for expressing such revisions within the same causal token stream. Special pointer tokens are paired with predicted source addresses, allowing insertion, deletion, replacement, copying and movement to be localized explicitly. A lightweight localization head and dual-index rotary position encoding support both address prediction and conditioning on previously emitted pointers, while retaining a standard causal attention mask. A pretraining procedure combines ordinary language modeling with serialized repairs derived from random corruptions, model-generated edits and repeated text. Across model scales and editing baselines, CES models demonstrate complete applied edits with modestly higher clean-text bits per byte than clean autoregressive models. Supervised and reinforcement-learning post-training provide a grammatical-error-correction proof of concept, producing more beneficial than harmful changes under reference-blind model judging. Correct repair programs also improve subsequent text prediction while the original context and its cached states are retained. CES provides a common interface for generation and localized revision, with potential extensions to iterative refinement and additional pointer-addressed transformations.