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Notes on artificial intelligence
262 notes, page 5 of 11
Toward cryptographically verifiable authorization for autonomous AI agents: A security hypothesis, preliminary formal model, and proof-of-concept implementation
The authors propose a model for Cryptographically Verifiable Agent Authorization (CVA), which aims to provide cryptographic evidence that an AI agent's authorization request satisfies the applicable policy in a…
Antigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design
Researchers have developed a new model called AAMFM to improve the design of functional antibodies. This model learns unified representations of antibody sequences and structures, considering antigen context, which…
Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference
The researchers propose a decentralized collaborative learning paradigm for an ensemble of Tsetlin Machines (TMs) using consensus-based inference. Each agent has its own private TM model, and they combine individual…
Co-Evolving LLM Evaluators and Policies via DynamicRubric
The paper introduces DynamicRubric, a framework that co-evolves language model evaluators and policies by generating weighted binary rubrics for each candidate set and aggregating the resulting judgments into…
Test Case Prioritization for DNNs via Neural Collapse Instability
A new method, Neural-Collapse-Inspired Prioritization (NCIP), is proposed to improve test case prioritization for deep neural networks (DNNs) in safety-critical domains where testing budgets are limited. The method…
AutoJourn: Multi-Perspective Summarisation, Bias Detection and Bias Neutralisation for LLM-Generated News in Automated Journalism
The researchers present AutoJourn, a system for generating news articles using large language models (LLMs) that consider multiple perspectives and aim to reduce bias in AI-generated news. The system includes…
Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA
The paper presents a diagnostic framework called FiT (Find before Fine-Tune) to evaluate the suitability of small language models for cybersecurity question-answering tasks. The study finds that fine-tuning these…
Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark
Researchers have created a new benchmark called Corpus OF Eeg-To-Text (COFETT) to evaluate the feasibility of translating brain signals into text using Electroencephalography (EEG) in real-world scenarios. The study…
Automated Discovery Has No Universally Superior Harness
A study investigates the effectiveness of different automated discovery systems (OpenEvolve and TTT-Discover) in various machine learning models and problems. The research finds that no single system consistently…
Automated Discovery Has No Universally Superior Harness
A study evaluates various discovery harnesses, such as OpenEvolve and TTT-Discover, used in automated discovery systems. The research finds that no single harness performs consistently well across different models and…
Human Grounded Evaluation of Large Language Models for Optical Network Automation
The paper introduces a new evaluation pipeline named HuGLEN for comparing large language models (LLMs) used in network automation. The pipeline uses an LLM to evaluate other LLMs and rates them based on a quality…
O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning
Researchers have developed a new method for Industrial Video Anomaly Detection (IVAD) that aims to identify unusual objects and events in industrial processes. This method is designed to work in complex settings where…
RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control
The paper proposes a system called Real-Time Self-Hosted Computer-Use Agent (RT-SHCUA) for unmanned aerial vehicles (UAVs). This system aims to address the challenges of directly applying SHCUAs to UAV control, which…
SelectInfer: Selective Neuron Loading and Computation for On-Device LLMs
Researchers present SelectInfer, a neuron-level optimization framework for Large Language Models (LLMs) that can run efficiently on resource-constrained edge devices. It reduces memory footprint and computation by…
SGA: Plug&Play Geometric Verification for Educational Video Synthesis
Researchers have proposed a new tool called Symbolic Geometric Agent (SGA) to improve the spatial correctness and visual legibility of educational videos generated by Large Language Models (LLMs). The SGA intercepts…
The Aura in the Machine: Genealogy and the Status of the Work of Art in the Generative Era
This research paper examines Generative Artificial Intelligence (AI) as a continuation of historical processes rather than a revolutionary breakthrough. It discusses the impact on authorship and creativity, identifies…
ANet Patu-1: The Value of Connection in the Agent Network
The paper introduces ANet Patu-1, a self-organizing consensus protocol for networks of AI agents. The authors model the network's value as a function of coordination-group size and derive properties an optimal…
ANet Patu-1: The Value of Connection in the Agent Network
The paper introduces ANet Patu-1, a self-organizing consensus protocol for networks of AI agents. The authors compare the value of connection in different types of networks and find that an optimal collaboration…
Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents
The researchers evaluate the performance of language model security agents, focusing on costs rather than just success rates. They use offensive Cybench challenges and defensive Splunk BOTS v1 investigation challenges…
Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents
The paper suggests that traditional methods for evaluating security agents, focusing on their offensive capabilities under generous inference budgets, are insufficient as they don't account for the cost of each…
BrainPilot: Automating Brain Discovery with Agentic Research
BrainPilot is a multi-agent system designed to accelerate brain science research by integrating evidence from various scales, modalities, and disciplines. The system uses a principal investigator (PI) agent and…
Can We Trust Item Response Theory for AI Evaluation?
The paper examines the reliability of using Item Response Theory (IRT) for evaluating AI systems in large-scale benchmarks. The authors simulate response matrices under three common IRT models, comparing four…
Can We Trust Item Response Theory for AI Evaluation?
This research investigates the reliability of using Item Response Theory (IRT) for evaluating Artificial Intelligence (AI). The study finds that classical estimators can become infeasible in large benchmark settings…
In-Place Tokenizer Expansion for Pre-trained LLMs
Researchers have developed an in-place method called Tokenizer Expansion to upgrade a pre-trained model's tokenizer when language priorities shift, focusing on improving efficiency for users of languages added later…
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