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- DeepGCNs: Can GCNs Go As Deep As CNNs?1,662
This work presents new ways to successfully train very deep GCNs by borrowing concepts from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures, and building a very deep 56-layer GCN.
- Can GCNs Go as Deep as CNNs1,662
This work presents new ways to successfully train very deep GCNs by borrowing concepts from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures, and building a very deep 56-layer GCN.
- CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society420
This paper proposes a novel communicative agent framework named role-playing, using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions, and showcases how role-playing can be used to generate conversational data for studying the behaviors and capabilities of chat agents.
- PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks278
A novel model called NodeShuffle is proposed, which uses a Graph Convolutional Network (GCN) to better encode local point information from point neighborhoods to improve state-of-the-art upsampling methods.
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- DeepGCNs: Making GCNs Go as Deep as CNNs223
This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs, achieving very promising performance in part segmentation and semantic segmentation on point clouds and in node classification of protein functions across biological protein-protein interaction (PPI) graphs.
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- FLAG: Adversarial Data Augmentation for Graph Neural Networks133
This work proposes a simple but effective solution, FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training, and boosts performance at test time.
- Mindstorms in Natural Language-Based Societies of Mind124
This work assemble and experiment with several of NLSOMs, leveraging mindstorms in them to solve some practical AI tasks: visual question answering, image captioning, text-to-image synthesis, 3D generation, egocentric retrieval, embodied AI, and general language-based task solving.
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- Robust Optimization as Data Augmentation for Large-scale Graphs118
This work proposes FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training, which universally works in node classification, link prediction, and graph classification tasks.
- Room-Temperature Assembled MXene-Based Aerogels for High Mass-Loading Sodium-Ion Storage93
Room temperature graphene oxide-assisted assembly of 3D Ti3C2Tx MXene aerogels have been realized by introducing interfacial mediators (amino-propyltriethoxysilane, Mn2+, Fe2+, Zn2+, and Co2+).
- Vision-Language Models Do Not Understand Negation92
A data-centric approach wherein CLIP models are finetune on large-scale synthetic datasets containing millions of negated captions is explored, which can result in a 10% increase in recall on negated queries and a 28% boost in accuracy on multiple-choice questions with negated captions.
- Achieving High‐Performance 3D K+‐Pre‐intercalated Ti3C2Tx MXene for Potassium‐Ion Hybrid Capacitors via Regulating Electrolyte Solvation Structure86
It is found that the optimized solvation structure of the concentrated ether-based electrolyte leads to the formation of a thin and inorganic-rich solid electrolyte interphase (SEI) on the K + -pre-intercalated Ti 3 C 2 T x electrode, which is beneficial for interfacial stability and reaction kinetics.
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- Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-Training79
The resulting method, called SGDiff, allows for the semantic manipulation of generated images by modifying scene graph nodes and connections, and outperforms state-of-the-art methods, as measured by both the Inception Score and Fr\'echet Inception Distance metrics.
- Can Large Language Model Agents Simulate Human Trust Behavior?76
This study finds that GPT-4 agents manifest high behavioral alignment with humans in terms of trust behavior, indicating the feasibility of simulating human trust behavior with LLM agents, and probes the biases of agent trust and differences in agent trust towards other LLM agents and humans.
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- A Scalable Communication Protocol for Networks of Large Language Models55
This work introduces Agora, a meta protocol that leverages existing communication standards to make LLM-powered agents solve complex problems efficiently and observes the emergence of self-organising, fully automated protocols that achieve complex goals without human intervention.
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- ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization48
This work introduces ScoreFlow, a simple yet high-performance framework that leverages efficient gradient-based optimization in a continuous space and incorporates Score-DPO, a novel variant of the direct preference optimization method that accounts for quantitative feedback.
- OIL: Observational Imitation Learning46
Observational Imitation Learning is proposed, a novel imitation learning variant that supports online training and automatic selection of optimal behavior by observing multiple imperfect teachers and outperforms conventional imitation learning (IL) and reinforcement learning (RL) baselines and even humans in simulation.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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