Research Lab

Research Systems for AI, Industry, and Scientific Computing

Explore our ongoing research across AI safety, predictive maintenance, surrogate simulations, and geotechnology in one standalone public page.

SerenAI Benchmark

SerenAI Benchmark v1

Version 1 Benchmark

SerenAI vs Its Base Model

This is our Version 1 benchmark: SerenAI compared against its base model.

SerenAI is a text-based world AI model. Instead of reading a prompt as isolated text, it represents the scene as a changing environment with states, actions, constraints, and consequences.

The model is trained with cause-and-effect data, so it can connect what happened, why it happened, and what is likely to happen next. This gives SerenAI a stronger ability to understand the environment and the situation behind a user request.

The first benchmark focuses on situational reasoning: tracking changes over time, identifying hidden causes from visible effects, and producing responses that fit the current context. The goal is not only to improve scores, but to make the model more grounded in how events unfold.

SerenAI Benchmark v1 comparing SerenAI with its base model

What the benchmark measures

SerenAI v1 is evaluated against its base model to show how cause-and-effect training changes the model behavior in environment-aware text reasoning tasks.

Comparison

SerenAI vs Base Model

Model type

Text-based world AI model

Training signal

Cause-and-effect data

Primary skill

Environment and situation understanding

AI Detection Systems

Hallucination Risk in Large Language Models

“Recent studies have shown that the percentage of hallucinated content is quite high among popular LLMs, ranging from 17% to 19% up to 45% of the content. If left without serious attention and the appropriate corrections, AI hallucinations can lead to critical limitations of AI applications that negatively impact human civilization and its progress.”
AI-generated content versus human content over time

Environmental Efficiency and Ethical AI Usage

Research published in Nature demonstrates that AI systems can contribute to a significant reduction in CO2 emissions. This environmental benefit represents a crucial advancement in sustainable technology deployment.

We strongly advocate for the responsible use of AI-generated content. Our tools are designed to assist researchers and writers in refining their original work, rather than replacing human creativity and critical thinking.

Environmental impact of AI usage

Panda v3.0 Detection Model

Advanced neural architecture optimized for LLM-generated content detection.

Core Detection Features

Perplexity Analysis94%
Burstiness Score91%
Entropy Mapping88%
N-Gram Frequency86%
Semantic Coherence92%
Stylometric Fingerprint85%
Token Probability90%

Accuracy

0.95

F1 Score

0.96

Backed by Research

Large Language Models Detection ResearchNortheastern University - November 2025
Comparative Study: AI Detection MethodsUniversity of Maryland - October 2025
GPT-4ClaudeGeminiReasoning AIKimi AILlamaPhi-4Others
Geotechnology

AI-Powered Geological Monitoring

We integrate artificial intelligence with autonomous drone systems to monitor landslides, seismic activity, and terrain instability, delivering real-time insights faster and more affordably than traditional methods.

Our geotechnology capabilities include AI-powered landslide detection, continuous seismic monitoring, high-resolution 3D terrain mapping, automated risk assessment, and scalable regional monitoring networks.

Landslide DetectionSeismic Monitoring3D Terrain MappingRisk AssessmentRegional Monitoring
Drone-based geotechnology monitoring