{"id":"d454f06c-06f6-4ddb-b91d-e24503c354f4","arxiv_id":"2607.13416","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":7,"one_line_summary":"Simulator-guided tree search around a language model raises analog power-converter topology success from 12–33% to 65% on a strict 6-component benchmark and extends generation to 7–9 components.","lead":"EXPLORE couples a language model with a tree search that tests candidate circuit designs in a simulator, improving success on a strict 6-component power-converter task from 12–33% to 65%. It matters because it shows a practical way to spend extra computation at generation time so LLM-based design automation scales beyond small circuits.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Fine-tuning data provenance is the load-bearing ambiguity: if the 32k set contains EXPLORE-generated topologies, the headline gain conflates test-time search with training on the search's own output distribution.","rationale":"The reader's weakest_assumption identified exactly the data-provenance confound: the fine-tuning sets in Section 4.1 are never characterized as Stage-1 random or Stage-2 EXPLORE-native. My review confirms this is the most load-bearing risk to the central claim. The paper's own Section 3.2 and Appendix B.3 demonstrate that Stage-2 data is not just more plentiful but qualitatively different (far higher efficiency yield), so its presence in the fine-tuning set could plausibly alter model behavior in ways that benefit EXPLORE's search. The baselines share the same model, so the relative comparison is not automatically invalid, but the claim of 'test-time scaling' as a decoding strategy requires showing the gain holds when the model is not fine-tuned on the search's own outputs. Other issues (error bars, missing RL baseline, budget accounting) are real but secondary: the gap sizes are large, the random-100/500 robustness checks mitigate selection bias, and the RL baseline claim is about positioning rather than the central quantitative result. I therefore agree with the conditional verdict and recommend no change. The concrete test (random-only vs EXPLORE-native fine-tuning) would settle the concern; if the gap persists under random-only data, the claim is strengthened and the paper could move toward acceptance.","tokens_in":14633,"tokens_out":11553,"duration_ms":122941,"concrete_test":"Fine-tune two FM-32k models from the same 3-5 component checkpoint: Model A on the original 32k set (as used in the paper), Model B on 32k circuits generated purely by Stage-1 random enumeration with rejection sampling to match the Stage-2 efficiency distribution if needed. Evaluate EXPLORE and S+F on the 6-comp benchmark with both models, using identical hyperparameters (c=4, p=0.99, k=3, budget=100). If EXPLORE's success-rate advantage over S+F declines by more than 10 percentage points under Model B, the headline result is substantially attributable to training on EXPLORE-generated data. Also report the fraction of Stage-2 samples in the 1k and 32k sets.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim that EXPLORE is a better decoding strategy under a fixed 100-generation budget depends on the model being identically conditioned across baselines. Section 4.1 states only that models are 'finetuned with 1k and 32k 6-component circuits' but never specifies whether these circuits come from Stage-1 random enumeration or Stage-2 model-native (EXPLORE-guided) collection described in Section 3.2. Section 3.2 explicitly assembles a 350k 6-component dataset using a two-stage pipeline whose Stage-2 reuses the trained model with EXPLORE; if the 32k fine-tuning set is drawn from this corpus, then the model's prior is shaped by the very search procedure being tested. The paper's own appendix (B.3) shows Stage-2 data has a dramatically different efficiency distribution (23.3% >90% eff vs 8.3% for random). If the 32k set is enriched with such topologies, all baselines (Greedy, Beam, S+F) share a model that has already been tuned to produce search-friendly sequences, potentially inflating EXPLORE's absolute success rate and its gap over S+F. The FM-1k result (0.52 vs 0.20) suggests the gain may survive with less data, but the composition of the 1k set is also unstated. Without a random-only vs EXPLORE-native training ablation, the headline 65% cannot be cleanly attributed to test-time search rather than to exploiting the search's own data collection.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes EXPLORE, a test-time search framework for analog topology generation. It combines an encoder-decoder LM (Flan-T5-base with the LaMAGIC float-input adjacency-matrix formulation) with PUCT-style MCTS, top-k expansion, p-filtering of high-confidence structural tokens, and NGSPICE simulation rewards. The authors also construct a 350k 6-component corpus via a two-stage pipeline in which Stage 2 uses the trained model plus EXPLORE itself as a data generator. On a 6-component benchmark at tolerance 0.01, EXPLORE reports success 0.65 vs 0.12 (Greedy), 0.33 (Sampling+Filter), 0.32 (Beam Search), and 0.31 (MCTS-Base) with the FM-32k model, with lower MSE; results extend to 7–9 components. The paper includes uniform-random validation sets (App. B.4) and sweeps of the exploration constant c and p-filtering threshold p.","tokens_in":14912,"tokens_out":8759,"duration_ms":99152,"significance":"If the reported gains are reproducible and cleanly attributable to test-time search, EXPLORE would be a meaningful advance for LM-based analog design: it demonstrates that a fixed simulator budget can be spent more effectively on guided partial expansions than on independent sampling, and it introduces a practical search-based data-collection loop. I credit the authors for pre-empting the curated-benchmark objection with the random-100/random-500 splits in App. B.4, for the MCTS-Base ablation that isolates LM-prior guidance, and for the systematic p-filtering analysis. However, the headline claim is currently confounded by the unknown composition of the fine-tuning data, and the lack of variance reporting makes it hard to assess how much of the 0.65 vs 0.33 gap is signal. The stated 10-component all-zero regime is appropriately framed as outside current capability.","major_comments":[{"comment":"The two-stage pipeline in §3.2 explicitly uses the trained model plus EXPLORE to assemble the 350k 6-component corpus, and App. B.3 shows that this Stage-2 data is far richer than random enumeration (23.3% vs 8.3% above 90% efficiency). Section 4.1 states only that models are 'finetuned with 1k and 32k 6-component circuits' and does not disclose whether these sets are Stage-1 random, Stage-2 EXPLORE-native, or a mix. If the 32k set is enriched with EXPLORE-generated topologies, then every decoder shares a model already fine-tuned on the output distribution of the search being tested, so the headline 0.65 vs 0.33 conflates test-time search with training on search-native data. This is not full circularity because NGSPICE targets come from LaMAGIC's validation set, but an ablation separating 'more random data' from 'EXPLORE-native data' (e.g., random-only vs mixed 1k/32k, evaluated with all","section":"§3.2, §4.1, App. B.3"},{"comment":"The fairness claim is undermined by the footnote 'All methods use up to 100 Transformer generations' while Greedy is defined as one-shot generation in §4.1. If Greedy uses only one generation, the 12%→65% comparison is not under the same budget; if it is run 100 times, a deterministic greedy decoder would not improve, so the comparison needs clarification. More generally, the unit 'Transformer generations' is not defined for Beam Search (one run with width 20 counts as 20 generations?) or for MCTS (one rollout with k=3 and b=1 may generate several sequences, and p-filtering changes the number of tokens per generation). Because the central claim is superior performance under a fixed 100-generation budget, the paper must define the budget unit precisely and report the actual number of evaluated sequences per target for each method.","section":"Table 1, §4.2"},{"comment":"All success rates are single point estimates on 100 or 500 targets, with no seeds, standard errors, or confidence intervals. MCTS and Sampling+Filter are stochastic, and the models themselves are trained once, so run-to-run variance is unreported. The large gaps—e.g., 0.65 vs 0.31 in Table 1—are suggestive, but the smaller differences (Table 3: 0.84 vs 0.70; Table 4: 0.73 vs 0.59) could be within noise. Please report means over at least three seeds, or at minimum exact binomial 95% confidence intervals for the 100-sample benchmarks, and show variance on the iteration-wise curves in Fig. 4.","section":"Tables 1–4, Fig. 4"}],"minor_comments":[{"comment":"The main-text abstract says 'over 20×' lower MSE, but the front-page abstract excerpt in the submitted text says 'over 20%'. Make the two versions consistent.","section":"Abstract"},{"comment":"The Greedy row is not actual greedy decoding but 'the first per-target sample of the S+F generations.' This is an important disclosure; move it from the footnote into the main text or the table caption so readers are not misled.","section":"Table 4"},{"comment":"Please state explicitly how many of the 350k 6-component circuits come from Stage 1 vs Stage 2. Currently only quality distribution statistics are given, not counts.","section":"§3.2"},{"comment":"The pseudocode condition 'len(node.top-p tokens)==1' is confusing; I assume it means the top-1 token has probability ≥ p. Please clarify the notation and define 'top-p tokens' here.","section":"Algorithm 1 / §3.1"},{"comment":"The runtime breakdown lists total time 9.22h for n=100; specify the hardware, whether this includes cache warm-up, and whether the same budget was used for the baseline runtimes.","section":"Fig. 4d / §4.1"},{"comment":"The conclusion claims superiority 'over prior RL-based search methods,' but the experiments compare only to decoding baselines (Greedy, Beam, S+F, MCTS-Base). Either add an RL/BO baseline from §2.2 on 6-comp or temper the conclusion to 'decoding strategies'.","section":"§5"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take on arXiv:2607.13416. The headline result — EXPLORE gets 65% success at t=0.01 on a 6-component benchmark versus 33% for sampling-and-filter, under the same 100-generation budget — is internally consistent and checked against two random-subset validations. That is a real advance for this subfield: it shows structured search around an LM prior can push analog topology generation beyond the 3–5-component regime, and the p-filtering of structural tokens is a sensible, well-motivated trick that saves a lot of simulator budget.\n\nWhat's actually new here is the domain adaptation: the PUCT-style MCTS is borrowed from AlphaCode and PG-TD, but the observation that more than half the tokens in the FM representation are structural, and the decision to skip them via a p=0.99 threshold, is specific and useful. The two-stage data-collection pipeline is also a nice engineering contribution, and the paper is honest about the 10-component regime being out of reach.\n\nSoft spots, in order of size. First, and most important: the paper never states where the 1k and 32k fine-tuning circuits come from. Section 3.2 describes a 350k corpus built partly by EXPLORE itself, and if the 32k set is drawn from that, then the headline number conflates test-time search quality with training on the search's own output. The comparison to S+F uses the same model, so the decoding comparison is still fair as stated, but the interpretation as a pure test-time-scaling effect is not clean. An ablation with a random-only training set would settle this, and the 1k result (0.52 vs 0.20) suggests the gain might survive, but we need the numbers.\n\nSecond, the conclusion claims superiority over \"prior RL-based search methods,\" but none of those appear in the comparison tables. The only RL-ish baseline is their own MCTS-Base. A direct run of Fan et al. or a reasonable RL baseline would be needed to support that sentence.\n\nThird, no error bars or multiple seeds anywhere. The margins are large enough that the main conclusions probably survive, but it's sloppy for a stochastic search method.\n\nMinor: the random-500 appendix substitutes the Greedy row with a first-sample proxy, which is a little odd; and the runtime numbers are for a single configuration. Neither changes the main picture.\n\nOverall: this is a worthwhile paper for anyone working on LLM-guided search or analog EDA. It deserves a serious referee. I'd send it out with a request for the data-provenance ablation, an RL baseline, and error bars. If those come back, it's a solid venue-level contribution.","headline":"A credible test-time-search recipe for analog topology generation, with a real but addressable data-provenance confound.","tokens_in":15569,"tokens_out":3468,"would_cite":true,"duration_ms":32993,"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":"Simulator-guided search lifts analog topology generation success from 12% to 65% on a six-component benchmark.","keywords":["analog topology generation","Monte Carlo Tree Search","test-time scaling","language models","power converter design","p-filtering","adjacency-matrix formulation","simulator-guided search"],"falsifier":"Retrain the model on 32k six-component topologies drawn only from random enumeration, then rerun EXPLORE's test-time search on the same validation set; if the 65% success rate drops toward the 33% sampling-and-filter level, the gain is driven by data provenance rather than by test-time search. A smaller check: report the success-rate curve with the 32k set replaced by 32k independent Stage-1 random circuits while holding the search algorithm and simulator budget unchanged.","tokens_in":14397,"feed_emoji":"⚡","tokens_out":5082,"duration_ms":51406,"temperature":0.7,"pith_summary":"This paper argues that analog circuit topology generation should be treated as a search problem, not a one-pass generation problem: instead of decoding a single circuit from a language model, the model's token probabilities steer a Monte Carlo tree search, and each candidate is checked with a circuit simulator before the search back-propagates a reward. On a benchmark of six-component power converters with a tight tolerance of 0.01, the method raises the success rate from 12% for one-shot decoding and 33% for sampling-and-filtering to 65% under the same budget of 100 transformer generations, with more than twenty times lower mean squared error. The same search also produces nonzero success at seven to nine components, a regime where one-shot decoding nearly collapses. If this holds, it means expensive simulator calls can be concentrated on genuine topology decisions rather than on forced syntax tokens, making larger design spaces reachable with the same model.","feed_headline":"Search lifts analog circuit design success from 12% to 65%","feed_subtitle":"Simulator-guided tree search beats one-shot decoding on six-component power converters.","key_machinery":"LM-guided MCTS with p-filtering: a Monte Carlo tree search whose node selection uses PUCT scores that multiply the exploration term by the language model's token probability. Because over half the tokens in the FM adjacency-matrix formulation are structural and carry no topology choice, p-filtering auto-commits any token with model probability at least 0.99 and only spawns top-k children at genuine decision points; partial sequences are beam-completed and simulated, and the resulting voltage/efficiency reward is backpropagated. This concentrates a fixed simulator budget on edge decisions between component pairs.","core_discovery":"EXPLORE claims that test-time search, guided by language-model priors and validated by a circuit simulator, is a better decoding strategy for analog topology generation than greedy, beam, or sampling-and-filter decoding. On the FM adjacency-matrix formulation, a PUCT-style tree search that auto-commits near-certain structural tokens via p-filtering (p=0.99), expands only low-confidence decision tokens, beam-completes partial topologies, and simulates each candidate reaches 65% success at tolerance 0.01 on six components versus 33% for sampling-and-filter and 12% for one-shot decoding, under 100 transformer generations, with over 20x lower MSE. It keeps an advantage at 7-9 components, while a","pith_inferences":["Editorial: the 32k fine-tuning set is assembled with 'model-native' data collection, and the paper does not state how much of it consists of EXPLORE's own outputs; an ablation using only random-enumeration data at the same size would separate test-time search quality from training-on-search-output effects.","Editorial: p-filtering's threshold sweep suggests formulations that remove structural tokens entirely could concentrate search budget even more; compressed adjacency representations are a natural next target.","Editorial: the clean zero at 10 components implies the current simulator-budget regime will not scale indefinitely; replacing some simulations with a learned surrogate could extend the approach.","Editorial: the reward's equal 0.5/0.5 weighting of voltage and efficiency error is untuned; per-target weighting or a Pareto treatment could raise effective success without changing the search."],"forward_implications":["At fixed inference budget (100 generations), structured search outperforms all four tested decoding baselines on 6-component circuits at tolerance 0.01, and lowers MSE by over 20x versus sampling-and-filter.","The method extends generation to 7-9 components where one-shot decoding scores near zero, with EXPLORE reaching 0.21, 0.26, and 0.16 success respectively.","Filtering near-certain structural tokens skips 24-48% of token expansions, reducing simulator calls without hurting final search quality when p=0.99.","The same search loop doubles as a data-collection engine, cutting the fraction of sub-2%-efficiency 6-component topologies from 66.1% to 18.2% and raising the over-90%-efficiency fraction from 8.3% to 23.3%.","All methods fail on 10-component circuits (0.00 success), so the paper frames its 7-9 component results as a scalability trend rather than practical readiness."],"fun_headline_variants":["EXPLORE: Tree search boosts analog circuit design success to 65%","LLM + guided search: analog topology hits 65% success, up from 12%","Analog circuit generation: search beats one-shot, 65% vs 12%","Simulator-guided MCTS makes LLM analog design 5x more successful"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The load-bearing assumption is that the composition of the 32k fine-tuning set does not matter; if those topologies are mostly EXPLORE's own prior search outputs, the reported success-rate gap could partly reflect training on the search distribution rather than search quality itself.","fun_headline_variants_meta":{"raw":{"variants":["EXPLORE: Tree search boosts analog circuit design success to 65%","LLM + guided search: analog topology hits 65% success, up from 12%","Analog circuit generation: search beats one-shot, 65% vs 12%","Simulator-guided MCTS makes LLM analog design 5x more successful"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00109,"raw_usage":{"total_tokens":4395,"prompt_tokens":753,"completion_tokens":3642,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":497,"completion_tokens_details":{"reasoning_tokens":3554}},"tokens_in":497,"tokens_out":3642,"duration_ms":28249,"temperature":1.0,"reasoning_tokens":3554,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-02T05:16:13.463659+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Retrain the model on 32k six-component topologies drawn only from random enumeration, then rerun EXPLORE's test-time search on the same validation set; if the 65% success rate drops toward the 33% sampling-and-filter level, the gain is driven by data provenance rather than by test-time search. A smaller check: report the success-rate curve with the 32k set replaced by 32k independent Stage-1 random circuits while holding the search algorithm and simulator budget unchanged.","supporting_citations":[],"review_version":1}