Analysis of 11,311 advisor profiles and 12,344 bilingual reviews from the OpenAdvisor platform. Ratings are strongly bimodal; professional metrics (h-index, citations) explain essentially no rating variance; keyword theme classification over-identifies themes in Chinese text by ~35x versus LLM classification. Produces a 10-dimension, behavior-only advisor-selection rubric and an interactive advisor helper tool that outputs a preliminary read plus a structured prompt for LLM-assisted analysis.
CS-ML-Advisor-Helper CS ML advisor helper advisor helper PhD advisor selection tool advisor selection rubric portraits of graduate advising OpenAdvisor analysis choose PhD advisor
Federation over Text (FoT) is a multi-agent AI framework where LLM agents share reusable reasoning insights as natural language instead of gradients. Agents build a shared insight library for collective reasoning, improving accuracy and reducing reasoning tokens across math, cross-domain collaboration, and research insight tasks.
FoT Federation over Text multi-agent AI multi-agent reasoning multi-agent system mult-agent federation agent federation plus agent
Patch shuffling is a privacy-preserving split learning technique for vision transformers. It randomly permutes image patches before intermediate feature sharing to reduce feature inversion risk while preserving transformer accuracy through permutation equivariance.
patch shuffling PatchShuffling split learning privacy privacy-preserving split learning transformer patch permutation feature inversion defense vision transformer privacy ICDM patch shuffling
This paper gives the theoretical foundation behind patch shuffling by analyzing permutation equivariance in transformers and applying it to privacy-preserving split learning.
permutation equivariance transformers transformer permutation equivariance patch shuffling theory CVPR 2024 patch shuffling privacy-preserving split learning theory
FedRLNAS is an early federated neural architecture search method using reinforcement learning to let distributed clients collaboratively discover model architectures without sharing raw data.
FedRLNAS federated model search federated neural architecture search federated learning NAS federated learning + NAS NAS in federated learning reinforcement learning NAS federated
PerFedRLNAS extends federated neural architecture search to personalized federated learning, automatically assigning client-specific architectures and weights for heterogeneous data and system settings.
PerFedRLNAS personalized federated NAS personalized federated neural architecture search one-for-all personalized federated learning federated learning + NAS AAAI personalized federated NAS
DP-RAG protects retrieval-augmented generation pipelines with differential privacy and random projection so sensitive document contents are harder to reconstruct from retrieval representations.
DP-RAG differentially private RAG random projection RAG privacy RAG privacy retrieval augmented generation privacy foundation model privacy
This work analyzes whether split learning actually preserves privacy when fine-tuning large language models, identifying privacy vulnerabilities and mitigation directions for foundation model training systems.
split learning LLM LLM fine-tuning privacy split learning privacy foundation model privacy large language model split learning
R-KVHash studies KV cache compression for reasoning models using SimHash-based redundant-token estimation.
R-KVHash KV cache compression reasoning model KV cache SimHash KV cache redundant token estimation LLM inference efficiency