All notes, newest first
2,343 notes. Notes 1,551 to 1,600, page 32 of 47.
POO-LPSP: Parallel Osprey Optimized Least Penalty-Squared Prioritization Methods for Priority Derivation in the Analytic Hierarchy Process
This research introduces the Parallel Osprey Optimized Least Penalty-Squared Prioritization (POO-LPSP) method, an improvement on previous Least Penalty-Squared Prioritization (LPSP) optimization models. The POO-LPSP…
QCNN with Rough Path Signature Kernels
The research paper proposes a hybrid quantum-classical architecture to address time series classification challenges, which are common in various scientific and engineering domains. The architecture integrates Quantum…
Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents
Researchers study a failure mode in tool-using Language Model (LLM) agents, where they violate policies without appearing to do so, leading to silent errors such as cancelled bookings or unverified claims. A…
Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents
Researchers found that LLM agents (Language Model-based tools) can violate policies without being detected because the resulting state changes are not reported by the tools or the agent. The researchers studied this…
Reasoning Consistency Scanning: A Framework for Auditing Chain-of-Thought Validity in AI Safety Evaluations
The paper introduces a method called Reasoning Consistency Scanning to assess the logical consistency between stated reasoning and answers in AI safety evaluation transcripts. The method can be applied to various AI…
Recursive Self-Improvement in AI: From Bounded Self-Refinement to Autonomous Research Loops
The text discusses the increasing trend of AI systems improving themselves through various methods such as revising outputs, adapting their own harnesses, and conducting AI research itself. The authors surveyed 1250…
Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26
The authors present Reward-Adaptive Iterative Discovery (RAID), a new approach for automated game testing using Reinforcement Learning (RL) in the gaming industry. The case study is based on EA SPORTS NHL 26's…
Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26
The researchers present Reward-Adaptive Iterative Discovery (RAID), a new approach for automated game testing in the gaming industry, specifically focusing on the ice hockey game EA SPORTS NHL 26. The goal is to reduce…
RLVP: Penalize the Path, Reward the Outcome
The paper 'RLVP: Penalize the Path, Reward the Outcome' by Bojie Li and Noah Shi proposes a method for training artificial agents to perform tasks effectively in real-world environments. This method addresses two main…
RLVP: Penalize the Path, Reward the Outcome
The paper 'RLVP: Penalize the Path, Reward the Outcome' discusses challenges faced by agents learning from real-world interactions and proposes a solution to improve their performance and adherence to constraints. The…
Selective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF
The paper proposes two methods for improving the efficiency of Reinforcement Learning from Human Feedback (RLHF) when applied to diffusion models, which are often inefficient due to high feedback requirements. The…
Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
The paper proposes a new method called Single-rollout Asynchronous Optimization (SAO) for Reinforcement Learning (RL) in post-training large language models (LLMs). SAO aims to improve stability and task effectiveness…
Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
The paper introduces Single-rollout Asynchronous Optimization (SAO), a new approach to reinforcement learning (RL) aimed at post-training large language models (LLMs). SAO addresses stability and off-policy challenges…
Single-Rollout Asynchronous Optimization for Agentic Reinforcement Learning
In this paper, the authors introduce Single-rollout Asynchronous Optimization (SAO), a new approach to reinforcement learning (RL) for post-training large language models (LLMs). SAO aims to improve training stability…
SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents
Researchers have created SkillCenter, a large-scale library of structured skills for autonomous AI agents. The library contains over 216,000 skills across 24 domains, obtained from various sources like peer-reviewed…
SkillCenter: A Large-Scale Source-Grounded Skill Library for Autonomous AI Agents
The paper introduces SkillCenter, a large-scale skill library for autonomous AI agents, containing 216,938 structured skills across 24 domain bundles. The skills are obtained from various sources like peer-reviewed…
SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis
Researchers propose SpaCellAgent, an autonomous multi-agent framework that automates spatiotemporal analysis in single-cell transcriptomics. It improves efficiency by over 40% while maintaining expert-level…
Stability of Flow Models for Graph Signals
The paper presents research on the stability of continuous flow models for generating signals on graphs. The authors analyze normalized flow models parameterized by Graph Neural Networks (GNNs) and demonstrate that…
SynthAVE: Scalable Synthetic Labeling for E-Commerce with LLM-Arena Validation
Researchers have developed a method called SynthAVE to generate large amounts of labeled data for e-commerce attribute extraction using synthetic labels validated by multiple large language models (LLMs). The benchmark…
The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents
The researchers explore the impact of biased judges in self-evolving agents, focusing on skill retirement, a mechanism that helps maintain the quality of an agent's knowledge base by retiring poorly performing skills…
The Blind Curator: How a Biased Judge Silently Disables Skill Retirement in Self-Evolving Agents
The authors investigate how a biased judge affects the retirement of skills in self-evolving agents, particularly in reference-free tasks. They show that a biased judge can cause a mechanism failure they call 'silent…
Towards Agentic AI Governance: A Preliminary Assessment
This paper is a systematic review of the emerging literature on agentic AI governance, focusing on the unique challenges posed by the increasing deployment of autonomous AI systems in 2025. The authors identify key…
Towards Agentic AI Governance: A Preliminary Assessment
This paper reviews emerging literature on agentic AI governance, a new field that deals with autonomous AI systems capable of planning and executing tasks. The authors analyze the unique features of agentic AI that…
Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation
The text discusses a scoping review of vision foundation models (VFMs) used in radiology. The study found that while these models are becoming common in radiological imaging, their development and evaluation methods…
When Prompts Ignore Structure: Graph-Based Attribute Reasoning for Calibrated VLMs
The paper proposes ARGTCA, a method to improve the calibration of Vision-Language Models (VLMs) by using Graph Attention Networks to capture inter-attribute dependencies and reduce overconfidence during test-time…
A Definition and Roadmap for World Models
The provided text discusses the concept of 'world models', which are internal simulators used in AI to understand the structure and dynamics of an environment. The authors propose a definition for world models, outline…
A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems
A new study presents a Physics-Informed Neural Network (PINN) framework to model the transient elastodynamic wave propagation in bimaterial systems, specifically focusing on a steel-aluminum specimen. The PINN…
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…
A toy framework for single and multi-agent human-AI curiosity ecosystems
The paper presents a basic framework to understand curiosity from the perspective of an ecosystem. It discusses how an individual agent's curiosity is influenced by factors like immediate uncertainty reduction, costs…
An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery
The researchers propose an experimental design framework to systematically evaluate the autonomous model discovery behavior of large language model coding agents, like Codex and Claude Code. The framework considers…
Analysis-by-Proxy: Localization Signals in VLMs Operating as Condition Encoders
Researchers investigate why Vision-Language Models (VLMs) struggle to maintain localization accuracy during image editing, even though they have strong localization capabilities on their own. The study suggests that…
Bridging Physical Reasoning and Task Generalization via Visual Action Outcome Reasoning Alignment
Researchers propose a new reward system called VAORA to help vision-language models (VLMs) perform better in interactive physical reasoning tasks. The new system addresses two common issues: hallucinated…
Bridging Physical Reasoning and Task Generalization via Visual Action Outcome Reasoning Alignment
Researchers propose a new reward system called VAORA to improve the performance of vision-language models in interactive physical reasoning tasks. The proposed system aims to address two main issues: hallucinated…
Bridging Physical Reasoning and Task Generalization via Visual Action Outcome Reasoning Alignment
Researchers have developed a new method called VAORA (Visual Action Outcome Reasoning Alignment) to improve the performance of vision-language models in interactive physical reasoning, especially in unseen tasks and…
Danus: Orchestrating Mathematical Reasoning Agents with Fact-Graph Memory
The paper introduces Danus, a system designed to effectively coordinate and scale mathematical reasoning agents by using a shared fact graph as a global memory-management mechanism. The system consists of a main agent…
Danus: Orchestrating Mathematical Reasoning Agents with Fact-Graph Memory
The paper proposes a system called Danus that aims to help mathematical reasoning agents tackle complex, research-level problems by coordinating them effectively and managing their shared memory through a global fact…
Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities
The researchers introduce DataGovBench, a new benchmark for evaluating large language models (LLMs) in practical scenarios of real-world data analysis. The benchmark tests the models' ability to answer complex…
Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities
The paper introduces DataGovBench, a benchmark for evaluating the performance of Large Language Models (LLMs) in practical scenarios, such as complex data analysis and insight discovery from large multi-tabular…
Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
Researchers present TOFFEE, a system that generates high-quality data agent trajectories using Monte Carlo Tree Search with adaptive model selection and cross-task prefix reuse. This system aims to help data agents…
Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
The paper introduces TOFFEE, a system designed to generate high-quality data agent trajectories using Monte Carlo Tree Search (MCTS) with adaptive model selection and cross-task prefix reuse. This is aimed at…
DepthWeave-KV: Token-Adaptive Cross-Layer Residual Factorization for Long-Context KV Cache Compression
The research introduces DepthWeave-KV, a new cache compression method for long-context language models. This method adapts to the needs of different tokens by factorizing key and value states across neighboring…
Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
The researchers introduce a method called a recall-controlled probe cascade that predicts early on if an interaction between a large language model (LLM) agent and a task is likely to fail. This prediction allows for…
Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
Researchers propose a method to predict early on if large language models (LLMs) are likely to fail at solving multi-step tasks and abort the process before consuming too much compute. This method uses probes on hidden…
DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail
Researchers present DT-Guard, a content safety guardrail model for large language models in open-world applications. The model uses a Reasoning-Active Training, Reasoning-Free Inference paradigm and aims to improve…
EcoVision: AI-Powered Drone Imaging for Salt Marsh Vegetation Monitoring and Dominance Mapping
A new AI-powered drone imaging system called EcoVision has been developed for monitoring and mapping the dominance of two types of halophytic grasses in salt marshes. The system uses high-resolution RGB images from…
Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs
The researchers evaluate the performance of uncertainty estimation methods across 22 languages in high-, mid-, and low-resource settings using two human-curated question and answer datasets. They compare open and…
Finding H. pylori in the Fine Print: Evidence-Linked Multi-Agent Case Finding from Gastric Biopsy Reports
Researchers evaluated a system called nMAS for extracting information about Helicobacter pylori infections from gastric biopsy reports. The system correctly identified the presence of H. pylori and associated…
Finding H. pylori in the Fine Print: Evidence-Linked Multi-Agent Case Finding from Gastric Biopsy Reports
Researchers evaluated a system called nMAS that can automatically extract information about Helicobacter pylori infection from gastric biopsy reports. The system had an accuracy of 98.61% in classifying four binary…
Finding H. pylori in the Fine Print: Evidence-Linked Multi-Agent Case Finding from Gastric Biopsy Reports
Researchers evaluated a system called nMAS for extracting information from gastric biopsy reports to identify Helicobacter pylori infection. The system achieved high accuracy in classifying samples, and could…
FreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM Inference
The paper introduces FreqDepthKV, an inference-time cache compression method for long-context LLM (Large Language Model) inference that reduces memory and bandwidth costs while maintaining task accuracy on various…
Page 32 of 47
