Three papers tackling agent infrastructure from different angles: Microsoft proposes a brain-inspired six-mechanism memory architecture that compresses memory stores by 58% while retaining 97.2% precision on real codebase data; Megagon Labs challenges the step-by-step reasoning default, showing that full-horizon planning saves 2–4.7x tokens on data-centric tasks; and a neuroscience-informed framework turns multi-agent topology selection (Chain / Star / Mesh) from guesswork into computable diagnostics.
Three papers on the most pressing question for agent platforms in 2026: can safety constraints in multi-agent systems actually hold up during execution? 2605.10481 names a new failure mode — 'constraint drift': safety rules written at design time silently weaken as they pass through agent delegation, memory read/write, and tool calls, arriving at the output already distorted. 2605.07728 (SARC) proposes an architectural fix: compile regulations into four enforceable checkpoints embedded in the agent execution loop — no more relying on prompt reminders — and is open-sourced. 2605.13851 uses psychology experiments to show that when a multi-agent system's coordinator is invisible, the system's protective behaviors drop significantly — a direct design warning for mainstream orchestrator-based architectures.