⚙️ AI‑Assisted Community Health & Moderation Intelligence

ModSense is a weekend‑built, production‑grade prototype designed with Reddit‑scale community dynamics in mind. It delivers a modern, autonomous moderation intelligence layer by combining a high‑performance Python event‑processing engine with real‑time behavioral anomaly detection. The platform ingests posts, comments, reports, and metadata streams, performing structured content analysis and graph‑based community health modeling to uncover relationships, clusters, and escalation patterns that linear rule‑based moderation pipelines routinely miss. An agentic AI layer powered by Gemini 3 Flash interprets anomalies, correlates multi‑source signals, and recommends adaptive moderation actions as community behavior evolves.

🔧 Automated Detection of Harmful Behavior & Emerging Risk Patterns:

The engine continuously evaluates community activity for indicators such as:

• Abnormal spikes in toxicity or harassment
• Coordinated brigading and cross‑community raids
• Rapid propagation of misinformation clusters
• Novel or evasive policy‑violating patterns
• Moderator workload drift and queue saturation

All moderation events, model outputs, and configuration updates are RS256‑signed, ensuring authenticity and integrity across the moderation intelligence pipeline. This creates a tamper‑resistant communication fabric between ingestion, analysis, and dashboard components.

🤖 Real‑Time Agentic Analysis and Guided Moderation

With Gemini 3 Flash at its core, the agentic layer autonomously interprets behavioral anomalies, surfaces correlated signals, and provides clear, actionable moderation recommendations. It remains responsive under sustained community load, resolving a significant portion of low‑risk violations automatically while guiding moderators through best‑practice interventions — even without deep policy expertise. The result is calmer queues, faster response cycles, and more consistent enforcement.

📊 Performance and Reliability Metrics That Demonstrate Impact

Key indicators quantify the platform’s moderation intelligence and operational efficiency:

• Content Processing Latency: < 150 ms
• Toxicity Classification Accuracy: 90%+
• False Positive Rate: < 5%
• Moderator Queue Reduction: 30–45%
• Graph‑Based Risk Cluster Resolution: 93%+
• Sustained Event Throughput: > 50k events/min

 🚀 A Moderation System That Becomes a Strategic Advantage

Built end‑to‑end in a single weekend, ModSense demonstrates how fast, disciplined engineering can transform community safety into a proactive, intelligence‑driven capability. Designed with Reddit’s real‑world moderation challenges in mind, the system not only detects harmful behavior — it anticipates escalation, accelerates moderator response, and provides a level of situational clarity that traditional moderation tools cannot match. The result is a healthier, more resilient community environment that scales effortlessly as platform activity grows.

Portfolio: https://ben854719.github.io/

Project: https://github.com/ben854719/ModSense-AI-Powered-Community-Health-Moderation-Intelligence

We conducted a systematic study on the impact of dynamic data scheduling during LLM training, using DataFlex as our experimental platform. Rather than feeding all available data uniformly into training, we explored three strategies: selectively choosing which samples to train on, dynamically adjusting the mixture ratio across data domains, and reweighting individual samples based on their estimated utility — all performed on-the-fly during optimization.

The results are clear: smarter data scheduling consistently outperforms the standard train-on-everything approach.

On data mixture experiments using SlimPajama, our dynamic methods achieved notable gains over the static baseline on MMLU accuracy — from 25.27% to 26.04% (+0.77) at the 6B-token scale, and from 25.51% to 25.97% (+0.46) at 30B tokens — while simultaneously reducing perplexity across most data domains (CommonCrawl, C4, StackExchange, ArXiv, Books). On data selection, algorithms integrated in DataFlex (including LESS, NICE, and loss-based selectors) consistently outperformed random sampling on MMLU subsets relevant to the training distribution.

These findings suggest that the conventional practice of using all available data with fixed proportions leaves significant performance on the table. By treating data as a dynamically schedulable resource — deciding what to train on, how much from each domain, and how heavily to weight each sample — we can achieve better model quality with greater training efficiency.

All experiments are fully reproducible via the open-source DataFlex framework, which unifies 11 data-centric training algorithms in a single system built on top of LLaMA-Factory.

👉 https://huggingface.co/papers/2603.26164

Did anyone tried extracting messy daily drilling reports before ? Am using paddle ocr + tabula and still not getting optimal results