In-Context Graphical Inference

• URL: http://arxiv.org/abs/2606.05042v1
• Date: Wed, 03 Jun 2026 16:04:00 GMT
• Title: In-Context Graphical Inference
• Abstract summary: We introduce an autoregressive Graph Transformer that restores the sequential elimination structure that makes exact inference correct.<n>We prove that TT compression errors propagate at most lincarly through the autoregressive chain, that the Dirichlet-Multinomial loss is a proper scoring rule, and that WCP maintains coverage with a quantifiable degradation under estimated density ratios.
• Score: 9.39195684989942
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Marginal inference in discrete graphical models forces a choice between exactness and scalability: exact algorithms are intractable for high-treewidth graphs, while iterative approximations (Belief Propagation, variational methods) sacrifice convergence guarantees on frustrated topologies. We argue that this dichotomy stems from a mismatched inductive bias: iterative methods abandon the sequential elimination structure that makes exact inference correct. We introduce In-Context Graphical Inference (ICG-I), an autoregressive Graph Transformer that restores this structure by mimicking Variable Elimination with learned, Tensor- Train-compressed intermediate factors, paired with a Dirichlet output layer and Weighted Conformal Prediction for calibrated, distribution-free coverage guarantees under topological shift. We prove that TT compression errors propagate at most lincarly through the autoregressive chain, that the Dirichlet-Multinomial loss is a proper scoring rule, and that WCP maintains coverage with a quantifiable degradation under estimated density ratios. We conducted intensive experiments to evaluate ICG-I and achieved state-of-the-art performance across all benchmarks. ICG-I reduces MAE from 0.041 (best baseline) to 0.020 on standard instances and achieves 0.048 on N=500 frustrated spin glasses where BP diverges entirely.

Related papers

• Self-Supervised Topologically Invariant Manifold Learning for Railway Image Quality Assessment [20.081901964453074]
  Existing blind image quality assessment (BIQA) methods rely on synthetic subjective annotations, limiting generalization in real-world domains.<n>We propose a fully self-supervised BIQA framework based on topologically invariant manifold learning under boundary constraints.<n>We show that our framework secures superior zero-shot transferability across standard synthetic and wild benchmarks.
  arXiv  Detail & Related papers  (2026-08-15T13:08:06Z)
• Cross-Domain Lossy Compression via Constrained Minimum Entropy Coupling [5.404926467615337]
  We study cross-domain lossy compression through the lens of minimum entropy coupling (MEC) with rate and classification constraints.<n>Motivated by logarithmic-loss distortion, we adopt an information-based objective that maximizes the coupling strength between the source and reconstruction.<n> Experiments on MNIST super-resolution and SVHN denoising show that increasing the available rate improves classification accuracy and yields more informative reconstructions.
  arXiv  Detail & Related papers  (2026-05-11T00:19:11Z)
• Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration [76.08899010904652]
  CapCal is a training-free framework that mechanically decouples positional bias from ranking decisions.<n>It achieves superior performance among training-free methods while preserving single-pass efficiency.
  arXiv  Detail & Related papers  (2026-04-11T10:47:22Z)
• Variational Rectification Inference for Learning with Noisy Labels [74.85528327499662]
  We propose variational rectification inference (VRI) to formulate the adaptive rectification for loss functions.<n>VRI is constructed as a hierarchical Bayes by treating the rectifying vector as a latent variable.<n>By introducing the variational term in VRI, the conditional posterior is estimated accurately and avoids collapsing to a Dirac delta function.
  arXiv  Detail & Related papers  (2026-03-18T01:25:08Z)
• Co-optimization for Adaptive Conformal Prediction [9.881784717196675]
  We propose a framework that learns prediction intervals by jointly optimizing a center $m(x)$ and a radius $h(x)$.<n>Experiments on synthetic and real benchmarks demonstrate that CoCP yields consistently shorter intervals and achieves state-of-the-art conditional-coverage diagnostics.
  arXiv  Detail & Related papers  (2026-03-02T10:43:19Z)
• Stability and Generalization of Push-Sum Based Decentralized Optimization over Directed Graphs [55.77845440440496]
  Push-based decentralized communication enables optimization over communication networks, where information exchange may be asymmetric.<n>We develop a unified uniform-stability framework for the Gradient Push (SGP) algorithm.<n>A key technical ingredient is an imbalance-aware generalization bound through two quantities.
  arXiv  Detail & Related papers  (2026-02-24T05:32:03Z)
• Generalizing GNNs with Tokenized Mixture of Experts [75.8310720413187]
  We show that improving stability requires reducing reliance on shift-sensitive features, leaving an irreducible worst-case generalization floor.<n>We propose STEM-GNN, a pretrain-then-finetune framework with a mixture-of-experts encoder for diverse computation paths.<n>Across nine node, link, and graph benchmarks, STEM-GNN achieves a stronger three-way balance, improving robustness to degree/homophily shifts and to feature/edge corruptions while remaining competitive on clean graphs.
  arXiv  Detail & Related papers  (2026-02-09T22:48:30Z)
• Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detection [2.8547732086436306]
  A fundamental limitation of supervised deep learning is "Generalization Collapse"<n>We propose Latent Sculpting, a hierarchical two-stage representation learning framework.<n>We report an 88.89% detection rate on "Infiltration" scenarios.
  arXiv  Detail & Related papers  (2025-12-19T11:37:02Z)
• DAG DECORation: Continuous Optimization for Structure Learning under Hidden Confounding [0.0]
  We study structure learning for linear Gaussian SEMs in the presence of latent confounding.<n>We propose textscDECOR, a single likelihood-based estimator that jointly learns a DAG and a correlated noise model.
  arXiv  Detail & Related papers  (2025-10-02T15:23:30Z)
• Fractal Graph Contrastive Learning [4.900366729482915]
  Fractal Graph Contrastive Learning (FractalGCL) is a theory-driven framework introducing two key innovations.<n>A fallback mechanism guarantees a performance lower bound even on non-fractal graphs.<n>Experiments show that FractalGCL not only delivers state-of-the-art results on standard benchmarks but also outperforms traditional and latest baselines on traffic networks by an average margin of about remarkably 4%.
  arXiv  Detail & Related papers  (2025-05-16T15:19:10Z)
• Distribution of Classification Margins: Are All Data Equal? [61.16681488656473]
  We motivate theoretically and show empirically that the area under the curve of the margin distribution on the training set is in fact a good measure of generalization. The resulting subset of "high capacity" features is not consistent across different training runs.
  arXiv  Detail & Related papers  (2021-07-21T16:41:57Z)
• Pruning Redundant Mappings in Transformer Models via Spectral-Normalized Identity Prior [54.629850694790036]
  spectral-normalized identity priors (SNIP) is a structured pruning approach that penalizes an entire residual module in a Transformer model toward an identity mapping. We conduct experiments with BERT on 5 GLUE benchmark tasks to demonstrate that SNIP achieves effective pruning results while maintaining comparable performance.
  arXiv  Detail & Related papers  (2020-10-05T05:40:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.