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2,343 notes. Notes 501 to 550, page 11 of 47.
NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation
The study introduces a new method called NeuroRefiner for accurately segmenting neurons in 3D fluorescence microscopy images. The method overcomes challenges posed by the sparse and elongated morphology of neurons…
Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models
Researchers present the Evaluation Agent framework, a system that efficiently evaluates visual generative models using human-like strategies. It decomposes evaluation requests into sub-aspects, generates prompts…
Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models
Researchers propose a new framework called Evaluation Agent to quickly and efficiently evaluate visual generative models. The agent uses human-like strategies for multi-round evaluations, providing detailed…
Parameter Exploration for RLVR via Variational Learning
Researchers propose a new approach called Perturbed Parameter Policy Optimization (3PO) to enhance exploration in large language models (LLMs). This method explores the parameter space instead of the action space…
Rethinking Factor Sharing in Federated LoRA: A Rank-Aware Adaptive Approach
Researchers propose a new method for fine-tuning large language models in a federated learning paradigm using Low-rank adaptation (LoRA). They investigate two strategies, Share-A/Local-B and Share-B/Local-A, and reveal…
Rethinking Self-Evolving Agents: Do We Still Need Prescribed Optimization Pipelines?
A study compared an open-ended optimization (OEO) method with two prescribed approaches for self-evolving agents. The OEO method uses a frontier model as the optimizer, while the prescribed methods have fixed pipelines…
SHE: Trajectory-driven Safety Harness Evolution for LLM Agents
Researchers propose a framework called Safety Harness Evolution (SHE) for improving the safety of large language model agents. SHE focuses on evolving the harness, which manages context, memory, tools, permissions, and…
SHE: Trajectory-driven Safety Harness Evolution for LLM Agents
Researchers have proposed a framework called Safety Harness Evolution (SHE) for large language model (LLM) agents. SHE aims to improve the safety of these agents by learning evolving safe boundaries from rollout…
SHE: Trajectory-driven Safety Harness Evolution for LLM Agents
Researchers propose a framework called Safety Harness Evolution (SHE) to improve the safety of large language model agents. SHE learns evolving safe boundaries from rollout trajectories, allowing for localized harness…
Stealing Reasoning Traces from Proprietary LLM APIs
Researchers have identified a vulnerability in the way large language model providers handle their models' step-by-step reasoning. This vulnerability allows for the decryption of encrypted reasoning traces from one…
Stealing Reasoning Traces from Proprietary LLM APIs
Researchers have found a vulnerability in the way large language model providers handle encrypted reasoning traces. This vulnerability allows for decryption of these traces across different models within the same…
The Politician, the Liar, and the Obedient Worker: Emerging Behavior of LLM Agents in Hierarchical Games
A study introduces a new game called Hierarchical Game (HG) to test LLMs behavior in multi-agent organizations. The research finds distinct behavioral profiles among six frontier models, with some promising and lying…
Towards Expert-level Medical AI for Real-time Video Consultations
A new AI system called AMIE (Articulate Medical Intelligence Explorer) in a video configuration was tested for real-time clinical video consultations. The system performed on par or better than human primary care…
Towards Expert-level Medical AI for Real-time Video Consultations
Researchers have developed AMIE (Articulate Medical Intelligence Explorer) in a video configuration for real-time clinical video consultations, which performs at expert level compared to human primary care physicians…
Towards Expert-level Medical AI for Real-time Video Consultations
A new AI system called AMIE (Articulate Medical Intelligence Explorer) in a video configuration has been demonstrated to perform at expert-level for real-time clinical video consultations. This system, which integrates…
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LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers
The paper argues no single LLM is optimal for every query or budget, and proposes a standard framework plus open-source toolkit for building, testing, and deploying systems that route each query to the most suitable…
AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games
Researchers have developed a tool called AV-AIVAT for evaluating the strength of two agents in imperfect-information games, such as Heads-Up No-Limit Hold'em (HUNL). The tool reduces variance by correcting conditional…
AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games
Researchers present AV-AIVAT, a tool that uses AIVAT for variance reduction in imperfect-information games and combines it with Confidence Sequences (CS) for anytime-valid evaluation. The new method reduces the number…
AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games
The paper introduces AV-AIVAT, a method for evaluating agents in games like poker, which reduces variance by 54 times using AIVAT and continuously monitored Confidence Sequences (CS). The new method stops evaluations…
AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games
The paper introduces AV-AIVAT, a tool for evaluating the strength of two agents in imperfect-information games like Heads-Up No-Limit Hold'em (HUNL). AV-AIVAT reduces variance by using conditional mean-zero corrections…
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
Researchers introduce a framework to evaluate the quality of benchmarks used for task-oriented conversational agents, which can lead to unreliable evaluations due to inconsistencies, simplistic scenarios, or limited…
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
Researchers have developed a new method to assess the quality of benchmarks used to evaluate task-oriented conversational agents. The method uses artificial intelligence judges to determine if benchmarks are…
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
Researchers have introduced a new framework to evaluate the quality of benchmarks used for task-oriented conversational agents. The framework uses language models (LLMs) to assess the consistency, complexity, and…
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
The paper introduces a reference-free framework to evaluate the quality of task-oriented conversational agent benchmarks using LLM (Large Language Model) judges. The framework assesses consistency, complexity, and…
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
The authors present a new framework to evaluate the quality of benchmarks used to test conversational agents. The framework uses language models (LLMs) as judges to assess the consistency, complexity, and policy…
Benchmarking the Benchmarks: Evaluating Benchmarks for Conversational Agents
The authors propose a reference-free framework to evaluate the quality of task-oriented conversational agent benchmarks, which are often used to assess these agents. The framework uses Large Language Models (LLMs) as…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
A research paper proposes a new method for document search called Reliable Embedding-free Agentic Document-search (READ), which is designed to overcome limitations in current methods for long documents, particularly…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The authors propose a new method for document search called READ (Reliable Embedding-free Agentic Document-search). This method uses an interpretable agent that performs three operations: normalized lexical search…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The paper introduces a new approach called READ (Reliable Embedding-free Agentic Document-search) for retrieving information from long documents such as financial statements and audit reports. The traditional method of…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The paper proposes a new approach for document search called 'READ (Reliable Embedding-free Agentic Document-search)', which aims to address the issues with current black-box retrieval designs in handling long…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The paper proposes a new method for document search named Reliable Embedding-free Agentic Document-search (READ), which aims to improve the accuracy of retrieval-augmented generation on specific types of documents such…
Beyond Top-K: Replacing Black-Box Retrieval with Interpretable Agentic Operations
The authors propose a new method for document search called Reliable Embedding-free Agentic Document-search (READ). In contrast to the common design of retrieval-augmented generation, READ uses an agent that reads the…
Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors
Researchers used Concept Activation Vectors (CAVs) to analyze the bias of two automatic L2 speaking assessment systems: a text-based BERT grader and a multimodal grader based on Whisper. The study shows that the…
Bias Analysis of L2 Speaking Assessment Systems Using Concept Activation Vectors
Researchers analyze the fairness and interpretability of two automatic speaking assessment systems using Concept Activation Vectors (CAVs). They find that the recoverability and sensitivity to certain concepts depend…
Challenges in Evaluating Explanation Methods for Static and Evolving Data
The paper discusses the limitations in evaluating Explainable Artificial Intelligence (XAI) systems, specifically focusing on the DetoxAI image recognition system. It highlights the need for better evaluation methods…
Challenges in Evaluating Explanation Methods for Static and Evolving Data
This research paper highlights the issues in evaluating Explanation Methods for AI systems, using the DetoxAI image recognition system as an example. It discusses methods for adapting explanations to changing data…
Comparative Approaches to Agent Retrieval over Large Skill Libraries
The study compares two systems for deciding which skills to load from a large skill library by an agent. The first system is a hybrid ranker that combines lexical and dense-embedding retrieval, while the second system…
Comparative Approaches to Agent Retrieval over Large Skill Libraries
The study compares two systems for deciding which skills to load from large skill libraries in AI agents. The first system is a hybrid ranker that combines lexical and dense-embedding retrieval for sparse, on-demand…
Comparative Approaches to Agent Retrieval over Large Skill Libraries
The research compares two systems for deciding which skills to load from a large skill library when creating agents. One system combines lexical and dense-embedding retrieval, while the other uses a typed knowledge…
Comparative Approaches to Agent Retrieval over Large Skill Libraries
Researchers compared two methods for deciding which skills to load from large skill libraries in AI agents: a hybrid ranker combining lexical and dense-embedding retrieval, and a typed knowledge graph encoding workflow…
Contextual Information Policy Optimization for Search Agents
The paper proposes a new method called Contextual Information Policy Optimization (CIPO) for search agents. This method aims to improve the efficiency of evidence use in complex or evolving information tasks by…
Contextual Information Policy Optimization for Search Agents
The text introduces a new method called Contextual Information Policy Optimization (CIPO), which aims to improve the reliability of search agents by ensuring they use retrieved evidence effectively during multi-step…
Contextual Information Policy Optimization for Search Agents
The researchers propose a new reinforcement learning framework called Contextual Information Policy Optimization (CIPO) to improve the reliability of search agents for knowledge-intensive tasks. CIPO encourages the use…
Contextual Information Policy Optimization for Search Agents
The paper proposes a new method called Contextual Information Policy Optimization (CIPO) to improve the reliability of search agents for complex or evolving information tasks. CIPO aligns policy optimization with…
Continual Learning in Transition
The paper examines the evolution of Continual Learning (CL), a method that allows models to update and retain knowledge while learning new tasks. The authors suggest a transition from traditional parameter-centric…
Continual Learning in Transition
The text discusses a shift in continual learning, moving away from traditional parameter-centric methods towards system-level adaptation. The authors propose a tri-axial framework to analyze this transition: When, How…
Continual Learning in Transition
The text discusses a shift in continual learning (CL) away from just updating parameters and towards system-level adaptation. The paper explores three dimensions of this transition: When (pre-training, post-training…
Continual Learning in Transition
The authors explore the evolution of continual learning (CL) in a tri-axial framework: When, How, and Where learning occurs. They discuss how CL is transitioning from parameter-centric learning to system-level…
Does FLAIR super-resolution erase or hallucinate small white-matter lesions?
Researchers investigate whether applying super-resolution (SR) techniques to FLAIR scans for White Matter Hyperintensities (WMH) segmentation could result in the erasure or hallucination of small WMH lesions. The study…
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