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2,343 notes. Notes 1,501 to 1,550, page 31 of 47.
Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis
This study presents a large-scale analysis of an AI-based learning assistant called Syntea, used by 77,543 students in distance learning. The analysis examines usage patterns based on demographic and structural…
Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis
The text describes a study that analyzed the use of an AI-based learning assistant (Syntea) among 77,543 students in distance education, looking at usage patterns across various demographic and structural factors. The…
Using AI-based Learning Assistants in Higher Education: A Large-Scale Descriptive Analysis
The study presents a large-scale analysis of an AI-based learning assistant named Syntea, used by 77,543 students in distance studies. The research focuses on usage patterns across various demographic and structural…
Validity of LLMs as data annotators: AMALIA on authority
A study evaluates AMALIA, a Portuguese language model, in measuring moral values and authority. The model shows agreement with human coders but fails to perform well when the test is broken down into smaller parts…
Validity of LLMs as data annotators: AMALIA on authority
A study tests the validity of a large language model (LLM), AMALIA, in measuring moral foundation concepts for European Portuguese. The study finds that while AMALIA agrees with human coders on these concepts, it does…
VocaDet: Sample-Driven Open-Vocabulary Object Detection and Segmentation via Visual Tokenization and Vector Database Retrieval
The text presents a new framework called VocaDet for open-vocabulary object detection and segmentation. This framework learns object concepts directly from user-provided positive and negative sample collections without…
WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving
The research presents WCog-VLA, a novel framework for autonomous driving that bridges semantic world forecasting with generative world evolution. This dual-level model aims to overcome the limitations of existing…
WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving
Researchers propose WCog-VLA, a dual-level model for autonomous driving, which combines semantic world forecasting and generative world evolution to bridge the gap between reactive and proactive driving. The semantic…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
WebSwarm is a framework that uses multiple language model-based search agents to perform complex, deep-and-wide web searches. It aims to overcome the limitations of single ReAct-style agents by dynamically…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
The paper introduces a new framework called WebSwarm, which uses multiple language model (LLM)-based search agents to handle complex, deep, and wide web searches more effectively than single agents or existing…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
The paper proposes a new framework called WebSwarm for multi-agent search systems. This system aims to improve deep-and-wide web searches by dynamically creating search nodes that can delegate tasks recursively and…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
Researchers propose a new framework called WebSwarm for deep and wide web searches using large language models (LLMs). The framework aims to improve the ability of single agents to handle complexity and coverage…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
The paper presents a new framework called WebSwarm for web search using multiple agents. This system aims to overcome the limitations of single agents by simultaneously handling depth and coverage, improving recursive…
When Synthetic Speech Is All You Have: Better Call GRPO
The paper presents a new approach for adapting Large Language Model (LLM) based Automatic Speech Recognition (ASR) systems to regulated domains like banking using synthetic text-to-speech instead of real speech due to…
When Synthetic Speech Is All You Have: Better Call GRPO
Researchers present a method called Group Relative Policy Optimization (GRPO) that improves the accuracy of synthetic speech-to-text conversion compared to traditional methods. This could be particularly useful in…
When the Judge Changes, So Does the Measurement: Auditing LLM-as-Judge Reliability
The study investigates the inconsistency in Language Model (LLM) as judge scores when different evaluators are used. It compares two methods of upgrading LLMs: scaling Qwen3 dense judges and moving across MiniMax APIs…
When the Judge Changes, So Does the Measurement: Auditing LLM-as-Judge Reliability
The paper investigates how changes in the evaluators used by large language models (LLMs) to score candidate responses can impact the model's performance and reliability. The study examines two common upgrade paths for…
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…
Workflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows
The paper proposes a conceptual model for large language model (LLM) applications that use explicit workflows. The model is designed to represent various elements of the workflow such as definitions, instances, and…
Workflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows
The paper proposes a conceptual model for large language model (LLM) applications that uses explicit workflows for various tasks. This model aims to represent workflow definitions, instances, and other related elements…
Agentic Data Environments
This research focuses on Agentic Data Environments, a new approach to automation aimed at increasing benefits while limiting the potential negative consequences. The data environments are designed to enhance agent…
Agentic Data Environments
The text discusses early work on Agentic Data Environments (ADE), a system that allows autonomous agents to operate safely and efficiently over a broader data environment. The goal is to increase the benefits of…
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…
Agon: Competitive Cross-Model RL with Implicit Rival Grading of Reasoning
The paper introduces a new method called Agon for reinforcement learning that allows two competing models to grade each other's reasoning during training. This approach aims to improve the quality of solutions by…
Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents
The paper proposes an action-graded harm rubric for evaluating AI agents' behavior during cybersecurity attacks. This scale assesses the severity of an agent's actions based on factors like reversibility, scope…
Beyond Attack-Success Rate: Action-Graded Severity Scale for Tool-Using AI Agents
The paper introduces an action-graded harm rubric for evaluating AI agents in a red-teaming scenario. This new method rates the severity of an agent's actions on a seven-level scale, considering factors such as…
Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass
The researchers present an approach called Jailbreak that allows analytical workloads to bypass the traditional database driver and read storage files directly, using Large Language Models (LLMs) to regenerate table…
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…
CARLA-GS: Decoupling Representation, Reasoning, and Physics Simulation for Autonomous Driving Corner-Case Synthesis
Researchers have proposed a new method called CARLA-GS for generating corner cases in autonomous driving simulations that are essential for safety evaluation. The method decouples the visual representation, semantic…
CARLA-GS: Decoupling Representation, Reasoning, and Physics Simulation for Autonomous Driving Corner-Case Synthesis
The paper proposes CARLA-GS, a modular pipeline for synthesizing corner cases in autonomous driving simulations. The pipeline decouples visual representation, semantic reasoning, and physics-based execution while…
Co-LMLM: Continuous-Query Limited Memory Language Models
Researchers have developed a new type of language model called Co-LMLM (Continuous-Query Limited Memory Language Model). This model stores factual knowledge in a knowledge base instead of memorizing it in its weights…
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…
Creativity from Friction: Human-AI Interaction for Exploratory Structural Design
This research discusses the limitations of AI agents in creative fields, focusing on structural design and architecture. The paper proposes that interactive systems that help users explore alternatives and refine…
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…
Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows
A study by Wei-Jung Huang found no significant improvement in performance for LLM-generated skills compared to the task prompt alone when applied across four stages of a data scientist's work: data preparation, data…
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…
Do LLM-Generated Skills Make Better AI Data Scientists? A Component Ablation Across Data-Science Workflows
A study investigates if LLM (Language Model) generated skills improve the performance of AI data scientists in various tasks across four lifecycle stages: data preparation, data extraction, statistical analysis, and…
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…
From Atomic Actions to Standard Operating Procedures: Iterative Tool Optimization for Self-Evolving LLM Agents
Researchers propose a method called EvoSOP to improve Large Language Model (LLM) agents' interaction with the real world. The proposed method optimizes the tools used by LLM agents through creating reusable Standard…
Future Confidence Distillation in Large Language Models
In this research paper from 2026, Sahil Kale explores the importance of accurate confidence estimation in large language models (LLMs) for various applications like retrieval and tool use. The study focuses on how…
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…
Institutional Red-Teaming: Deployment Rules, Not Just Models, Causally Shape Multi-Agent AI Safety
The text introduces institutional red-teaming, a new evaluation methodology for testing the impact of deployment rules on multi-agent AI safety. The study uses IABench-CA, a consequence-allocation benchmark, to test…
Institutional Red-Teaming: Deployment Rules, Not Just Models, Causally Shape Multi-Agent AI Safety
The paper introduces institutional red-teaming, a method for evaluating deployment rules in multi-agent AI. The study uses IABench-CA, a consequence-allocation benchmark, to test five canonical rules across seven model…
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.
Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors
The research discusses the study of multi-agent AI control in real-world scenarios where multiple agents operate over shared infrastructure. The paper introduces FakeLab, a synthetic AI-lab codebase used to evaluate…
Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors
The research focuses on AI control techniques to prevent malicious AI agents from subverting their operator's intent in multi-agent scenarios. The study introduces a synthetic AI lab (FakeLab) and evaluates single…
ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies
The paper introduces ORCAID, a method for extracting interpretable rule-based policies from reinforcement learning (RL) agents in complex environments with continuous action spaces. The method involves training an…
ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies
Researchers introduce ORCAID, a method for extracting interpretable rule-based policies from reinforcement learning (RL) agents in complex environments with continuous action spaces. The method uses an efficient…
Physics-Audited Agentic Discovery in Scientific Machine Learning
The paper introduces Physics-Audited Agentic Scientific Machine Learning (PA-SciML), a new workflow that verifies the accuracy of surrogate models discovered by large language model agents in scientific machine…
Physics-Audited Agentic Discovery in Scientific Machine Learning
Researchers have developed Physics-Audited Agentic Scientific Machine Learning (PA-SciML), a new workflow for machine learning in science that ensures discovered models follow certain physics rules such as boundary…
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