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2,343 notes. Notes 1,451 to 1,500, page 30 of 47.
OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice
A new benchmark, OmniFood-Bench, is introduced to evaluate the performance of Large Vision-Language Models (VLMs) in nutrient reasoning and personalized health advice for dietary management. The benchmark assesses VLMs…
OpenCoF: Learning to Reason Through Video Generation
The paper introduces OpenCoF, a framework that includes a new dataset and a fine-tuned video model for improving Chain-of-Frame (CoF) reasoning in video generation. The authors argue that existing models lack specific…
Pose-to-Biomechanics: Bridging 3D Human Pose Estimation and Biomechanical Attribute Prediction
The research proposes a lightweight plug-in called BioModule that can be added to any 3D pose estimator to predict biomechanical attributes from standard 17-joint 3D skeletons. It's trained and evaluated on an aligned…
Pose-to-Biomechanics: Bridging 3D Human Pose Estimation and Biomechanical Attribute Prediction
A new method called BioModule is proposed for predicting biomechanical attributes from standard 3D skeletons recovered from human motion, without requiring modifications to the upstream pose model. The method is…
ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
ProjAgent is a new system for generating code at the repository level. It introduces a method called 'procedural similarity' to retrieve relevant functions from the repository, even if those functions have different…
ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
The paper proposes ProjAgent, a new system for generating code at the repository level by introducing 'procedural similarity' as a retrieval signal. This method decomposes the target function into steps and retrieves…
ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
The paper introduces ProjAgent, a system for generating code at the repository level that considers procedural similarity as a new method for retrieving appropriate functions. ProjAgent deconstructs target functions…
ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
The paper proposes ProjAgent, a system for generating code at the repository level. It introduces 'procedural similarity' as a new way to retrieve relevant functions, addressing a limitation of existing methods that…
ProjAgent: Procedural Similarity Retrieval for Repository-Level Code Generation
The paper presents ProjAgent, a system for generating code at the repository level that uses 'procedural similarity' as a new signal in the retrieval process. This system decomposes the target function into…
Psychological Competence as a Missing Dimension in AI Evaluation
The paper discusses the need to consider psychological competence as an important aspect in evaluating AI systems that interact directly with humans, particularly in roles like advisors, coaches, tutors, and…
Psychological Competence as a Missing Dimension in AI Evaluation
The text discusses a need to evaluate human-AI interaction beyond just technical performance, focusing on how AI systems can affect users' cognition, emotions, decision-making, and behavior. The proposed evaluation…
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
Researchers propose a proactive memory agent for long-horizon tasks, where decision-relevant information can get buried as trajectories grow. The memory agent updates a structured bank from the recent trajectory and…
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
Researchers present a proactive memory agent for long-horizon tasks that helps an action agent remember decision-relevant state as trajectories grow. The memory agent updates a structured memory bank from the recent…
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…
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
Researchers introduce a proactive memory agent for long-horizon tasks, aiming to improve decision-making in scenarios where relevant state information can become buried over time. The memory agent updates a structured…
Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
Researchers propose a proactive memory agent for long-horizon tasks where decision-relevant information can get buried as trajectories grow. The agent updates a structured memory bank from the recent trajectory and…
Self-Adaptive Anomaly Detection with Reinforcement Learning and Human Feedback in Connected Vehicles
The paper presents an online anomaly detection framework for connected vehicles that uses a deep Q-network with self-attention to select suitable detectors, an ensemble of statistical drift detectors to monitor input…
Self-Adaptive Anomaly Detection with Reinforcement Learning and Human Feedback in Connected Vehicles
The paper proposes an online anomaly detection framework for autonomous cyber-physical systems (CPS), specifically connected vehicles, that combines three coordinated mechanisms: a deep Q-network with self-attention to…
SLORR: Simple and Efficient In-Training Low-Rank Regularization
The paper introduces SLORR, a simple and efficient method for in-training low-rank regularization of neural networks. It addresses the limitations of existing methods by being stateless and preserving the original…
SLORR: Simple and Efficient In-Training Low-Rank Regularization
The paper introduces SLORR, a simple and efficient low-rank regularization method for compressing neural networks during training. It addresses the limitations of existing methods by being stateless…
SLORR: Simple and Efficient In-Training Low-Rank Regularization
The paper introduces SLORR, a simple and efficient in-training low-rank regularization framework for neural networks. It aims to improve compressibility without significant accuracy loss, while avoiding the limitations…
SLORR: Simple and Efficient In-Training Low-Rank Regularization
The paper introduces SLORR, a simple and efficient in-training low-rank regularization framework for compressing neural networks without significant accuracy loss or relying on stateful cached quantities. The authors…
SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling
The paper proposes SMetric, a new scheduling method for Large Language Models (LLMs) in serving agents. The method aims to balance the session-centric workload of agents and increase Key-Value (KV$) reuse by…
SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling
The paper proposes a new method for scheduling Language Model (LLM) requests called SMetric, which aims to improve the performance of serving agents in terms of tokens per second (TPS). The current methods prioritize…
SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling
The paper 'SMetric: Rethink LLM Scheduling for Serving Agents with Balanced Session-centric Scheduling' presents a new approach to scheduling Language Model (LLM) requests for AI agents, rather than humans. The…
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…
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
The authors propose SolarChain-Eval, a benchmark for evaluating the trustworthiness of economic agents in decentralized energy markets. This benchmark ensures that agents make hourly decisions based on a…
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
The paper proposes SolarChain-Eval, a benchmark to evaluate the trustworthiness of economic agents in decentralized energy markets using a physics-constrained Markov Decision Process. The benchmark evaluates agents'…
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
The paper proposes SolarChain-Eval, a benchmark for evaluating the trustworthiness of economic agents in decentralized energy markets. The benchmark is designed to assess the performance of AI systems in cyber-physical…
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
The authors propose SolarChain-Eval, a benchmark for evaluating the trustworthiness of autonomous agents in decentralized energy markets. It assesses market utility, physical safety, slippage, action smoothness…
Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS
This research focuses on creating a five-stage pipeline to protect pedestrians' privacy in autonomous vehicles (AVs) by using a face-swapping method called Roop, which outperforms Ghost-v2 and strikes a balance between…
Swapping Faces, Saving Features: A Dual-Purpose Pipeline for Pedestrian Privacy in ITS
The paper focuses on creating a five-stage pipeline for protecting pedestrians' privacy in AI models used by autonomous vehicles (AVs) while maintaining the image attributes needed to train the models. The proposed…
The complexities of patient-centred conversational artificial intelligence
Researchers evaluated the performance of large language models in patient-chatbot conversations and found significant variations in communication patterns and emotional expression among real patients. They developed a…
The complexities of patient-centred conversational artificial intelligence
Researchers analyzed 2,053 conversations between patients and AI chatbots powered by large language models (LLMs) for symptom assessment. They found that communication patterns and emotional expressions vary widely…
The Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agentic Inequality
The paper proposes a new dimension for analyzing disparities in AI usage called the 'Context Access Divide' (CAD). This divide is based on whether an AI system can autonomously access a user's stored knowledge or…
The Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agentic Inequality
The text introduces a new concept called the 'Context Access Divide' (CAD). This divide refers to the qualitative difference in AI utility experienced by two users who have similar access to AI agents, but one system…
The Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agentic Inequality
The text introduces a concept called 'Context Access Divide' in the field of AI, which refers to a qualitative difference in AI utility experienced by two users with similar access levels. This divide arises when one…
The Context Access Divide: Interaction-Level Architecture as a Complementary Dimension of Agentic Inequality
The text discusses a new concept called the 'Context Access Divide' in AI agents, which is a divide between users who can autonomously access their own knowledge corpus (Dynamic Context Retrieval) and those who need to…
The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
The authors of this paper introduce a new method to evaluate the effects of post-training quantization on large language models, as this technique is commonly used to deploy models in resource-limited settings. The…
The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
The authors investigate how the quantization process used to deploy large language models in resource-limited settings can lead to unnoticed behavioral changes, even when task performance seems unaffected. They…
The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
This research study introduces a new metric, called 'correctness agreement', to evaluate the impact of post-training quantization on large language models. The authors show that even when task performance seems…
The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
This research paper introduces a new evaluation metric called 'correctness agreement' for large language models that undergo post-training quantization, a method used to deploy these models in resource-constrained…
Towards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment Guidance
A large language model named HCC-STAR has been developed to provide personalized treatment recommendations, risk scores, and survival estimates for patients with Hepatocellular Carcinoma (HCC). The model reads through…
Towards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment Guidance
Researchers present HCC-STAR, a large language model that reads electronic medical records (EMRs) of patients with Hepatocellular Carcinoma (HCC), a common and deadly type of liver cancer. The model suggests treatments…
TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories
The paper introduces TRACE, a new agent watermark system designed to withstand manipulation by resellers who may alter or replace an original agent. The system works by embedding two channels into the agent's behavior…
TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories
Researchers introduce TRACE, a watermark system for large language model (LLM) agents that can maintain its integrity even when resellers rebrand or substitute cheaper models. TRACE consists of two channels: a…
Two Axes of LLM Abstention: Answer Correctness and Question Answerability
The paper discusses the issue of language models providing incorrect or unanswerable responses. The author proposes two separate axes for abstention: one for answers the model would get wrong, and another for questions…
Two Axes of LLM Abstention: Answer Correctness and Question Answerability
A study finds that large language models often struggle with two types of errors: answering questions incorrectly and attempting to answer unanswerable or misleading questions. The researchers propose a method to…
UltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic Editing
UltraX is a new framework designed to improve the quality of large-scale pre-training data for Large Language Models (LLMs). It addresses the limitations of existing methods by offering fine-grained instance-level…
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…
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