The paper investigates whether AI agents truly understand computer architecture by comparing their performance when the same 15‑dimensional accelerator design space is presented either as meaningful architectural knobs or as anonymous variables. Using the AutoTuring framework, the authors find that agents with architectural knowledge outperform blind agents on a nine‑kernel FP16 GEMM benchmark, yet a critic loop can largely recover this advantage. The study highlights that architectural knowledge and structured critique act as substitutes rather than complements in improving agent performance.
Agents are increasingly asked to design hardware, and increasingly reported to succeed. Such reports establish that a design improved; they cannot establish why. An agent that improves an accelerator...
The paper introduces $ au^ au$-Bench, a benchmark that turns the construction of AI agents into a measurable task. In this environment a developer agent receives real business records, client requirements, a production API, an existing codebase, and constraints on cost and models, and must deliver a complete customer‑service agent. The benchmark evaluates performance by deploying the agent against simulated users, revealing that current state‑of‑the‑art models achieve only 23.9% success while an expert‑written reference scores 82.2%.
The paper introduces the concept of substrate blindness, where AI agents lack execution context in their planning. By providing a 128 MB RAM and 10 s wall‑time contract to large language models, the authors show that agents generate code that uses less memory, runs faster, and incorporates structural changes such as bounded blocking and in‑place buffers. Across three leading models, contract disclosure improved resource usage and correctness, demonstrating that minimal execution contracts can guide agents to produce more efficient programs.
arXiv:2607. 13034v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires.
arXiv:2607. 21268v1 Announce Type: cross Abstract: In many social-science research tasks, such as economics, LLM-based agents must produce outputs for which no cheap, task-complete, machine-readable correctness signal exists.
We study what happens when a single general-purpose large language model acts as the sole researcher on a long-horizon neural architecture design problem. The agent receives a scientific question, an initial hypothesis and motivation, a compute budget, and research affordances (source and experiment management, experiment tracking, literature access, and persistent memory), then autonomously proposes, implements, evaluates, and records experiments over an extended period.
Eureka is a task‑conditioned Meta‑Agent architecture that transforms long‑horizon scientific tasks into dynamic obligation graphs with explicit acceptance semantics. During execution it constructs Macro‑Agents equipped with specialized state, memory, operators, tools, verifiers, and local topology, using receding‑horizon planning, architecture promotion, and minimal‑sufficient compilation. The system demonstrates strong empirical performance, completing all 170 recursive tasks, generating 3,948 certificates without false acceptances, and achieving significant reductions in input size, recomputation, and consistent serialization across 16,000 concurrent executions.
arXiv:2606. 28279v1 Announce Type: cross Abstract: We present HORIZON, a self-evolving agent framework that treats hardware design as repository-level code evolution.
The paper introduces Gauntlet, a framework that lets large language models autonomously build game-playing agents from a bare contract—just a game description, raw observation/action interface, and an empty policy file. In a single session, the model experiments with the game, compiles a standalone controller, and the resulting program is evaluated on held‑out instances without further model calls. The authors demonstrate that these compiled agents can win full‑scale games such as StarCraft II and Civilization, marking the first time a language‑agent system has achieved standalone victory in such complex titles.
arXiv:2607. 02436v1 Announce Type: cross Abstract: Agentic coding assistants are increasingly given extra capabilities, such as browser based testing tools and design oriented system prompts, on the assumption that more capability yields better software.
arXiv:2608.21356v1 Announce Type: cross Abstract: For sixty years, machine verification has been a major cost overhead, affordable only for exceptional artifacts. Here we report that generative AI in...
Large Language Model agents are being explored for chip design, but most methods work directly at RTL. This study compares Direct RTL Design, Agent-based HLS Design, Post-Compiler HLS Refinement, and Post-HLS RTL Refinement, and proposes a combined workflow called Agent-based HLS with RTL Refinement (AHRR). Using FPGAs for evaluation, AHRR achieves a 2.6× geometric‑mean speedup over Direct RTL Design across an 11‑task benchmark suite, demonstrating that higher‑level abstractions and subsequent RTL refinement can improve chip design efficiency.