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arxiv: 2604.15657 · v1 · submitted 2026-04-17 · 💻 cs.AR

Recognition: unknown

Understanding Inference-Time Token Allocation and Coverage Limits in Agentic Hardware Verification

Vihaan Patel , Vidya Chhabria , Aman Arora
Authors on Pith no claims yet

Pith reviewed 2026-05-10 07:57 UTC · model grok-4.3

classification 💻 cs.AR
keywords hardware verificationcoverage closureLLM agentstoken allocationagentic systemscoverage holesdomain specializationinference-time computation
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The pith

Domain-specialized agentic systems close hardware coverage holes with 4-13x fewer tokens than general baselines while reaching 95-99% coverage.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how LLM-based agents allocate computation during the coverage closure phase of hardware verification, a step that consumes the most time in design flows. It compares a general-purpose agent to an enhanced domain-specialized version using detailed instrumentation of token usage across six categories and develops a taxonomy of coverage holes that remain difficult to close. A sympathetic reader cares because the work identifies concrete limits of purely LLM-driven stimulus generation and shows how specialization improves efficiency without losing coverage quality. The results characterize where agentic methods succeed or hit fundamental barriers in large designs.

Core claim

A two-tier agentic framework demonstrates that domain specialization redirects token allocation toward coverage-directed reasoning and error recovery, yielding comparable or higher coverage levels of 95-99 percent with 4-13 times fewer tokens and 2-4 times faster convergence to targets than a general-purpose baseline across tested designs.

What carries the argument

Taxonomy of coverage holes that separates methodology-bound ceilings such as tied-off hardware and dead code from reasoning frontiers such as protocol sequencing and narrow timing conditions, supported by six-category token tracking instrumentation.

Load-bearing premise

The efficiency gains and coverage hole taxonomy observed with the tested designs and agents extend to other hardware verification scenarios without confounding effects from prompt variations or unmeasured token tracking factors.

What would settle it

Re-running the comparison on an independent set of large designs and finding that the enhanced system achieves less than 4x token reduction or that coverage holes fall outside the proposed taxonomy categories.

Figures

Figures reproduced from arXiv: 2604.15657 by Aman Arora, Vidya Chhabria, Vihaan Patel.

Figure 1
Figure 1. Figure 1: An overview of the proposed framework informs benchmark design, guides human escalation decisions, and provides actionable feedback to designers and verifi￾cation engineers (e.g., updating the coverage model or the specification). (2) Where are inference-time tokens allocated during agentic verification? Token allocation analysis, similar to profiling, can guide efficient agent design, including multi￾agen… view at source ↗
Figure 3
Figure 3. Figure 3: Enhanced Agent - High Level Architecture [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Average token allocation across designs. [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: Coverage achieved vs. cumulative tokens across all designs. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison between CovAgent’s base and enhanced tiers [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 9
Figure 9. Figure 9: Cost vs. coverage tradeoff for various execution configurations [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗
Figure 8
Figure 8. Figure 8: Average token allocation across designs with GPT-5-mini. [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
read the original abstract

Coverage closure is the most time-consuming phase of hardware verification, and recent large language model (LLM)-based coding agents offer a promising approach to automated stimulus generation. However, prior LLM-based flows do not systematically analyze which coverage holes remain difficult to close or how inference-time computation is allocated during agentic verification. As a result, the efficiency limits and failure modes of LLM-based coverage closure remain poorly understood, particularly for large designs. We present an empirical study using a two-tier agentic framework comprising a base Codex agent and an enhanced domain-specialized LangGraph system. Our framework enables a taxonomy of coverage holes: methodology-bound ceilings (integration tied-off hardware, infeasible boundaries, dead code) and reasoning frontiers (protocol sequencing, multi-module pipeline warm-up, narrow timing conditions), exposing fundamental limits of purely LLM-driven approaches. We further instrument the system to track token usage across six categories, including system prompt, design comprehension, stimulus generation, coverage feedback, error recovery, and agentic overhead. We show that domain specialization shifts token allocation toward coverage-directed reasoning and improves efficiency. Across designs, the enhanced system achieves comparable or higher coverage (95-99%) while using 4-13x fewer tokens and converging to coverage targets 2-4x faster than a general-purpose baseline. Our results characterize the limits of LLM-based coverage closure, inform benchmark design and human escalation strategies, and guide profile-driven agent design for hardware verification.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents an empirical study of LLM-based agents for hardware verification coverage closure. It introduces a two-tier agentic framework consisting of a base Codex agent and an enhanced domain-specialized LangGraph system, along with a taxonomy classifying coverage holes into methodology-bound ceilings (e.g., integration tied-off hardware, dead code) and reasoning frontiers (e.g., protocol sequencing, narrow timing conditions). The work instruments token usage across six categories (system prompt, design comprehension, stimulus generation, coverage feedback, error recovery, agentic overhead) and reports that the enhanced system achieves 95-99% coverage while using 4-13x fewer tokens and converging 2-4x faster than a general-purpose baseline across designs.

Significance. If the results hold after addressing experimental controls, the work would be significant for characterizing the practical limits of purely LLM-driven coverage closure in hardware verification. The taxonomy and token-category instrumentation provide concrete guidance on when agentic approaches hit fundamental ceilings versus where additional reasoning or human escalation is needed, and the efficiency metrics could inform profile-driven agent design and benchmark construction in the field.

major comments (2)
  1. [Abstract] Abstract: The central quantitative claims (4-13x token reduction, 2-4x faster convergence, 95-99% coverage) are reported without any details on the number of designs tested, statistical methods, error bars, data exclusion rules, or instrumentation of the six token categories. This absence makes the reliability and reproducibility of the efficiency results difficult to assess and is load-bearing for the paper's main contribution.
  2. [Abstract] Abstract and experimental description: The enhanced system is described as 'domain-specialized' and incorporating additional design-specific context and refined prompts, yet the comparison to the 'general-purpose baseline' provides no evidence that the baseline received equivalent prompt engineering or context. Without an ablation isolating the two-tier LangGraph architecture from specialization effects, the attribution of the 4-13x token savings and 2-4x speedup to the agentic framework (rather than prompt/domain differences) cannot be supported.
minor comments (1)
  1. The taxonomy of coverage holes is conceptually useful but would benefit from one or two concrete examples per category drawn from the tested designs to clarify the distinction between methodology-bound ceilings and reasoning frontiers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We appreciate the referee's feedback, which highlights important aspects for improving the presentation of our empirical study on LLM-based agents for hardware verification. We provide point-by-point responses to the major comments and commit to revisions that enhance the manuscript's rigor and clarity.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central quantitative claims (4-13x token reduction, 2-4x faster convergence, 95-99% coverage) are reported without any details on the number of designs tested, statistical methods, error bars, data exclusion rules, or instrumentation of the six token categories. This absence makes the reliability and reproducibility of the efficiency results difficult to assess and is load-bearing for the paper's main contribution.

    Authors: We agree that the abstract should provide more context on the experimental parameters to support the key claims. The full paper specifies the designs evaluated, details the six token categories and their instrumentation in the methods section, and presents results from multiple runs. We will revise the abstract to include the number of designs, a summary of the statistical approach (repeated independent trials with reported ranges), and a reference to the token usage tracking. This will improve reproducibility without exceeding length limits. revision: yes

  2. Referee: [Abstract] Abstract and experimental description: The enhanced system is described as 'domain-specialized' and incorporating additional design-specific context and refined prompts, yet the comparison to the 'general-purpose baseline' provides no evidence that the baseline received equivalent prompt engineering or context. Without an ablation isolating the two-tier LangGraph architecture from specialization effects, the attribution of the 4-13x token savings and 2-4x speedup to the agentic framework (rather than prompt/domain differences) cannot be supported.

    Authors: The baseline is explicitly the general-purpose Codex agent without domain specialization or the LangGraph two-tier structure, while the enhanced system combines both as our proposed approach. This reflects the practical deployment of domain-specialized agentic systems. We recognize the value of isolating the architecture's contribution and will add an ablation study in the revised version, comparing the base agent, prompt-specialized base agent, and full enhanced system. We will also update the abstract and experimental description to better clarify the baseline setup and the integrated nature of the enhancements. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical measurements only

full rationale

The paper is an empirical study that reports measured outcomes (coverage percentages, token counts, convergence times) from running a two-tier agentic framework on specific hardware designs. No equations, derivations, fitted parameters, or predictions appear in the provided text. Claims rest on direct experimental comparisons rather than any self-referential definitions or self-citation chains that reduce the result to its inputs by construction. The central efficiency numbers are presented as observed deltas, not as outputs forced by prior author work or ansatzes.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The work rests on standard domain assumptions about coverage metrics being objective and measurable; it introduces a new taxonomy without external validation.

axioms (1)
  • domain assumption Coverage metrics in hardware verification are well-defined, objective, and can be reliably measured by standard tools.
    Invoked throughout the description of coverage closure and hole classification.
invented entities (1)
  • Taxonomy of coverage holes (methodology-bound ceilings and reasoning frontiers) no independent evidence
    purpose: Classify difficult-to-close coverage points into infeasible versus reasoning-limited categories.
    New classification derived from observations in the agent runs.

pith-pipeline@v0.9.0 · 5560 in / 1245 out tokens · 21094 ms · 2026-05-10T07:57:31.963631+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

38 extracted references · 9 canonical work pages · 3 internal anchors

  1. [1]

    Why First-Silicon Success Is Getting Harder for System Companies,

    H. Foster, “Why First-Silicon Success Is Getting Harder for System Companies,” Sep. 2025

    2025

  2. [2]

    Chip-Chat: Chal- lenges and Opportunities in Conversational Hardware Design,

    J. Blocklove, S. Garg, R. Karri, and H. Pearce, “Chip-Chat: Chal- lenges and Opportunities in Conversational Hardware Design,” in2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD), Sep. 2023, pp. 1–6

    2023

  3. [3]

    RTL-Repo: A Benchmark for Evaluating LLMs on Large-Scale RTL Design Projects,

    A. Allam and M. Shalan, “RTL-Repo: A Benchmark for Evaluating LLMs on Large-Scale RTL Design Projects,” May 2024

    2024

  4. [4]

    CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation,

    M. DeLorenzo, V . Gohil, and J. Rajendran, “CreativEval: Evaluating Creativity of LLM-Based Hardware Code Generation,” Apr. 2024

    2024

  5. [5]

    RTLCoder: Fully Open-Source and Efficient LLM-Assisted RTL Code Generation Technique,

    S. Liu, W. Fang, Y . Lu, J. Wang, Q. Zhang, H. Zhang, and Z. Xie, “RTLCoder: Fully Open-Source and Efficient LLM-Assisted RTL Code Generation Technique,” Oct. 2024

    2024

  6. [6]

    VerilogEval: Evaluating Large Language Models for Verilog Code Generation,

    M. Liu, N. Pinckney, B. Khailany, and H. Ren, “VerilogEval: Evaluating Large Language Models for Verilog Code Generation,” Dec. 2023

    2023

  7. [7]

    RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model,

    Y . Lu, S. Liu, Q. Zhang, and Z. Xie, “RTLLM: An Open-Source Benchmark for Design RTL Generation with Large Language Model,” in2024 29th Asia and South Pacific Design Automation Conference (ASP-DAC), Jan. 2024, pp. 722–727

    2024

  8. [8]

    AutoChip: Automating HDL Generation Using LLM Feedback,

    S. Thakur, J. Blocklove, H. Pearce, B. Tan, S. Garg, and R. Karri, “AutoChip: Automating HDL Generation Using LLM Feedback,” Jun. 2024

    2024

  9. [9]

    VeriGen: A Large Language Model for Verilog Code Generation,

    S. Thakur, B. Ahmad, H. Pearce, B. Tan, B. Dolan-Gavitt, R. Karri, and S. Garg, “VeriGen: A Large Language Model for Verilog Code Generation,”ACM Transactions on Design Automation of Electronic Systems, p. 3643681, Feb. 2024

    2024

  10. [10]

    ChatCPU: An Agile CPU Design and Verification Platform with LLM,

    X. Wang, G.-W. Wan, S.-Z. Wong, L. Zhang, T. Liu, Q. Tian, and J. Ye, “ChatCPU: An Agile CPU Design and Verification Platform with LLM,” inProceedings of the 61st ACM/IEEE Design Automation Conference, ser. DAC ’24. New York, NY , USA: Association for Computing Machinery, Nov. 2024, pp. 1–6

    2024

  11. [11]

    MG-Verilog: Multi- grained Dataset Towards Enhanced LLM-assisted Verilog Generation,

    Y . Zhang, Z. Yu, Y . Fu, C. Wan, and Y . C. Lin, “MG-Verilog: Multi- grained Dataset Towards Enhanced LLM-assisted Verilog Generation,” Jul. 2024

    2024

  12. [12]

    GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models,

    Y . Fu, Y . Zhang, Z. Yu, S. Li, Z. Ye, C. Li, C. Wan, and Y . C. Lin, “GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models,” 2025. [Online]. Available: https://arxiv.org/abs/2309.10730

    work page arXiv 2025

  13. [13]

    UVLLM: An automated universal rtl verification framework using LLMs,

    Y . Hu, J. Ye, K. Xu, J. Sun, S. Zhang, X. Jiao, D. Pan, J. Zhou, N. Wang, W. Shan, X. Fang, X. Wang, N. Guan, and Z. Jiang, “UVLLM: An Automated Universal RTL Verification Framework using LLMs,” 2024. [Online]. Available: https://arxiv.org/abs/2411.16238

    work page arXiv 2024

  14. [14]

    ChIRAAG: ChatGPT Informed Rapid and Automated Assertion Gen- eration,

    B. Mali, K. Maddala, V . Gupta, S. Reddy, C. Karfa, and R. Karri, “ChIRAAG: ChatGPT Informed Rapid and Automated Assertion Gen- eration,” in2024 IEEE Computer Society Annual Symposium on VLSI (ISVLSI). Knoxville, TN, USA: IEEE, Jul. 2024, pp. 680–683

    2024

  15. [15]

    AssertLLM: Generating Hardware Verification Assertions from Design Specifications via Multi-LLMs,

    W. Fang, M. Li, M. Li, Z. Yan, S. Liu, H. Zhang, and Z. Xie, “AssertLLM: Generating Hardware Verification Assertions from Design Specifications via Multi-LLMs,” in2024 IEEE LLM Aided Design Workshop (LAD), Jun. 2024, pp. 1–1

    2024

  16. [16]

    Au- toBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design,

    R. Qiu, G. L. Zhang, R. Drechsler, U. Schlichtmann, and B. Li, “Au- toBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design,” inProceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD, ser. MLCAD ’24. New York, NY , USA: Association for Computing Machinery, Sep. 2024, pp. 1–10

    2024

  17. [17]

    LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation,

    Z. Zhang, G. Chadwick, H. McNally, Y . Zhao, and R. Mullins, “LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation,” Oct. 2023

    2023

  18. [18]

    VerilogReader: LLM-Aided Hardware Test Generation,

    R. Ma, Y . Yang, Z. Liu, J. Zhang, M. Li, J. Huang, and G. Luo, “VerilogReader: LLM-Aided Hardware Test Generation,” in2024 IEEE LLM Aided Design Workshop (LAD), Jun. 2024, pp. 1–5

    2024

  19. [19]

    Prompt. Verify. Repeat. LLMs in the Hardware Verification Cycle,

    M. Hassan, M. Nadeem, K. Qayyum, C. K. Jha, and R. Drechsler, “Prompt. Verify. Repeat. LLMs in the Hardware Verification Cycle,” in 2025 IEEE International Conference on Omni-layer Intelligent Systems (COINS), 2025, pp. 1–6

    2025

  20. [20]

    From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification

    J. Ye, Y . Hu, K. Xu, D. Pan, Q. Chen, J. Zhou, S. Zhao, X. Fang, X. Wang, N. Guan, and Z. Jiang, “From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification,” 2025. [Online]. Available: https://arxiv.org/abs/2504.19959

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  21. [21]

    Evaluating Large Language Models Trained on Code

    M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. de Oliveira Pinto, J. Kaplan, H. Edwards, Y . Burda, N. Joseph, G. Brockman, A. Ray, R. Puri, G. Krueger, M. Petrov, H. Khlaaf, G. Sastry, P. Mishkin, B. Chan, S. Gray, N. Ryder, M. Pavlov, A. Power, L. Kaiser, M. Bavarian, C. Winter, P. Tillet, F. P. Such, D. Cummings, M. Plappert, F. Chantzis, E. Barnes, A. Her...

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  22. [22]

    Agent ai with lang- graph: A modular framework for enhancing machine translation using large language models.arXiv preprint arXiv:2412.03801, 2024

    J. Wang and Z. Duan, “Agent AI with LangGraph: A Modular Framework for Enhancing Machine Translation Using Large Language Models,” 2024. [Online]. Available: https://arxiv.org/abs/2412.03801

    work page arXiv 2024

  23. [23]

    CovAgent GitHub,

    Blinded, “CovAgent GitHub,” mar 2026. [Online]. Available: blinded

    2026

  24. [24]

    Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

    C. Snell, J. Lee, K. Xu, and A. Kumar, “Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters,” 2024. [Online]. Available: https://arxiv.org/abs/2408.03314

    work page Pith review arXiv 2024

  25. [25]

    Inference scaling laws: An empirical analysis of compute-optimal inference for problem-solving with language models.arXiv preprint arXiv:2408.00724,

    Y . Wu, Z. Sun, S. Li, S. Welleck, and Y . Yang, “Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models,” 2025. [Online]. Available: https://arxiv.org/abs/2408.00724

    work page arXiv 2025

  26. [26]

    ReAct: Synergizing Reasoning and Acting in Language Models

    S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y . Cao, “React: Synergizing reasoning and acting in language models,” 2023. [Online]. Available: https://arxiv.org/abs/2210.03629

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  27. [27]

    Comprehensive verilog design problems: A next-generation benchmark dataset for evaluating large language models and agents on rtl design and verification,

    N. Pinckney, C. Deng, C.-T. Ho, Y .-D. Tsai, M. Liu, W. Zhou, B. Khailany, and H. Ren, “Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification,” 2025. [Online]. Available: https://arxiv.org/abs/2506.14074

    work page arXiv 2025

  28. [28]

    DSP Slice: Floating Point Units,

    S. Mehta, “DSP Slice: Floating Point Units,” https://github.com/samidhm/DSP Slice/tree/main/Floating Point Units, 2020

    2020

  29. [29]

    tpu like design: TPU-like design with pooling unit,

    V . Patel and UT-LCA, “tpu like design: TPU-like design with pooling unit,” https://github.com/UT- LCA/tpu like design/tree/master/design ws vedant, 2019

    2019

  30. [30]

    uart: Verilog implementation of a simple UART core,

    J. Str ¨ombergson, “uart: Verilog implementation of a simple UART core,” https://github.com/secworks/uart, 2014

    2014

  31. [31]

    sha1: Verilog implementation of the SHA-1 hash function,

    ——, “sha1: Verilog implementation of the SHA-1 hash function,” https://github.com/secworks/sha1, 2014

    2014

  32. [32]

    chacha: Verilog implementation of the ChaCha stream cipher,

    ——, “chacha: Verilog implementation of the ChaCha stream cipher,” https://github.com/secworks/chacha, 2014

    2014

  33. [33]

    SD-card-controller: SD/SDHC card controller for Wish- bone bus,

    M. Czerski, “SD-card-controller: SD/SDHC card controller for Wish- bone bus,” https://github.com/mczerski/SD-card-controller, 2013

    2013

  34. [34]

    trng: True Random Number Generator core imple- mented in Verilog,

    J. Str ¨ombergson, “trng: True Random Number Generator core imple- mented in Verilog,” https://github.com/secworks/trng, 2014

    2014

  35. [35]

    FreeCores CAN,

    I. Mohor, “FreeCores CAN,” mar 2006. [Online]. Available: https://github.com/freecores/can/tree/master

    2006

  36. [36]

    AMBA AXI Crossbar,

    D. Pretet, “AMBA AXI Crossbar,” sep 2025. [Online]. Available: https://github.com/dpretet/axi-crossbar/tree/main

    2025

  37. [37]

    High throughput JPEG decoder,

    “High throughput JPEG decoder,” October 2020. [Online]. Available: https://github.com/ultraembedded/core jpeg

    2020

  38. [38]

    Ethernet MAC Design,

    I. Mohor and O. Kindgren, “Ethernet MAC Design,” june 2010. [Online]. Available: https://github.com/freecores/ethmac

    2010