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NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation
Researchers propose NeuroRefiner, a multi-agent system for improving the accuracy of 3D neuron segmentation in fluorescence microscopy. The system addresses challenges posed by the sparse and elongated morphology of…
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…
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