Synthetic Media
New empirical research examines whether AI-generated social media posts can corrupt disaster social sensing systems — and whether current detection methods can tell synthetic crisis content from the real thing.
AI Detection
The em dash became a popular signal for spotting AI-generated text. But as detection heuristics spread, models began adapting, exposing the fragility of stylometric fingerprints for synthetic content detection.
fake news
New research explores how large language models generate scenario-based fake news through discourse structures — and how detectors can be trained to catch it. A closer look at the arms race between synthetic text generation and authenticity.
diffusion models
Autoregressive language models generate text token by token. A new wave of diffusion-based LLMs aims to produce entire passages in parallel, promising dramatic speedups and rethinking how synthetic text is created.
LLM
From KV caches to prompt and semantic caches, LLM serving relies on multiple caching layers to slash latency and GPU costs. Here's a technical breakdown of the four caches that make large-scale AI inference practical.
AI Text Detection
A new detection method models text as a graph of sentence transitions, exposing the tell-tale over-regularity of AI-generated writing. The approach flags synthetic prose by measuring how unnaturally smooth its flow of ideas is compared to human text.
watermarking
Major AI models are increasingly embedding invisible statistical watermarks into the text they generate. Here's how these hidden signals work, why they matter for digital authenticity, and what they mean for detecting synthetic content.
watermarking
LLM watermarking promises to help detect AI-generated text, but embedding these statistical signatures comes at a cost. New analysis shows how watermarking degrades output quality, reasoning, and reliability, raising hard questions for digital authenticity efforts.
Model Provenance
New research traces the lineage of large language models by analyzing spectral fingerprints in their weight matrices, offering a forensic tool for model provenance and AI authenticity.
AI Detection
New research tackles the challenge of identifying undisclosed LLM-generated content in official parliamentary texts, raising the bar for synthetic-text detection in high-stakes civic and governmental settings.
LLM
A technical breakdown of how LLM quantization works, comparing INT8, INT4, GPTQ, and AWQ methods that shrink large models for faster, cheaper inference without destroying accuracy.
LLM
New analysis challenges the standard practice of extracting embeddings from the final token of decoder-only LLMs, showing intermediate layers and alternative pooling strategies often produce richer semantic representations.