Research & Science
EVALS.md · 22 documents
Intelligent Research Assistant - Technical Documentation
The Intelligent Research Assistant is a comprehensive AI-powered research platform built with a modular, scalable architecture. It combines document processing, vector search, multi-agent orchestration, fine-tuning capabilities, RLHF (Reinforcement Learning from Human Feedback), and enterprise-grade security into a unified system.
RAG System Testing Methodologies: A Comprehensive Guide
**Document Version:** 1.0
GenAI Benchmarks & Evaluation — Product-Based Companies
Understanding how to **benchmark, evaluate, and compare LLMs** is essential for roles at Google, OpenAI, Anthropic, Cohere, and AI research teams. This file covers the most important benchmarks, evaluation methodologies, and how to build custom evaluation harnesses.
Research Report: Using LLMs as Oracle for Entity Matching Ground Truth
Comprehensive research on using Large Language Models (particularly DeepSeek, GPT-4, and Claude) for entity matching ground truth generation. This report covers LLM accuracy benchmarks, prompt engineering best practices, multi-LLM ensemble approaches, cost-benefit analysis, validation strategies, and patterns for converting LLM labels into regression tests.
Arxiv Papers in cs.CV on 2023-04-20
- **Arxiv ID**: http://arxiv.org/abs/2304.10029v1
Arxiv Papers in cs.CV on 2024-07-15
- **Arxiv ID**: http://arxiv.org/abs/2407.10366v1
Analysis of Paper 50
- **Title:** Expanding Queries for Code Search Using Semantically Related API Class-names
Voice AI Leaderboards, Benchmarks, and Evaluation Gaps (Jan 2025 -- Feb 2026)
> **Last updated**: 2026-02-20
Discussion Report: Flat Band Evaluation Methodology & Strategic Direction
**Participants**: Ryotaro, Masaki Adachi
LLM Evaluation — Deep Dive
> Frontier-lab interview-grade reference on evaluating LLMs and LLM-powered products.
RAG Evaluation Frameworks: Comprehensive Analysis for VERA
**Research Focus**: Grounding verification metrics and evaluation methodologies for retrieval-augmented generation systems
ProtoExtract — Evaluation Approach Using OmniDocBench Methodology
Define a rigorous, evidence-based evaluation framework for the ProtoExtract
Evaluating generative systems
title: "LLM evaluation, chapter 2: Evaluating generative systems"
American Mass-Market Brand Psychology
- Being sophisticated
UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation
[🐙 GitHub](https://github.com/xmed-lab/UniEval) [🤗 UniBench](https://huggingface.co/datasets/yili7eli/UniBench) [📄 arXiv](https://arxiv.org/abs/2505.10483)
🔬 Open Deep Research
<img width="1388" height="298" alt="full_diagram" src="https://github.com/user-attachments/assets/12a2371b-8be2-4219-9b48-90503eb43c69" />
VaultGres Project Context
**VaultGres** is a high-performance, PostgreSQL-compatible relational database management system (RDBMS) written in Rust. It delivers ACID compliance, advanced query optimization, modern concurrency with MVCC (Multi-Version Concurrency Control), and enterprise-grade security features.
State-of-the-Art in PII/PHI Detection for Handwritten Medical Documents: A Comprehensive Review
This report consolidates a comprehensive review of existing datasets, methods, and performance metrics for the detection of Personally Identifiable Information (PII) and Protected Health Information (PHI) in handwritten medical documents. The digitization of healthcare has led to a massive volume of unstructured data, including scanned prescriptions and clinical notes. While invaluable for research and patient care, this data is rich with sensitive information. Manual redaction is unscalable and
Universal and Transferable Adversarial Attacks on Aligned Language Models
Based on my reading of the paper, the central research question seems to be:
Enhancing Budget-Aware Gating for Retrieval Augmented Generation (RAG)
**Author:** <Your Name>
New Papers
https://www.promptingguide.ai/
Codex [OpenAI] [2021.07] [Close]
Paper:[Evaluating Large Language Models Trained on Code](https://arxiv.org/abs/2107.03374)