Pith. sign in

REVIEW 4 major objections 5 minor 59 references

QuantumMind: Constraint-Grounded Agentic Reasoning for Speedup Analysis in Quantum Computing

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read QuantumMind separates proposal generation from deterministic validation and reports a 17.3-point ODS gain over the strongest of seven baselines on 582 paired tasks.

desk verdict A well-designed auditable agentic workflow whose headline advantage is currently only as strong as the authors' own rubric. read the letter →

arxiv 2608.07743 v1 pith:NLF3KSWP submitted 2026-08-07 cs.AI cs.MA

Yijing Zuo , Zhe Fu , Zihan Nie , Zhihui Zhu , Haohan Wang This is my paper · ORCID
classification cs.AIcs.MA
keywords quantumspeedupagenticreasoningdeterministicvalidationevidencegraphopen-discoveryevaluationtypedstatetransitionscomputinghypothesisgeneration
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

A quantum-speedup claim is meaningful, the paper argues, only when it preserves the original task, respects access and output models, exposes required promises, and stays within a defensible complexity scope. To enforce this, QuantumMind separates a typed proposal plane—where one generic agent executes nine fixed schema-validated actions against an immutable shared state—from a deterministic authorization plane in which a ten-check validator alone issues the verdict. Completed runs are compiled into a Quantum Acceleration Evidence Graph and passed through a downward-only research screen that can demote or defer but never upgrade a decision. On 582 identical open-discovery tasks, QuantumMind scores 53.1 mean ODS versus 35.8 for the strongest control, wins 355 paired tasks, and passes the graph audit on 99.8% of tasks, leading the authors to conclude that typed state transitions and deterministic evidence control contribute beyond fluent generation alone.

What carries the argument

The central mechanism is an asymmetric trust architecture in which a single generic agent, instantiated by a fixed table of nine typed actions (formalization, structure analysis, primitive matching, barrier and prior-art assessment, scheme generation, critique, novelty, and consistency review), proposes candidates against an immutable typed shared state with a scope order None < Query < Gate < EndToEnd, while deterministic mechanisms control what may be retained. The authorization plane runs exactly ten public checks (B1–B10), covering selection, structure, asymptotic certificate, access, output, promises, barriers, scope, novelty, and evaluator isolation, and returns a verdict of Invalid, Negative, Conditional, or Positive under a fixed precedence. Two read-only sidecars follow: the Quantum Acceleration Evidence Graph compiles support dependencies and applies five hard graph checks, and a downward-only research screen classifies output alignment, access upgrades, oracle risk, and classical-baseline status; neither can strengthen the decision.

What would settle it

If independent quantum-algorithm researchers, blind to system identity, ranked the 582 paired outputs for follow-up value and the ranking did not reproduce QuantumMind's 17.3-point ODS advantage—or if a token-matched ablation erased the gap within noise—the reported result would be revealed as an artifact of an internally aligned rubric rather than a genuine gain in hypothesis quality.

Watch

Extended reading notes

Core claim

The paper's central claim is that separating typed, role-specialized agentic generation from deterministic authorization and read-only auditing yields materially more valid quantum-speedup hypotheses than unrestricted prompting or multi-agent dialogue. Under the frozen Open-Discovery Score, QuantumMind obtains a 17.3-point mean advantage over the strongest of seven task-adapted controls (53.1 versus 35.8), with 355 wins, 98 ties, and 129 losses against that baseline, a 99.8% graph-audit pass rate versus 43.6%, and first place in all seven task families. Because the reviewer-only quality dimensions are nearly indistinguishable across systems, the paper attributes the separation not to more persuasive prose but to the production of validator-consistent, auditable states.

Load-bearing premise

The load-bearing premise is that the frozen Open-Discovery Score, whose anchors, cap weights, and scale parameter were hand-set by the same team that designed QuantumMind's validator and audit, correctly measures the research utility of a quantum-speedup hypothesis.

Editorial extensions

If this is right

  • If the central claim is correct, fluent generation alone is insufficient for trustworthy quantum-speedup hypotheses; the decisive factor is whether the produced artifacts survive deterministic checks.
  • The architecture is conservative by construction: a run can pass all ten validator checks and still be demoted by the research screen, as in the approximate marked-set counting example.
  • The 99.8% graph-Pass rate and 0.2% InvalidState rate indicate that nearly all QuantumMind runs remain coherent under the shared validator and audit, whereas controls often produce structurally invalid artifacts.
  • Ranking first in every task family suggests the benefit of typed state transitions and deterministic evidence control is not confined to a single problem structure.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A focused ablation that matches inference budget across systems could separate the contribution of the typed-state architecture from the larger number of prompt calls QuantumMind uses.
  • The same proposal/authorization split might generalize to other domains with specifiable constraints, such as protocol verification or formal proof search, where a deterministic checker can gate generative proposals.
  • If the hand-set ODS weights were replaced by an externally calibrated rubric based on expert follow-up rankings, the reported 17.3-point gap would be a stricter test of the architecture's value.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper introduces QuantumMind, an agentic workflow for generating and conservatively screening quantum-acceleration hypotheses. The system separates a typed proposal plane of nine fixed, schema-validated LLM actions from a deterministic authorization plane consisting of a ten-check validator (B1–B10), a Quantum Acceleration Evidence Graph (QAEG) audit, and a downward-only research screen that can demote but never upgrade a decision. Evaluation is performed on 582 paired open-discovery tasks against seven prompting and agentic controls under a frozen Open-Discovery Score (ODS-v1). The reported headline result is 53.1 mean ODS for QuantumMind versus 35.8 for the strongest baseline, with 355 wins, 98 ties, and 129 losses, a 99.8% graph-PASS rate versus 43.6%, and first place in all seven task families. The paper's central claim is that typed state transitions and deterministic evidence control contribute beyond fluent generation alone, with the principal separation appearing in artifact-level consistency rather than in reviewer-assigned quality dimensions.

Significance. If the empirical claim were externally validated, the paper would make a useful contribution: the separation of open-ended generation from deterministic, audit-based authorization is a sensible design pattern for domains where fluent prose can masquerade as scientific support, and the downward-only screen is a clean way to prevent evaluative components from strengthening a verdict. The paper also deserves credit for reporting a detailed paired protocol, for making the B1–B10/QAEG/screen pipeline explicit in equations, and for candidly acknowledging several limitations, including inference-budget asymmetry and the fact that ODS is not a probability of correctness or novelty. The significance is currently limited by the absence of external calibration of ODS, by the lack of released code, data, registry, and evaluator implementations, and by the post hoc exclusion of 46 runs from the 628 completed runs. These issues do not undermine the internal logic of the architecture, but they do mean the headline comparative advantage is, at present, an internal-consistency result rather than an established claim about better research outcomes.

major comments (4)
  1. [Open-Discovery Evaluation (Eqs. 11–15)] The central 17.3-point advantage is measured under ODS-v1, whose disposition anchors (Eq. 12), prior fusion weights (Eq. 13), and cap weights (Eq. 14) directly reward QAEG acceptance, graph-PASS status, and valid-state production—the exact properties QuantumMind's B1–B10/QAEG pipeline is engineered to satisfy. The paper itself states that ODS is 'not a probability of correctness or novelty' and that QAEG and ODS were designed by the same team, yet the abstract and conclusion present the ODS gap as the main result without external calibration. This is load-bearing: unless ODS differences are shown to track expert-judged research utility or downstream follow-up success, the reported advantage is an internal-consistency result. Please provide calibration evidence, for example a correlation between ODS and blinded expert ratings on a held-out sample of runs, or substantially re-scope the abstract and conclusion to say that the result is about validator-consistent artifact production under a self-authored rubric.
  2. [Experiments / Limitations] The strongest baseline comparison is not call- or token-matched: QuantumMind executes a fixed sequence of nine typed prompts, while the Limitations state that some controls use a single call. The reported 17.3-point ODS advantage and the 355-win count therefore conflate architectural choices with a roughly nine-fold inference-budget asymmetry. The claimed conclusion that 'typed state transitions and deterministic evidence control contribute beyond fluent generation alone' requires a call- or token-matched ablation against the strongest controls, or at minimum a control that receives a comparable number of sampled or staged calls. Without such an ablation, the paper should be explicit that it compares complete system designs and cannot separate orchestration from budget effects.
  3. [Experiments Protocol] The evaluation uses 582 tasks obtained by excluding 46 runs belonging to 23 'duplicate-card collision groups' from 628 completed runs. This exclusion is post hoc: the paper does not state whether the collision groups are balanced across methods, nor does it report how the headline ODS differences change under any reasonable assignment of the excluded runs. If the excluded runs are correlated with system performance, the paired comparison could be biased. Please justify the exclusion rule a priori, report results on all 628 runs, or provide sensitivity bounds under worst-case pairings of the excluded runs.
  4. [Experiments Protocol and Overall] No code, task set, registry contents, QAEG implementation, ODS-v1 implementation, or saved artifacts are released, so the 4,656 system–task records and all B1–B10/QAEG outcomes are unverifiable from the manuscript. Since ODS-v1 is a new evaluator with several hand-set constants (disposition anchors, cap weights, soft-plus scale, epsilon clip), reproducibility requires at least the frozen evaluator implementation, the task and registry files, and raw per-task scores. I ask for a release or a detailed appendix containing the scoring code and the complete per-task results; without this, the empirical contribution cannot be independently checked.
minor comments (5)
  1. [Table 1] The row 'B4–B6' groups three checks under a single deterministic requirement; please spell out what each of B4, B5, and B6 separately checks, since the subscripted names are otherwise opaque.
  2. [QAEG section, Eq. (8)] G6 is described as 'diagnostic only' but its exact inputs and outputs are never defined; please state what G6 measures and why it is excluded from the hard checks.
  3. [Figure 2] The phrase 'under the probe’s assumptions' in the Input field is unclear; specify what assumptions are being attributed to the probe and how they are represented in the problem card.
  4. [Table 2, Panel A] The column label 'Strong' is defined only in the caption as the ODS-≥70 rate; consider renaming it 'ODS≥70' to avoid confusion with the 'Strong' disposition terminology used elsewhere.
  5. [Results / Illustrative run] The illustrative run reports ODS 65.8 but is described as 'selected for compactness and interpretability rather than maximum ODS'; please state the maximum ODS in the cohort so readers can gauge how representative the example is.

Circularity Check

2 steps flagged · score 7.0 of 10

Headline ODS advantage is substantially a self-authored rubric: the score's prior and cap are defined over QuantumMind's own validator, screen, and audit outcomes, making the 17.3-point gap partially circular by construction.

  1. self definitional [Open-Discovery Evaluation, Eqs. (12)–(15)]
    "The frozen disposition anchors are (π(ℓ1), . . . , π(ℓ8)) = (.04, .10, .12, .36, .45, .70, .84, .95). ... D_r = σ(logit π(q_r) + 0.5 a_r − 1.5(1−g_r)), ... C_r = min{1, 0.25 f_r, 0.58 u_r, 0.65 b_r, 0.68 1−a_r}. ... ODS(r) = 100 clip((P_r − sp(40(P_r − C_r)))/40, 0, 1)."

    ODS is the dependent variable behind the central claim ('exceeds the strongest baseline by 17.3 points'). Its construction directly injects q_r, a_r, and g_r — the downward-screen disposition and QAEG acceptance/pass flags produced by Eqs. (5)–(10), whose criteria QuantumMind is explicitly engineered to satisfy. The hand-set disposition anchors π and cap weights in C_r reward exactly the valid/graph-pass/claim-accepted states that B1–B10 and QAEG enforce. Thus 'research utility' is defined, in part, as the system's own designed behavior; the 17.3-point advantage is therefore an internal-consistency result by construction, not an externally calibrated measure.

  2. other [Results, 'Overall comparison'; Table 2; Conclusion]
    "The empirical advantage is therefore concentrated in producing artifacts that remain coherent under the shared validator and graph audit. ... The main empirical advantage is artifact-level consistency rather than more persuasive prose."

    The reported differentiators — 99.8% vs 43.6% graph-PASS, 66.0% vs 0.2% claim acceptance, and the 355-win matrix — are not external measures of hypothesis quality; they restate compliance with G1–G5 and B1–B10, checks defined in this paper by the same team. Presenting 'audit pass' as the main separation is a renamed version of the architecture's own design objective (typed states + deterministic validation), so the headline result does not independently demonstrate that QuantumMind's hypotheses are better; it demonstrates that the system matches the rubric the authors built. The conclusion's cautious scope cannot repair the fact that the primary quantitative evidence is the system's agreement with its own checks.

full rationale

QuantumMind's B1–B10 guarantee and QAEG are honest formal constructions; they do not by themselves constitute circularity because they are labeled deterministic consistency checks. The circularity concerns the evaluation endpoint. ODS (Eqs. 11–15) is authored by the same team, and its prior D_r and cap C_r are explicit functions of the downward-screen disposition q_r and QAEG flags a_r and g_r — the very outputs produced by QuantumMind's own checks. The abstract's '17.3 points' and '99.8% vs 43.6%' therefore partly reduce, by construction, to the system agreeing with the rubric its designers wrote. The paper's own Limitations disclaim external significance: ODS is 'not a probability of correctness or novelty.' The controls are also not inference-budget matched (admitted), which further entangles the comparison, though that is a confound rather than a circularity. The few self-citations (Fu et al. 2025; Geng et al. 2026) appear only as related-work pointers and are not load-bearing. Because the reported advantage is dominated by the self-authored validator/audit terms and the conclusion is essentially a restatement of the ODS construction, the circularity score is 7 rather than 2; some residual content (blinded reviewer scores, illustrative run, honest scope caveats) keeps it from 9.

Assumptions & free parameters 6 free parameters · 5 assumptions · 2 invented entities

The central empirical result rests on a largely uncalibrated evaluator and on assumptions of registry correctness and task fidelity. The hand-set ODS constants and the registry labels are the main unfalsifiable inputs; QAEG and ODS themselves are new constructs introduced by the paper with no external validation.

free parameters (6)
  • ODS disposition anchors = (0.04, 0.10, 0.12, 0.36, 0.45, 0.70, 0.84, 0.95)
    Hand-chosen anchors in Eq. (12) that map screen dispositions to prior probabilities; no external calibration is shown, and they are load-bearing for the ODS gap.
  • ODS cap weights = (0.25, 0.58, 0.65, 0.68)
    Hand-chosen weights in Eq. (14) penalizing invalid states, unaccepted claims, generic wrappers, and graph failures; these directly condition the final score and favor validator-aligned systems.
  • ODS T/E/R exponent weights = 0.4, 0.4, 0.2
    Weights in Eq. (11) for technical validity, epistemic auditability, and research utility medians; chosen without sensitivity analysis.
  • ODS soft-plus scale = 40
    In Eq. (15), the scale parameter 40 controls the penalty curve, a hand-set constant.
  • ODS prior fusion weights = 0.5, -1.5, 0.7, 0.3
    Weights in Eq. (13) mixing graph pass and review scores into the prior; hand-set.
  • ODS epsilon clip floor/ceiling = 0.02
    Clip bounds in Eq. (11) are arbitrary and set before scoring; they affect small scores near the extremes.
assumptions (5)
  • domain assumption The primitive and barrier registry labels (structures, access models, outputs, promises, scopes, complexity classes) are correct and complete for the 582 tasks.
    Invoked in the Registry-Grounded Authorization section and the Registry-relative guarantee; if labels are wrong, B1-B10 and QAEG certify incorrect claims.
  • domain assumption The public task specification x faithfully represents the original computational problem and is immutable after formalization.
    Used in Eq. (2) and B4-B6; if the task is misrepresented, the entire authorization is representative only of the serialized card, not the real problem.
  • ad hoc to paper ODS-v1 is a valid measure of research utility and auditability with the stated constants.
    The paper assumes the median/MAD aggregation and cap/prior formula produce a meaningful score; no external validation is provided.
  • domain assumption The blinded LLM judge scores (T/E/R) are unbiased enough for comparison.
    The paper acknowledges LLM-judge biases exist and uses blinded skeptical judges, but does not calibrate against human experts.
  • standard math Standard quantum complexity results (Grover, quantum counting, walks, period finding, HHL) have the claimed asymptotic behavior under the stated access models.
    These are background for the registry entries, treated as inputs from prior literature.
invented entities (2)
  • Quantum Acceleration Evidence Graph (QAEG)
    purpose: Deterministic graph projection of run state exposing dependencies between task facts, registry entries, checks, and claims; used for audit G1-G5 and the research screen.
    QAEG is a new representation introduced by this paper; its audit checks are authored by the same team and there is no external benchmark showing the graph's judgments track true scientific value.
  • Open-Discovery Score v1 (ODS)
    purpose: Frozen rubric to score auditability and research utility; the paper's main outcome measure.
    ODS is introduced by this paper, with hand-set constants, and is used only for evaluation; no external validation shows it predicts expert assessment or downstream usefulness.

how reviews work

0 comments
Cite this review

Pith. "Pith review of QuantumMind: Constraint-Grounded Agentic Reasoning for Speedup Analysis in Quantum Computing." pith.science (2026). https://pith.science/paper/NLF3KSWP

@misc{pith2026260807743,
  author       = {Pith},
  title        = {Pith review of: QuantumMind: Constraint-Grounded Agentic Reasoning for Speedup Analysis in Quantum Computing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NLF3KSWP}},
  note         = {Machine review of arXiv:2608.07743}
}
read the original abstract

Identifying a meaningful quantum speedup requires more than matching a classical problem to a familiar quantum primitive: the claim must preserve the task, respect access and output models, expose required promises, and remain within a defensible complexity scope. We present QuantumMind, an auditable agentic workflow for generating and conservatively screening quantum-acceleration hypotheses. A fixed sequence of typed, role-specialized actions formalizes the public task, analyzes structure and classical bottlenecks, matches a source-linked registry of quantum primitives and barriers, and constructs a scoped candidate scheme. A deterministic ten-check validator assigns the authoritative verdict; completed states are compiled into a Quantum Acceleration Evidence Graph and passed through a downward-only research screen that cannot strengthen the decision. We evaluate QuantumMind against seven task-adapted prompting and agentic controls on 582 identical open-discovery tasks. Under the frozen Open-Discovery Score (ODS), QuantumMind obtains 53.1 mean ODS, exceeding the strongest baseline by 17.3 points (48.2% relative), and wins 355 of 582 paired tasks against that baseline. It passes the graph audit on 99.8% of tasks, compared with 43.6% for the strongest baseline, and ranks first in all seven task families. The results indicate that typed state transitions and deterministic evidence control contribute beyond fluent generation alone.

Figures

Figures reproduced from arXiv: 2608.07743 by the authors.

Figure 1
Figure 1. QuantumMind separates typed proposal construction from deterministic authorization. QAEG exposes the support [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Original-cohort run FDV1-00009, abridged from [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

59 extracted references · 24 canonical work pages

  1. [1]

    Proceedings 35th annual symposium on foundations of computer science , pages=

    Algorithms for quantum computation: discrete logarithms and factoring , author=. Proceedings 35th annual symposium on foundations of computer science , pages=. 1994 , organization=

    1994

  2. [2]

    Proceedings of the twenty-eighth annual ACM symposium on Theory of computing , pages=

    A fast quantum mechanical algorithm for database search , author=. Proceedings of the twenty-eighth annual ACM symposium on Theory of computing , pages=

  3. [3]

    arXiv preprint quant-ph/0005055 , year=

    Quantum amplitude amplification and estimation , author=. arXiv preprint quant-ph/0005055 , year=

    arXiv

  4. [4]

    SIAM Journal on Computing , volume=

    Quantum walk algorithm for element distinctness , author=. SIAM Journal on Computing , volume=. 2007 , publisher=

    2007

  5. [5]

    Physical review letters , volume=

    Quantum algorithm for linear systems of equations , author=. Physical review letters , volume=. 2009 , publisher=

    2009

  6. [6]

    Nature , volume=

    Discovering faster matrix multiplication algorithms with reinforcement learning , author=. Nature , volume=. 2022 , publisher=

    2022

  7. [7]

    Nature , volume=

    Faster sorting algorithms discovered using deep reinforcement learning , author=. Nature , volume=. 2023 , publisher=

    2023

  8. [8]

    Nature , volume=

    Mathematical discoveries from program search with large language models , author=. Nature , volume=. 2024 , publisher=

    2024

Show all 59 references
  1. [9]

    Nature , volume=

    Solving olympiad geometry without human demonstrations , author=. Nature , volume=. 2024 , publisher=

    2024

  2. [10]

    AAAI , volume=

    AutoSciLab: A self-driving laboratory for interpretable scientific discovery , author=. AAAI , volume=

  3. [11]

    AAAI , volume=

    Generating novel leads for drug discovery using LLMs with logical feedback , author=. AAAI , volume=

  4. [12]

    arXiv preprint arXiv:2210.03629 , year=

    React: Synergizing reasoning and acting in language models , author=. arXiv preprint arXiv:2210.03629 , year=

    arXiv

  5. [13]

    arXiv preprint arXiv:2308.08155 , year=

    Autogen: Enabling next-gen llm applications via multi-agent conversation , author=. arXiv preprint arXiv:2308.08155 , year=

    arXiv

  6. [14]

    International Conference on Learning Representations , volume=

    MetaGPT: Meta programming for a multi-agent collaborative framework , author=. International Conference on Learning Representations , volume=

  7. [15]

    AAAI , volume=

    Verilogcoder: Autonomous verilog coding agents with graph-based planning and abstract syntax tree (ast)-based waveform tracing tool , author=. AAAI , volume=

  8. [16]

    AAAI , volume=

    Rag-enhanced collaborative llm agents for drug discovery , author=. AAAI , volume=

  9. [17]

    AAAI , volume=

    Failure localization in multi-agent code generation via knowledge-guided and transferable reasoning , author=. AAAI , volume=

  10. [18]

    AAAI , volume=

    Deep quantum error correction , author=. AAAI , volume=

  11. [19]

    AAAI , volume=

    AI-powered algorithm-centric quantum processor topology design , author=. AAAI , volume=

  12. [20]

    ACM Computing Surveys (Csur) , volume=

    Knowledge graphs , author=. ACM Computing Surveys (Csur) , volume=. 2021 , publisher=

    2021

  13. [21]

    arXiv preprint arXiv:2408.06292 , year=

    The ai scientist: Towards fully automated open-ended scientific discovery , author=. arXiv preprint arXiv:2408.06292 , year=

    arXiv

  14. [22]

    Advanced Materials , volume=

    SciAgents: automating scientific discovery through bioinspired multi-agent intelligent graph reasoning , author=. Advanced Materials , volume=. 2025 , publisher=

    2025

  15. [23]

    International Conference on Learning Representations , volume=

    Moose-chem: Large language models for rediscovering unseen chemistry scientific hypotheses , author=. International Conference on Learning Representations , volume=

  16. [24]

    arXiv preprint arXiv:2411.15114 , year=

    Re-bench: Evaluating frontier ai r&d capabilities of language model agents against human experts , author=. arXiv preprint arXiv:2411.15114 , year=

    arXiv

  17. [25]

    arXiv preprint arXiv:2504.01848 , year=

    PaperBench: Evaluating AI's Ability to Replicate AI Research , author=. arXiv preprint arXiv:2504.01848 , year=

    arXiv

  18. [26]

    arXiv preprint arXiv:2504.00255 , year=

    Scireplicate-bench: Benchmarking llms in agent-driven algorithmic reproduction from research papers , author=. arXiv preprint arXiv:2504.00255 , year=

    arXiv

  19. [27]

    arXiv preprint arXiv:2412.07978 , year=

    Agents for self-driving laboratories applied to quantum computing , author=. arXiv preprint arXiv:2412.07978 , year=

  20. [28]

    arXiv preprint arXiv:2508.20134 , year=

    QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming , author=. arXiv preprint arXiv:2508.20134 , year=

    arXiv

  21. [29]

    arXiv preprint arXiv:2510.16779 , year=

    QuanBench: Benchmarking Quantum Code Generation with Large Language Models , author=. arXiv preprint arXiv:2510.16779 , year=

  22. [30]

    Proceedings of the 18th International Natural Language Generation Conference , pages=

    QCoder Benchmark: Bridging Language Generation and Quantum Hardware through Simulator-Based Feedback , author=. Proceedings of the 18th International Natural Language Generation Conference , pages=

  23. [31]

    arXiv preprint quant-ph/9607014 , year=

    A quantum algorithm for finding the minimum , author=. arXiv preprint quant-ph/9607014 , year=

    arXiv

  24. [32]

    arXiv preprint arXiv:1509.02374 , year=

    Quantum walk speedup of backtracking algorithms , author=. arXiv preprint arXiv:1509.02374 , year=

    arXiv

  25. [33]

    Proceedings of the thirty-ninth annual ACM symposium on Theory of computing , pages=

    Search via quantum walk , author=. Proceedings of the thirty-ninth annual ACM symposium on Theory of computing , pages=

  26. [34]

    International colloquium on automata, languages, and programming , pages=

    Quantum counting , author=. International colloquium on automata, languages, and programming , pages=. 1998 , organization=

    1998

  27. [35]

    Algorithmica , volume=

    Quantum complexities of ordered searching, sorting, and element distinctness , author=. Algorithmica , volume=. 2002 , publisher=

    2002

  28. [36]

    Proceedings 39th Annual Symposium on Foundations of Computer Science (Cat

    Quantum oracle interrogation: Getting all information for almost half the price , author=. Proceedings 39th Annual Symposium on Foundations of Computer Science (Cat. No. 98CB36280) , pages=. 1998 , organization=

    1998

  29. [37]

    Journal of the ACM (JACM) , volume=

    Quantum lower bounds by polynomials , author=. Journal of the ACM (JACM) , volume=. 2001 , publisher=

    2001

  30. [38]

    Judging the judges: A systematic study of position bias in llm-as-a-judge , author=. Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics , pages=

  31. [39]

    Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Large language models are not fair evaluators , author=. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  32. [40]

    Proceedings of Machine Learning and Systems , volume=

    Xgrammar: Flexible and efficient structured generation engine for large language models , author=. Proceedings of Machine Learning and Systems , volume=

  33. [41]

    Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

    Learning to generate structured output with schema reinforcement learning , author=. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

  34. [42]

    arXiv preprint arXiv:2203.11171 , year=

    Self-consistency improves chain of thought reasoning in language models , author=. arXiv preprint arXiv:2203.11171 , year=

    arXiv

  35. [43]

    Nature Physics , volume=

    Read the fine print , author=. Nature Physics , volume=. 2015 , publisher=

    2015

  36. [44]

    AAAI , volume=

    Agentswift: Efficient llm agent design via value-guided hierarchical search , author=. AAAI , volume=

  37. [45]

    Advances in neural information processing systems , volume=

    Chain-of-thought prompting elicits reasoning in large language models , author=. Advances in neural information processing systems , volume=

  38. [46]

    arXiv preprint arXiv:2305.14325 , year=

    Improving factuality and reasoning in language models through multiagent debate , author=. arXiv preprint arXiv:2305.14325 , year=

    arXiv

  39. [47]

    arXiv preprint arXiv:2407.01502 , year=

    Ai agents that matter , author=. arXiv preprint arXiv:2407.01502 , year=

    arXiv

  40. [48]

    Journal of the American Statistical association , volume=

    Alternatives to the median absolute deviation , author=. Journal of the American Statistical association , volume=. 1993 , publisher=

    1993

  41. [49]

    AAAI , volume=

    BAMAS: Structuring budget-aware multi-agent systems , author=. AAAI , volume=

  42. [50]

    Proceedings of the 51st annual ACM SIGACT symposium on theory of computing , pages=

    A quantum-inspired classical algorithm for recommendation systems , author=. Proceedings of the 51st annual ACM SIGACT symposium on theory of computing , pages=

  43. [51]

    arXiv preprint arXiv:2409.11363 , year=

    Core-bench: Fostering the credibility of published research through a computational reproducibility agent benchmark , author=. arXiv preprint arXiv:2409.11363 , year=

    arXiv

  44. [52]

    1) , author=

    The open provenance model core specification (v1. 1) , author=. Future generation computer systems , volume=. 2011 , publisher=

    2011

  45. [53]

    AAAI , volume=

    A ^2 Flow: Automating Agentic Workflow Generation via Self-Adaptive Abstraction Operators , author=. AAAI , volume=

  46. [54]

    Advances in Neural Information Processing Systems , volume=

    MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges? , author=. Advances in Neural Information Processing Systems , volume=

  47. [55]

    AAAI , volume=

    AgentODRL: A Large Language Model-based Multi-agent System for ODRL Generation , author=. AAAI , volume=

  48. [56]

    NLP4Science , pages=

    Hypothesis generation with large language models , author=. NLP4Science , pages=

  49. [57]

    arXiv preprint arXiv:2502.09858 , year=

    Automated hypothesis validation with agentic sequential falsifications , author=. arXiv preprint arXiv:2502.09858 , year=

    arXiv

  50. [58]

    The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

    Llm evaluators recognize and favor their own generations , author=. The Thirty-eighth Annual Conference on Neural Information Processing Systems , year=

  51. [59]

    arXiv preprint arXiv:2505.14599 , year=

    Toward reliable scientific hypothesis generation: Evaluating truthfulness and hallucination in large language models , author=. arXiv preprint arXiv:2505.14599 , year=

    arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.