AI Agents

Ai2’s Shippy shows that production agents in high-stakes domains are not just model wrappers. Reliability comes from tool design, sandboxing, explicit limits, and workflow-level evaluation.

A new arXiv paper argues that one-shot benchmark gains are not enough to judge agent optimizers. In a two-phase continual-learning setup, methods diverged sharply once new tasks were introduced.

A new arXiv paper proposes the DVM-HALL framework and the Net Human-Agent Score to rethink loyalty in autonomous commerce. The model treats trust, delegation, machine experience, and verifiable execution risk as core parts of brand choice.

A new arXiv paper introduces Experience Memory Graph, a framework that stores failure fixes as structured graph edits. Instead of repeated reflection loops, agents retrieve relevant correction patterns and act once.

SPyCE proposes a training framework where a multimodal agent’s policy and skill library improve together. Instead of treating trajectories only as rewards or static memories, it distills them into reusable execution and workflow skills.

A new arXiv paper surveys 21 proposed permission systems for AI agents and compares them with five commercial agents. Its central argument: agent safety needs user-level permissions, not just one-size-fits-all product policies.