LLM-generated research ideas cluster more around bridge-like opportunities and synthesis methods than the broader distribution seen in human papers.
others (2024)
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
years
2026 8verdicts
UNVERDICTED 8representative citing papers
BREW uses block voting and window-shifting verification to reach TPR 0.965 and FPR 0.02 under 10% synonym substitution, addressing high false-positive issues in prior multi-bit LLM watermarking.
Luminol-AIDetect detects machine-generated text zero-shot by extracting perplexity-based features from an input and its shuffled version, using density estimation to exploit greater dispersion in MGT perplexity under shuffling.
IRM derives implicit reward signals from off-the-shelf LLMs to detect generated text zero-shot and reports better results than prior zero-shot and supervised detectors on the DetectRL benchmark.
SHARE models are the first causal LMs pretrained exclusively for SSH and match general models like Phi-4 on SSH texts despite using 100 times fewer tokens, paired with a non-generative MIRROR interface to support scholarly review.
WaveDetect reformulates machine-generated text detection as a time-frequency signal processing task by applying continuous wavelet transform to token probability sequences to reveal spectral fingerprints.
A distribution-free framework applies knockoff filtering to rewrite-based detectors to achieve finite-sample FDR control for human vs. LLM text detection.
LLMSniffer improves detection of LLM-generated code on GPTSniffer and Whodunit benchmarks by fine-tuning GraphCodeBERT via two-stage supervised contrastive learning plus preprocessing and MLP classification.
citing papers explorer
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Measuring the Gap Between Human and LLM Research Ideas
LLM-generated research ideas cluster more around bridge-like opportunities and synthesis methods than the broader distribution seen in human papers.
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Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking
BREW uses block voting and window-shifting verification to reach TPR 0.965 and FPR 0.02 under 10% synonym substitution, addressing high false-positive issues in prior multi-bit LLM watermarking.
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Luminol-AIDetect: Fast Zero-shot Machine-Generated Text Detection based on Perplexity under Text Shuffling
Luminol-AIDetect detects machine-generated text zero-shot by extracting perplexity-based features from an input and its shuffled version, using density estimation to exploit greater dispersion in MGT perplexity under shuffling.
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Zero-Shot Detection of LLM-Generated Text via Implicit Reward Model
IRM derives implicit reward signals from off-the-shelf LLMs to detect generated text zero-shot and reports better results than prior zero-shot and supervised detectors on the DetectRL benchmark.
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SHARE: Social-Humanities AI for Research and Education
SHARE models are the first causal LMs pretrained exclusively for SSH and match general models like Phi-4 on SSH texts despite using 100 times fewer tokens, paired with a non-generative MIRROR interface to support scholarly review.
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WaveDetect: Robust Framework for Machine-Generated Text Detection via Wavelet Transform
WaveDetect reformulates machine-generated text detection as a time-frequency signal processing task by applying continuous wavelet transform to token probability sequences to reveal spectral fingerprints.
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A Distribution-Free Framework for Rewrite-Based Human-text Detection via Knockoff Filtering
A distribution-free framework applies knockoff filtering to rewrite-based detectors to achieve finite-sample FDR control for human vs. LLM text detection.
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LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning
LLMSniffer improves detection of LLM-generated code on GPTSniffer and Whodunit benchmarks by fine-tuning GraphCodeBERT via two-stage supervised contrastive learning plus preprocessing and MLP classification.