Botao 'Amber' Hu

Botao 'Amber' Hu, Helena Rong

As the "agentic web" takes shape-billions of AI agents (often LLM-powered) autonomously transacting and collaborating-trust shifts from human oversight to protocol design. In 2025, several inter-agent protocols crystallized this shift, including Google's Agent-to-Agent (A2A), Agent Payments Protocol (AP2), and Ethereum's ERC-8004 "Trustless Agents," yet their underlying trust assumptions remain under-examined. This paper presents a comparative study of trust models in inter-agent protocol design: Brief (self- or third-party verifiable claims), Claim (self-proclaimed capabilities and identity, e.g. AgentCard), Proof (cryptographic verification, including zero-knowledge proofs and trusted execution environment attestations), Stake (bonded collateral with slashing and insurance), Reputation (crowd feedback and graph-based trust signals), and Constraint (sandboxing and capability bounding). For each, we analyze assumptions, attack surfaces, and design trade-offs, with particular emphasis on LLM-specific fragilities-prompt injection, sycophancy/nudge-susceptibility, hallucination, deception, and misalignment-that render purely reputational or claim-only approaches brittle. Our findings indicate no single mechanism suffices. We argue for trustless-by-default architectures anchored in Proof and Stake to gate high-impact actions, augmented by Brief for identity and discovery and Reputation overlays for flexibility and social signals. We comparatively evaluate A2A, AP2, ERC-8004 and related historical variations in academic research under metrics spanning security, privacy, latency/cost, and social robustness (Sybil/collusion/whitewashing resistance). We conclude with hybrid trust model recommendations that mitigate reputation gaming and misinformed LLM behavior, and we distill actionable design guidelines for safer, interoperable, and scalable agent economies.

Botao 'Amber' Hu

Improvisation-the art of spontaneous creation that unfolds moment-to-moment without a scripted outcome-requires practitioners to continuously sense, adapt, and create anew. It is a fundamental mode of human creativity spanning music, dance, and everyday life. The open-ended nature of improvisation produces a stream of novel, unrepeatable moments-an aspect highly valued in artistic creativity. In parallel, open-endedness (OE)-a system's capacity for unbounded novelty and endless "interestingness"-is exemplified in natural or cultural evolution and has been considered "the last grand challenge" in artificial life (ALife). The rise of generative AI now raises the question in computational creativity (CC) research: What makes a "good" improvisation for AI? Can AI learn to improvise in a genuinely open-ended way? In this work-in-progress paper, we report insights from in-depth interviews with 6 experts in improvisation across dance, music, and contact improvisation. We draw systemic connections between human improvisational arts and the design of future experiential AI agents that could improvise alone or alongside humans-or even with other AI agents-embodying qualities of improvisation drawn from practice: active listening (umwelt and awareness), being in the time (mindfulness and ephemerality), embracing the unknown (source of randomness and serendipity), non-judgmental flow (acceptance and dynamical stability, balancing structure and surprise (unpredictable criticality at edge of chaos), imaginative metaphor (synaesthesia and planning), empathy, trust, boundary, and care (mutual theory of mind), and playfulness and intrinsic motivation (maintaining interestingness).

Botao 'Amber' Hu, Danlin Huang

The recent more-than-human turn in design calls for attentiveness to nonhuman beings. Yet -- as Thomas Nagel's famous ``What is it like to be a bat?'' thought experiment highlights -- human experience is constrained by our own sensorium and an irreducible gap in phenomenal access to nonhuman \emph{Umwelten}. Grounded in eco-phenomenology and eco-somatics, this paper proposes \textbf{Experiencing More-than-Human through Human Augmentation} (MtHtHA, or ``>HtH+''), a design approach that repurposes human augmentation technologies -- typically aimed at enhancing human capabilities for human optimization -- to create temporary, embodied, first-person experiences that modulate the human sensorium to approximate nonhuman sensory experiences, cultivating ecological awareness, empathy, and care across species boundaries. We articulate seven design principles, report five design cases -- EchoVision (bat-like echolocation), FeltSight (star-nosed-mole tactile navigation), FungiSync (fungal network attunement), TentacUs (octopus-like distributed agency), and City of Sparkles (urban data from an AI's perspective) -- and discuss implications for more-than-human aesthetics and design practice.