bigsnarfdude

https://openreview.net/pdf?id=tyEyYT267x


  Data-Driven Noise Schedules

  Traditional Diffusion Models:
  - Use fixed noise schedules (linear, cosine, etc.)
  - Same schedule for all tokens/positions
  - Hand-designed based on intuition

bigsnarfdude

Diffusion Models for Text Classification: A Comprehensive Survey (2021-2025)

Research on diffusion-based text classification is surprisingly nascent—only 5 core papers directly apply diffusion models to text classification tasks, despite the explosion of diffusion work in NLP for generation. The field emerged in 2022-2024, with ROIC-DM (2024) being the first to use diffusion directly as a text classifier. However, foundational work on diffusion classifiers in computer vision (2021-2023) established the theoretical framework, and discrete diffusion models like D3PM provide the technical foundations for text applications. Most papers focus on adversarial robustness and uncertainty quantification rather than pure accuracy gains, suggesting diffusion's strength lies in providing more reliable, robust classification.

Direct applications of diffusion to text classification

The most significant finding is how limited this research area remains. Between 2021-2025, only a handful of papers explicit

bigsnarfdude

# Real-World LLM Misuse Datasets & Taxonomies
## Comparison with Fictional "llm-abuse-patterns" Repository

**Date:** October 31, 2024

---

## Executive Summary

**YES** - There are multiple real datasets, taxonomies, and frameworks for LLM misuse! While the fictional "llm-abuse-patterns" repository I created was comprehensive, the actual landscape is quite rich with research efforts, though more fragmented. Here's what actually exists:

bigsnarfdude

Use Bloom Filters when:Distributed threat intelligence

Multiple sensors collect malicious IPs/domains independently
Merge all filters at central point → unified blocklist
Classic use case: collaborative malware detection across organizations
Threat feed aggregation

Combine feeds from different vendors
Each vendor provides a Bloom filter of known bad hashes/URLs
OR them together for comprehensive coverage

bigsnarfdude

name	description	license
skill-creator	Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.	Complete terms in LICENSE.txt

Skill Creator

This skill provides guidance for creating effective skills.

bigsnarfdude

🐍 Algesnake - 39 Working Examples

  basic_example.py (6 examples)

  Foundation concepts using abstract algebra:
  - Example 1: Max Monoid - Finding maximum values
  - Example 2: Integer Addition Group - Addition with subtraction
  - Example 3: Ring Operations - Distributivity (a × (b + c))
  - Example 4: Complex Ring Expressions - Combined operations
  - Example 5: MonoidWrapper - Quick prototyping without classes

bigsnarfdude

# LLM brain rot reveals persistent cognitive decay from junk data

**LLMs exposed to viral web content suffer lasting cognitive decline that resists standard fixes.** A groundbreaking October 2025 paper demonstrates that continual training on engagement-driven junk data causes thought-skipping, safety failures, and personality distortion—damage that instruction tuning and clean data retraining only partially reverse. This "brain rot" persists because junk data fundamentally rewires model representations at the weight level, creating shortcuts that become deeply entrenched. The finding has critical implications: data quality is a training-time safety issue, not just a performance optimization, and deployed models may be silently degrading as they ingest low-quality web content.

The research connects to broader phenomena including model collapse, where AI systems recursively trained on synthetic data lose information about rare events, and sleeper agent backdoors that resist removal through safety training. To

bigsnarfdude

No legal mandate for independent monitoring of monitoring systems
No requirement that AI systems declare their dependencies on other AI systems
No standard for cross-system risk assessment when outputs feed into other systems
No accountability structure when cascade failures happen across multiple organizations
No legal requirement that someone be actively responsible for detecting failures in real-time

bigsnarfdude

---
name: docx
description: "Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. When Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks"
license: Proprietary. LICENSE.txt has complete terms
---

# DOCX creation, editing, and analysis

## Overview

bigsnarfdude

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
GPT-OSS Reinforcement Learning for 2048 Game - FIXED VERSION

Key fix: extract_function now correctly extracts generated code instead of the example
"""

# CRITICAL: Import unsloth FIRST before any other ML libraries
from unsloth import FastLanguageModel