A practical framework for matching ride-hailing fraud mechanisms with graph structures, anomaly levels, and GNN architectures—without mistaking a promising research map for deployment proof.
A comparison of three ways to guide an AI assistant when turning formal software requirements into readable, semantically disciplined language.
GARDO shows how selective regularization, moving reference policies, and quality-gated diversity incentives can reduce reward hacking without suffocating diffusion-model learning.
OptRot shows how a simple proxy for weight outliers can improve GPTQ compression without calibration data during rotation learning—and why the same geometry can backfire at W4A4.
A procedural self-critique loop can make LLM planners markedly more reliable—but only when reflection is converted into explicit rule checking, state tracking, and conservative approval.
ERIQ and GenieReasoner reveal why understanding the right action and physically executing it are separate engineering problems that robotics teams must diagnose separately.
A practical reading of why LLM memorization becomes hard to remove once training entangles recall with general capability.
A controlled study of LLM-generated neural networks shows why moderate prompt context can improve architecture synthesis—and why more examples eventually break the pipeline.
A systems-level reading of how AI scale can amplify ecological, social, linguistic, and institutional risks—and what organizations can do about it.
A four-year experiment in hands-on NLP ethics shows why responsibility is learned through difficult choices, public explanation, and repeated practice—not compliance slides.