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2,343 notes. Notes 1,351 to 1,400, page 28 of 47.
A Glimpse into Long-term Physical Coexistence with Intelligent Robots
The paper introduces PHILIA, a multi-robot agent designed to facilitate long-term physical coexistence with intelligent robots. The agent aims to support diverse user interfaces, maintain long-horizon memory of users…
A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Researchers introduce REGRIND, a method that enables robots to learn dexterous manipulation policies from a single human demonstration. The method retargets hand-object motion to a robot reference and trains an RL…
A Multimodal Dataset for Large Language Model Applications in the Energy Domain
This paper introduces a new open-access dataset called mAIEnergy, which is designed to support the development of Large Language Models (LLMs) in the energy sector. The dataset contains various types of data including…
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…
Active Offline-to-Online Reinforcement Learning
Researchers propose an active policy selection method for fine-tuning in offline-to-online reinforcement learning (O2O-RL) settings, where the goal is to allocate a limited online interaction budget effectively among…
Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming
Researchers have developed a new method called AHA for automated red-teaming of production language models like Claude Code and Codex. This method uses one agentic research environment to discover reusable…
Agent Hacks Agent: Autoresearch for Production-Agent Red-Teaming
Researchers present AHA, an automated red-teaming approach for production language models like Claude Code and Codex. AHA discovers reusable vulnerability knowledge about other agents by proposing a vulnerability…
Agentic Routing: The Harness-Native Data Flywheel
The authors propose a new method for routing large language model agents called Harness-Native agentic routing. This method selects either a single best-fit model or multiple complementary models based on the full…
Agentic Routing: The Harness-Native Data Flywheel
The authors propose a new method of model selection for large language model agents called Harness-Native agentic routing. This method selects the best-fit model or multiple complementary models based on the full…
Agentic Skill Optimization over Lie Algebroids
The text introduces LASKO, a new framework for optimizing skills in agentic systems. LASKO models skills as structured artifacts and edit policies as sections of a Lie algebroid. The framework achieves significant…
An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory
This research paper introduces an explainable AI system designed to detect conversational scams that can span multiple weeks or months and build trust to request money or sensitive information. The system, when tested…
An Explainable Agentic System for Detection of Conversational Scams with Summary-Based Memory
The paper presents an 'explainable agentic system' for detecting conversational scams over multiple messages, which existing systems often fail to do effectively. The new system achieves high accuracy in both…
Auditing the Risk Claims of Distributional Reinforcement Learning
In a study titled 'Auditing the Risk Claims of Distributional Reinforcement Learning', Hari Prasad investigates whether the risk claims made by distributional reinforcement learning agents are accurate. The study finds…
Auditing the Risk Claims of Distributional Reinforcement Learning
The research paper 'Auditing the Risk Claims of Distributional Reinforcement Learning' by Hari Prasad questions the accuracy of risk claims made by distributional reinforcement learning agents, which are used for…
AutoMatBench: An Automatic Optimization Toolkit for the Acceleration of Material Properties Prediction Benchmarking
The paper presents AutoMatBench, an automatic optimization toolkit that addresses the limitations of MatBench, a widely used material property prediction benchmarking tool. MatBench lacks the ability to reflect the…
BackgroundMellow: A Multi-Modal Cohesive Framework for Narrative-Driven Rich Cinematic Soundscape Generation
The paper presents BackgroundMellow, a new framework for generating synchronized and immersive audio for long-form narratives. The framework treats sound generation as an orchestration problem and uses a…
Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal
A study investigates the limitations of current Requirements Engineering (RE) practices in supporting explainability requirements, particularly within industrial contexts. The research focuses on Daimler Truck and…
Evaluating RE Practices for Explainability: Synthesizing Insights from Daimler Truck into an Explainable RE Framework Proposal
This research paper reports early findings from an ongoing study investigating how requirements engineering (RE) practices support explainability in AI-based systems, particularly in the industrial context of Daimler…
Evidence-Backed Video Question Answering
The authors propose Evidence-Backed Video Question Answering (E-VQA), a new task that requires models to not only provide answers but also the specific spatial and temporal evidence from videos. They introduce…
Extending LLM Context via Associative Recurrent Memory
In this research, the authors propose a new method called Associative Recurrent Memory Transformer (ARMT) to extend the context length of large language models (LLMs). This is crucial for many real-world applications…
From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence
The paper proposes a roadmap for developing artificial general intelligence that can reason and act in the physical world. The authors identify three gaps in current progress: inconsistencies in model roles…
From World Action Models to Embodied Brains: A Roadmap for Open-World Physical Intelligence
The paper discusses the development of artificial intelligence that can act in the physical world. It highlights the importance of World Action Models (WAMs) and identifies three gaps in current research: model roles…
HCRMap: Pressure-Aware Hot-Expert Residency Mapping for 3.5D MoE Chiplet Inference
The paper proposes HCRMap, a framework for managing hot expert replicas in 3.5D Mixture-of-Experts (MoE) inference systems. The aim is to dynamically place and reuse hot experts across different memory tiers to…
Heuristic Learning for Active Flow Control Using Coding Agents
A new method for active flow control is proposed in this research paper. Instead of optimizing neural network parameters, the authors use coding agents to search directly for explicit executable feedback laws. The…
Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias
A research study presents an approach to understanding and mitigating bias in large language models (LLMs) used as judges, focusing on the hidden state of these models rather than just their input-output level. The…
Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias
The research investigates bias in Large Language Models (LLMs) acting as judges by exploring representation-level accounts, focusing on the hidden state of the LLM, complementing input-output level studies. The study…
Introducing Human-Centeredness in AI-Assisted Lexicography
The text introduces a framework for human-centered AI in lexicography, aiming to enhance opportunities while addressing concerns about automation and cultural diversity.
Introducing Human-Centeredness in AI-Assisted Lexicography
This research introduces a framework for using AI in lexicography that prioritizes human involvement and understanding. The framework addresses concerns about the impact on lexicographers' roles and linguistic…
Invariant Learning Dynamics of Transformers in Inductive Reasoning Tasks
The authors present a theoretical framework explaining the development of inductive reasoning abilities in Transformer language models. This framework applies to a wide range of inductive tasks and provides insights…
Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning
A new method using reinforcement learning (RL) has been proposed to improve the accuracy of decoding continuous three-dimensional motor imagery signals from non-invasive EEG data for brain-computer interfaces (BCIs)…
Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning
Researchers proposed a new method for improving the accuracy of decoding continuous three-dimensional motor imagery signals using non-invasive EEG-based BCIs. They introduced a two-stage decoding framework that applies…
LightMem-Ego: Your AI Memory for Everyday Life
The research paper introduces LightMem-Ego, a lightweight multimodal memory system designed for AI assistants on mobile and wearable devices. The system continuously captures and organizes egocentric visual and audio…
LoRA-Based Cascaded Multimodal Fusion for Action Recognition in Medical Training Environments
The paper introduces a new framework for recognizing actions and activities in medical training environments using a cascaded Low-Rank Adaptation (LoRA)-based multimodal fusion approach. This method allows for the…
MAGIC: Transition-Aware Generation of Navigable Multi-Scene Game Worlds with Large Language Models
The paper presents MAGIC, a system that generates connected multi-scene game worlds using large language models. The system overcomes three challenges in single-scene methods: cross-scene consistency, in-scene…
MAGIC: Transition-Aware Generation of Navigable Multi-Scene Game Worlds with Large Language Models
The research paper introduces MAGIC, a system that generates multi-scene game worlds using large language models (LLMs). The system overcomes three obstacles in single-scene methods: cross-scene consistency, in-scene…
Metacognition in LLMs: Foundations, Progress, and Opportunities
This paper provides a comprehensive overview of metacognition in Large Language Models (LLMs), highlighting the importance of this aspect for effective learning, problem solving, and communication. It discusses recent…
Metacognition in LLMs: Foundations, Progress, and Opportunities
This research paper provides a comprehensive overview of the current state of metacognition in Large Language Models (LLMs). The authors discuss recent advancements, methods, and benchmarks for measuring and evaluating…
MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agents
The paper introduces MM-ToolSandBox, a new evaluation framework for visual tool-calling agents. It supports multi-image, multi-turn tasks and offers a stateful execution environment with over 500 tools across 16…
MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agents
Researchers introduce MM-ToolSandBox, a new evaluation framework for visual tool-calling agents. This framework supports over 500 tools across 16 application domains and can handle multi-image, multi-turn tasks with…
MM-ToolSandBox: A Unified Framework for Evaluating Visual Tool-Calling Agents
Researchers have introduced MM-ToolSandBox, a tool for evaluating visual agents that can perform tasks by calling tools based on visual inputs. The framework includes over 500 tools across 16 application domains and…
Omni-Decision: A Progressive Evidence-State Agent System for Omni-Modal QA
The paper introduces Omni-Decision, a training-free system designed to answer questions across multiple modalities such as videos, audio, images, web pages, and computation results. It focuses on maintaining a…
Omni-Decision: A Progressive Evidence-State Agent System for Omni-Modal QA
The paper introduces Omni-Decision, a system aimed at answering questions where evidence is spread across multiple modalities like videos, audio, images, web pages, and computation results. The system maintains a…
Playful AI in Professional Email: A Field Experiment on Tone and Recipient Engagement
A study examined how using a large language model (GPT-5) to rewrite work emails in either a playful or professional tone affects recipient behavior. The study found that playful editing increased emotional positivity…
Playful AI in Professional Email: A Field Experiment on Tone and Recipient Engagement
A study found that using a large language model (GPT-5) to write work emails in a playful tone increased emotional positivity in recipients, while using a professional tone decreased it. However, neither condition…
Playful AI in Professional Email: A Field Experiment on Tone and Recipient Engagement
A study found that using a playful tone in work emails generated by GPT-5 increased emotional positivity in responses, while a professional tone decreased it. However, neither condition directly affected open rates…
Proxy Exploration and Reusable Guidance: A Modular LLM Post-Training Paradigm via Proxy-Guided Update Signals
The researchers propose a new method called Proxy-guided Update Signal Transfer (PUST) for improving the domain-specific capabilities of large language models (LLMs). Instead of using the primary model for exploration…
Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
Researchers investigate if large language models (LLMs) can reproduce human biases in decision-making, specifically in route choice, without explicitly specifying parameters for Cumulative Prospect Theory (CPT)…
Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
This research explores the ability of large language models (LLMs) to reproduce human biases in route choice decisions, traditionally described using Cumulative Prospect Theory (CPT). The study finds that LLMs can…
StoryTeller: Training-Free Narrative Grounding for Long-Form Audio Description
The authors propose a new framework called StoryTeller for long-form audio description (AD). Unlike modern video-language models, StoryTeller maintains a narrative memory that carries forward story-relevant information…
StructAgent: Harness Long-horizon Digital Agents with Unified Causal Structure
In this paper, researchers introduce StructAgent, a framework designed to improve long-horizon digital agents' performance in tasks with evolving contexts and complex workflows. The framework introduces a unified state…
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