AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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5,133 papers
This paper combines symbolic regression with LLMs for feature engineering (SymboLLM-FE) to solve the dual challenges of poor interpretability and numerous iterations by employing a statistical prior-grounded LLM refinement mechanism and single-digit LLM calls.
PASK (Parser-Aware Structural KV Persistence), which turns parser-derived structure into layer-group-specific KV persistence decisions by using task-error sensitivity to set minimum protection floors and attention-output distortion to allocate residual KV capacity.
Let $n\ge2$ be even, let $\lambda=(\lambda_1,\ldots,\lambda_n)\in\mathbb{R}^n$ have pairwise distinct coordinates, and define the difference-power matrix \[ A_d(\lambda) := \bigl[(\lambda_r-\lambda_s)^d\bigr]_{r,s=1}^n, \qquad d\in\mathbb{N}. \] In 1928, Colombo proved that $\det A_{n-1}(\lambda)\ne0$---and hence $\det A_{n-1}(\lambda)>0$---and that $\operatorname{rank} A_d(\lambda)=d+1$ for $0\le d<n-1$. He conjectured that \[ \det A_d(\lambda)\ne0 \qquad\text{for every } d\ge n-1. \] For even $d$, the conjectured nonsingularity follows from previously published results on distance-power matrices. The remaining open cases were therefore the supercritical odd exponents $d\ge n+1$. We prove nonsingularity for all these odd exponents, thereby completing Colombo's conjecture. Consequently, \[ \operatorname{rank} A_d(\lambda)=\min\{n,d+1\} \qquad(d\in\mathbb{N}). \] Our proof converts a hypothetical kernel vector into a real binary form having more projective real linear factors, counted with multiplicity, than its real Waring length permits.
TransMod constructs a shared zone-level spatial representation that aligns mobility systems with different spatial granularities into a common space, thereby reducing structural mismatch and distributional shift and provides robust forecasting performance under limited target data.
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A simple and broadly applicable residual guided procedure that greedily constructs the hidden layer using a closed form residual decrease criterion and yields a progressive training process with a guaranteed monotonic decrease of the training objective.
SinkSLOT is proposed, which addresses both limitations of the standard Sinkhorn-Knopp algorithm by putting forth the expected sliced lifted transport plan as a natural way to sparsify the Gibbs kernel with a non-independent prior coupling.
A literature-guided, patient-independent comparison of ten BCG feature groups using a 512-sensor capacitive pressure mat recorded simultaneously with respiratory polygraphy in 155 patients undergoing in-hospital evaluation for obstructive sleep apnea shows that a compact, interpretable subset of the full feature library achieves clinically relevant performance under patient-independent validation and provides an empirical basis for feature selection in future BCG systems.
This work proposes COMPASS (Continual Online foundation Model-based PPM with Adaptive SubSpaces), the first framework for online continual fine-tuning of FMs for PPM, which outperforms three SOTA non-FM competitors and two update strategy baselines.
D-TAIA is introduced, a framework for a joint next activity and remaining time prediction task via parameter-efficient fine-tuning of an FM backbone that combines domain-aware triplet loss pre-training with FAISS-based nearest neighbor retrieval for remaining time prediction, and adopts the TAIA inference strategy to preserve pre-trained sequential reasoning during fine-tuning.
The experiments show that CATTLE can learn generalized context from a single source data set and is rank-wise and statistically superior to nine state-of-the-art baselines, including machine learning, deep learning, and transfer learning methods using large-scale pre-trained models.
HARTS is the first system to demonstrate arbitrary-rollout-tree prefix-sharing speedups on a real hybrid-attention model, and its numerical differences are comparable to baseline self-rerun variation, and its reward trend is similar to the baseline over the first 120 steps of SWE-bench training.
A generative modeling framework for characterizing EV energy consumption under real-world operating conditions, providing an essential foundation for uncertainty-aware fleet planning in large-scale operational settings is demonstrated.
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GPT-Lab
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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.
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