Repository Analysis

Doragd/Algorithm-Practice-in-Industry

搜索、推荐、广告、用增等工业界实践文章收集(来源:知乎、Datafuntalk、技术公众号)

3.6 Likely human-written View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of Doragd/Algorithm-Practice-in-Industry, a HTML project with 4,515 GitHub stars. SynthScan v2.0 examined 1,807,242 lines of code across 601 source files, recording 3340 pattern matches distributed across 14 syntactic categories. The overall adjusted score of 3.6 places this repository in the Likely human-written band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

3.6
Adjusted Score
3.6
Raw Score
100%
Time Factor
2026-07-14
Last Push
4.5K
Stars
HTML
Language
1.8M
Lines of Code
601
Files
3.3K
Pattern Hits
2026-07-14
Scan Date
0.13
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 0HIGH 80MEDIUM 3177LOW 83

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 3340 distinct pattern matches across 14 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

AI Slop Vocabulary2312 hits · 4276 pts
SeverityFileLineSnippetContext
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html272 Sequential recommendation aims to predict users' next interaction with items by analyzing their CODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html272 Sequential recommendation aims to predict users' next interaction with items by analyzing their CODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html1042 Post-training quantization at the 2-bit level enables low-cost deployment and inference accelerationCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html1262 Evaluation remains a critical bottleneck for interactive agent development. Existing evaluation methCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html2060 Scientific discovery is often a collective process: researchers share partial results, inspect faileCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html2459 Multiple-choice (MCQA) benchmarks are the standard for evaluating pretrained large language models, CODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html6278 The deployment of Large Language Model (LLM) agents for computer automation is accelerating, yet theCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html7703 Most existing deep learning-based PET image denoising methods assume a fixed and known dose reductioCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html8102 While recent advancements in generative AI have substantially accelerated static 3D model creation wCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html8444 Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previouslCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html8444 Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previouslCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html9983 In abdominal CT imaging, developing a low-dose, no-reference image quality assessment (No-reference CODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html10154 While existing AI-generated image detectors report high performance, we identify that this is largelCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html10496 Current cross-view localization methods predominantly rely on satellite imagery as the aerial modaliCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html382 Shared-account usage is common on streaming and e-commerce platforms, where multiple users share oneCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html2526 Real-world systems must continuously adapt to novel concepts from limited data without forgetting prCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html3039 Multi-Layer Perceptron (MLP) models are the foundation of contemporary point cloud processing. HowevCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html3039 Multi-Layer Perceptron (MLP) models are the foundation of contemporary point cloud processing. HowevCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html3381 Open-set semantic mapping enables language-driven robotic perception, but current instance-centric aCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html3381 Open-set semantic mapping enables language-driven robotic perception, but current instance-centric aCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html3438 Transformer-based encoder-decoder networks have recently achieved impressive results in handwritten CODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html5946 Mapping is crucial in robotics for localization and downstream decision-making. As robots are deployCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html1620 Although learned representations underlie neural networks' success, their fundamental propertiesCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html2019 AI agents need to plan to achieve complex goals that involve orchestrating perception, sub-goal decoCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html2076 Reasoning in Large Language Models (LLMs) often suffers from inefficient long chain-of-thought traceCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html2076 Reasoning in Large Language Models (LLMs) often suffers from inefficient long chain-of-thought traceCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html2589 We introduce CAPA, a parameter-efficient test-time optimization framework that adapts pre-trained 3DCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html3900 Early diagnosis of Alzheimer's Disease (AD) is crucial for delaying its progression. While AI-baCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html4242 To understand and identify the unprecedented risks posed by rapidly advancing artificial intelligencCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html4413 High-fidelity generative video editing has seen significant quality improvements by leveraging pre-tCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html4812 Referring Image Segmentation (RIS) aims to segment a target object described by a natural language eCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html4869 Current meta-learning methods are constrained to narrow task distributions with fixed feature and laCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html2408 Agentic coding tools present new opportunities to transform research workflows. The performance of aCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html767 Recommendation systems help users recommend relevant items from a large collection of choices. PreseCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html767 Recommendation systems help users recommend relevant items from a large collection of choices. PreseCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html3206 Labels are critical for both training and evaluating deep learning segmentation models, but are ofteCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html3377 Recent multimodal large language models (MLLMs) have made remarkable progress on fine-grained percepCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html4574 Text-based evaluations of Theory of Mind (ToM) in Large Language Models (LLMs) often involve cognitiCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html5372 Autoregressive diffusion models have enabled high-quality video generation, yet their sequential natCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html5372 Autoregressive diffusion models have enabled high-quality video generation, yet their sequential natCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html5657 Referring Camouflaged Object Detection (Ref-COD) requires segmenting hidden targets guided by refereCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html5657 Referring Camouflaged Object Detection (Ref-COD) requires segmenting hidden targets guided by refereCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html7595 Transparent objects pose a fundamental challenge for depth estimation and 3D reconstruction due to tCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html8336 As mobile robots become more integrated into everyday human environments, social robot navigation isCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html8450 Existing Stochastic 3D Human Motion Prediction models are fundamentally constrained by hard-coding tCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html8963 The widespread adoption of social media platforms has transformed online communication by enabling uCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html9134 We used a large language model (GPT-4.1) to annotate the text of about 9,000 support conversations aCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html9533 Hairstyle transfer aims to synthesize a photorealistic portrait by transplanting the hairstyle from CODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html9590 Low-count Positron Emission Tomography (PET) reconstruction is severely hindered by the dissipative CODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html822 Personalization in social robots refers to the ability of the robot to meet the needs and/or prefereCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html877 In the era of explosive growth in academic literature, the burden of literature review on scholars aCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html1319 Despite the significant advancements represented by Vision-Language Models (VLMs), current architectCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html1319 Despite the significant advancements represented by Vision-Language Models (VLMs), current architectCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html1889 Diffusion Language Models (DLMs) have emerged as a compelling alternative to autoregressive approachCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html1946 Key Information Extraction (KIE) from visually-rich documents (VrDs) is a critical task, for which rCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html1946 Key Information Extraction (KIE) from visually-rich documents (VrDs) is a critical task, for which rCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html2573 Reviewer assignment is increasingly critical yet challenging in the LLM era, where rapid topic shiftCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html2858 Large Language Models (LLMs) have recently demonstrated remarkable reasoning abilities, yet hallucinCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html2858 Large Language Models (LLMs) have recently demonstrated remarkable reasoning abilities, yet hallucinCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html4283 Deep research has emerged as a transformative capability for autonomous agents, empowering Large LanCODE
2252 more matches not shown…
Modern AI Meta-Vocabulary841 hits · 1681 pts
SeverityFileLineSnippetContext
MEDIUMpapers/recsys/recsys2025.md41|[You Say Search, I Say Recs: A Scalable Agentic Approach to Query Understanding and Exploratory Search at Spotify](httpCODE
MEDIUMpapers/recsys/recsys2025.md86|[Multi-Agentic Recommender Systems: Foundations, Design Patterns, and E-Commerce Applications - An Industrial Tutorial]CODE
MEDIUMpapers/recsys/recsys2025.md189|[A Tutorial on Agentic LLM for Recommender Systems](https://doi.org/10.1145/3705328.3748007)|Chengkai Huang, Junda Wu, CODE
MEDIUMpapers/ecir/ecir2025.md163|[Towards Interpretable Radiology Report Generation via Concept Bottlenecks Using a Multi-agentic RAG](https://doi.org/1CODE
MEDIUMpapers/wsdm/wsdm2025.md13|[A Shopping Agent for Addressing Subjective Product Needs](https://doi.org/10.1145/3701551.3704124)|Preetam Prabhu SrikCODE
MEDIUM…perBotV2/conf_summary/data/papers/recsys/recsys2025.md39| 31 | | [You Say Search, I Say Recs: A Scalable Agentic Approach to Query Understanding and Exploratory Search at SpoCODE
MEDIUM…perBotV2/conf_summary/data/papers/recsys/recsys2025.md84| 76 | | [Multi-Agentic Recommender Systems: Foundations, Design Patterns, and E-Commerce Applications - An IndustrialCODE
MEDIUM…perBotV2/conf_summary/data/papers/recsys/recsys2025.md192| 184 | | [A Tutorial on Agentic LLM for Recommender Systems](https://doi.org/10.1145/3705328.3748007) | | 0 | RecentCODE
MEDIUMpaperBotV2/conf_summary/data/papers/kdd/kdd2022.md218| 210 | | [Real-Time Rideshare Driver Supply Values Using Online Reinforcement Learning](https://doi.org/10.1145/35346CODE
MEDIUMpaperBotV2/conf_summary/data/papers/naacl/naacl2025.md30| 22 | | [Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers](https://dCODE
MEDIUMpaperBotV2/conf_summary/data/papers/naacl/naacl2025.md131| 123 | | [SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters](https://doi.org/10.18653/v1/2025.naacl-lonCODE
MEDIUMpaperBotV2/conf_summary/data/papers/naacl/naacl2025.md150| 142 | | [AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Prediction](https://dCODE
MEDIUMpaperBotV2/conf_summary/data/papers/naacl/naacl2025.md216| 208 | | [SafeQuant: LLM Safety Analysis via Quantized Gradient Inspection](https://doi.org/10.18653/v1/2025.naacl-loCODE
MEDIUMpaperBotV2/conf_summary/data/papers/naacl/naacl2025.md278| 270 | | [CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models](https://doi.org/10.18653CODE
MEDIUMpaperBotV2/conf_summary/data/papers/naacl/naacl2025.md302| 294 | | [PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines](hCODE
MEDIUMpaperBotV2/conf_summary/data/papers/naacl/naacl2024.md293| 285 | | [GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives](https://doi.org/10.1CODE
MEDIUMpaperBotV2/conf_summary/data/papers/iclr/iclr2022.md416| 408 | | [Learning to Extend Molecular Scaffolds with Structural Motifs](https://openreview.net/forum?id=ZTsoE8G3GG) CODE
MEDIUMpaperBotV2/conf_summary/data/papers/wsdm/wsdm2025.md98| 90 | | [A Shopping Agent for Addressing Subjective Product Needs](https://doi.org/10.1145/3701551.3704124) | | 0 | CODE
MEDIUMpaperBotV2/conf_summary/data/papers/sigir/sigir2024.md383| 375 | | [Preventing and Detecting Misinformation Generated by Large Language Models](https://doi.org/10.1145/3626772CODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html712 As recommender systems transition toward agentic, multi-turn conversational interfaces, evaluation pCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html1042 Post-training quantization at the 2-bit level enables low-cost deployment and inference accelerationCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html1152 Does personalizing what a reader sees pay off, and where does it stop? Using a social web highlighteCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html3941 Speech carries more information than just words: a child's voice, a fearful tone, or a noisy bacCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html4397 Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals actCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html5879 Data tells stories that shape society; the data journalist's job is to turn raw information intoCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html5879 Data tells stories that shape society; the data journalist's job is to turn raw information intoCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html6278 The deployment of Large Language Model (LLM) agents for computer automation is accelerating, yet theCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260610.html7475 Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model perCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html492 Deep Research agents are rapidly emerging as primary consumers of modern retrieval systems. Unlike hCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html492 Deep Research agents are rapidly emerging as primary consumers of modern retrieval systems. Unlike hCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html1158 Large-scale Vision-Language Foundation Models (VLFMs), such as CLIP, now underpin a wide range of coCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html3381 Open-set semantic mapping enables language-driven robotic perception, but current instance-centric aCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260305.html4920 As mental health issues continue to rise globally, there is an increasing demand for accessible and CODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html993 This paper proposes a novel method for Text Style Transfer (TST) based on parameter-efficient fine-tCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html993 This paper proposes a novel method for Text Style Transfer (TST) based on parameter-efficient fine-tCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html1734 We present a domain-grounded framework and benchmark for tool-aware plan generation in contact centeCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html2190 This paper reformulates Transformer/Attention mechanisms in Large Language Models (LLMs) through meaCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html3045 Bio-pharmaceutical innovation has shifted: many new drug assets now originate outside the United StaCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html3045 Bio-pharmaceutical innovation has shifted: many new drug assets now originate outside the United StaCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html4641 Task-specialized models form the backbone of agentic healthcare systems, enabling the agents to answCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260217.html4869 Current meta-learning methods are constrained to narrow task distributions with fixed feature and laCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html2408 Agentic coding tools present new opportunities to transform research workflows. The performance of aCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html2408 Agentic coding tools present new opportunities to transform research workflows. The performance of aCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html1325 Legal information processing spans retrieval, entailment and judgment prediction problems, requiringCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html3719 We equipped an LLM-based search agent with access to a Boolean retrieval engine to search the MS MARCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html6341 When should an intelligent assistant speak up without being asked? Continuous egocentric video offerCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html8450 Existing Stochastic 3D Human Motion Prediction models are fundamentally constrained by hard-coding tCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260714.html8849 Large language models (LLMs) in financial applications fail most consequentially when they are confiCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html2573 Reviewer assignment is increasingly critical yet challenging in the LLM era, where rapid topic shiftCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html2858 Large Language Models (LLMs) have recently demonstrated remarkable reasoning abilities, yet hallucinCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260128.html2972 As LLMs increasingly act as autonomous agents in interactive and multi-agent settings, understandingCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20251225.html2921 Large language models(LLMs) excel at text generation and knowledge question-answering tasks, but theCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20251225.html3833 Zero-shot object navigation (ZSON) requires a robot to locate a target object in a previously unseenCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260313.html1553 The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained bCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260313.html1553 The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained bCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260313.html2864 Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such asCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260313.html2864 Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such asCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260313.html5885 Reinforcement learning (RL) has emerged as a promising paradigm for enhancing image editing and textCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260313.html6683 Longitudinal brain MRI is essential for characterizing the progression of neurological diseases suchCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260313.html7937 The acquisition of large-scale physical interaction data, a critical prerequisite for modern robot lCODE
781 more matches not shown…
Synthetic Comment Markers70 hits · 372 pts
SeverityFileLineSnippetContext
HIGHpapers/cikm/cikm2022.md457|[An Empirical Study on How People Perceive AI-generated Music](https://doi.org/10.1145/3511808.3557235)|Hyeshin Chu, JoCODE
HIGHpapers/kdd/kdd2025.md26|[Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Search](https://doi.org/1CODE
HIGHpapers/ecir/ecir2024.md7|[Overview of PAN 2024: Multi-author Writing Style Analysis, Multilingual Text Detoxification, Oppositional Thinking AnaCODE
HIGHpapers/wsdm/wsdm2024.md105|[Unlocking Human Curiosity](https://doi.org/10.1145/3616855.3637631)|Elizabeth Reid|Google Inc, Mountain View, CA 94043CODE
HIGHpaperBotV2/conf_summary/data/papers/www/www2025.md139| 131 | | [Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation](https://doi.org/1CODE
HIGHpaperBotV2/conf_summary/data/papers/www/www2025.md378| 370 | | [Supernotes: Driving Consensus in Crowd-Sourced Fact-Checking](https://doi.org/10.1145/3696410.3714934) | |CODE
HIGHpaperBotV2/conf_summary/data/papers/kdd/kdd2025.md19| 11 | | [Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Search](https:/CODE
HIGHpaperBotV2/conf_summary/data/papers/naacl/naacl2022.md122| 114 | | [An Exploration of Post-Editing Effectiveness in Text Summarization](https://doi.org/10.18653/v1/2022.naacl-CODE
HIGHpaperBotV2/conf_summary/data/papers/naacl/naacl2022.md134| 126 | | [Reframing Human-AI Collaboration for Generating Free-Text Explanations](https://doi.org/10.18653/v1/2022.naCODE
HIGHpaperBotV2/conf_summary/data/papers/naacl/naacl2025.md67| 59 | | [DART: An AIGT Detector using AMR of Rephrased Text](https://doi.org/10.18653/v1/2025.naacl-short.59) | | 0 CODE
HIGHpaperBotV2/conf_summary/data/papers/naacl/naacl2025.md87| 79 | | [MixRevDetect: Towards Detecting AI-Generated Content in Hybrid Peer Reviews](https://doi.org/10.18653/v1/202CODE
HIGHpaperBotV2/conf_summary/data/papers/naacl/naacl2025.md231| 223 | | [Have LLMs Reopened the Pandora's Box of AI-Generated Fake News?](https://doi.org/10.18653/v1/2025.naacl-lonCODE
HIGHpaperBotV2/conf_summary/data/papers/naacl/naacl2025.md535| 527 | | [Kill two birds with one stone: generalized and robust AI-generated text detection via dynamic perturbationsCODE
HIGHpaperBotV2/conf_summary/data/papers/naacl/naacl2024.md198| 190 | | [Ghostbuster: Detecting Text Ghostwritten by Large Language Models](https://doi.org/10.18653/v1/2024.naacl-lCODE
HIGHpaperBotV2/conf_summary/data/papers/ecir/ecir2024.md147| 139 | | [Overview of PAN 2024: Multi-author Writing Style Analysis, Multilingual Text Detoxification, Oppositional TCODE
HIGHpaperBotV2/conf_summary/data/papers/wsdm/wsdm2024.md108| 100 | | [Unlocking Human Curiosity](https://doi.org/10.1145/3616855.3637631) | | 0 | Human Curiosity has always beeCODE
HIGHpaperBotV2/conf_summary/data/papers/sigir/sigir2023.md440| 432 | | [SocialDial: A Benchmark for Socially-Aware Dialogue Systems](https://doi.org/10.1145/3539618.3591877) | | CODE
HIGHpaperBotV2/conf_summary/data/papers/sigir/sigir2024.md27| 19 | | [Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images](https://doi.org/10.1145/36CODE
HIGHpaperBotV2/conf_summary/data/papers/sigir/sigir2024.md201| 193 | | [Diffusion Models for Generative Outfit Recommendation](https://doi.org/10.1145/3626772.3657719) | | 0 | OuCODE
HIGHpaperBotV2/conf_summary/data/papers/sigir/sigir2024.md301| 293 | | [Label Hierarchical Structure-Aware Multi-Label Few-Shot Intent Detection via Prompt Tuning](https://doi.orgCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260610.html7589 AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assisCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260108.html8957 The rapid advancement of generative models has significantly enhanced the quality of AI-generated imCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20251204.html4071 A quarter century ago, Wikipedia's decentralized, crowdsourced, and consensus-driven model replaCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260430.html5423 Recent methods demonstrate that large-scale pretrained models, such as CLIP vision transformers, effCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260410.html3371 Recent research shows that greater numbers of people are turning to Large Language Models (LLMs) forCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20251121.html4853 UV unwrapping flattens 3D surfaces to 2D with minimal distortion, often requiring the complex surfacCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260129.html712 "Compression Tells Intelligence", is supported by research in artificial intelligence, partiCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260129.html5830 Recent multimodal large language models (MLLMs) have demonstrated strong capabilities in image qualiCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20251009.html2972 Generative Artificial Intelligence is reshaping online communication by enabling large-scale productCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260304.html4454 Generative artificial intelligence (AI) offers scalable support for formative feedback, yet most AI-CODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260122.html1221 In this paper, we present LookBench (We use the term "look" to reflect retrieval that mirrorCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260319.html4348 This paper describes the design, implementation, and evaluation of a browser extension that providesCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260211.html5708 The emerging paradigm of AI co-scientists focuses on tasks characterized by repeatable verification,CODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260211.html5708 The emerging paradigm of AI co-scientists focuses on tasks characterized by repeatable verification,CODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20251218.html3727 The rapid development of Generative AI is bringing innovative changes to education and assessment. ACODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20251218.html4639 The misuse of AI-driven video generation technologies has raised serious social concerns, highlightiCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260226.html2296 Effectively addressing client resistance is a sophisticated clinical skill in psychological counseliCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260318.html5480 As autonomous LLM-based agents increasingly populate social platforms, understanding the dynamics ofCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260318.html8729 Despite recent advances in deep generative modeling, skin lesion classification systems remain constCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20251107.html2298 The proliferation of AI-generated content has created an absurd communication theater where senders CODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20251111.html5822 Large language models (LLMs) are transforming the landscape of medicine, yet two fundamental challenCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260423.html3354 This paper presents the Duluth approach to SemEval-2026 Task 6 on CLARITY: Unmasking Political QuestCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260618.html5997 Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information. ACODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260618.html5997 Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information. ACODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260317.html4523 Indirectness is a common feature of daily communication, yet is underexplored in NLP research for boCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260707.html602 Public institutions increasingly use large language models (LLMs) to answer citizens' questions,CODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260603.html8045 While multimodal deep learning has advanced medical imaging analysis, existing black-box systems \teCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20251125.html3998 The rapid progress of GANs and Diffusion Models poses new challenges for detecting AI-generated imagCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260506.html1099 Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is wideCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260623.html6848 Some professional authors are beginning to use AI tools to help produce their fiction writing. Are rCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260604.html6646 'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic AsCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260203.html10154 Despite being trained on balanced datasets, existing AI-generated image detectors often exhibit systCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260612.html6563 The integration of Large Language Models (LLMs) and Multimodal LLMs (MLLMs) into scientific peer-revCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20251211.html3222 This article presents the creation of an Estonian-language dataset for document-level subjectivity, CODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20251010.html2516 The modern information environment (MIE) is increasingly complex, shaped by a wide range of techniquCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260130.html9812 Large multimodal models (LMMs) have demonstrated outstanding capabilities in various visual perceptiCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260424.html675 Learning robust representations of authorial style is crucial for authorship attribution and AI-geneCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260127.html5765 Generative Artificial Intelligence (GenAI) is rapidly becoming embedded in Saudi Arabia's digitaCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260408.html7646 Harmful content detectors-particularly disinformation classifiers-are predominantly developed and evCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20251119.html2638 Multi-label sentiment classification plays a vital role in natural language processing by detecting CODE
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Excessive Try-Catch Wrapping57 hits · 76 pts
SeverityFileLineSnippetContext
LOWlegacy/translate.py28 except Exception as e:CODE
LOWlegacy/arxiv.py129 except Exception as e:CODE
LOWlegacy/maintain.py184 except Exception as e:CODE
MEDIUMlegacy/conf.py65 print(f"Error occurred when requesting data from {URL}: {e}")CODE
MEDIUMlegacy/conf.py77 print(f"Error: Failed to save results to {filename}: {e}")CODE
MEDIUMlegacy/citer.py26 print(f"Error: Failed to save results to {filename}: {e}")CODE
LOWlegacy/citer.py61 except Exception as e:CODE
MEDIUMlegacy/citer.py62 print(f"Error: Failed to fill_citation for {paper_item['paper_url']}: {str(e)}")CODE
LOWlegacy/citer.py77 except Exception as e:CODE
MEDIUMlegacy/citer.py78 print(f"Error: Failed to update citation for {paper_item['paper_url']}: {str(e)}")CODE
LOWlegacy/citer.py93 except Exception as e:CODE
MEDIUMlegacy/citer.py94 print(f"Error: Failed to update results for {conf}: {str(e)}")CODE
LOWlegacy/citer.py108 except Exception as e:CODE
MEDIUMlegacy/citer.py109 print(f"Error: Failed to update citation for {paper_item['paper_url']}: {str(e)}")CODE
LOWlegacy/citer.py126 except Exception as e:CODE
MEDIUMlegacy/citer.py127 print(f"Error: Failed to update results for {conf}: {str(e)}")CODE
MEDIUMlegacy/citer.py14def load_results(filename='results.json'):CODE
LOWlegacy/crawler.py84 except Exception as e:CODE
MEDIUMlegacy/crawler.py85 print(f"Error occurred while searching paper info: {e}")CODE
MEDIUMlegacy/crawler.py113 print(f"Error: {e} at url: {url}")CODE
MEDIUMlegacy/crawler.py139 print(f"Error: Failed to save results to {filename}: {e}")CODE
LOWpaperBotV2/conf_summary/conf_daily.py156 except Exception as exc:CODE
LOWpaperBotV2/conf_summary/get_free_abstract.py30 except Exception as e:CODE
LOWpaperBotV2/conf_summary/get_free_abstract.py42 except Exception as e:CODE
LOWpaperBotV2/conf_summary/get_free_abstract.py155 except Exception as e:CODE
LOWpaperBotV2/conf_summary/get_free_abstract.py210 except Exception as e:CODE
LOWpaperBotV2/conf_summary/crawler.py89 except Exception as e:CODE
MEDIUMpaperBotV2/conf_summary/crawler.py90 print(f"Error occurred while searching paper info: {e}")CODE
MEDIUMpaperBotV2/conf_summary/crawler.py142 print(f"Error: Failed to fetch {url} with status code {response.status}. Attempt {retry_count+1}CODE
MEDIUMpaperBotV2/conf_summary/crawler.py144 print(f"Error fetching {url}: {str(e)}. Attempt {retry_count+1}/{max_retries+1}")CODE
LOWpaperBotV2/conf_summary/crawler.py147 except Exception as e:CODE
MEDIUMpaperBotV2/conf_summary/crawler.py211 print(f"Error: Failed to save results to {filename}: {e}")CODE
LOWpaperBotV2/industry_practice/maintain.py45 except Exception as e:CODE
LOWpaperBotV2/industry_practice/maintain.py139 except Exception as e:CODE
LOWpaperBotV2/industry_practice/maintain.py179 except Exception as e:CODE
LOWpaperBotV2/industry_practice/maintain.py297 except Exception as e:CODE
LOWpaperBotV2/industry_practice/maintain.py331 except Exception as e:CODE
LOWpaperBotV2/industry_practice/maintain.py359 except Exception as e:CODE
MEDIUMpaperBotV2/industry_practice/maintain.py51def parse_issue(issue):CODE
LOWpaperBotV2/industry_practice/generate_industry_html.py86 except Exception as e:CODE
LOWpaperBotV2/industry_practice/generate_industry_html.py126 except Exception as e:CODE
LOWpaperBotV2/industry_practice/generate_industry_html.py268 except Exception as e:CODE
LOWpaperBotV2/arxiv_daily/arxiv_feishu_msg.py34 except Exception as e:CODE
LOWpaperBotV2/arxiv_daily/arxiv_feishu_msg.py59 except Exception as e:CODE
LOWpaperBotV2/arxiv_daily/generate_arxiv_html.py123 except Exception as e:CODE
LOWpaperBotV2/arxiv_daily/generate_arxiv_html.py138 except Exception as e:CODE
LOWpaperBotV2/arxiv_daily/generate_arxiv_html.py189 except Exception as e:CODE
LOWpaperBotV2/arxiv_daily/generate_arxiv_html.py208 except Exception as e:CODE
LOWpaperBotV2/arxiv_daily/generate_arxiv_html.py997 except Exception as e:STRING
LOWpaperBotV2/arxiv_daily/arxiv.py104 except Exception as exc:CODE
LOWpaperBotV2/arxiv_daily/arxiv.py251 except Exception as e:CODE
LOWpaperBotV2/arxiv_daily/arxiv.py344 except Exception as exc:CODE
LOWpaperBotV2/arxiv_daily/arxiv.py411 except Exception as exc:CODE
LOWpaperBotV2/arxiv_daily/arxiv.py497 except Exception as e:CODE
LOWpaperBotV2/arxiv_daily/arxiv.py519 except Exception as e:CODE
LOWpaperBotV2/arxiv_daily/arxiv.py651 except Exception as e:CODE
LOWpaperBotV2/arxiv_daily/arxiv.py692 except Exception as exc:CODE
AI Response Leakage6 hits · 32 pts
SeverityFileLineSnippetContext
HIGHpapers/wsdm/wsdm2025.md58|[Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation](https://doi.org/10.1145/37CODE
HIGHpaperBotV2/conf_summary/data/papers/www/www2025.md95| 87 | | [MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification](https://doi.org/10.1145/369641CODE
HIGHpaperBotV2/conf_summary/data/papers/emnlp/emnlp2020.md910| 902 | | [Seq2Edits: Sequence Transduction Using Span-level Edit Operations](https://doi.org/10.18653/v1/2020.emnlp-mCODE
HIGHpaperBotV2/conf_summary/data/papers/wsdm/wsdm2025.md53| 45 | | [Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation](https://doi.org/CODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260305.html987 The goal of Open-Vocabulary Compositional Zero-Shot Learning (OV-CZSL) is to recognize attribute-objCODE
HIGHpaperBotV2/arxiv_daily/output/arxiv_20260113.html2459 Retrieval-Augmented Generation (RAG) systems are usually defined by the combination of a generator aCODE
Deep Nesting20 hits · 20 pts
SeverityFileLineSnippetContext
LOWlegacy/conf.py97CODE
LOWlegacy/citer.py99CODE
LOWpaperBotV2/conf_summary/convert_to_md.py88CODE
LOWpaperBotV2/conf_summary/conf_daily.py201CODE
LOWpaperBotV2/conf_summary/update_readme_papers.py9CODE
LOWpaperBotV2/conf_summary/get_free_abstract.py52CODE
LOWpaperBotV2/conf_summary/get_free_abstract.py120CODE
LOWpaperBotV2/conf_summary/get_free_abstract.py177CODE
LOWpaperBotV2/conf_summary/get_free_abstract.py284CODE
LOWpaperBotV2/conf_summary/crawler.py94CODE
LOWpaperBotV2/conf_summary/crawler.py219CODE
LOWpaperBotV2/industry_practice/maintain.py62CODE
LOWpaperBotV2/industry_practice/maintain.py145CODE
LOWpaperBotV2/industry_practice/generate_industry_html.py92CODE
LOWpaperBotV2/arxiv_daily/generate_arxiv_html.py350CODE
LOWpaperBotV2/arxiv_daily/generate_arxiv_html.py748CODE
LOWpaperBotV2/arxiv_daily/arxiv.py157CODE
LOWpaperBotV2/arxiv_daily/arxiv.py329CODE
LOWpaperBotV2/arxiv_daily/arxiv.py396CODE
LOWpaperBotV2/arxiv_daily/arxiv.py500CODE
Cross-Language Confusion4 hits · 20 pts
SeverityFileLineSnippetContext
HIGHpaperBotV2/arxiv_daily/generate_arxiv_html.py817 if (savedDate === currentPageDate && savedYear && savedMonth) {CODE
HIGHpaperBotV2/arxiv_daily/generate_arxiv_html.py925 localStorage.setItem('selectedYear', displayYear.toString());CODE
HIGHpaperBotV2/arxiv_daily/generate_arxiv_html.py926 localStorage.setItem('selectedMonth', displayMonth.toString());CODE
HIGHpaperBotV2/arxiv_daily/generate_arxiv_html.py955 if (hasPapers && currentDateObj.getTime() !== today.getTime()) {CODE
Slop Phrases8 hits · 16 pts
SeverityFileLineSnippetContext
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260206.html3770 DARWIN is an evolutionary GPT model, utilizing a genetic-algorithm like optimization structure with CODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20260204.html3169 GFlowPO: Generative Flow Network as a Language Model Prompt OptimizerCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20251125.html437 Recent advances in Large Language Models (LLMs) have opened new avenues for sequential recommendatioCODE
MEDIUMpaperBotV2/arxiv_daily/output/arxiv_20251114.html1376 Despite the remarkable success of the LLaVA architecture for vision-language tasks, its design inherCODE
MEDIUMpaperBotV2/arxiv_daily/data/20260206.json587 "ori_summary": "DARWIN is an evolutionary GPT model, utilizing a genetic-algorithm like optimization structure with CODE
MEDIUMpaperBotV2/arxiv_daily/data/20251114.json1692 "ori_summary": "Despite the remarkable success of the LLaVA architecture for vision-language tasks, its design inherCODE
MEDIUMpaperBotV2/arxiv_daily/data/20251125.json128 "ori_summary": "Recent advances in Large Language Models (LLMs) have opened new avenues for sequential recommendatioCODE
MEDIUMpaperBotV2/arxiv_daily/data/20260204.json1142 "title": "GFlowPO: Generative Flow Network as a Language Model Prompt Optimizer",CODE
Unused Imports7 hits · 7 pts
SeverityFileLineSnippetContext
LOWlegacy/update.py1CODE
LOWlegacy/update.py6CODE
LOWlegacy/conf.py7CODE
LOWpaperBotV2/conf_summary/crawler.py10CODE
LOWpaperBotV2/industry_practice/generate_industry_html.py10CODE
LOWpaperBotV2/arxiv_daily/__init__.py4CODE
LOWpaperBotV2/arxiv_daily/__init__.py4CODE
Hyper-Verbose Identifiers7 hits · 7 pts
SeverityFileLineSnippetContext
LOWpaperBotV2/conf_summary/convert_to_md.py57def calculate_optimal_abstract_length(papers_count):CODE
LOWpaperBotV2/conf_summary/update_readme_papers.py9def get_all_meetings_and_years(papers_dir):CODE
LOWpaperBotV2/conf_summary/get_free_abstract.py52def get_papers_with_empty_abstracts(results_data: dict, conf_pattern_func=None, max_papers=None) -> list:CODE
LOWpaperBotV2/industry_practice/maintain.py336def update_industry_practice_page():CODE
LOWpaperBotV2/arxiv_daily/arxiv.py329def rough_analyze_papers_cocurrent(results, max_workers=10):CODE
LOWpaperBotV2/arxiv_daily/arxiv.py396def fine_analyze_papers_cocurrent(papers, max_workers=10):CODE
LOWpaperBotV2/arxiv_daily/arxiv.py500def get_papers_from_all_categories(run_status=None):CODE
Over-Commented Block3 hits · 3 pts
SeverityFileLineSnippetContext
LOWlegacy/maintain.py101 }]COMMENT
LOWpaperBotV2/conf_summary/process_papers_loop.sh1#!/bin/bashCOMMENT
LOWpaperBotV2/industry_practice/maintain.py241 "tag": "text",COMMENT
AI Structural Patterns2 hits · 2 pts
SeverityFileLineSnippetContext
LOWlegacy/translate.py50CODE
LOWpaperBotV2/conf_summary/crawler.py219CODE
Fake / Example Data2 hits · 2 pts
SeverityFileLineSnippetContext
LOWpaperBotV2/arxiv_daily/output/arxiv_20260508.html7589 Reinforcement learning with verifiable rewards, particularly Group Relative Policy Optimization (GRPCODE
LOWpaperBotV2/arxiv_daily/data/20260508.json1862 "ori_summary": "Reinforcement learning with verifiable rewards, particularly Group Relative Policy Optimization (GRPCODE
Modern Structural Boilerplate1 hit · 1 pts
SeverityFileLineSnippetContext
LOWpaperBotV2/arxiv_daily/__init__.py6__all__ = ['PRERANK_PROMPT', 'FINERANK_PROMPT']CODE