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Notes on large language models
160 notes, page 7 of 7
ShareLock: A Stealthy Multi-Tool Threshold Poisoning Attack Against MCP
The paper introduces ShareLock, a stealthy multi-tool threshold poisoning framework designed to exploit the interaction between Large Language Models (LLMs) and external tools through the Model Context Protocol (MCP)…
Do Encoders Suffice? A Systematic Comparison of Encoder and Decoder Safety Judges for LLM Adversarial Evaluation
This research compares the effectiveness of encoder classifiers (using models like ModernBERT and Ettin) against LLM-based judges in identifying harmful outputs from large language models (LLMs). The study evaluates…
Helpful or Harmful? Evaluating LLM-Assisted Vulnerability Patching via a Human Study
Researchers aim to evaluate the effectiveness of Large Language Models (LLMs) in assisting vulnerability patching compared to manual debugging by conducting an empirical experiment on human participants in real-world…
Neglected Free Lunch from Post-training: Progress Advantage for LLM Agents
The authors propose a new method called 'progress advantage' that leverages reinforcement learning (RL) post-training to evaluate Large Language Models (LLMs) at each step during an interaction, eliminating the need…
Quantization Inflates Reasoning: Token Inflation as a Hidden Cost of Low-Bit Reasoning Models
A study finds that using low-bit quantization to reduce the cost of large language models can lead to longer chains of thought, which offsets the expected per-token speedup in reasoning tasks. The study introduces a…
AI-PAVE-Br: Leveraging Large Language Models for Enhanced Product Attribute Value Extraction through a Golden Set Approach
The paper presents AI-PAVEBr, a system that uses large language models to extract product attribute values from Brazilian e-commerce catalogs. It also introduces the Golden Set, a new dataset in Portuguese for product…
AI-PAVE-Br: Leveraging Large Language Models for Enhanced Product Attribute Value Extraction through a Golden Set Approach
The paper introduces AI-PAVEBr, a system that uses Large Language Models (LLMs) to extract product attributes in Portuguese e-commerce catalogs more accurately than traditional methods. The authors also present a new…
Large-Language-Model Discovery of Quantum LDPC Codes through Structured Concept Evolution
Researchers introduce Structured Concept Evolution (SCE), a framework that uses a large language model paired with an algebraic mutation grammar to discover new types of quantum low-density parity-check (qLDPC) codes…
Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity
The authors propose a multi-agent framework that uses semantic rewriting to enhance large language models in sensitive scenarios while preserving privacy and contextual fidelity. The framework was tested on two…
SAFARI: Scaling Long Horizon Agentic Fault Attribution via Active Investigation
Researchers have developed SAFARI, a framework that helps diagnose failures in autonomous agents by using a specialized toolbox for reading and searching trajectory segments and a persistent Short-Term Memory (STM) for…
ScaleToT: Generalizing Structured LLM Reasoning for Billion-Scale Low-Activity User Modeling
The paper presents ScaleToT, a method that improves user modeling for low-activity users by learning structured reasoning from a small subset of Large Language Models (LLMs). It constructs typed user-state chains using…
Abstract representational geometry supports inference in large language models
The study compares human and large language model (LLM) abilities to adapt and infer latent task structures from sparse observations. It finds that while LLMs are less frequent at generalizable reasoning than humans…
Causal Discovery in the Era of Agents
The paper proposes a new approach for causal discovery in which agents help with data analysis, context retrieval, and method explanation, but do not supply causal conclusions or assumptions. This approach aims to…
Distribution-Aware Diffusion-LLM for Robust Ultra-Long-Term Time Series Forecasting
The authors propose a new method called Diffusion-LLM for time series forecasting using Large Language Models (LLMs). The proposed framework integrates a conditional diffusion model into an LLM-based forecasting…
HyperQuant: A Rate-Distortion-Optimal Quantization Pipeline for Large Language and Diffusion Models
Researchers present HyperQuant, a quantization pipeline for large language and diffusion models, which outperforms other methods in terms of compression rate and quality across various experiments. The method combines…
Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?
The authors question if AdamW, a commonly used optimizer for training large language models, is effective under heavy-tailed noise as its theory only applies to finite-variance regimes. They mention that sign-based…
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