Research repositories

This paper derives Simple Graph Convolution (SGC) by removing intermediate nonlinearities from a graph convolutional network and collapsing its weight matrices into one linear classifier. The resulting method applies a fixed, parameter-free graph propagation filter to node features before multinomial logistic regression. A spectral analysis relates propagation with self-loops to low-pass filtering and proves that self-loops shrink the normalized-Laplacian spectrum. Experiments compare SGC with graph neural-network baselines on citation and social networks and adapt it to text classification, geolocation, relation extraction, zero-shot image classification, graph classification, and molecular prediction, emphasizing accuracy, training time, stability, and known failure cases.

This paper develops stochastic-gradient variational inference for directed probabilistic models with continuous latent variables and intractable posteriors. Its reparameterization expresses posterior samples as differentiable transformations of parameter-free noise, yielding the Stochastic Gradient Variational Bayes estimator. For independent observations with local latent variables, the Auto-Encoding Variational Bayes algorithm jointly trains a probabilistic encoder and generative decoder, producing the variational auto-encoder when both are neural networks. Experiments on MNIST and Frey Face compare AEVB with wake-sleep using variational lower bounds, and on low-dimensional MNIST also compare estimated marginal likelihood with Monte Carlo EM. Appendix visualizations show learned manifolds and generated samples.

The paper presents AGENTIC R AG-R1, a reinforcement-learning framework for retrieval-augmented multi-step reasoning. It combines fine-grained actions, stack memory with push, pop, revision, and summarization, hierarchical outcome and process rewards, and information-aware rollout rejection. Experiments report comparisons, ablations, step-budget scaling, model-size effects, timing, and downstream agent evaluations.

The paper introduces DoCtOR, a reflection framework for large-language-model multi-agent systems. DoCtOR uses a process-reward-based ProFA diagnosis module to identify the first incorrect reasoning step and responsible agent in a failed trajectory, a counterfactual correction module to propose and score an alternative action, and a reflector that gives targeted feedback to the decisive error agent. The authors evaluate the framework on HotPotQA, ChartQAPro, and Mind2Web, compare it with Reflexion, Retroformer, and COPPER, and separately evaluate ProFA against automated failure-attribution baselines on held-in and held-out Who & When data. Additional experiments study model size, test-set size, trajectory scope, correctness threshold, and component ablations.