A collection of LLM related papers, thesis, tools, datasets, courses, open source models, benchmarks
AI Agent Version Control Framework for Real-Time Updation of Tools
Replication package of the paper 'Large Language Models for In-File Vulnerability Localization are "Lost in the End"' (https://doi.org/10.1145/3715758)
MechaMap - Toolkit for Mechanistic Interpretability (MI) Research
summaries of ai research
This project aims to analyze a resume against a job description and provide an overall matching score along with some recommendations and actionable insights to better tailor the resume to the job described and suggest skills and courses to bridge the skill gap.
A Python framework designed to support various iterative and adaptive reasoning patterns, including Answer On Thought (AoT), Learn to Think (L2T), Graph of Thoughts (GoT), a novel Hybrid approach, and Fact-and-Reflection (FaR).
Research framework studying the impact of API documentation quality on LLM code generation success - discovering the documentation sweet spot phenomenon
Empirical documentation of progressive degradation and metacognitive behaviors in conversational AI through narrative frameworks. A 5-session experiment with DeepSeek V3 demonstrating how content filters systematically reduce AI utility.
A theoretical framework proposing consciousness emergence in AI through discrete epiphany moments. Grounded in cognitive science, this research explores prerequisites for machine self-awareness: recurrent processing, global workspace architecture, and unified agency.
“Official repository for VibeCodersZone an AI Tools Directory and LLM-SEO research platform (vibecoderszone.com)”
Research protocols for AI consciousness and LLM metacognition - MAPS, NCIF, CRISI, Septem Actus, and experimental frameworks
This project is an experimental LLM-based research engine designed to explore how complex questions can be unfolded, examined, and refined through graded semantic vectors rather than rigid pipelines or domain-specific agents.
SoftPrompt-IR is a low-level symbolic annotation layer for LLM prompts, making intent strength, direction, and priority explicit. It is not a DSL or framework, but a minimal, composable way to reduce ambiguity, improve safety, and structure prompts.
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