mlsanityData to 14 Oct 2026

Design Conductor 2.0: An agent builds a TurboQuant inference accelerator in 80 hours

arXiv 2605.05170 ↗First posted 6 May 2026cs.AR cs.AIcited by 2

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

Driven by a rapid co-evolution of both harness and underlying models, LLM agents are improving at a dizzying pace. In our prior work (performed in Dec. 2025), we introduced "Design Conductor" (or just "Conductor"), a system capable of building a 5-stage Linux-capable RISC-V CPU in 12 hours. In this work, we introduce an updated multi-agent harness powered by frontier models released in April 2026, which is able to handle 80x larger tasks, at higher quality, fully autonomously. Following a brief introduction, we examine 4 designs that the system produced autonomously, including "VerTQ", an LLM inference accelerator which hard-wires support for TurboQuant in a 240-cycle pipeline, starting from the TurboQuant arXiv paper. VerTQ includes heavy compute processing, with 5129 FP16/32 units; the design was mapped to an FPGA at 125 MHz and consumes 5.7 mm^2 in TSMC 16FF (8 attention pipes). We review the key new characteristics that enabled these results. Finally, we analyze Design Conductor's token usage and other empirical characteristics, including its limitations.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 3 of its 13 references that are in the map. First posted on arXiv 6 May 2026.

Builds on

3 of 13 references.

References · 3

  1. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  2. 2025TurboQuant: Online Vector Quantization with Near-optimal Distortion Ratecited by 125
  3. 2026Design Conductor: An agent autonomously builds a 1.5 GHz Linux-capable RISC-V CPUcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

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  3. 2025LLM-DSE: Searching Accelerator Parameters with LLM Agents
  4. 2026FlexLLM: Composable HLS Library for Flexible Hybrid LLM Accelerator Design
  5. 2025An Inquiry into Datacenter TCO for LLM Inference with FP8
  6. 2026Agent-X: Full Pipeline Acceleration of On-device AI Agents
  7. 2026D2K-Bench: Can LLM Agents Turn Expert Designs into Efficient GPU Kernels?
  8. 2025RAPID-LLM: Resilience-Aware Performance analysis of Infrastructure for Distributed LLM Training and Inference ↗