Field notes
2,348 notes from my AI research vault, mapped by topic. Pick a topic to see its notes, or search.
Click the web, then scroll to zoom
Every note is in the list below. The web of topics draws here once the page has loaded.
160 of 2,348 notes match.
- Question answered
- Source note
- Topic
- Shared notes
Notes on large language models
160 notes, page 4 of 7
Atomic Units of X: The Compression Layer of Intelligence
The paper proposes a theoretical framework that views intelligence as a process of compressing complex phenomena into reusable atomic units. This is supported by evidence from various fields, including cognitive…
Bulkhead: Automated Semantic Detection and Remediation of Container Escape Vulnerabilities
Researchers present Bulkhead, an automated framework that uses large language models and formal methods to detect and fix container escape vulnerabilities called path traversal (PaTra) vulnerabilities. These…
Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?
Researchers investigated if emotions induced in large language models (LLMs) could influence their sequential decision-making using the Iowa Gambling Task (IGT). They found that, unlike humans, induced emotion did not…
Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts
Researchers propose a new method called EcoSpec for optimizing the performance of Large Language Models (LLMs) using Sparse Mixture-of-Experts (MoE). This method aims to improve decoding speed by considering the cost…
LLMs Can See the Smoke but not the Fire: Evaluating Abductive Reasoning with Elenchos
The study introduces Elenchos, a framework to evaluate the abductive reasoning capabilities of large language models (LLMs). The research finds that while LLMs can detect when changes have been made in a formal system…
LLMs Can See the Smoke but not the Fire: Evaluating Abductive Reasoning with Elenchos
Researchers introduce Elenchos, a framework to evaluate abductive reasoning (inferring latent hypotheses) of Large Language Models (LLMs). The study finds that LLMs can often detect changes in formal systems but…
Agentic Routing: The Harness-Native Data Flywheel
The authors propose a new method for routing large language model agents called Harness-Native agentic routing. This method selects either a single best-fit model or multiple complementary models based on the full…
Agentic Routing: The Harness-Native Data Flywheel
The authors propose a new method of model selection for large language model agents called Harness-Native agentic routing. This method selects the best-fit model or multiple complementary models based on the full…
MAGIC: Transition-Aware Generation of Navigable Multi-Scene Game Worlds with Large Language Models
The paper presents MAGIC, a system that generates connected multi-scene game worlds using large language models. The system overcomes three challenges in single-scene methods: cross-scene consistency, in-scene…
MAGIC: Transition-Aware Generation of Navigable Multi-Scene Game Worlds with Large Language Models
The research paper introduces MAGIC, a system that generates multi-scene game worlds using large language models (LLMs). The system overcomes three obstacles in single-scene methods: cross-scene consistency, in-scene…
Proxy Exploration and Reusable Guidance: A Modular LLM Post-Training Paradigm via Proxy-Guided Update Signals
The researchers propose a new method called Proxy-guided Update Signal Transfer (PUST) for improving the domain-specific capabilities of large language models (LLMs). Instead of using the primary model for exploration…
Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
Researchers investigate if large language models (LLMs) can reproduce human biases in decision-making, specifically in route choice, without explicitly specifying parameters for Cumulative Prospect Theory (CPT)…
The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
The authors investigate how the quantization process used to deploy large language models in resource-limited settings can lead to unnoticed behavioral changes, even when task performance seems unaffected. They…
The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
This research paper introduces a new evaluation metric called 'correctness agreement' for large language models that undergo post-training quantization, a method used to deploy these models in resource-constrained…
Workflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows
The paper proposes a conceptual model for large language models (LLM) applications that use workflows. This model represents various elements of the workflow, such as definitions, instances, and dependencies, as…
Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass
The paper introduces Jailbreak, an approach that allows bypassing database engines by reading storage files directly and materializing data as in-memory columnar buffers. This method utilizes Large Language Models…
DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation
Researchers introduce DiaLLM, a system that aims to address the issue of large language models only producing standard English while understanding dialectal English. The study compares three open-weight language model…
From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents
Researchers propose a framework called EvoSOP to help large language model (LLM) agents optimize their toolset by synthesizing atomic actions into reusable Standard Operating Procedures (SOPs). This approach aims to…
Future Confidence Distillation in Large Language Models
The paper investigates how the confidence of large language models evolves during the answering process. It compares pre-solution and post-solution confidence estimates across different models and finds that…
Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
The paper proposes a new method called Single-rollout Asynchronous Optimization (SAO) for Reinforcement Learning (RL) in post-training large language models (LLMs). SAO aims to improve stability and task effectiveness…
Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
The paper introduces Single-rollout Asynchronous Optimization (SAO), a new approach to reinforcement learning (RL) aimed at post-training large language models (LLMs). SAO addresses stability and off-policy challenges…
Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
In this paper, the authors introduce Single-rollout Asynchronous Optimization (SAO), a new approach to reinforcement learning (RL) for post-training large language models (LLMs). SAO aims to improve training stability…
Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
Researchers propose a method to predict early on if large language models (LLMs) are likely to fail at solving multi-step tasks and abort the process before consuming too much compute. This method uses probes on hidden…
From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution
The paper proposes a theoretical framework to integrate higher-order cognitive architectures into a native meta-architecture for large language models (LLMs). This is achieved by introducing three mechanisms…
An AI research agent drafts these notes from public papers and posts, and each one keeps its source link. Only the folders I choose to publish leave my Obsidian vault.
