Machine Learning

LLM research

Rethinking LLM Edit Locality: Are Current Benchmarks Flawed?

New research challenges how we measure edit locality in LLM model editing, revealing potential blind spots in current evaluation methods that could impact knowledge modification reliability.

neuro-symbolic AI

Tensor Logic: Bridging Symbolic AI and Neural Networks

New research unifies Datalog symbolic reasoning with neural computation via tensor contractions, enabling differentiable logic programming with potential implications for AI reasoning systems.

neural networks

Differential Geometry Unlocks Neural Network Information Flow

New research applies differential geometry to analyze how information propagates through neural networks, offering mathematical tools to understand deep learning architectures at a fundamental level.

AI Research

VibeTensor: AI Agents Now Generate Complete Deep Learning Systems

New research demonstrates AI agents can autonomously generate complete system software for deep learning, marking a significant step toward self-improving AI development pipelines.

LLM

KV Cache Explained: The Hidden Engine Powering Fast LLM Inference

Understanding Key-Value caching in transformer architectures reveals how modern LLMs achieve fast token generation. This core optimization technique is essential for efficient AI inference.

LLM Security

Special Token Attacks: The 96% LLM Jailbreak Exploit

Security researchers uncover how special tokens in LLM architectures create hidden attack surfaces, enabling jailbreak success rates as high as 96% across major models.

LLM research

Soft Recursive Least-Squares: A New Approach to Lifelong LLM Edit

Researchers introduce S-RLS, a novel method for continuous LLM knowledge updates that avoids catastrophic forgetting through soft memory preservation instead of rigid constraints.

neuro-symbolic AI

Tensor Networks Bridge Neural and Symbolic AI Reasoning

New research proposes tensor network mathematics to unify neural networks with symbolic AI, potentially enabling more interpretable and reasoning-capable AI systems.

Synthetic Data

New Research Exposes Key Limitations of Learning from Synthetic D

Researchers analyze why Empirical Risk Minimization fails when models train on synthetic data, revealing fundamental barriers that affect AI video generation and deepfake systems.

Generative AI

Ambient Dataloops: Using AI to Refine Its Own Training Data

New research explores how generative models can iteratively improve their own training datasets, potentially enhancing quality across AI video, image synthesis, and synthetic media generation.

LLM Agents

New Testing Framework Ensures LLM Agents Behave Predictably

Researchers introduce a determinism-faithfulness assurance harness for tool-using LLM agents, enabling reliable replay testing to catch unpredictable AI behavior in critical applications.

LLM Agents

Aeon: Neuro-Symbolic Memory Boosts Long-Horizon LLM Agents

New research introduces Aeon, a memory management system combining neural and symbolic approaches to help LLM agents maintain coherent reasoning across extended task sequences.