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)
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).
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.
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.
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.
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.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 272 | Sequential recommendation aims to predict users' next interaction with items by analyzing their | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 272 | Sequential recommendation aims to predict users' next interaction with items by analyzing their | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 1042 | Post-training quantization at the 2-bit level enables low-cost deployment and inference acceleration | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 1262 | Evaluation remains a critical bottleneck for interactive agent development. Existing evaluation meth | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 2060 | Scientific discovery is often a collective process: researchers share partial results, inspect faile | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 2459 | Multiple-choice (MCQA) benchmarks are the standard for evaluating pretrained large language models, | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 6278 | The deployment of Large Language Model (LLM) agents for computer automation is accelerating, yet the | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 7703 | Most existing deep learning-based PET image denoising methods assume a fixed and known dose reductio | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 8102 | While recent advancements in generative AI have substantially accelerated static 3D model creation w | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 8444 | Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previousl | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 8444 | Class-Incremental Learning (CIL) aims to continuously learn new classes without forgetting previousl | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 9983 | In abdominal CT imaging, developing a low-dose, no-reference image quality assessment (No-reference | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 10154 | While existing AI-generated image detectors report high performance, we identify that this is largel | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 10496 | Current cross-view localization methods predominantly rely on satellite imagery as the aerial modali | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 382 | Shared-account usage is common on streaming and e-commerce platforms, where multiple users share one | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 2526 | Real-world systems must continuously adapt to novel concepts from limited data without forgetting pr | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 3039 | Multi-Layer Perceptron (MLP) models are the foundation of contemporary point cloud processing. Howev | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 3039 | Multi-Layer Perceptron (MLP) models are the foundation of contemporary point cloud processing. Howev | CODE |
| MEDIUM⚡ | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 3381 | Open-set semantic mapping enables language-driven robotic perception, but current instance-centric a | CODE |
| MEDIUM⚡ | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 3381 | Open-set semantic mapping enables language-driven robotic perception, but current instance-centric a | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 3438 | Transformer-based encoder-decoder networks have recently achieved impressive results in handwritten | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 5946 | Mapping is crucial in robotics for localization and downstream decision-making. As robots are deploy | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 1620 | Although learned representations underlie neural networks' success, their fundamental properties | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 2019 | AI agents need to plan to achieve complex goals that involve orchestrating perception, sub-goal deco | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 2076 | Reasoning in Large Language Models (LLMs) often suffers from inefficient long chain-of-thought trace | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 2076 | Reasoning in Large Language Models (LLMs) often suffers from inefficient long chain-of-thought trace | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 2589 | We introduce CAPA, a parameter-efficient test-time optimization framework that adapts pre-trained 3D | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 3900 | Early diagnosis of Alzheimer's Disease (AD) is crucial for delaying its progression. While AI-ba | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 4242 | To understand and identify the unprecedented risks posed by rapidly advancing artificial intelligenc | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 4413 | High-fidelity generative video editing has seen significant quality improvements by leveraging pre-t | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 4812 | Referring Image Segmentation (RIS) aims to segment a target object described by a natural language e | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 4869 | Current meta-learning methods are constrained to narrow task distributions with fixed feature and la | CODE |
| MEDIUM⚡ | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 2408 | Agentic coding tools present new opportunities to transform research workflows. The performance of a | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 767 | Recommendation systems help users recommend relevant items from a large collection of choices. Prese | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 767 | Recommendation systems help users recommend relevant items from a large collection of choices. Prese | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 3206 | Labels are critical for both training and evaluating deep learning segmentation models, but are ofte | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 3377 | Recent multimodal large language models (MLLMs) have made remarkable progress on fine-grained percep | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 4574 | Text-based evaluations of Theory of Mind (ToM) in Large Language Models (LLMs) often involve cogniti | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 5372 | Autoregressive diffusion models have enabled high-quality video generation, yet their sequential nat | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 5372 | Autoregressive diffusion models have enabled high-quality video generation, yet their sequential nat | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 5657 | Referring Camouflaged Object Detection (Ref-COD) requires segmenting hidden targets guided by refere | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 5657 | Referring Camouflaged Object Detection (Ref-COD) requires segmenting hidden targets guided by refere | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 7595 | Transparent objects pose a fundamental challenge for depth estimation and 3D reconstruction due to t | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 8336 | As mobile robots become more integrated into everyday human environments, social robot navigation is | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 8450 | Existing Stochastic 3D Human Motion Prediction models are fundamentally constrained by hard-coding t | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 8963 | The widespread adoption of social media platforms has transformed online communication by enabling u | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 9134 | We used a large language model (GPT-4.1) to annotate the text of about 9,000 support conversations a | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 9533 | Hairstyle transfer aims to synthesize a photorealistic portrait by transplanting the hairstyle from | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 9590 | Low-count Positron Emission Tomography (PET) reconstruction is severely hindered by the dissipative | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 822 | Personalization in social robots refers to the ability of the robot to meet the needs and/or prefere | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 877 | In the era of explosive growth in academic literature, the burden of literature review on scholars a | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 1319 | Despite the significant advancements represented by Vision-Language Models (VLMs), current architect | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 1319 | Despite the significant advancements represented by Vision-Language Models (VLMs), current architect | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 1889 | Diffusion Language Models (DLMs) have emerged as a compelling alternative to autoregressive approach | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 1946 | Key Information Extraction (KIE) from visually-rich documents (VrDs) is a critical task, for which r | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 1946 | Key Information Extraction (KIE) from visually-rich documents (VrDs) is a critical task, for which r | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 2573 | Reviewer assignment is increasingly critical yet challenging in the LLM era, where rapid topic shift | CODE |
| MEDIUM⚡ | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 2858 | Large Language Models (LLMs) have recently demonstrated remarkable reasoning abilities, yet hallucin | CODE |
| MEDIUM⚡ | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 2858 | Large Language Models (LLMs) have recently demonstrated remarkable reasoning abilities, yet hallucin | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 4283 | Deep research has emerged as a transformative capability for autonomous agents, empowering Large Lan | CODE |
| 2252 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | papers/recsys/recsys2025.md | 41 | |[You Say Search, I Say Recs: A Scalable Agentic Approach to Query Understanding and Exploratory Search at Spotify](http | CODE |
| MEDIUM | papers/recsys/recsys2025.md | 86 | |[Multi-Agentic Recommender Systems: Foundations, Design Patterns, and E-Commerce Applications - An Industrial Tutorial] | CODE |
| MEDIUM | papers/recsys/recsys2025.md | 189 | |[A Tutorial on Agentic LLM for Recommender Systems](https://doi.org/10.1145/3705328.3748007)|Chengkai Huang, Junda Wu, | CODE |
| MEDIUM | papers/ecir/ecir2025.md | 163 | |[Towards Interpretable Radiology Report Generation via Concept Bottlenecks Using a Multi-agentic RAG](https://doi.org/1 | CODE |
| MEDIUM | papers/wsdm/wsdm2025.md | 13 | |[A Shopping Agent for Addressing Subjective Product Needs](https://doi.org/10.1145/3701551.3704124)|Preetam Prabhu Srik | CODE |
| MEDIUM | …perBotV2/conf_summary/data/papers/recsys/recsys2025.md | 39 | | 31 | | [You Say Search, I Say Recs: A Scalable Agentic Approach to Query Understanding and Exploratory Search at Spo | CODE |
| MEDIUM | …perBotV2/conf_summary/data/papers/recsys/recsys2025.md | 84 | | 76 | | [Multi-Agentic Recommender Systems: Foundations, Design Patterns, and E-Commerce Applications - An Industrial | CODE |
| MEDIUM | …perBotV2/conf_summary/data/papers/recsys/recsys2025.md | 192 | | 184 | | [A Tutorial on Agentic LLM for Recommender Systems](https://doi.org/10.1145/3705328.3748007) | | 0 | Recent | CODE |
| MEDIUM | paperBotV2/conf_summary/data/papers/kdd/kdd2022.md | 218 | | 210 | | [Real-Time Rideshare Driver Supply Values Using Online Reinforcement Learning](https://doi.org/10.1145/35346 | CODE |
| MEDIUM | paperBotV2/conf_summary/data/papers/naacl/naacl2025.md | 30 | | 22 | | [Watching the AI Watchdogs: A Fairness and Robustness Analysis of AI Safety Moderation Classifiers](https://d | CODE |
| MEDIUM | paperBotV2/conf_summary/data/papers/naacl/naacl2025.md | 131 | | 123 | | [SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters](https://doi.org/10.18653/v1/2025.naacl-lon | CODE |
| MEDIUM | paperBotV2/conf_summary/data/papers/naacl/naacl2025.md | 150 | | 142 | | [AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Prediction](https://d | CODE |
| MEDIUM | paperBotV2/conf_summary/data/papers/naacl/naacl2025.md | 216 | | 208 | | [SafeQuant: LLM Safety Analysis via Quantized Gradient Inspection](https://doi.org/10.18653/v1/2025.naacl-lo | CODE |
| MEDIUM | paperBotV2/conf_summary/data/papers/naacl/naacl2025.md | 278 | | 270 | | [CodeTree: Agent-guided Tree Search for Code Generation with Large Language Models](https://doi.org/10.18653 | CODE |
| MEDIUM | paperBotV2/conf_summary/data/papers/naacl/naacl2025.md | 302 | | 294 | | [PROMPTEVALS: A Dataset of Assertions and Guardrails for Custom Production Large Language Model Pipelines](h | CODE |
| MEDIUM | paperBotV2/conf_summary/data/papers/naacl/naacl2024.md | 293 | | 285 | | [GRASP: A Disagreement Analysis Framework to Assess Group Associations in Perspectives](https://doi.org/10.1 | CODE |
| MEDIUM | paperBotV2/conf_summary/data/papers/iclr/iclr2022.md | 416 | | 408 | | [Learning to Extend Molecular Scaffolds with Structural Motifs](https://openreview.net/forum?id=ZTsoE8G3GG) | CODE |
| MEDIUM | paperBotV2/conf_summary/data/papers/wsdm/wsdm2025.md | 98 | | 90 | | [A Shopping Agent for Addressing Subjective Product Needs](https://doi.org/10.1145/3701551.3704124) | | 0 | | CODE |
| MEDIUM | paperBotV2/conf_summary/data/papers/sigir/sigir2024.md | 383 | | 375 | | [Preventing and Detecting Misinformation Generated by Large Language Models](https://doi.org/10.1145/3626772 | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 712 | As recommender systems transition toward agentic, multi-turn conversational interfaces, evaluation p | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 1042 | Post-training quantization at the 2-bit level enables low-cost deployment and inference acceleration | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 1152 | Does personalizing what a reader sees pay off, and where does it stop? Using a social web highlighte | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 3941 | Speech carries more information than just words: a child's voice, a fearful tone, or a noisy bac | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 4397 | Multimodal Large Language Models (MLLMs) can listen and see, but how do audio and visual signals act | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 5879 | Data tells stories that shape society; the data journalist's job is to turn raw information into | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 5879 | Data tells stories that shape society; the data journalist's job is to turn raw information into | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 6278 | The deployment of Large Language Model (LLM) agents for computer automation is accelerating, yet the | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 7475 | Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model per | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 492 | Deep Research agents are rapidly emerging as primary consumers of modern retrieval systems. Unlike h | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 492 | Deep Research agents are rapidly emerging as primary consumers of modern retrieval systems. Unlike h | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 1158 | Large-scale Vision-Language Foundation Models (VLFMs), such as CLIP, now underpin a wide range of co | CODE |
| MEDIUM⚡ | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 3381 | Open-set semantic mapping enables language-driven robotic perception, but current instance-centric a | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 4920 | As mental health issues continue to rise globally, there is an increasing demand for accessible and | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 993 | This paper proposes a novel method for Text Style Transfer (TST) based on parameter-efficient fine-t | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 993 | This paper proposes a novel method for Text Style Transfer (TST) based on parameter-efficient fine-t | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 1734 | We present a domain-grounded framework and benchmark for tool-aware plan generation in contact cente | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 2190 | This paper reformulates Transformer/Attention mechanisms in Large Language Models (LLMs) through mea | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 3045 | Bio-pharmaceutical innovation has shifted: many new drug assets now originate outside the United Sta | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 3045 | Bio-pharmaceutical innovation has shifted: many new drug assets now originate outside the United Sta | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 4641 | Task-specialized models form the backbone of agentic healthcare systems, enabling the agents to answ | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260217.html | 4869 | Current meta-learning methods are constrained to narrow task distributions with fixed feature and la | CODE |
| MEDIUM⚡ | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 2408 | Agentic coding tools present new opportunities to transform research workflows. The performance of a | CODE |
| MEDIUM⚡ | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 2408 | Agentic coding tools present new opportunities to transform research workflows. The performance of a | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 1325 | Legal information processing spans retrieval, entailment and judgment prediction problems, requiring | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 3719 | We equipped an LLM-based search agent with access to a Boolean retrieval engine to search the MS MAR | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 6341 | When should an intelligent assistant speak up without being asked? Continuous egocentric video offer | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 8450 | Existing Stochastic 3D Human Motion Prediction models are fundamentally constrained by hard-coding t | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260714.html | 8849 | Large language models (LLMs) in financial applications fail most consequentially when they are confi | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 2573 | Reviewer assignment is increasingly critical yet challenging in the LLM era, where rapid topic shift | CODE |
| MEDIUM⚡ | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 2858 | Large Language Models (LLMs) have recently demonstrated remarkable reasoning abilities, yet hallucin | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260128.html | 2972 | As LLMs increasingly act as autonomous agents in interactive and multi-agent settings, understanding | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20251225.html | 2921 | Large language models(LLMs) excel at text generation and knowledge question-answering tasks, but the | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20251225.html | 3833 | Zero-shot object navigation (ZSON) requires a robot to locate a target object in a previously unseen | CODE |
| MEDIUM⚡ | paperBotV2/arxiv_daily/output/arxiv_20260313.html | 1553 | The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained b | CODE |
| MEDIUM⚡ | paperBotV2/arxiv_daily/output/arxiv_20260313.html | 1553 | The effectiveness upper bound of retrieval-augmented generation (RAG) is fundamentally constrained b | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260313.html | 2864 | Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such as | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260313.html | 2864 | Multimodal Large Language Models (MLLMs) are increasingly used to carry out visual workflows such as | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260313.html | 5885 | Reinforcement learning (RL) has emerged as a promising paradigm for enhancing image editing and text | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260313.html | 6683 | Longitudinal brain MRI is essential for characterizing the progression of neurological diseases such | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260313.html | 7937 | The acquisition of large-scale physical interaction data, a critical prerequisite for modern robot l | CODE |
| 781 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | papers/cikm/cikm2022.md | 457 | |[An Empirical Study on How People Perceive AI-generated Music](https://doi.org/10.1145/3511808.3557235)|Hyeshin Chu, Jo | CODE |
| HIGH | papers/kdd/kdd2025.md | 26 | |[Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Search](https://doi.org/1 | CODE |
| HIGH | papers/ecir/ecir2024.md | 7 | |[Overview of PAN 2024: Multi-author Writing Style Analysis, Multilingual Text Detoxification, Oppositional Thinking Ana | CODE |
| HIGH | papers/wsdm/wsdm2024.md | 105 | |[Unlocking Human Curiosity](https://doi.org/10.1145/3616855.3637631)|Elizabeth Reid|Google Inc, Mountain View, CA 94043 | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/www/www2025.md | 139 | | 131 | | [Contextualized Counterspeech: Strategies for Adaptation, Personalization, and Evaluation](https://doi.org/1 | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/www/www2025.md | 378 | | 370 | | [Supernotes: Driving Consensus in Crowd-Sourced Fact-Checking](https://doi.org/10.1145/3696410.3714934) | | | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/kdd/kdd2025.md | 19 | | 11 | | [Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Search](https:/ | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/naacl/naacl2022.md | 122 | | 114 | | [An Exploration of Post-Editing Effectiveness in Text Summarization](https://doi.org/10.18653/v1/2022.naacl- | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/naacl/naacl2022.md | 134 | | 126 | | [Reframing Human-AI Collaboration for Generating Free-Text Explanations](https://doi.org/10.18653/v1/2022.na | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/naacl/naacl2025.md | 67 | | 59 | | [DART: An AIGT Detector using AMR of Rephrased Text](https://doi.org/10.18653/v1/2025.naacl-short.59) | | 0 | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/naacl/naacl2025.md | 87 | | 79 | | [MixRevDetect: Towards Detecting AI-Generated Content in Hybrid Peer Reviews](https://doi.org/10.18653/v1/202 | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/naacl/naacl2025.md | 231 | | 223 | | [Have LLMs Reopened the Pandora's Box of AI-Generated Fake News?](https://doi.org/10.18653/v1/2025.naacl-lon | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/naacl/naacl2025.md | 535 | | 527 | | [Kill two birds with one stone: generalized and robust AI-generated text detection via dynamic perturbations | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/naacl/naacl2024.md | 198 | | 190 | | [Ghostbuster: Detecting Text Ghostwritten by Large Language Models](https://doi.org/10.18653/v1/2024.naacl-l | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/ecir/ecir2024.md | 147 | | 139 | | [Overview of PAN 2024: Multi-author Writing Style Analysis, Multilingual Text Detoxification, Oppositional T | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/wsdm/wsdm2024.md | 108 | | 100 | | [Unlocking Human Curiosity](https://doi.org/10.1145/3616855.3637631) | | 0 | Human Curiosity has always bee | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/sigir/sigir2023.md | 440 | | 432 | | [SocialDial: A Benchmark for Socially-Aware Dialogue Systems](https://doi.org/10.1145/3539618.3591877) | | | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/sigir/sigir2024.md | 27 | | 19 | | [Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images](https://doi.org/10.1145/36 | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/sigir/sigir2024.md | 201 | | 193 | | [Diffusion Models for Generative Outfit Recommendation](https://doi.org/10.1145/3626772.3657719) | | 0 | Ou | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/sigir/sigir2024.md | 301 | | 293 | | [Label Hierarchical Structure-Aware Multi-Label Few-Shot Intent Detection via Prompt Tuning](https://doi.org | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260610.html | 7589 | AI is increasingly used to support scientific peer review, from manuscript screening, reviewer assis | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260108.html | 8957 | The rapid advancement of generative models has significantly enhanced the quality of AI-generated im | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20251204.html | 4071 | A quarter century ago, Wikipedia's decentralized, crowdsourced, and consensus-driven model repla | CODE |
| HIGH⚡ | paperBotV2/arxiv_daily/output/arxiv_20260430.html | 5423 | Recent methods demonstrate that large-scale pretrained models, such as CLIP vision transformers, eff | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260410.html | 3371 | Recent research shows that greater numbers of people are turning to Large Language Models (LLMs) for | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20251121.html | 4853 | UV unwrapping flattens 3D surfaces to 2D with minimal distortion, often requiring the complex surfac | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260129.html | 712 | "Compression Tells Intelligence", is supported by research in artificial intelligence, parti | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260129.html | 5830 | Recent multimodal large language models (MLLMs) have demonstrated strong capabilities in image quali | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20251009.html | 2972 | Generative Artificial Intelligence is reshaping online communication by enabling large-scale product | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260304.html | 4454 | Generative artificial intelligence (AI) offers scalable support for formative feedback, yet most AI- | CODE |
| HIGH⚡ | paperBotV2/arxiv_daily/output/arxiv_20260122.html | 1221 | In this paper, we present LookBench (We use the term "look" to reflect retrieval that mirror | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260319.html | 4348 | This paper describes the design, implementation, and evaluation of a browser extension that provides | CODE |
| HIGH⚡ | paperBotV2/arxiv_daily/output/arxiv_20260211.html | 5708 | The emerging paradigm of AI co-scientists focuses on tasks characterized by repeatable verification, | CODE |
| HIGH⚡ | paperBotV2/arxiv_daily/output/arxiv_20260211.html | 5708 | The emerging paradigm of AI co-scientists focuses on tasks characterized by repeatable verification, | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20251218.html | 3727 | The rapid development of Generative AI is bringing innovative changes to education and assessment. A | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20251218.html | 4639 | The misuse of AI-driven video generation technologies has raised serious social concerns, highlighti | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260226.html | 2296 | Effectively addressing client resistance is a sophisticated clinical skill in psychological counseli | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260318.html | 5480 | As autonomous LLM-based agents increasingly populate social platforms, understanding the dynamics of | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260318.html | 8729 | Despite recent advances in deep generative modeling, skin lesion classification systems remain const | CODE |
| HIGH⚡ | paperBotV2/arxiv_daily/output/arxiv_20251107.html | 2298 | The proliferation of AI-generated content has created an absurd communication theater where senders | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20251111.html | 5822 | Large language models (LLMs) are transforming the landscape of medicine, yet two fundamental challen | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260423.html | 3354 | This paper presents the Duluth approach to SemEval-2026 Task 6 on CLARITY: Unmasking Political Quest | CODE |
| HIGH⚡ | paperBotV2/arxiv_daily/output/arxiv_20260618.html | 5997 | Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information. A | CODE |
| HIGH⚡ | paperBotV2/arxiv_daily/output/arxiv_20260618.html | 5997 | Text-rich images often contain privacy-sensitive, transactional, or decision-relevant information. A | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260317.html | 4523 | Indirectness is a common feature of daily communication, yet is underexplored in NLP research for bo | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260707.html | 602 | Public institutions increasingly use large language models (LLMs) to answer citizens' questions, | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260603.html | 8045 | While multimodal deep learning has advanced medical imaging analysis, existing black-box systems \te | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20251125.html | 3998 | The rapid progress of GANs and Diffusion Models poses new challenges for detecting AI-generated imag | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260506.html | 1099 | Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is wide | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260623.html | 6848 | Some professional authors are beginning to use AI tools to help produce their fiction writing. Are r | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260604.html | 6646 | 'Your AI Text is not Mine': Redefining and Evaluating AI-generated Text Detection under Realistic As | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260203.html | 10154 | Despite being trained on balanced datasets, existing AI-generated image detectors often exhibit syst | CODE |
| HIGH⚡ | paperBotV2/arxiv_daily/output/arxiv_20260612.html | 6563 | The integration of Large Language Models (LLMs) and Multimodal LLMs (MLLMs) into scientific peer-rev | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20251211.html | 3222 | This article presents the creation of an Estonian-language dataset for document-level subjectivity, | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20251010.html | 2516 | The modern information environment (MIE) is increasingly complex, shaped by a wide range of techniqu | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260130.html | 9812 | Large multimodal models (LMMs) have demonstrated outstanding capabilities in various visual percepti | CODE |
| HIGH⚡ | paperBotV2/arxiv_daily/output/arxiv_20260424.html | 675 | Learning robust representations of authorial style is crucial for authorship attribution and AI-gene | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260127.html | 5765 | Generative Artificial Intelligence (GenAI) is rapidly becoming embedded in Saudi Arabia's digita | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260408.html | 7646 | Harmful content detectors-particularly disinformation classifiers-are predominantly developed and ev | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20251119.html | 2638 | Multi-label sentiment classification plays a vital role in natural language processing by detecting | CODE |
| 10 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | legacy/translate.py | 28 | except Exception as e: | CODE |
| LOW | legacy/arxiv.py | 129 | except Exception as e: | CODE |
| LOW | legacy/maintain.py | 184 | except Exception as e: | CODE |
| MEDIUM | legacy/conf.py | 65 | print(f"Error occurred when requesting data from {URL}: {e}") | CODE |
| MEDIUM | legacy/conf.py | 77 | print(f"Error: Failed to save results to {filename}: {e}") | CODE |
| MEDIUM | legacy/citer.py | 26 | print(f"Error: Failed to save results to {filename}: {e}") | CODE |
| LOW | legacy/citer.py | 61 | except Exception as e: | CODE |
| MEDIUM | legacy/citer.py | 62 | print(f"Error: Failed to fill_citation for {paper_item['paper_url']}: {str(e)}") | CODE |
| LOW | legacy/citer.py | 77 | except Exception as e: | CODE |
| MEDIUM | legacy/citer.py | 78 | print(f"Error: Failed to update citation for {paper_item['paper_url']}: {str(e)}") | CODE |
| LOW | legacy/citer.py | 93 | except Exception as e: | CODE |
| MEDIUM | legacy/citer.py | 94 | print(f"Error: Failed to update results for {conf}: {str(e)}") | CODE |
| LOW | legacy/citer.py | 108 | except Exception as e: | CODE |
| MEDIUM | legacy/citer.py | 109 | print(f"Error: Failed to update citation for {paper_item['paper_url']}: {str(e)}") | CODE |
| LOW | legacy/citer.py | 126 | except Exception as e: | CODE |
| MEDIUM | legacy/citer.py | 127 | print(f"Error: Failed to update results for {conf}: {str(e)}") | CODE |
| MEDIUM | legacy/citer.py | 14 | def load_results(filename='results.json'): | CODE |
| LOW | legacy/crawler.py | 84 | except Exception as e: | CODE |
| MEDIUM | legacy/crawler.py | 85 | print(f"Error occurred while searching paper info: {e}") | CODE |
| MEDIUM | legacy/crawler.py | 113 | print(f"Error: {e} at url: {url}") | CODE |
| MEDIUM | legacy/crawler.py | 139 | print(f"Error: Failed to save results to {filename}: {e}") | CODE |
| LOW | paperBotV2/conf_summary/conf_daily.py | 156 | except Exception as exc: | CODE |
| LOW | paperBotV2/conf_summary/get_free_abstract.py | 30 | except Exception as e: | CODE |
| LOW | paperBotV2/conf_summary/get_free_abstract.py | 42 | except Exception as e: | CODE |
| LOW | paperBotV2/conf_summary/get_free_abstract.py | 155 | except Exception as e: | CODE |
| LOW | paperBotV2/conf_summary/get_free_abstract.py | 210 | except Exception as e: | CODE |
| LOW | paperBotV2/conf_summary/crawler.py | 89 | except Exception as e: | CODE |
| MEDIUM | paperBotV2/conf_summary/crawler.py | 90 | print(f"Error occurred while searching paper info: {e}") | CODE |
| MEDIUM⚡ | paperBotV2/conf_summary/crawler.py | 142 | print(f"Error: Failed to fetch {url} with status code {response.status}. Attempt {retry_count+1} | CODE |
| MEDIUM⚡ | paperBotV2/conf_summary/crawler.py | 144 | print(f"Error fetching {url}: {str(e)}. Attempt {retry_count+1}/{max_retries+1}") | CODE |
| LOW⚡ | paperBotV2/conf_summary/crawler.py | 147 | except Exception as e: | CODE |
| MEDIUM | paperBotV2/conf_summary/crawler.py | 211 | print(f"Error: Failed to save results to {filename}: {e}") | CODE |
| LOW | paperBotV2/industry_practice/maintain.py | 45 | except Exception as e: | CODE |
| LOW | paperBotV2/industry_practice/maintain.py | 139 | except Exception as e: | CODE |
| LOW | paperBotV2/industry_practice/maintain.py | 179 | except Exception as e: | CODE |
| LOW | paperBotV2/industry_practice/maintain.py | 297 | except Exception as e: | CODE |
| LOW | paperBotV2/industry_practice/maintain.py | 331 | except Exception as e: | CODE |
| LOW | paperBotV2/industry_practice/maintain.py | 359 | except Exception as e: | CODE |
| MEDIUM | paperBotV2/industry_practice/maintain.py | 51 | def parse_issue(issue): | CODE |
| LOW | paperBotV2/industry_practice/generate_industry_html.py | 86 | except Exception as e: | CODE |
| LOW | paperBotV2/industry_practice/generate_industry_html.py | 126 | except Exception as e: | CODE |
| LOW | paperBotV2/industry_practice/generate_industry_html.py | 268 | except Exception as e: | CODE |
| LOW | paperBotV2/arxiv_daily/arxiv_feishu_msg.py | 34 | except Exception as e: | CODE |
| LOW | paperBotV2/arxiv_daily/arxiv_feishu_msg.py | 59 | except Exception as e: | CODE |
| LOW | paperBotV2/arxiv_daily/generate_arxiv_html.py | 123 | except Exception as e: | CODE |
| LOW | paperBotV2/arxiv_daily/generate_arxiv_html.py | 138 | except Exception as e: | CODE |
| LOW | paperBotV2/arxiv_daily/generate_arxiv_html.py | 189 | except Exception as e: | CODE |
| LOW | paperBotV2/arxiv_daily/generate_arxiv_html.py | 208 | except Exception as e: | CODE |
| LOW | paperBotV2/arxiv_daily/generate_arxiv_html.py | 997 | except Exception as e: | STRING |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 104 | except Exception as exc: | CODE |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 251 | except Exception as e: | CODE |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 344 | except Exception as exc: | CODE |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 411 | except Exception as exc: | CODE |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 497 | except Exception as e: | CODE |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 519 | except Exception as e: | CODE |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 651 | except Exception as e: | CODE |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 692 | except Exception as exc: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | papers/wsdm/wsdm2025.md | 58 | |[Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation](https://doi.org/10.1145/37 | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/www/www2025.md | 95 | | 87 | | [MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification](https://doi.org/10.1145/369641 | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/emnlp/emnlp2020.md | 910 | | 902 | | [Seq2Edits: Sequence Transduction Using Span-level Edit Operations](https://doi.org/10.18653/v1/2020.emnlp-m | CODE |
| HIGH | paperBotV2/conf_summary/data/papers/wsdm/wsdm2025.md | 53 | | 45 | | [Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation](https://doi.org/ | CODE |
| HIGH | paperBotV2/arxiv_daily/output/arxiv_20260305.html | 987 | The goal of Open-Vocabulary Compositional Zero-Shot Learning (OV-CZSL) is to recognize attribute-obj | CODE |
| HIGH⚡ | paperBotV2/arxiv_daily/output/arxiv_20260113.html | 2459 | Retrieval-Augmented Generation (RAG) systems are usually defined by the combination of a generator a | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | legacy/conf.py | 97 | CODE | |
| LOW | legacy/citer.py | 99 | CODE | |
| LOW | paperBotV2/conf_summary/convert_to_md.py | 88 | CODE | |
| LOW | paperBotV2/conf_summary/conf_daily.py | 201 | CODE | |
| LOW | paperBotV2/conf_summary/update_readme_papers.py | 9 | CODE | |
| LOW | paperBotV2/conf_summary/get_free_abstract.py | 52 | CODE | |
| LOW | paperBotV2/conf_summary/get_free_abstract.py | 120 | CODE | |
| LOW | paperBotV2/conf_summary/get_free_abstract.py | 177 | CODE | |
| LOW | paperBotV2/conf_summary/get_free_abstract.py | 284 | CODE | |
| LOW | paperBotV2/conf_summary/crawler.py | 94 | CODE | |
| LOW | paperBotV2/conf_summary/crawler.py | 219 | CODE | |
| LOW | paperBotV2/industry_practice/maintain.py | 62 | CODE | |
| LOW | paperBotV2/industry_practice/maintain.py | 145 | CODE | |
| LOW | paperBotV2/industry_practice/generate_industry_html.py | 92 | CODE | |
| LOW | paperBotV2/arxiv_daily/generate_arxiv_html.py | 350 | CODE | |
| LOW | paperBotV2/arxiv_daily/generate_arxiv_html.py | 748 | CODE | |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 157 | CODE | |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 329 | CODE | |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 396 | CODE | |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 500 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | paperBotV2/arxiv_daily/generate_arxiv_html.py | 817 | if (savedDate === currentPageDate && savedYear && savedMonth) { | CODE |
| HIGH | paperBotV2/arxiv_daily/generate_arxiv_html.py | 925 | localStorage.setItem('selectedYear', displayYear.toString()); | CODE |
| HIGH | paperBotV2/arxiv_daily/generate_arxiv_html.py | 926 | localStorage.setItem('selectedMonth', displayMonth.toString()); | CODE |
| HIGH | paperBotV2/arxiv_daily/generate_arxiv_html.py | 955 | if (hasPapers && currentDateObj.getTime() !== today.getTime()) { | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260206.html | 3770 | DARWIN is an evolutionary GPT model, utilizing a genetic-algorithm like optimization structure with | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20260204.html | 3169 | GFlowPO: Generative Flow Network as a Language Model Prompt Optimizer | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20251125.html | 437 | Recent advances in Large Language Models (LLMs) have opened new avenues for sequential recommendatio | CODE |
| MEDIUM | paperBotV2/arxiv_daily/output/arxiv_20251114.html | 1376 | Despite the remarkable success of the LLaVA architecture for vision-language tasks, its design inher | CODE |
| MEDIUM | paperBotV2/arxiv_daily/data/20260206.json | 587 | "ori_summary": "DARWIN is an evolutionary GPT model, utilizing a genetic-algorithm like optimization structure with | CODE |
| MEDIUM | paperBotV2/arxiv_daily/data/20251114.json | 1692 | "ori_summary": "Despite the remarkable success of the LLaVA architecture for vision-language tasks, its design inher | CODE |
| MEDIUM | paperBotV2/arxiv_daily/data/20251125.json | 128 | "ori_summary": "Recent advances in Large Language Models (LLMs) have opened new avenues for sequential recommendatio | CODE |
| MEDIUM | paperBotV2/arxiv_daily/data/20260204.json | 1142 | "title": "GFlowPO: Generative Flow Network as a Language Model Prompt Optimizer", | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | legacy/update.py | 1 | CODE | |
| LOW | legacy/update.py | 6 | CODE | |
| LOW | legacy/conf.py | 7 | CODE | |
| LOW | paperBotV2/conf_summary/crawler.py | 10 | CODE | |
| LOW | paperBotV2/industry_practice/generate_industry_html.py | 10 | CODE | |
| LOW | paperBotV2/arxiv_daily/__init__.py | 4 | CODE | |
| LOW | paperBotV2/arxiv_daily/__init__.py | 4 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | paperBotV2/conf_summary/convert_to_md.py | 57 | def calculate_optimal_abstract_length(papers_count): | CODE |
| LOW | paperBotV2/conf_summary/update_readme_papers.py | 9 | def get_all_meetings_and_years(papers_dir): | CODE |
| LOW | paperBotV2/conf_summary/get_free_abstract.py | 52 | def get_papers_with_empty_abstracts(results_data: dict, conf_pattern_func=None, max_papers=None) -> list: | CODE |
| LOW | paperBotV2/industry_practice/maintain.py | 336 | def update_industry_practice_page(): | CODE |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 329 | def rough_analyze_papers_cocurrent(results, max_workers=10): | CODE |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 396 | def fine_analyze_papers_cocurrent(papers, max_workers=10): | CODE |
| LOW | paperBotV2/arxiv_daily/arxiv.py | 500 | def get_papers_from_all_categories(run_status=None): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | legacy/maintain.py | 101 | }] | COMMENT |
| LOW | paperBotV2/conf_summary/process_papers_loop.sh | 1 | #!/bin/bash | COMMENT |
| LOW | paperBotV2/industry_practice/maintain.py | 241 | "tag": "text", | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | legacy/translate.py | 50 | CODE | |
| LOW | paperBotV2/conf_summary/crawler.py | 219 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | paperBotV2/arxiv_daily/output/arxiv_20260508.html | 7589 | Reinforcement learning with verifiable rewards, particularly Group Relative Policy Optimization (GRP | CODE |
| LOW | paperBotV2/arxiv_daily/data/20260508.json | 1862 | "ori_summary": "Reinforcement learning with verifiable rewards, particularly Group Relative Policy Optimization (GRP | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | paperBotV2/arxiv_daily/__init__.py | 6 | __all__ = ['PRERANK_PROMPT', 'FINERANK_PROMPT'] | CODE |