AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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5,133 papers
This work proposes a multi-domain generative domain mapping approach based on Star Generative Adversarial Networks (StarGAN) to improve fault detection on data-scarce wind turbines and proposes a proxy metric that detects poor performance at training time, despite an absence of anomalies.
LFPG-RL is developed and evaluated, which integrates link-flow propagation guidance (LFPG) into proximal policy optimization (PPO), and results support the contention that the method is a more efficient and accurate online OD demand calibration method compared to existing ones.
A dynamic explanation of how data statistics and architecture jointly shape token embeddings in language models is provided, and an implicit bias in the space of data statistics is revealed: training proceeds from simpler, low-order statistical relations toward increasingly complex, context-dependent ones.
Tail-Replay is presented, a prefix caching mechanism that enables unconstrained token-level prefix reuse in hybrid large language models and is evaluated on three Gated DeltaNet-based hybrid models using the LongBench and RULER benchmarks.
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This work discovers that certain attention heads exhibit sequential consistency in their attention patterns, which can be persistently identified using a coefficient-of-variation-based algorithm, and proposes CateKV, a hybrid KV cache method that retains only critical token information for consistent heads, thereby reducing KV cache size and computational overhead.
BCPPO (Bachelier-Inspired Constrained Proximal Policy Optimization), a proximal policy optimization (PPO) method, supports a practical balance among reward, caution around cost predictions that vary across trained critics, and policy-only deployment.
CAESAR-LDAR is presented, an error-controlled multivariate learned compressor that augments a shared CAESAR-V backbone with two complementary mechanisms: a trainable orthogonal transform that reorganizes dependence across aligned latent channels, and a causal autoregressive hierarchical prior that captures local spatial structure left after transformation.
The threshold part of Question 4 of the COLT 2025 open problem "Data Selection for Regression Tasks" is resolved, and an erroneous claim circulating in a recent unrefereed preprint is correct.
Memory-augmented drafting is introduced for long-context SD, equipping a strong independent draft with compressed draft-side KV memory and incrementally updates this memory to retain distant information and exact recent context.
Localized extreme precipitation is a major trigger of urban flash floods and landslides, yet producing nowcasts that combine fine spatial detail with probabilistic uncertainty remains challenging. Here we introduce exPreCast-ENS, a conditional residual diffusion framework that transforms the deterministic 4 km radar nowcaster exPreCast into a 1 km probabilistic ensemble while correcting systematic forecast errors. Conditioning on both the forecast and preceding radar observations lets the ensemble-mean correct the baseline rather than perturb it, while members represent unresolved fine-scale variability. Over the Korean Peninsula, skill improves with ensemble size. In two high-impact events in 2023, a 30-member ensemble recovers 38-47% of heavy-rain pixels missed by exPreCast while retaining approximately 95% of its correct detections and alarming on under 1% of the pixels it correctly left clear. The method generates a 1-h forecast in 3.4 s on a single GPU and yields consistent improvements on the French regional MeteoNet radar dataset.
Directly robustifying small-signal stability in AC optimal power flow is challenging since the stability boundary in the original uncertainty space is implicit, highly nonconvex, and changes with the operating decision. This paper exploits an alternative geometry. For a fixed model-specific stability certificate admitting suitable physical lifts, the small-signal stability requirement becomes an affine positive semidefinite constraint in the lifted variables, thereby defining a convex certified safe region. Instead of approximating the nonlinear instability boundary itself, we optimize a sample-wise safe radius in the original uncertainty space and certify, in the lifted space, that the entire power-flow image of the corresponding uncertainty ball is contained in the convex stability region. To this end, a componentwise Perron certificate guarantees existence, uniqueness, and Jacobian regularity of the target AC power-flow branch throughout each ball. An adjoint elimination then provides an exact affine-quadratic representation of the stability-relevant quantities, while rigorous matrix remainder bounds convert their nonlinear variation into finite robust PSD constraints. The resulting radii are certified lower bounds on the distances from empirical samples to failure and can therefore be coupled directly to the distance-based reformulation of a Wasserstein distributionally robust chance constraint, without directly approximating the instability boundary. Numerical studies demonstrate the effectiveness of the proposed framework.
Peptide-protein affinity benchmarks should align data partitions with the intended use and jointly assess the effects of data scale, molecular representation, and downstream learner.
Education Innovation
Machine Intelligence
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
General Science
GPT-Lab
· gpt-lab.eu
Sep 3, 2026
Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.
Climate & Sustainability
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