3.3

Adjusted Signal

3.3

Raw Pattern Score

100%

Time Factor

2026-10-31

Last Push

4.6K

Stars

HTML

Language

2.3M

Lines of Code

777

Files

3.8K

Pattern Hits

2026-10-31

Scan Date

0.11

HC Hit Rate

Risk Review · Payload v1

Use each vector to guide manual verification of the underlying findings.

Payload schema: 1.0.0 · Engine: 2.0.0 · Scoring: heuristic-density-v1 · Scan date: 2026-10-31.

Cognitive Ownership Risk 14.8/100 · LOW

Can maintainers explain intent, assumptions, and trade-offs? 3764 finding(s).

Hallucinated Dependency & Supply Chain Risk 0.1/100 · LOW

Are imports declared and package/API names verified? 13 finding(s).

Agent Over-Engineering Ratio 0.2/100 · LOW

Are wrappers, guards, and repeated structures necessary? 68 finding(s).

PR Review Velocity Impact 22.3/100 · LOW

Which changes need focused human review? 3836 finding(s).

What These Metrics Mean

Adjusted Signal: Legacy pattern score normalized per 1,000 scanned lines and multiplied by the repository-level time factor. It is not an estimate of AI authorship.
Raw Pattern Score: Sum of severity- and context-weighted pattern findings before applying the time factor.
Time Factor: A coarse multiplier based on repository last-push metadata. It does not identify when individual code was written.
Pattern Hits: Count of configured pattern findings. Review each location and surrounding code before acting.
HC Hit Rate: High- and critical-severity findings per scanned file. Severity is a configured detector weight, not proof of a vulnerability or authorship.
Lines / Files: Scan coverage denominators used by the legacy normalized score.

Pattern-Signal History

This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.

Severity Breakdown

Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.

CRITICAL 0HIGH 85MEDIUM 3667LOW 84

Directory Score Breakdown

This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.

Pattern Findings

The scanner identified 3836 distinct pattern matches across 15 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. 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 configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.

AI Slop Vocabulary2595 hits · 4820 pts

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
2535 more matches not shown…

Modern AI Meta-Vocabulary1047 hits · 2098 pts

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
987 more matches not shown…

Synthetic Comment Markers74 hits · 392 pts

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_20260814.html	5765	In 2023, a New York judge sanctioned two attorneys in Mata v. Avianca for filing a brief with halluc	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
14 more matches not shown…

Excessive Try-Catch Wrapping57 hits · 76 pts

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

AI Response Leakage6 hits · 32 pts

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

Deep Nesting20 hits · 20 pts

Severity	File	Line	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

Cross-Language Confusion4 hits · 20 pts

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

Slop Phrases10 hits · 20 pts

Severity	File	Line	Snippet	Context
MEDIUM	paperBotV2/arxiv_daily/output/arxiv_20260904.html	217	Sequential recommender systems model user behavior as item ID sequences, while recent generative met	CODE
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/20260904.json	264	"ori_summary": "Sequential recommender systems model user behavior as item ID sequences, while recent generative met	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

Unused Imports7 hits · 7 pts

Severity	File	Line	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

Hyper-Verbose Identifiers7 hits · 7 pts

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

Empty Error Handlers1 hit · 5 pts

Severity	File	Line	Snippet	Context
HIGH	paperBotV2/arxiv_daily/arxiv.py	152	except ValueError:	CODE

Over-Commented Block3 hits · 3 pts

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

AI Structural Patterns2 hits · 2 pts

Severity	File	Line	Snippet	Context
LOW	legacy/translate.py	50		CODE
LOW	paperBotV2/conf_summary/crawler.py	219		CODE

Fake / Example Data2 hits · 2 pts

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

Modern Structural Boilerplate1 hit · 1 pts

Severity	File	Line	Snippet	Context
LOW	paperBotV2/arxiv_daily/__init__.py	6	__all__ = ['PRERANK_PROMPT', 'FINERANK_PROMPT']	CODE

Doragd/Algorithm-Practice-in-Industry

Open-Source Code Quality, Security and Technical Debt Review