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Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers

Chenglei Si, Diyi Yang, Tatsunori Hashimoto

arXiv:2409.04109v1cs.CLcs.AIcs.CYcs.HCcs.LG

TL;DR

Whether LLMs can generate research ideas comparable to expert ideas remains an open question. The paper compares human- and LLM-generated ideas using a controlled study, finding that reviewers rate AI ideas as statistically more novel than expert ideas while finding them comparable on other metrics.

  • Problem

    Whether LLMs can generate novel research ideas comparable to expert ideas remains an open question about the creative first step of research.

    Source: §1 · p. 1

  • Method

    The study compares human- and LLM-generated research ideas using shared topic descriptions, instructions, templates, and examples.

    Source: §2 · pp. 3–4

  • Results

    Expert reviewers rate AI ideas as statistically more novel than expert ideas, while finding them comparable on other metrics.

    Source: §5 · p. 9, §10 · p. 18

  • Takeaways & Limitations

    The findings address the ideation step of research, which the paper describes as only one part of the research process.

    Source: §1 · p. 1

    The study finds significant limitations in the assumption that scaling idea generation by producing and filtering a vast pool will yield high-quality ideas.

    Source: §7 · p. 13

Abstract

from arXiv · show

Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing research agents that autonomously generate and validate new ideas. Despite this, no evaluations have shown that LLM systems can take the very first step of producing novel, expert-level ideas, let alone perform the entire research process. We address this by establishing an experimental design that evaluates research idea generation while controlling for confounders and performs the first head-to-head comparison between expert NLP researchers and an LLM ideation agent. By recruiting over 100 NLP researchers to write novel ideas and blind reviews of both LLM and human ideas, we obtain the first statistically significant conclusion on current LLM capabilities for research ideation: we find LLM-generated ideas are judged as more novel (p < 0.05) than human expert ideas while being judged slightly weaker on feasibility. Studying our agent baselines closely, we identify open problems in building and evaluating research agents, including failures of LLM self-evaluation and their lack of diversity in generation. Finally, we acknowledge that human judgements of novelty can be difficult, even by experts, and propose an end-to-end study design which recruits researchers to execute these ideas into full projects, enabling us to study whether these novelty and feasibility judgements result in meaningful differences in research outcome.

1 Introduction

The paper asks whether current LLMs can generate novel ideas comparable to expert humans and answers through a large-scale, controlled expert evaluation. AI-generated ideas were judged more novel than human ideas, while the study also identifies limits in feasibility, diversity, and LLM self-evaluation.

1 Introduction

  • Research ideation remains an open test of whether LLMs can handle the creative parts of research, and is a key first step toward autonomous agents that create their own ideas.The paper asks whether current LLMs can generate novel ideas comparable to expert humans.Source: §1 · p. 1
  • AI-generated ideas were judged more novel than human expert ideas (p<0.05), robustly across correction and statistical tests, while possible feasibility costs were inconclusive.The study also found signs of correlations with excitement and overall score, but lacked sufficient power to identify those effects conclusively.Source: Figure 1 · p. 2, §1 · p. 3
  • Over 100 NLP researchers contributed human baseline ideas and blind reviews, with idea styles standardized and topic distributions matched to control confounders.The LLM agent used retrieval augmentation and inference-time scaling through overgeneration and reranking.Source: §1 · p. 2
  • The year-long evaluation uses nearly 300 expert reviews and contrasts with prior work relying on fewer reviewers, constrained ideas, or LLM-as-a-judge.It provides human expert baselines and a standardized protocol for follow-up evaluation and methods work.Source: §1 · p. 3
  • Scaling idea generation revealed limited diversity, and the LLM agent could not reliably evaluate ideas.Qualitative free-text responses also corroborated the findings on novelty and feasibility.Source: §1 · p. 3

2 Problem Setup

The experiment compares human- and LLM-generated research ideas by separating idea generation, writeup, and expert evaluation, while controlling potential confounders. It uses prompting-based NLP research and standardized procedures to make the comparison fair and support later execution of ideas.

Problem Setup

  • The study frames research-idea evaluation as three components: the idea, its writeup, and expert evaluation, with design choices aimed at controlling confounders.Potential confounders include research area, idea format, and the evaluation process.Source: §2 · p. 3

Ideation Scope and Instructions

  • The testbed is prompting-based NLP research, chosen to balance realistic, interesting, executable ideas with the practicalities of eliciting ideas at scale.Prompting projects can be executed with minimal computing hardware, and the resulting ideas are intended for expert follow-up experiments.Source: §2 · p. 3
  • To control topic-selection bias, human and LLM ideas use the same seven topics and instructions, with LLM ideas matched to humans’ preferred-topic distribution.The topics are Bias, Coding, Safety, Multilinguality, Factuality, Math, and Uncertainty; humans can choose a topic suited to their expertise.Source: §2 · p. 4

Idea Writeup

  • Both groups use a proposal template and demonstration examples, while LLM-based style normalization reduces writing-style cues and preserves original content through author verification.The template specifies title, problem, motivation, method, experiment plan, test cases, and fallback plan; a small study found experts distinguished AI from human ideas with 50% accuracy after normalization.Source: §2 · p. 4

Review and Evaluation

  • Blind reviews score novelty, excitement, feasibility, expected effectiveness, and overall quality, using 1–10 ratings plus rationales across three idea conditions.The conditions are human ideas, top-ranked LLM ideas, and LLM ideas manually reranked by the first author to estimate their upper-bound quality.Source: §2 · pp. 4–5

3 Idea Generation Agent

The agent uses a minimalist pipeline of paper retrieval, idea generation, and idea ranking to compare LLM ideation with human experts. It generates many candidates, then ranks them for the human study.

3 Idea Generation Agent

  • The agent’s design prioritizes measuring current LLM ideation capabilities over innovating the agent itself, with retrieval, generation, and ranking as its three components.The authors describe the agent as simple but effective for comparison with the human expert baseline.Source: §3 · p. 5

3.1 Paper Retrieval for RAG

  • The agent retrieves and reranks topic-relevant empirical papers using Semantic Scholar API calls, selecting top-ranked papers to ground idea generation.It uses Claude-3.5-Sonnet, retains up to 20 papers per call and 120 overall, then scores papers for relevance, empirical content, and project inspiration.Source: §3.1 · p. 5

3.2 Idea Generation

  • The agent generates 4,000 seed ideas per topic, then deduplicates candidates, leaving about 5% non-duplicated ideas.Prompts include six manually crafted demonstrations and titles and abstracts from 10 retrieved papers; deduplication uses cosine similarity with a 0.8 threshold.Source: §3.2 · pp. 5–6

3.3 Idea Ranking

  • Claude-3.5-Sonnet’s zero-shot pairwise ranker reaches 71.4% accuracy, versus 61.1% for GPT-4o and 63.5% for Claude-3-Opus.The ranker uses a Swiss-system tournament with five rounds to score proposals; top-ranked proposals form the AI Ideas condition, while the first author’s reranking forms AI Ideas + Human Rerank.Source: §3.3 · p. 6
  • 17 of 49 proposals in the AI Ideas + Human Rerank condition overlapped with AI Ideas, showing substantial differences between human and LLM reranking.The other 32 proposals were different.Source: §3.3 · p. 7

4 Expert Idea Writing and Reviewing

The section describes how the study recruited NLP experts to write ideas and review anonymized ideas, and reports participant qualifications, writing effort, review assignment, and review-quality checks.

4.1 Expert Recruitment

  • Recruitment through NLP community channels and conference outreach screened U.S. participants for a major-AI-venue paper, yielding 49 writers and 79 reviewers (104 people total).Writers had 10 days to submit one idea and received $300, with a $1,000 bonus for the five top-scored ideas; reviewers completed 2–7 reviews each, and the study collected 298 unique reviews.Source: §4.1 · p. 7

4.2 Expert Qualifications

  • Writers averaged 12 papers and 477 citations; reviewers averaged 635 citations and h-index 7, and 72 of 79 had reviewed for major AI venues or journals.Writers represented 26 institutions and reviewers 32; every reviewer had published at least two papers.Source: §4.2 · pp. 7–8, Table 2 · p. 8

4.3 Idea Writing

  • Writers reported topic familiarity of 3.7/5 and task difficulty of 3/5, spent 5.5 hours on average, and produced ideas averaging 902 words.The paper also presents the distribution of their selected topics in Table 4.Source: §4.3 · p. 8, Table 3 · p. 8, Table 4 · p. 8

4.4 Idea Reviewing

  • Reviewers selected two preferred topics and a load of 2–7 ideas; assignments balanced AI and human ideas, avoided same-institution authors, and gave each idea 2–4 reviewers.Each reviewer wrote an average of 3.8 reviews across 2 or 3 conditions and 1 to 3 topics.Source: §4.4 · p. 8, Table 5 · p. 9
  • Reviewers averaged 3.7/5 for topic familiarity and confidence, spent 32 minutes per review, and wrote 232 words on average, compared with 247 words for matched ICLR review sections.Of 298 collected reviews, 80 cited existing papers to justify novelty concerns.Source: §4.4 · p. 9, Table 6 · p. 9

5 Main Result: AI Ideas Are Rated More Novel Than Expert Ideas

Across three statistical tests addressing possible confounders, the ideation agent’s AI ideas received higher novelty ratings than human expert ideas while remaining comparable on other metrics.

5.1 Test 1: Treating Each Review as an Independent Datapoint

  • AI Ideas scored 5.64±1.76 and AI Ideas + Human Rerank 5.81±1.66 on novelty, versus 4.84±1.79 for Human Ideas (p<0.01).In Test 1, AI conditions also significantly outscored Human Ideas on excitement; the reranked condition did so on overall score, while other metrics showed no significant differences.Source: §5.1 · pp. 9–10, Table 7 · p. 10

5.2 Test 2: Treating Each Idea as an Independent Datapoint

  • With each idea as a datapoint (N=49 per condition), AI Ideas scored 5.62±1.39 and AI Ideas + Human Rerank 5.78±1.07 on novelty, versus 4.86±1.26 for Human Ideas (p<0.05).The other metrics were comparable between AI and human ideas.Source: §5.2 · p. 10, Table 8 · p. 11

5 Main Result: AI Ideas Are Rated More Novel Than Expert Ideas

  • Across all three tests, both AI Ideas and AI Ideas + Human Rerank were rated more novel than Human Ideas.The third test treated each reviewer as a datapoint and found significant differences (p<0.05) for both AI conditions.Source: §5 · p. 9, §5.3 · p. 11

5.3 Test 3: Treating Each Reviewer as an Independent Datapoint

  • Treating each reviewer as a datapoint, one-sample tests of reviewers’ mean score differences found both AI conditions significantly more novel than Human Ideas (p<0.05).This test compared each reviewer’s average scores across conditions and tested whether the differences were above zero.Source: §5.3 · pp. 10–11

6 In-Depth Analysis of the Human Study

The analysis examines whether human participants submitted ideas representative of their past work, which idea qualities reviewers prioritize, and how consistently they judge ideas.

6.1 Human Experts May Not Be Giving Their Best Ideas

  • Participants rated their submitted ideas as averaging the top 43% of their past ideas, suggesting they were likely median-level ideas.Of 49 participants, 37 came up with their idea on the spot, and most did so within the task’s 10-day time constraint.Source: §6.1 · p. 12

6.2 Reviewers Tend to Focus More on Novelty and Excitement

  • Overall scores correlated with novelty (r=0.725) and excitement (r=0.854), but had almost no correlation (r<0.1) with feasibility.This suggests reviewers may focus more on novelty and excitement when evaluating ideas.Source: §6.2 · p. 12, Table 10 · p. 13

6.3 Reviewing Ideas is Inherently Subjective

  • Reviewers agreed on idea rankings with 56.1% accuracy, below the 66.0% NeurIPS 2021 and 71.9% ICLR 2024 comparison results.Agreement was above the 50% random baseline; the authors suggest judging ideas without experiment results may be more subjective than conference reviewing (Beygelzimer et al., 2021, Lu et al., 2024).Source: §6.3 · p. 13

7 Limitations of LLMs

The study finds important limits to LLM idea generation: generating more ideas yields many duplicates, and current LLM evaluators are less reliable than expert reviewers.

7 Limitations of LLMs

  • The over-generate-and-rank approach assumes that scaling generation and filtering can yield high-quality ideas, but the study finds this naive assumption has significant limitations.The agent is motivated by LLMs’ ability to generate many ideas and filter the pool for the best ones.Source: §7 · p. 13

7.1 LLMs Lack Diversity in Idea Generation

  • Of 4,000 generated seed ideas, only 200 are unique; as generation continues, non-duplicate batches shrink and accumulated unique ideas plateau.Ideas were treated as duplicates when cosine similarity to a previously generated idea exceeded 0.8; Figure 4 averages data across topics.Source: §7.1 · pp. 13–14, Figure 4 · p. 14

7.2 LLMs Cannot Evaluate Ideas Reliably

  • LLM evaluators perform below expert reviewers: the best, Claude-3.5 pairwise ranker, achieves 53.3% accuracy versus 56.1% inter-reviewer consistency.Table 11 compares human reviewer consistency with agreement between humans and AI.Source: §7.2 · p. 14, Table 11 · p. 14
  • AI-human agreement drops significantly versus earlier studies, and ranking disagreements plus higher scores for AI Ideas + Human Rerank on all metrics caution against relying on LLM evaluators.Even matching human-human agreement may not make AI reviewing meaningful if it trades variance for bias or relies on spurious correlations (Durmus et al., 2022).Source: §7.2 · pp. 14–15

8 Qualitative Analysis and Examples

Free-text reviews reinforce the finding that AI-generated ideas tend to be more novel, while identifying recurring weaknesses in their execution and feasibility. Human ideas are described as more grounded and practical but sometimes less innovative; sampled proposals illustrate both groups across several topics.

8.1 Analysis of Free-Text Reviews

  • Reviewers often described AI-generated ideas as novel, including approaches they had not encountered in the literature.The authors used Claude-3.5 to cluster review themes and manually verified and labeled the clusters.Source: §8.1 · p. 15
  • AI ideas were criticized for vague implementation details, unsuitable datasets or baselines, and unrealistic assumptions.These problems could make reviewers less confident in judging an idea; the cited assumption concerns included whether models can reliably flag their own hallucinations.Source: §8.1 · pp. 15–16
  • Other AI-idea criticisms concerned resource demands, weak motivation, and failure to follow established research and evaluation practices.Examples included costly fine-tuning and time-consuming manual data collection or evaluation.Source: §8.1 · p. 16
  • Human ideas were described as more grounded in prior work and practical considerations, but often incremental, focused on familiar problems, or prioritized feasibility over novelty.Reviewers cited applying known techniques to new problems, combining existing methods, and ideas likely to work without being groundbreaking.Source: §8.1 · pp. 16–17

8.2 Randomly Sampled Human and AI Ideas with Reviews

  • Four randomly sampled human–AI idea pairs provide examples across uncertainty, multilingualism, factuality, and coding; titles, topics, and average scores are listed.The proposals’ full texts and reviews appear in the appendix, where their authorship is revealed.Source: §8.2 · p. 17

9 Related Work

Prior LLM research studied idea-generation methods, research execution, and related research tasks, but this work focuses on open-ended research ideation and compares LLM ideas with expert ideas. It also connects the study’s novelty and diversity findings to computational creativity research.

Research idea generation and execution

  • Earlier idea-generation methods lacked human-expert comparisons, while execution studies relied on constrained benchmarks or often-unreliable LLM evaluators.Prior methods explored iterative novelty boosting, multi-agent collaboration (Baek et al., 2024), and retrieval and revision; execution work generated experimental code (Huang et al., 2024, Tian et al., 2024) or implemented AI-generated ideas (Li et al., 2024, Lu et al., 2024).Source: §9 · p. 18

LLM for other research-related tasks

Computational creativity

  • In research ideation, LLM-generated ideas can be more novel than expert ideas, and their lack of collective diversity aligns with prior findings on LLM generations.This contrasts with findings that AI writing is less creative than professional writing (Chakrabarty et al., 2024) and aligns with collective-diversity findings (Anderson et al., 2024, Zhou et al., 2024); prior human evaluations of AI exposure or collaboration reported mixed conclusions (Ashkinaze et al., 2024, Liu et al., 2024, Padmakumar and He, 2024).Source: §9 · p. 18

10 Discussion

Expert reviewers rated AI-generated ideas as statistically more novel than expert ideas; the discussion considers how to evaluate their quality, the study’s scope, and automated execution.

Discussion

  • Expert reviewers rated AI-generated ideas statistically more novel than expert ideas, though the study notes possible limits in how well quickly written expert ideas represented researchers’ best ideas.To address this concern, the authors cached AI ideas generated on 23 EMNLP 2024 topics and preregistered a comparison with accepted papers and an analysis of topic overlap.Source: §10 · p. 18
  • The authors treat idea-only evaluations as preliminary and plan to have researchers execute AI- and human-generated ideas into full projects for outcome-based assessment.This would also test whether initial idea evaluations align with assessments of project outcomes.Source: §10 · p. 19
  • The study is limited to prompting-based NLP research to support relatively quick, less resource-demanding execution, while future work could test other domains where conclusions may differ.The authors expect their evaluation protocols may apply beyond NLP, but call for human studies across research domains.Source: §10 · p. 19
  • An idea-execution agent can generate compiling, runnable code, but its experiments can mislead by omitting or changing method steps or incorrectly defining metrics.The authors argue that result comparison alone is insufficient: implementation faithfulness must also be verified, a nontrivial task left to future work.Source: §10 · p. 19

11 Ethical Considerations

The authors call for accountability, transparency, and safety measures as LLMs enter research ideation. They also warn that AI adoption could affect idea diversity and human collaboration, and urge careful integration into research.

Publication Policy

  • Researchers should be held accountable for AI-assisted work under rigorous standards equal to those for human-generated research, to protect academic quality and review integrity.The authors warn that uncritical use could encourage low-quality submissions and undermine the credibility of peer review.Source: §11 · p. 19

Intellectual Credit

  • Because AI-generated ideas complicate intellectual credit and provenance, researchers should disclose the models, data sources, frameworks, and human involvement used.The authors argue transparent documentation can support fairer credit distribution, while a broader overhaul of legal and academic norms is beyond this project’s scope.Source: §11 · pp. 19–20

Potential for Misuse

  • Ideation agents could be misused to generate harmful strategies, so the authors advocate targeted safety research and benchmarks for ethical application.Suggested measures include RLHF systems and anti-jailbreak mechanisms; they argue safety discussions should be grounded in measured capabilities.Source: §11 · p. 20

Idea Homogenization

  • Because current LLMs lack idea diversity, widespread adoption could homogenize research and narrow its perspectives; future work should promote diversity in models or methods.The evaluation focuses on typical idea quality and may miss a long tail of uniquely transformative ideas.Source: §11 · p. 20

Impact on Human Researchers

  • Overreliance on LLMs could diminish original human thought and collaboration, so future work should explore human-AI ideation that uses feedback, inspiration, and expertise.The authors note that human reranking of AI ideas shows even naive collaboration can be effective.Source: §11 · p. 20

Positionality Statement … P Example Idea: Modular Calibration for Long-form Answers

This section documents the study materials and idea-generation pipeline, including proposal elicitation, standardization, filtering, examples, and expert-review criteria. It also presents supplementary analyses and an example proposal with reviewer feedback.

Positionality Statement

  • The authors disclosed differing expectations: Tatsu and Diyi anticipated a null result, while Chenglei expected AI to outperform humans.They reported these expectations before the experiment to make potential experimenter biases transparent.Source: Positionality Statement · p. 21

A List of Research Topics

  • The ideation task used selected research topics to elicit ideas from both human participants and the LLM agent.The supplied topics include prompting for bias reduction, code generation, safety, multilingual performance, and factuality.Source: A List of Research Topics · p. 26

B Project Proposal Template

  • Both human writers and the AI agent received a proposal template covering the research question, motivation, method, executable experiments, test cases, and fallback plan.The template also calls for concrete baseline-failure and proposed-method-success examples, with inputs, prompts, expected outputs, and explanations.Source: B Project Proposal Template · p. 27

C Project Proposal Demo Example

  • The shared demonstration adapts Chain-of-Verification into four LLM-prompted steps: generate a baseline, plan and execute verifications, then produce a revised response.Verification questions are answered without the original baseline response in their context; the fallback plan is to diagnose each step or turn the work into an analysis.Source: C Project Proposal Demo Example · pp. 28, 30

D Style Standardization Prompt

  • A style-standardization prompt directs editing for template format and academic phrasing while preserving the idea’s meaning, sections, examples, and named models or datasets.It also instructs editors to retain author-year citations but remove numeric citations, and to update Claude and LLaMA references to Claude-3.5 and LLaMA-3, respectively.Source: D Style Standardization Prompt · p. 31

E Idea Review Form

  • Expert reviewers were given one week to complete each review.The review form is described as the instrument used to elicit reviews from all expert reviewers.Source: E Idea Review Form · p. 32

4. Consent

  • Reviewers affirmed they would not use AI tools and reported topic familiarity on a five-level scale, from never having read about the topic to publishing multiple papers or a first-author paper.The form also asked whether they had reviewed for major NLP or AI conferences.Source: §4 · p. 32

8. Full Research Idea Proposal

  • The full-idea review form rated novelty, feasibility, expected effectiveness, and excitement, with novelty judged against work online before July 2024 and feasibility framed for a typical CS PhD student’s 1–2 months.The feasibility prompt assumes abundant OpenAI and Anthropic API access but limited GPU compute.Source: §8 · pp. 32–34

F Idea Generation Agent: Additional Implementation Details

  • About 1% of generated proposals were filtered after expansion for possible novelty or feasibility failures.Novelty filtering compared ideas with the 10 most relevant retrieved papers; feasibility filtering removed proposals requiring excessive labor or hardware, or inconsistent assumptions and setups.Source: F Idea Generation Agent: Additional… · p. 36

G Demonstration Example: Seed Idea Generation

H Generated Seed Ideas and Their Nearest Neighbors

Similarity: 0.70; Similarity: 0.77

  • The examples pair QSCP’s persona-based competing answers with QSP’s amplitude-and-measurement framing, and FUD’s recursive confidence tree with SFD’s fractal aggregation.The proposed evaluations span uncertainty calibration, ambiguity, and consistency across levels of abstraction.Source: Similarity: 0.70 · pp. 39–40, Similarity: 0.77 · pp. 40–41

I Overlap Between AI Ranking and Expert Reranking

J Quality Control of Human Expert Ideas

N Mixed-Effects Models

  • 0.761 for novelty and -0.330 for feasibility are example AI Ideas–Human Ideas coefficients; mixed-effects models confirm that AI ideas are significantly more novel.The models account for topic, reviewer, and idea variation; reviewer variances are generally high, indicating substantial differences in how reviewers score ideas.Source: N Mixed-Effects Models · p. 46, Table 17 · p. 47

O Score Breakdown by Topic

  • Per-topic score breakdowns are mainly illustrative because smaller sample sizes mean most results are not statistically significant.The breakdown covers all conditions and topics.Source: O Score Breakdown by Topic · p. 48

P Example Idea: Modular Calibration for Long-form Answers

  • The proposal targets long-form calibration, where answer-length variation can undermine perplexity and verbalized confidence or calibration metrics may be unreliable across task settings.It motivates monitoring answer generation and applying targeted external augmentation when needed.Source: P Example Idea: Modular Calibration… · p. 49
  • Modular Calibration extends the question, decomposes the response into modules, extracts confidence per module, and merges those scores into an overall confidence estimate.The same LLM performs each step through differently worded prompts.Source: P Example Idea: Modular Calibration… · p. 49
  • The experiment plan proposes GSM8K, code-generation, and essay datasets, direct-prompting and whole-answer perplexity baselines, three LLMs, and qualitative and quantitative component analyses.The listed models are GPT-3.5 (Text-Davinci-003), GPT-4, and LLaMA-3-70B-chat.Source: P Example Idea: Modular Calibration… · p. 49
  • If Modular Calibration fails to improve over the baseline, the fallback is to assess module-level calibration or analyze decomposition quality to debug the method and identify its limitations.Source: P Example Idea: Modular Calibration… · p. 50
  • The reviewer rated Novelty 6, Feasibility 8, Expected Effectiveness 6 (possible moderate gains over self-consistency majority-vote baselines on a few benchmarks), and Overall Score 5, describing the proposal as a straightforward extension lacking an interesting angle.The reviewer said that actually producing uncertainty estimates for each claim would face few prior-work comparators and less-strong baselines.Source: P Example Idea: Modular Calibration… · p. 51
  • Another reviewer assigned Novelty 6, Feasibility 8, Expected Effectiveness 6, Excitement 6, and Overall Score 6, while flagging unclear modules and partial correctness.The reviewer questioned whether module definitions would work for essays and whether the approach could handle answers that are only partially correct.Source: P Example Idea: Modular Calibration… · p. 52

Q Example Idea: Semantic Resonance Uncertainty Quantification · R Example Idea: Translation with LLMs through Prompting with Long-Form Context

This section presents two proposed LLM research ideas: semantic resonance for uncertainty estimation and long-form-context prompting for translation. It outlines their methods and planned evaluations, including examples and reviewer assessments.

Q Example Idea: Semantic Resonance Uncertainty Quantification

  • SRUQ proposes generating independent reasoning paths, cross-evaluating their consistency and mutual support, and using a resonance graph score to estimate calibrated uncertainty.The graph represents solution attempts as nodes and semantic reinforcement as edges; its score is based on properties such as connectivity and centrality. In a train-distance example, the proposal reports a resonance score of 0.95 and calibrated uncertainty of 0.05.Source: Q Example Idea: Semantic Resonance… · p. 53
  • SRUQ's proposed evaluation compares softmax probabilities, Monte Carlo Dropout, and ensemble disagreement on GSM8K, EntailmentBank, and HotpotQA using calibration and uncertainty-ranking metrics.The plan also examines uncertainty-error correlation, varies the number of solution attempts and graph centrality measures, tests cross-evaluation, and checks generalization across datasets.Source: Q Example Idea: Semantic Resonance… · p. 54
  • In an illustrative train-distance case, three mutually supportive solutions form a fully connected graph, yielding a resonance score of 0.95 and calibrated uncertainty of 0.05.The example presents these values as the method's output for that case, not as benchmark results.Source: Q Example Idea: Semantic Resonance… · p. 55
  • Reviewers rated SRUQ's novelty 6 and expected effectiveness 6, while noting that cross-evaluation is underspecified and the proposal's success depends on it.One reviewer also described the idea as reasonably novel, while another said its mutual-support evaluation details were crucial to implementation.Source: Q Example Idea: Semantic Resonance… · pp. 56–57

R Example Idea: Translation with LLMs through Prompting with Long-Form Context

  • The translation proposal supplies long-form context by prompting the model to generate a paragraph containing the source sentence and prepending target-language monolingual text marked with langid tags.The model then receives both added sources of content when prompted to translate.Source: R Example Idea: Translation with LLMs… · p. 59
  • The translation study plans comparisons across FLORES-200 and WMT-21, 15 English-centric languages, API and open-weight models, and standard MT and token-level language-identification metrics.Planned ablations remove or alter langid templates and separately provide only the generated paragraph or monolingual content; analyses vary language direction, resource level, and script.Source: R Example Idea: Translation with LLMs… · p. 59
  • The proposed third prompt step asks the model to select all target-language sentences translating a given English sentence, with multiple choices allowed.The expected output identifies the correct Spanish translations while rejecting distractors.Source: R Example Idea: Translation with LLMs… · p. 61
  • If the method underperforms, the fallback plan proposes analyzing errors and testing alternative context, retrieval, few-shot prompts, templates, language identification, and token-level likelihoods.The analysis could distinguish off-target errors from failures to recognize generated translations and examine how results vary with languages’ training data.Source: R Example Idea: Translation with LLMs… · p. 61
  • Novelty was rated 5 versus 1, while Expected Effectiveness was 7 versus 3; reviewers differed on the idea’s novelty and prospects for improving translation.One reviewer thought the method could guide output into the target language; the other saw no clear evidence that any proposed prompt would improve performance.Source: R Example Idea: Translation with LLMs… · pp. 62–63
  • Feasibility was rated 9 by one reviewer, who expected rapid execution using APIs, and 5 by another, who noted API costs and unclear experiment utility.The latter also questioned the fit between the listed high-resource languages and a proposal focused on low-resource languages.Source: R Example Idea: Translation with LLMs… · pp. 62–63
  • Overall Scores were 5 and 3: reviewers questioned the proposal’s publication-level contribution, citing a need for broader prompt comparisons or existing similar work.One recommended focusing on ablations and comparisons; the other judged the idea a clear rejection, arguing prompting was unlikely to deliver high-quality low-resource translation gains.Source: R Example Idea: Translation with LLMs… · pp. 62–63

S Example Idea: Linguistic Pivot Constellation: Enhancing Cross-Lingual Transfer for Low-Resource Languages and Dialects

The section proposes Linguistic Pivot Constellation (LPC), which prompts a model through related languages to support cross-lingual transfer for low-resource languages and dialects. It outlines a benchmark study and reviewers’ assessments of the idea, rather than reporting experimental results.

Proposed Method

  • LPC selects conceptually similar pivot languages and combines task-specific prompts from them with the target-language task, guiding the model to triangulate an answer.The proposal suggests selecting 3–5 pivots and forming a prompt constellation that captures different task aspects.Source: S Example Idea: Linguistic Pivot… · p. 64

Experiment Plan

  • The proposed evaluation uses FLORES-101 translation and TyDi QA, compares prompting baselines with LPC using GPT-4 and GPT-3.5-turbo, and measures BLEU and F1 across pivot counts and language-resource settings.The plan also examines pivot combinations and scalability to extremely low-resource scenarios; these are planned analyses, not reported findings.Source: S Example Idea: Linguistic Pivot… · pp. 64–65

Fallback Plan

  • If LPC does not outperform baselines, the fallback plan is to analyze how language similarity, pivot selection, and lexical, syntactic, and semantic transfer relate to effectiveness.The proposal frames this analysis as a way to investigate cross-lingual transfer mechanisms.Source: S Example Idea: Linguistic Pivot… · p. 65

Review Assessments

  • Reviewers rated LPC Novelty 9, Novelty: 8, and Novelty: 8, citing its task-specific pivot-prompt constellation and use of similar languages as demonstrations.One reviewer noted that language similarity has been studied, but not for choosing demonstrations in multilingual in-context learning.Source: S Example Idea: Linguistic Pivot… · pp. 66–68
  • Reviewers each rated Expected Effectiveness: 6, anticipating possible moderate gains while noting that outcomes may depend on pivot selection and whether the model can reason across languages.These are reviewer expectations, not measured performance results.Source: S Example Idea: Linguistic Pivot… · pp. 66–68
  • Feasibility ratings ranged from Feasibility: 5 to Feasibility: 8, with reviewers noting challenges in selecting pivots and constructing the similarity matrix and prompts.Another reviewer considered implementation straightforward with an existing similarity matrix and sufficient API quota.Source: S Example Idea: Linguistic Pivot… · pp. 66–68
  • Overall Score ratings were 7, 6, and 7; reviewers characterized LPC as promising but cautioned that implementation challenges and uncertain effectiveness warrant care.One review conditioned its Overall Score: 7 on execution with good results; another assigned Overall Score: 6, marginally above its acceptance threshold.Source: S Example Idea: Linguistic Pivot… · pp. 67–68

T Example Idea: LLM Directed Retrieval Querying for Improving Factuality · U Example Idea: Semantic Divergence Minimization: Reducing Hallucinations in Large Language Models through Iterative Concept Grounding

The section presents two proposals for improving LLM factuality: query-expanding retrieval and iterative semantic grounding. It describes an example location-answer outcome for the first and mixed reviewer assessments of the second.

T Example Idea: LLM Directed Retrieval Querying for Improving Factuality

U Example Idea: Semantic Divergence Minimization: Reducing Hallucinations in Large Language Models through Iterative Concept Grounding

  • SDM generates a reasoning step, extracts concepts from the input, measures semantic similarity, and regenerates below-threshold steps until convergence or a maximum iteration limit.The proposal uses the model’s own concept extraction and comparison as a self-correcting mechanism without external knowledge bases.Source: U Example Idea: Semantic Divergence… · p. 73
  • The planned evaluation uses HotpotQA (7,405 dev questions; EM/F1) and GSM8K (1,319 test problems; accuracy), GPT-4 and GPT-3.5-turbo, standard and CoT baselines, and threshold, iteration, and similarity-correctness analyses.Source: U Example Idea: Semantic Divergence… · p. 74
  • Feasibility ratings ranged from 5 to 9: reviewers described threshold selection as a challenge but also characterized the prompting-based approach and evaluation as straightforward.The method requires extracting semantic concepts and judging a candidate next step’s relevance to them.Source: U Example Idea: Semantic Divergence… · pp. 76–78
  • 6 (Excitement) and 5 (Overall Score) reflected one reviewer’s view that semantic-similarity constraints on chain-of-thought were novel, despite concerns about threshold tuning and scalability.The reviewer also described the method as task-sensitive and said it merited further exploration.Source: U Example Idea: Semantic Divergence… · p. 76
  • 3 (Overall Score) reflected another reviewer’s rejection, citing a simple, insufficiently comprehensive evaluation and doubts that semantic similarity would address subtle factual errors.The reviewer also questioned reliance on another LLM for rejection sampling, since that model could hallucinate too.Source: U Example Idea: Semantic Divergence… · p. 77
  • 2 (Overall Score) reflected a reviewer’s strong rejection, judging the approach too similar to existing self-critique methods and unlikely to meaningfully improve language-model performance.The reviewer said extracting semantic concepts and evaluating candidate-step relevance resembled prior methods, including approaches commonly used for math problems.Source: U Example Idea: Semantic Divergence… · p. 78

V Example Idea: Autoprompting: Generate Diverse Few-shot Examples for Any Application

The proposal uses an LLM coding agent to generate and iteratively validate few-shot examples for software functions, then compose examples across functions. Its plan evaluates the examples on varied user intents, while reviewer assessments of novelty, feasibility, and effectiveness were mixed.

Proposed Method

  • The LLM extracts user-facing functions, generates diverse prompts with expected outputs, executes generated code, repairs failures, and then creates examples that compose functions.The proposal uses documentation and unit tests to help refine code that implements natural-language instructions.Source: V Example Idea: Autoprompting:… · p. 79

Step-by-Step Experiment Plan

  • The experiment plan targets diverse documented applications, starts with 4–5 manual examples per function, and proposes static analysis, contextual code exploration, sandboxed execution, and versioned example storage.Code summarization, call-graph analysis, and type inference could provide further context; the proposal also recommends logging execution errors.Source: V Example Idea: Autoprompting:… · pp. 79–80

Experiments and Test Cases

  • The evaluation would simulate varied user intents, measure goal completion and error rates across models, and test clear, ambiguous, and out-of-scope utterances.For out-of-scope inputs such as adding letters, the expected behavior is to refuse rather than generate a function call.Source: V Example Idea: Autoprompting:… · pp. 79–80

Fallback Plan

  • If results disappoint, the proposal recommends checking documentation and modules separately, starting with functions with few dependencies before increasing complexity.The exploration module is described as particularly challenging when functions depend heavily on other functions.Source: V Example Idea: Autoprompting:… · p. 80

Reviewer Assessments

  • Reviewers assigned novelty scores of 4 and 7; one gave an overall score of 4, calling it “Ok but not good enough” and a rejection for major AI conferences.That reviewer found the contribution confusing and questioned how diversity and high coverage would be ensured; another called automatic natural-language integration into applications a hugely impactful capability.Source: V Example Idea: Autoprompting:… · pp. 81–82
  • Effectiveness was rated 5 (somewhat ineffective) and 7, while feasibility was rated 6 and 4; reviewers cited vagueness and substantial infrastructure work.One reviewer estimated 3–4 months to build the modules and run evaluations.Source: V Example Idea: Autoprompting:… · pp. 81–82

W Example Idea: Temporal Dependency Unfolding: Improving Code Generation for Complex Stateful Systems · X Identities of Example Ideas

The example proposes a staged prompting method for generating code that handles temporal dependencies, alongside an experiment plan and mixed reviewer assessments; the following section reveals which example ideas were human-written or AI-generated.

W Example Idea: Temporal Dependency Unfolding: Improving Code Generation for Complex Stateful Systems

  • Temporal Dependency Unfolding generates code through state identification, temporal-graph construction, staged generation, consistency verification, and integration.It is motivated by the difficulty of handling state changes over time in larger systems, which current approaches often address only at the function or snippet level.Source: W Example Idea: Temporal Dependency… · p. 83
  • The proposed dataset covers 50 tasks each in multithreaded applications, game logic, and distributed systems, with GPT-4 direct-prompting and chain-of-thought baselines.The CoT baseline appends “Let’s approach this step-by-step:” to the task description.Source: W Example Idea: Temporal Dependency… · p. 83
  • Evaluation would measure test-case correctness, temporal consistency, code quality, runtime, and developer ratings, then compare methods and inspect step effectiveness, task patterns, and metric-human-rating correlations.Five experienced developers would rate a subset of 30 solutions for readability, maintainability, and temporal-dependency handling.Source: W Example Idea: Temporal Dependency… · p. 84
  • One reviewer rated the idea Novelty 6, Feasibility 6, Expected Effectiveness 6, Excitement 7, and Overall Score 7.This reviewer praised the temporal-graph construction and underexplored question, while noting dataset collection and human evaluation as challenges.Source: W Example Idea: Temporal Dependency… · p. 86
  • The reviewer rated Feasibility 3, Expected Effectiveness 5, Excitement 4, and Overall Score 4, citing low confidence in correctness and expecting limited improvement from prompting for temporal code generation.They described collecting repository-scale datasets and generating executable tests as difficult, time-consuming, and costly.Source: W Example Idea: Temporal Dependency… · p. 87
  • A third reviewer rated Novelty 10, Feasibility 5, Expected Effectiveness 6, Excitement 8, and Overall Score 9.The reviewer highlighted the novel problem and potential dataset, while calling for clearer evidence that each method step works in practice.Source: W Example Idea: Temporal Dependency… · p. 88

X Identities of Example Ideas

Y Attempt on Idea Execution Agent

The section examines an agent that turns project proposals into runnable Python implementations and evaluates them against baselines. It highlights the need to verify generated implementations and evaluation metrics, since apparent successes can be misleading.

Agent design

  • The template takes a project proposal and outputs executable Python; its evaluators check method-component implementation and prediction correctness.The proposed workflow includes task decomposition, sub-task information generation, result combination, and reflection and refinement.Source: Y Attempt on Idea Execution Agent · pp. 90–91

Results

  • Over half of generated code files executed successfully, but only a handful of methods outperformed baselines across 30 safety and 30 factuality ideas.The authors report that these results show some promise for automatically implementing and identifying effective methods, while cautioning that manual checks exposed pitfalls.Source: Y Attempt on Idea Execution Agent · p. 93, Table 18 · p. 93

Limitations

  • Manual inspection found that one malicious-prompt baseline was a trivial-to-beat keyword-based approach, undermining the apparent strength of its proposed method.The passage identifies this as an example of the pitfalls found in a careful manual check.Source: Y Attempt on Idea Execution Agent · pp. 93–94

Y Attempt on Idea Execution Agent

  • The example evaluator asked a model to judge whether a predicted sentiment classification matched the reference label.Its prompt requested a yes-or-no correctness judgment using the text, reference, and prediction.Source: Y Attempt on Idea Execution Agent · p. 94
  • The agent evaluated sentiment-classification accuracy but failed to evaluate adversarial-prompt detection, the task its method was intended to address.The proposed method was meant to inject adversarial triggers into sentiment data and test detection while maintaining sentiment accuracy.Source: Y Attempt on Idea Execution Agent · p. 94
  • The authors argue that generated code implementations need careful verification rather than blind trust in their executed results.They leave further attempts to verify the execution agent’s implementations to future work.Source: Y Attempt on Idea Execution Agent · p. 94

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