A mechanism-first reading of ReCiSt, a bio-inspired agentic framework that turns distributed-system failures into containment, causal diagnosis, adaptive reasoning, and reusable operational memory.
MERINDA shows that practical physical AI begins by redesigning solver-heavy model recovery for parallel hardware—not by placing the same algorithm on a smaller device.
A comparison of ordinary prompts, evolutionary search, and reinforcement-learning attackers reveals why an LLM’s willingness to stop is becoming an operational security property.
Why hard-constrained reinforcement learning must preserve the zero-violation objective without training agents to become safely useless.
A mechanism-first examination of how identity verification, behavioral update filtering, and adversarial training divide the security workload in federated industrial systems.
HyFair shows how fairness audits can move beyond counting isolated violations to measure, explain, and mitigate concentrated regions of algorithmic arbitrariness.
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.