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MM-IssueLoc: A Controlled Benchmark for Evaluating Visual Evidence in Multimodal Repository-Level Issue Localization
Researchers have created a new benchmark called MM-IssueLoc to evaluate the effectiveness of multimodal repository-level issue localization, which includes visual evidence such as screenshots and logs. Current…
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 multimodal repository-level issue localization, which includes visual evidence such as screenshots, error dialogs, and…
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
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…
Pretraining Data Can Be Poisoned through Computational Propaganda
The research discusses the potential for malicious content to be injected into large-scale pretraining datasets used for training language models (LMs). The authors propose a novel analysis method, HalfLife, to…
Pretraining Data Can Be Poisoned through Computational Propaganda
The research shows that harmful information can be introduced into language models during their pre-training phase by manipulating web-scale discussion interfaces, such as online forums or comment sections. A new…
The Industrialization of Research ; On AI-Driven Science and Its Consequences
This paper discusses the industrialization of research through the use of artificial intelligence (AI), referring specifically to the US Department of Energy's Genesis Mission. The author raises seven concerns about…
Experience Memory Graph: One-Shot Error Correction for Agents
Researchers propose a new framework called Experience Memory Graph (EMG) to help large language model agents recover from failures more effectively in complex, long-horizon tasks. Instead of using prompt-based…
Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study
Researchers propose a new method for feature selection in renewable energy prediction pipelines, called Cluster-based Sequential Feature Selection (CSFS). This method aims to address the gap in limited or unsystematic…
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…
Agentic Service-Oriented Computing: A Manifesto for the Next Frontier of Service-Oriented Computing
The text discusses a new research area called Agentic Service-Oriented Computing (ASOC), which focuses on engineering agents as services, managing their complex distributed workflows, and ensuring their reliability for…
Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
Researchers propose E3, a method to help large language model agents estimate the complexity of tasks and reduce unnecessary work. The proposed method reduces cost, tokens, and inspected files by significant margins on…
Silent Alarm: A J-Space Protocol for Comparing Danger Recognition Across Models and Quantization Levels
The research proposes JADR, a protocol for measuring the internal safety mechanisms of language models before their first response is generated. It uses Jacobian space to record top-k J-space tokens from prompts and…
A Multimodal Dataset for Large Language Model Applications in the Energy Domain
The paper introduces a new open-access dataset called mAIEnergy which is a multimodal corpus designed to support Large Language Model (LLM) applications in the energy sector. It contains various types of data including…
Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination
Researchers present G-Frame, a new framework that uses artificial intelligence (AI) to create a high-quality dataset and train a model called OmniChem. This model performs as well as another AI model (GPT 4o mini) on…
Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation
The text introduces IdeaGene-Bench (IG-Bench), a benchmark for evaluating AI systems' ability to follow the inheritance structure of scientific ideas, similar to how biological genomes evolve. IG-Bench uses the…
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
Researchers propose a proactive memory agent for long-horizon tasks in which an action agent must make decisions based on state information scattered across an expanding trajectory. The proposed memory agent updates a…
SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling
The paper proposes a new LLM (Language Model) scheduling method called SMETRIC for serving agents. This method aims to balance the workload between instances by focusing on the first request of each agent session and…
Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis
This research presents a large-scale descriptive analysis of an AI-based learning assistant named Syntea, used by 77,543 students in distance studies. The study examines usage patterns across various demographic and…
Agon: Competitive Cross-Model RL with Implicit Rival Grading of Reasoning
The paper introduces Agon, a new reinforcement learning approach that trains two reasoning models by having them compete against each other. This competitive grading system aims to improve the quality of thinking…
Creativity from Friction: Human-AI Interaction for Exploratory Structural Design
This research discusses the limitations of current AI systems in creative fields like architecture and structural design, which often require exploratory and iterative processes. The authors propose a new approach for…
Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows
A study investigates whether Language Model (LLM) generated skills can improve the performance of AI in data science tasks compared to traditional methods. The study tests this across four stages of a data science…
MIRA-Math: A Benchmark for Minimal Information Requesting and Mathematical Reasoning
The researchers introduce MIRA-Math, a benchmark for testing AI models' ability to ask for and integrate missing facts to solve mathematical problems. The benchmark contains 2310 instances from 22 mathematical families.
A toy framework for single and multi-agent human-AI curiosity ecosystems
This research paper introduces a simplified framework for understanding curiosity within single and multiple agent systems. The framework considers how an agent decides to ask questions based on factors like immediate…
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