Rethinking AI Hardware: A Three-Layer Cognitive Architecture for Autonomous Agen
arXiv:2604.13959v1 Announce Type: new Abstract: As AI moves from data centers to robots and wearables, scaling ever-larger models becomes insufficient. Physical AI operates under
arXiv:2605.15237v1 Announce Type: cross Abstract: Accelerating applications through the design of hardware accelerators can significantly enhance system performance and energy
arXiv:2603.30031v1 Announce Type: new Abstract: Current autonomous AI agents, driven primarily by Large Language Models (LLMs), operate in a state of cognitive weightlessness:
arXiv:2604.08590v1 Announce Type: cross Abstract: We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full
arXiv:2605.13848v1 Announce Type: new Abstract: Agentic LLM frameworks that rely on prompted orchestration, where the model itself determines workflow transitions, often suffer
arXiv:2604.04990v1 Announce Type: cross Abstract: AI coding agents select frameworks, scaffold infrastructure, and wire integrations, often in seconds. These are architectural
arXiv:2605.13850v1 Announce Type: new Abstract: Existing frameworks for LLM-based agent architectures describe systems from a single perspective: industry guides (Anthropic,
arXiv:2603.25719v1 Announce Type: new Abstract: We present an empirical study of how far general-purpose coding agents -- without hardware-specific training -- can optimize
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