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Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution
Researchers propose E3, a method to help large language model agents estimate the complexity of tasks and reduce unnecessary work. The proposed method reduces cost, tokens, and inspected files by significant margins on…
Tracing Agentic Failure from the Flow of Success
The paper proposes a new method called OAT (One-Class Attribution for Tracing) for identifying errors in language models without relying on expensive or difficult-to-collect data. This method is faster and more…
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…
Cognitive-structured Multimodal Agent for Multimodal Understanding, Generation, and Editing
Researchers propose a new type of multimodal agent called the Cognitive-structured Multimodal Agent (CMA) that can understand, generate, and edit visual and textual information. The agent externalizes visual…
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…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
The paper proposes a new framework called WebSwarm for multi-agent search systems. This system aims to improve deep-and-wide web searches by dynamically creating search nodes that can delegate tasks recursively and…
WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search
The paper presents a new framework called WebSwarm for web search using multiple agents. This system aims to overcome the limitations of single agents by simultaneously handling depth and coverage, improving recursive…
ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies
The paper introduces ORCAID, a method for extracting interpretable rule-based policies from reinforcement learning (RL) agents in complex environments with continuous action spaces. The method involves training an…
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…
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…
Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents
The paper presents Information Gain-based Rollout Policy Optimization (IGRPO), a new framework for improving large language model agents in long-horizon search tasks. IGRPO aims to allocate rollout budget more…
TacReasoner: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios
The paper proposes TacReasoner, a new framework that enhances the perception and representation of dynamic tactile signals for intelligent systems. It addresses two challenges: insufficient modeling of such signals and…
Unified Audio Intelligence Without Regressing on Text Intelligence
Researchers have developed a unified language model called Audex, which combines audio and text intelligence. The model is built on Nemotron-Cascade-2-30B-A3B, a strong text-only MoE LLM (Language Model). Audex uses a…
Weak-to-Strong Generalization via Direct On-Policy Distillation
Researchers propose a method called Direct On-Policy Distillation (Direct-OPD) for improving stronger language models by leveraging the knowledge gained from reinforcement learning on smaller, weaker models. This…
Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs
Researchers propose Task-Agnostic Pretraining (TAP), a two-stage framework to train Vision-Language-Action (VLA) models. TAP first learns transferable motor priors from unlabeled interaction data using self-supervised…
Neuron-Aware Data Selection for Annotation-Free LLM Self-Distillation
The paper proposes a new method called Neuron On-Policy Self-Distillation (Neuron-OPSD) for annotation-free self-distillation of large language models. This method uses the internal neuron activations to guide data…
Neuron-Aware Data Selection for Annotation-Free LLM Self-Distillation
In this paper, the authors propose a new method called Neuron On-Policy Self-Distillation (Neuron-OPSD) for annotation-free self-distillation of large language models. This method uses internal neuron activations to…
QFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
The paper proposes a new method called QFedAgent for multi-agent activity recognition in privacy-sensitive robotic sensing applications. The method uses a hybrid quantum-classical personalized Federated Learning…
CausalMix: Data Mixture as Causal Inference for Language Model Training
The paper presents CausalMix, a method that addresses the limitation of existing data mixing methods in Large Language Model (LLM) training by casting it as a causal inference problem. By using causal modeling to…
Human-Machine Collaboration on Generative Meta-Learning: Model and Algorithm
The research paper introduces a new framework called Generative Meta-Learning with Human Feedback (GMHF). This framework aims to improve machine learning models by bridging the gap between training and target domains…
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