{"id":"4934c40f-a4a1-4736-b31d-176635cabae0","arxiv_id":"2412.11014","paper_version":2,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":2,"one_line_summary":"A coopetitive multi-agent LLM framework, with a researcher, a prosecutor critic, and two revisers, pushes Verilog code generation to near-perfect pass rates on standard benchmarks.","lead":"The paper presents a multi-agent prompting framework that improves AI-generated Verilog hardware code by having one agent propose fixes, another critique them, and two others apply the corrections. A smart generalist might read it to see how far LLM agents can go in automating hardware design and how to guard against cascading errors.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The functional-correctness oracle is endogenous: the pipeline revises both code and testbench until the self-generated testbench passes, so pass@k does not measure quality against a fixed specification.","rationale":"The paper's only evidence for 'higher quality Verilog code generation' is pass@k on VerilogEval and RTLLM, computed with testbenches produced by AutoBench. Section 2.3.1 explicitly defines the evaluation as A(Code0, m) where the testbench is generated from the code and the module description. More importantly, Sections 2.3.5 and 2.3.6 introduce testbench revision agents that modify TB in lockstep with code corrections; the loop terminates when the self-consistent pair (Code_{k+1}, TB_{k+1}) passes Iverilog. This is a textbook endogenous oracle: the pass/fail signal is not a fixed specification, so a wrong implementation can pass if the testbench is generated or corrected to match its behavior. Baselines in Table 1 were evaluated on the official fixed testbenches of VerilogEval/RTLLM, so the comparison is invalid. The paper acknowledges neither the circularity nor the absence of independent verification. A re-evaluation against official testbenches with testbench revision disabled is the minimal check that would determine whether the claimed advantage is real. We therefore concur with the reader's REJECT verdict.","tokens_in":13594,"tokens_out":3857,"duration_ms":32650,"concrete_test":"Re-run CoopetitiveV+GPT-4 on VerilogEval-Machine and VerilogEval-Human using the official fixed testbenches that ship with the benchmark (the same testbenches used for all baselines in Table 1), and disable the testbench revision agents so that only module code may be corrected. If the official-testbench pass@10 scores drop materially below the reported 99.2%/99.1%, or if the ranking against GPT-4 and the SOTA baselines changes, the headline claim is not supported. A secondary check: require two independently generated AutoBench testbenches to both pass for each problem, or use formal equivalence checking against a reference implementation.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim of 'higher quality Verilog code generation' rests entirely on pass@k scores computed with testbenches generated and revised inside the pipeline. Section 2.3.1 defines the oracle as TB0, Pass0, E0 = A(Code0, m), so the testbench is created from the candidate code and the module description. More damagingly, Sections 2.3.5 and 2.3.6 introduce testbench revision agents that modify the testbench alongside the code whenever simulation fails; Section 2.4 confirms the loop terminates when the self-consistent pair (Code_{k+1}, TB_{k+1}) passes Icarus Verilog. This is a circular oracle: a buggy implementation can be deemed correct if the testbench is generated or corrected to match its behavior. All baseline numbers in Table 1 were obtained on the official fixed testbenches of VerilogEval/RTLLM, so the comparison is not apples-to-apples. The reported 99.2%/99.1% pass@10 for CoopetitiveV+GPT-4 is therefore not evidence of functional correctness without independent verification. The limitation section does not acknowledge this circularity.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes CoopetitiveV, a multi-agent LLM prompting framework for Verilog code generation. The system includes a code generation agent, an AutoBench-based testbench generation and simulation step, a research agent that proposes correction strategies, a prosecutor agent that critiques and refines those strategies, and separate revision agents for code and testbench. The authors report state-of-the-art pass@k numbers on VerilogEval-Machine, VerilogEval-Human, Verilog-V2, and RTLLM, including 99.2% and 99.1% pass@10 for CoopetitiveV+GPT-4, and claim that the framework mitigates degeneration and error propagation while improving code error correction.","tokens_in":13739,"tokens_out":6909,"duration_ms":61563,"significance":"If the results were valid, CoopetitiveV would be a useful contribution to agentic RTL code generation, with a clearly described multi-agent architecture, a broad comparison across models and datasets, and an ablation of the prosecutor agent. The paper also makes a reasonable distinction between single-agent degeneration and cooperation-only error propagation. However, the evaluation's validity hinges on an oracle that is generated and revised by the pipeline itself. Because the testbench is not independent of the implementation, the headline accuracy numbers cannot be interpreted as functional correctness against a fixed specification, and the comparison with baselines evaluated on official testbenches is not apples-to-apples.","major_comments":[{"comment":"The functional oracle is endogenous: the displayed equation TB0, Pass0, E0 = A(Code0, m) shows that AutoBench generates the testbench from the module description and the candidate code, and Figure 4 instructs the testbench generation agent to 'generate testbench based on the RTL code.' A module can therefore pass by satisfying whatever behaviors the generated testbench happens to check, rather than the full specification. Since Table 1 baselines are evaluated on the fixed official testbenches of VerilogEval and RTLLM, the comparison is not apples-to-apples.","section":"Section 2.3.1"},{"comment":"The correction loop revises the testbench together with the code. The research agent produces both code and testbench strategies (S_c, S_t), the revision agents L_c and L_t apply them in parallel, and the prosecutor refines both; Section 2.4 terminates when Pass_{k+1}=True is obtained from Icarus Verilog on the jointly revised pair (Code_{k+1}, TB_{k+1}). Consequently a buggy implementation can be certified if the testbench is edited to match its behavior. The reported pass@k therefore measures self-consistency between generated code and generated testbench, not correctness against a fixed specification.","section":"Sections 2.3.5, 2.3.6, 2.4"},{"comment":"The headline claims (99.2% and 99.1% pass@10 for CoopetitiveV+GPT-4) are computed under this circular oracle. The pattern of much higher pass@5/pass@10 than pass@1 is consistent with the pipeline searching over both code and testbench until a self-consistent pair passes. The paper does not report any verification of the final testbenches against reference testbenches or by independent human inspection; without such evidence, the central claim of 'higher quality Verilog code generation' is unsupported. The ablation in Section 3.3 suffers from the same issue and does not establish that the prosecutor improves functional correctness.","section":"Section 3.2, Table 1"},{"comment":"The limitations paragraph only notes the restriction to Verilog and does not acknowledge the dependence of the evaluation oracle on the method's own testbench generation and revision. This omission is material because the validity of every reported pass@k number is at stake. The paper should explicitly state that all pass/fail signals come from AutoBench-generated testbenches and should provide a separate evaluation on fixed reference testbenches before claiming state-of-the-art performance.","section":"Section 5, Limitations"}],"minor_comments":[{"comment":"The phrase 'the agents cannot collaborate with each other to form the generation pipeline, but also create' appears to be a typo; it should read 'can not only collaborate' or 'not only can collaborate.'","section":"Abstract"},{"comment":"In the paragraph explaining the pass@1 versus pass@5/pass@10 pattern, '20 trails' should be '20 trials.'","section":"Section 3.2"},{"comment":"The 'Open source' column marks CoopetitiveV as 'Y', but no repository or artifact link is provided in the paper; please clarify how the implementation can be accessed or reproduced.","section":"Table 1"},{"comment":"The pass@k estimator is defined with n=20, but it is not stated whether pass/fail is determined after the full correction loop or on the initial code generation. Since the correction loop is stochastic and may alter the testbench, the meaning of 'passed trials' should be made explicit.","section":"Equation (1)"},{"comment":"The figure caption and prompt text contain duplicated fragments such as 'Only use cooperation framework may keep propagatingthe error.' Please clean up the figure so the reader can follow the pipeline without confusion.","section":"Figure 4"}],"recommendation":"reject","confidential_remarks":"I recommend rejecting the current version because the central evaluation is invalidated by the self-referential oracle. The method may have merit, but any resubmission would need to evaluate on fixed reference testbenches, report the correctness of the generated testbenches themselves, and clearly separate the effect of testbench revision from code quality. On the present evidence, the reported accuracy numbers cannot be accepted as measurements of functional correctness."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: the central evaluation is compromised because the pass/fail oracle is generated and revised inside the same pipeline. The method itself is a sensible extension of multi-agent debate, and the ablation suggests the prosecutor agent helps, but the 99% pass numbers do not mean what they claim.\n\nWhat's actually new: a multi-agent setup for Verilog where a research agent proposes correction strategies, a prosecutor critiques and refines them, and separate revision agents edit code and testbench. That's an incremental twist on self-refine/debate, but it's applied cleanly to RTL generation, and the reported gains over single-agent and cooperation-only baselines are large.\n\nWhat's done well: the paper identifies two real failure modes—degeneration in single-agent correction and error propagation in purely cooperative pipelines—and the design directly targets both. The ablation (with vs. without prosecutor) on GPT-3.5 shows a clear lift, which is some evidence the mechanism is doing work.\n\nThe soft spot is load-bearing. AutoBench writes the testbench from the candidate code, and when simulation fails the pipeline revises both the code and the testbench until the pair passes. That makes the oracle self-referential: a wrong implementation can pass if the testbench is adjusted to match it. The baselines in Table 1 were evaluated on fixed official testbenches, so the comparison is not fair, and the 99.2%/99.1% pass@10 numbers are not evidence of functional correctness without independent verification against a fixed spec. The paper does not acknowledge this in the limitations. I'd also flag that no code or prompts are released, so the work is not reproducible as-is. Minor issues: a typo in the abstract ('cannot collaborate' instead of 'can collaborate', presumably) and a stray 'teacher agent' reference in the results discussion.\n\nBottom line: the framework is plausible and worth examining, but the current empirical claims are not supported. A serious referee should require a re-evaluation on fixed, independent testbenches and release of the pipeline. I wouldn't cite the numbers as they stand.","headline":"The method is a plausible multi-agent extension, but the evaluation oracle is self-referential—the pipeline writes and revises its own testbenches—so the near-perfect pass rates don't demonstrate functional correctness.","tokens_in":14380,"tokens_out":2417,"would_cite":false,"duration_ms":21073,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"The paper claims that a coopetitive multi-agent prompting loop, with a prosecutor agent that critiques a research agent's repair strategy, raises LLM Verilog generation to 99.2% pass@10 on VerilogEval-Machine and 99.1% on VerilogEval-Human.","keywords":["Verilog generation","multi-agent LLMs","coopetitive prompting","code repair","hardware design automation","testbench generation","pass@k evaluation","error propagation"],"falsifier":"Run CoopetitiveV with GPT-4 on VerilogEval and RTLLM but grade the generated modules with the datasets' official, independently written testbenches instead of AutoBench-generated ones. The central claim would be falsified if pass@10 on VerilogEval-Machine drops by more than a few points relative to the reported 99.2%.","tokens_in":13329,"feed_emoji":"🛠️","tokens_out":6013,"duration_ms":46784,"temperature":0.7,"pith_summary":"The paper proposes CoopetitiveV, a prompting framework in which several large language model agents cooperate to write Verilog code and then compete to correct it. The central claim is that adding a prosecutor agent, which challenges and refines the strategy of a research agent before revision agents apply it, fixes two failure modes: single-agent degeneration, where an agent repeats its own wrong fixes, and cooperation-only error propagation, where one agent's bad advice flows downstream. If true, the framework lifts pass@10 on VerilogEval-Machine to 99.2% and on VerilogEval-Human to 99.1% with GPT-4, near-saturating these benchmarks. This matters because hardware designers could delegate more RTL module generation and debugging to LLM agents, provided the verification signal driving corrections is trustworthy.","feed_headline":"CoopetitiveV reports 99 percent pass rates on VerilogEval","feed_subtitle":"A five-agent loop with a prosecutor role beats single-agent and cooperation-only pipelines on hardware code benchmarks.","key_machinery":"The load-bearing mechanism is the prosecutor agent inserted between the research agent and the revision agents. In the first correction round, the research agent's strategy goes directly to the revision agents, forming a cooperation-only loop. If simulation still fails, in later rounds the prosecutor agent must score, challenge, and refine that strategy into a more comprehensive one before the revision agents apply it, creating the competitive component. The whole loop is wrapped around Icarus Verilog simulation driven by AutoBench-generated testbenches, which supply the pass/fail signal that decides whether another round starts.","core_discovery":"The paper's central discovery is that competition, not just cooperation, among LLM agents is what prevents correction loops from stalling or compounding errors in Verilog generation. In the proposed pipeline, one agent generates code, another generates a testbench, a research agent analyzes simulation failures and proposes fixes, a prosecutor agent must critique and improve that proposal, and two revision agents apply the final guidance to code and testbench separately. The paper reports that this coopetitive loop, with GPT-4 or Claude-3.5, reaches 99.2% pass@10 on VerilogEval-Machine and 99.1% pass@10 on VerilogEval-Human, plus 100% syntax and 99.9% functionality pass@5 on RTLLM. It frames the result as evidence that the prosecutor's oversight interrupts the propagation of faulty correction strategies that plagues cooperation-only multi-agent pipelines.","pith_inferences":["Editorial inference: if AutoBench-generated testbenches were replaced by independent held-out testbenches, the reported pass rates would likely drop because the oracle and the code under test come from overlapping generation processes.","Editorial inference: the same prosecutor-plus-revision loop could be tested on Python or C++ repair, where official unit tests exist, to see whether competition helps beyond Verilog-specific syntax quirks.","Editorial inference: a per-round ablation would likely show that the prosecutor's benefit concentrates in the second correction round, since the paper only reports overall gains and not round-by-round behavior."],"forward_implications":["If the reported scores hold, LLM-based RTL generation reaches near-ceiling performance on VerilogEval and RTLLM, leaving testbench quality rather than code quality as the main remaining bottleneck.","The framework should reduce the number of generation attempts needed to obtain correct Verilog, because pass@5 and pass@10 are high even when pass@1 is moderate.","The competition mechanism can be applied to any iterative code repair loop to break chains of bad suggestions, not just hardware description languages.","The gap between machine and human VerilogEval scores shrinks from tens of points in the baselines to a few points, suggesting the correction loop generalizes beyond synthetic problems."],"supporting_citations":[{"why":"AutoBench generates the testbenches and runs the Icarus Verilog simulation that supplies the pass/fail signal driving every correction round.","marker":"(Qiu et al., 2024)"},{"why":"VerilogEval defines the Machine and Human datasets and the pass@k metric on which the headline scores are reported.","marker":"(Liu et al., 2023b)"},{"why":"RTLLM provides the third benchmark and the syntax/functionality pass@5 scores used in the main comparison.","marker":"(Lu et al., 2024)"},{"why":"Mage is the multi-agent baseline against which CoopetitiveV shows a small advantage on Verilog-Human and Verilog-V2.","marker":"(Zhao et al., 2024b)"},{"why":"CodeV is a specialized Verilog-generation baseline whose pass@1, pass@5, and pass@10 numbers define the prior state of the art that CoopetitiveV claims to exceed.","marker":"(Zhao et al., 2024a)"}],"fun_headline_variants":["CoopetitiveV: rival agents force better Verilog","Prosecutor critique lifts Verilog code generation to 99%","Competitive agent loop outperforms cooperation for Verilog","Coopetition, not cooperation, key to high-quality Verilog","99.2% pass rate: CoopetitiveV's secret is an arguing team"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The entire correction loop is driven by AutoBench-generated testbenches, and those testbenches are produced from the module description together with the candidate code; if they are not independent of the code under test, the reported pass@k scores overstate true functional correctness.","fun_headline_variants_meta":{"raw":{"variants":["CoopetitiveV: rival agents force better Verilog","Prosecutor critique lifts Verilog code generation to 99%","Competitive agent loop outperforms cooperation for Verilog","Coopetition, not cooperation, key to high-quality Verilog","99.2% pass rate: CoopetitiveV's secret is an arguing team"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000334,"raw_usage":{"total_tokens":1868,"prompt_tokens":976,"completion_tokens":892,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":592,"completion_tokens_details":{"reasoning_tokens":799}},"tokens_in":592,"tokens_out":892,"duration_ms":7453,"temperature":1.0,"reasoning_tokens":799,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-11T15:22:59.185515+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run CoopetitiveV with GPT-4 on VerilogEval and RTLLM but grade the generated modules with the datasets' official, independently written testbenches instead of AutoBench-generated ones. The central claim would be falsified if pass@10 on VerilogEval-Machine drops by more than a few points relative to the reported 99.2%.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"AutoBench generates the testbenches and runs the Icarus Verilog simulation that supplies the pass/fail signal driving every correction round."}],"review_version":1}