AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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OrthoPurify is proposed, a more efficient method to purify backdoored model weights via one-step orthogonal projection, which reduces the attack success rate to near zero while preserving the original performance across diverse benchmarks, without retraining the backdoored model or introducing inference-time overhead.
In Partial Label Learning (PLL), each instance is associated with a candidate label set, with exactly one label being true. While most studies implicitly assume balanced class distributions, real-world data often exhibit severe class imbalance distributions, leading to the Long-Tailed Partial Label Learning (LT-PLL) pr...
This work derives a reformulated pixel-level contrastive learning objective by modeling feature distributions with the von Mises-Fisher distribution, and introduces a Reliability-aware Filter based on the vMF-derived metrics, which performs adaptive class-wise pixel reliability assessment to identify unreliable pixels...
The results suggest that active, structure-preserving evidence seeking is a promising paradigm for open-domain table retrieval, and TableSeek, a structure-preserving agentic search framework for heterogeneous table corpora is presented.
Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning, but its practical scaling is constrained by expensive on-policy rollouts and the cost of obtaining reliable targets at scale. Existing methods address sample selection, incomplete supervision, or noisy labels separately, ofte...
FCCA, which estimates the signed input--error cross-covariance, whitens it with diagonal Fisher moments, truncates it in the resulting local metric, maps the selected directions back, and applies thin QR to obtain stable core coordinates, shows that a carefully selected fixed span can recover most of the benefit of mov...
This work introduces TCR-Bench, a diagnostic benchmark for Table Content-level Answerability in RAG, and tests whether a lightweight two-stage pipeline, Answerability-Aware Reranking (AAR), applying direct query-table answerability judgment, can recover performance.
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