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Notes on artificial intelligence
262 notes, page 6 of 11
Man, Machine, and Masterpiece: Artistic Ownership in the AI Era
The text discusses a prototype named ArtSplit that quantifies human and AI contributions in artistic work. However, it suggests that this approach doesn't align with artists' understanding of creative intent and…
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
Researchers have introduced a new benchmark called MM-IssueLoc for evaluating the effectiveness of systems in repository-level issue localization that also includes visual evidence such as screenshots, error dialogs…
MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
Researchers introduce MM-IssueLoc, a benchmark and evaluation protocol designed specifically for repository-level issue localization with visual evidence. The dataset includes 652 issue-PR instances across multiple…
OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios
A research paper titled 'OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios' introduces a new benchmark called OmniaBench, designed to evaluate the capabilities of general AI agents in diverse…
OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios
Researchers have introduced OmniaBench, a new benchmark for evaluating the abilities of general AI agents across various scenarios with explicit state spaces. The benchmark includes a hierarchical taxonomy covering…
Plover: Steering GUI Agents through Plan-Centric Interaction
The paper introduces Plover, a system for GUI automation that makes task plans visible and editable by users, allowing them to supervise, correct, or intervene in the system's behavior. The system supports natural…
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
The paper introduces SearchOS, a system-level multi-agent framework designed to improve the quality and completeness of search results in open-domain information-seeking tasks. The framework addresses the issue of…
Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
Researchers propose a graph neural network approach to detect and analyze reasoning structures in large language models (LLMs) generated texts for improved robustness against paraphrasing and obfuscation techniques…
Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution
The paper proposes a new method for detecting text generated by large language models (LLMs) and attributing authorship to them. The method goes beyond just analyzing surface-level linguistic features and instead…
CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems
The paper introduces Canonical Action Verification and Attestation (CAVA), a system designed to convert diverse agent activities into standardized action objects, addressing the challenge of governing heterogeneous AI…
Early Adoption of Agentic Coding Tools by GitHub Projects
The paper investigates the early adoption of automated coding tools by GitHub projects. It found that while intensive adoption is concentrated in a few projects, smaller projects tend to have higher participation…
Early Adoption of Agentic Coding Tools by GitHub Projects
This research investigates the adoption and management of agentic coding tools in software development on GitHub. The study analyzes 25,264 agentic pull requests from 2,361 popular repositories to understand (1) how…
Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code
Researchers introduce 'generative compilation', an approach to get feedback from compilers on partially generated code during the code generation process. The method aims to reduce errors and improve the functionality…
Groc-PO: Grounded Context Preference Optimization for Truthful Multimodal LLMs
Researchers propose a new method called Grounded Context Preference Optimization (Groc-PO) to improve the truthfulness and utility of Multimodal Large Language Models (MLLMs). Current MLLMs often make errors due to…
Rethinking Penetration Testing for AI-Enabled Systems: From Resource Compromise to Behavioral Objective Violation
The paper proposes a new approach to penetration testing for AI-enabled systems, focusing on evaluating whether adversaries can manipulate system behavior without directly compromising the underlying infrastructure…
SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing
Researchers present Safety Sentry, a lightweight guard model that makes decisions about executing, asking for help, or refusing actions in real-world environments. This model aims to improve safety and autonomy by…
When Bots Join the Team: Bot Adoption and the Institutional Fabric of Open-Source Software Projects
Researchers studied the impact of AI bots joining human teams in open-source software projects. They found that bot adoption leads to more repeated collaboration, less conflict, and more distinctive outputs. However…
A Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation Study
A new system called Pythia was developed, which automatically generates and optimizes extraction prompts for clinical concepts without the need for manual prompt engineering or fine-tuning. The researchers compared…
Agentic Service-Oriented Computing: A Manifesto for the Next Frontier of Service-Oriented Computing
The paper introduces Agentic Service-Oriented Computing (ASOC), a new research area focusing on engineering agents as services, orchestrating services through autonomous and semi-autonomous agents, and governing…
Audio-Native Speech Recognition with a Frozen Discrete-Diffusion Language Model
Researchers train a discrete diffusion language model to transcribe speech instead of using autoregressive decoders that emit one token at a time. The model is based on a mixture-of-experts called DiffusionGemma, and…
Multi-Perspective Agentic Program Repair via Code Property Graphs and Temporal Execution Graphs
A new automated program repair framework called CT-Repair is presented, which addresses two limitations of existing large language models in this area. The first issue is the large and repetitive nature of raw…
Multi-Perspective Agentic Program Repair via Code Property Graphs and Temporal Execution Graphs
The paper introduces CT-Repair, an agentic Automated Program Repair (APR) framework that addresses the limitations of large language models in APR. The framework uses Code Property Graphs (CPG) and Temporal Execution…
OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning
The paper introduces OOD-RL-Bench, a new framework for testing out-of-distribution (OOD) detection in reinforcement learning (RL). The framework is designed to evaluate the performance of detectors against various…
Resist and Update: Counterfactual Report Coordinates for Incentive-Compatible LLMs
The authors present a method for creating counterfactual report mediators, which help align language models by making their reports invariant to certain influences (like pressure or prestige) and responsive to others…
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