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Notes on ai
458 notes, page 18 of 20
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning
Researchers introduce a new method called PEEU (Planning Experience Exploration and Utilization) to improve the task planning capabilities of small, open-source Multimodal Language Learning Models (MLLMs). These models…
From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan
This research study explores the Wild West of AI-generated explicit content known as AI nudification on the anonymous online community 4chan. The authors identify over 24,000 such items and find that non-celebrity…
Inherited Circuits, Learned Semantics: How Fine-Tuning Creates Evasion Vulnerabilities Invisible to Standard Evaluation
A study shows that Language Models (LLMs) fine-tuned for security classification can miss vulnerabilities due to learning token-level indicator semantics that preserve accuracy on their training data but fail under…
Joint Learning of Experiential Rules and Policies for Large Language Model Agents
The paper introduces a new method called Joint Learning of Experiential Rules and Policies (JERP) for large language model agents operating in multi-step interactive environments. The main challenge addressed is…
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
The paper introduces OpenRCA 2.0, a new benchmark for root cause analysis (RCA) of Large Language Models (LLMs). The new dataset, OpenRCA 2.0, includes 500 instances and is the first cross-system RCA benchmark with…
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
Researchers introduce a new method, called PAVE, to support the rigorous evaluation of large language models (LLMs) in root cause analysis (RCA). The current datasets only label the root cause and not the path…
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
The paper introduces OpenRCA 2.0, a new benchmark for root cause analysis (RCA) that includes step-wise causal annotations for large language models (LLMs). The benchmark aims to test the capabilities of LLMs in…
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
The paper introduces OpenRCA 2.0, a new benchmark for root cause analysis (RCA) of large language models (LLMs). The benchmark includes step-wise causal annotations for LLMs and addresses the issue that existing…
The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development
The paper presents the Spec Growth Engine, a framework to address the structural failure modes introduced by AI coding agents in software development. These failure modes include context explosion and silent spec-code…
The Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development
The paper proposes a new framework called the Spec Growth Engine to improve AI coding agents during software development. The framework addresses two main issues: context explosion and silent spec-code drift. It uses a…
When Does Combining Language Models Help? A Co-Failure Ceiling on Routing, Voting, and Mixture-of-Agents Across 67 Frontier Models
The study discusses multi-model language models and how their accuracy is limited by a factor called 'beta', which is the rate at which all models are wrong on the same query. The authors find that across 67 models, a…
AI-Assisted Computational Reproducibility on the FABRIC Testbed
This paper discusses the use of an international research testbed, FABRIC, combined with a large language model (LLM) coding assistant called LoomAI, to simplify the reproduction of published experiments across various…
Autodata: An agentic data scientist to create high quality synthetic data
The authors introduce Autodata, an AI agent that acts as a data scientist to create high-quality synthetic training and evaluation data. They discuss how to train this data scientist agent to improve its data creation…
Autodata: An agentic data scientist to create high quality synthetic data
Researchers introduce Autodata, an AI agent that can act as a data scientist to create high-quality synthetic data for various tasks like computer science research, legal reasoning, and mathematical object reasoning…
Can Trustless Agents Be Trusted? An Empirical Study of the ERC-8004 Decentralized AI Agent Ecosystem
The paper presents the first empirical study of the ERC-8004 protocol, a trust layer for AI agent economies. The study reveals several issues with the current design: most registrations are placeholders, reputation…
Explainable Control Framework (XCF) based on Fuzzy Model-Agnostic Explanation and LLM Agent-Supported Interface
Researchers have developed an Explainable Control Framework (XCF) to make complex controllers more understandable for humans. The framework provides explanations about how controllers determine their actions in…
GUI agent: Guided Exploration of User-Sensitive Screens
The text discusses a paper that proposes a solution for a problem in Large Language Model (LLM) agents used within Graphical User Interface (GUI) environments. The current issue is that these agents, fine-tuned to…
Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
The paper introduces Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound (MAGR-BB), a method for inferring which agents are working together and what each…
Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
The paper presents Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound (MAGR-BB), a method to infer which agents in a multi-agent system are working…
On-Policy Self-Distillation with Sampled Demonstrations Reduces Output Diversity
The paper discusses a method called On-policy Self-Distillation, which uses a single model as both teacher and student to achieve high accuracy in pass@1. However, it is found that this method can lead to a decrease in…
SpeechEQ: Benchmarking Emotional Intelligence Quotient in Socially Aware Voice Conversational Models
The paper introduces SpeechEQ, a framework designed to evaluate the sociolinguistic reasoning of Speech-Language Models (SLMs) in multi-turn dialogues. The framework includes a dataset and evaluation protocol for…
Staying In Character: Perspective-Bounded Memory For Book-Based Role-Playing Agents
Researchers propose a new memory architecture called REVERIEMEM for book-based role-playing agents to improve the accuracy of the characters' knowledge boundaries and reduce stylistic monotony. They evaluate their…
Staying In Character: Perspective-Bounded Memory For Book-Based Role-Playing Agents
Researchers propose a three-layer memory architecture named REVERIEMEM for book-based role-playing agents to address the issues of Factual Overreach and Stylistic Monotony in current LLM role-playing systems. The…
The Unfireable Safety Kernel: Execution-Time AI Alignment for AI Agents and Other Escapable AI Systems
The authors present the Unfireable Safety Kernel, a Rust implementation that serves as an execution-time AI alignment layer for escapable AI systems. This system aims to provide authorization mechanisms for controlling…
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