arXiv:2607. 25387v2 Announce Type: replace Abstract: Learned restriction maps in sheaf graph neural networks are often treated as proof that the model has discovered useful edge geometry.
arXiv:2607. 16568v1 Announce Type: new Abstract: Function-preserving network growth techniques such as Net2Net and progressive stacking expand a model's capacity without destroying its learned function, but existing formulations either tolerate numerical perturbations or require a full rebuild of the training program.
arXiv:2607. 26533v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios.
arXiv:2609.23547v1 Announce Type: new Abstract: Graph prompt learning enables parameter-efficient adaptation of frozen Graph Neural Networks to downstream tasks through lightweight prompt parameters....
Graph Foundation Models (GFMs) aim to learn transferable knowledge from multi-domain graphs and adapt to unseen scenarios. As a fundamental source of relational semantics in graphs, the transferability of topological patterns has long been central to GFM research.
arXiv:2608.30520v1 Announce Type: new Abstract: Automated material handling systems in semiconductor fabs are operated by a material control system (MCS) that must schedule a relay route for every tr...
The paper studies generative flows that sample from an unnormalized target by training a flow to satisfy a balance condition. It shows that the training loss can certify sampler accuracy, can be driven to zero, and quantifies how fast gradient descent converges, with explicit bounds depending on the backward policy and graph structure. The authors provide theoretical guarantees, test them on finite graphs, and certify each theorem using a Lean 4 development.
The paper introduces Network Feasibility Geometry Reinforcement Learning (NFG‑RL), a method that enforces multi‑layer network constraints—such as interference, power‑rate coupling, flow conservation, service chains, capacity, latency, and reliability—by transporting a proto‑policy through a differentiable feasibility map. By compiling heterogeneous constraints into typed residual blocks and using a variational transport operator, NFG‑RL ensures almost‑sure feasible execution and shapes exploration and gradients to respect active constraints. Experiments on two wireless‑edge surrogate environments show that NFG‑RL boosts feasible utility by 37.5–41.5 %, cuts raw‑action violations by 48.5–60.8 %, and reduces P99 delay by 57.0–75.5 % compared to leading baselines.
arXiv:2608.30512v1 Announce Type: cross Abstract: Large-scale industrial robot fleets share constrained physical infrastructure, making vehicle travel times dependent on safety separation, intersecti...
arXiv:2607. 18323v1 Announce Type: cross Abstract: Exhaustive site-by-site interventions on a neural network's computational graph -- activation-patching sweeps, circuit-discovery searches, systematic ablation studies -- mutate the graph at every candidate site, and their cost is dominated by recomputation after each mutation.
The paper presents a theoretical framework linking balance objectives in Generative Flow Networks (GFlowNets) to sampling accuracy. It establishes bounds on terminal symmetric KL divergence in terms of expected balance loss and shows how approximate stationarity controls distribution error, accounting for parameterization incompleteness. The authors prove finite‑time convergence rates under various sampling and backward‑policy regimes, clarifying the roles of coverage, parameterization, and moving components in GFlowNet training.
arXiv:2512. 11839v2 Announce Type: replace Abstract: Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital infrastructure.