H-RePlan introduces a hierarchical replanning framework that separates device-local strategy recovery from global orchestrator replanning. It outperforms existing baselines by achieving higher completion and instruction adherence, with reduced token cost, through scope-aware recovery in multi-device agent systems.
LedgerAgent introduces a structured ledger to maintain task states separately in tool-calling agents. It renders these states into prompts and enforces policy constraints before tool execution, reducing policy violations and improving performance across customer-service domains.
The AI Economist Agent uses RAG, knowledge graphs, and LLMs to generate economic narratives grounded in theory and data. It enables model-based analysis, evidence retrieval, and report generation, ensuring economic coherence and traceability through explicit model computations.
UAV-VLN-FOV isolates the see-and-reach stage for precise evaluation of UAV navigation. 3DG-VLN enhances visual grounding and spatial alignment using dynamic 3D direction cues, achieving a 13.82% success rate improvement over baselines and validated in real-world trials.
A Task Manager introduces priority inference, related-event merging, and preemption to enable continuous operation in enterprise AI. It reduces high-priority queue latency by 14-77% and improves related-event correctness by over 20 percentage points at enterprise scale, addressing agent discovery noise as the primary bottleneck.
A multi-head attention feature extractor integrated with Soft Actor-Critic improves porosity prediction and process parameter optimization in laser powder bed fusion. The method achieves a convergence value of 322.79 in 14 episodes, outperforming DQN, PPO, TD3, and vanilla SAC with faster convergence and greater stability.
A new sensorimotor world model (SMWM) learns compact, action-relevant latent representations from offline trajectories. It uses inverse dynamics regularization to prevent representation collapse and align latent states with controllable environmental degrees of freedom, enabling stable training without complex regularizers or frozen components. SMWM achieves competitive planning performance in 2D and 3D control tasks.
A dual-agent framework converts natural-language experiment protocols into executable commands for robotic lab platforms. It uses a Parser Agent and a rule-based mapping engine to translate protocols, with a heterogeneous LLM Validation Agent ensuring accuracy and triggering self-correction. The framework successfully enables end-to-end autonomous execution of microplate-based experiments like the Bradford assay.
ScaffoldAgent introduces a utility-guided framework for dynamic outline optimization in open-ended deep research. It models outline evolution through Expansion, Contraction, and Revision operations, guided by a feedback mechanism that evaluates retrieval gain, structural coherence, and generation quality. Experiments show it improves long-form report generation and factual grounding compared to existing agents.
A new adaptive LLM tutoring system uses subject-aware prompting to enhance student engagement. It outperforms static models in simulation and shows real-world effectiveness, reducing interactions by 3 turns and increasing exercise conversion rates to 28.1% with a stochastic strategy.
RACL introduces a reasoning agent that controls metaheuristic search behavior without replacing optimizers or altering constraints. It improves or ties key policies in vehicle routing experiments, reducing average cost by 8.337% versus Fixed and 1.605% versus Stagnation-Triggered policies, with no significant computational overhead.
MedRLs enables long-context clinical reasoning by recursively inspecting patient data across text, images, sensors, and guidelines. It integrates specialized agents and a Clinical Evidence Graph Memory to connect observations with evidence and referral criteria, supporting sensor-triggered reasoning and uncertainty-gated clinician review.
This paper proposes a framework for applying deep reinforcement learning to game AI, aiming to create more believable and human-like characters. It addresses current limitations in deploying machine learning agents in games and identifies key research challenges that could accelerate AI adoption in the video game industry.
ScholarQuest is a large-scale benchmark for agentic academic paper search, built from 1,000 computer science topics and four research intents. It includes scalable answer construction and a shared retrieval backend, ScholarBase, enabling reproducible evaluation. Results show agentic methods outperform baseline retrieval, with the best agent achieving 0.314 Recall@100 and 0.355 Recall@All, indicating significant room for improvement.
MAMO introduces a multi-agent reinforcement learning approach to address the challenge of balancing cost minimization and constraint satisfaction in dynamic environments. It decouples task execution from reward weight selection, treating the choice of weights as a learning problem to enable more autonomous and robust solutions.
SPOT-E introduces a test-time method that uses visual spotlights to enhance evidence grounding in frozen vision-language models. It employs low-entropy anchors and an entropy-shaping objective to reduce answer uncertainty while preserving high-confidence tokens, improving robustness under visual corruptions across benchmarks and VLM families.
Vision-Language-Action models show severe layer-wise redundancy despite large parameter counts. A training-free compression method using Centered Kernel Alignment removes twin layers, reducing model depth by up to 50% and enabling 40-50% faster training and up to 30% faster inference without performance loss, validated across simulation and real-world robotic tasks.
A vision-based model with parameter-efficient adaptation scores student drawings in science education. It uses confidence-aware scoring to automatically evaluate high-confidence responses while deferring uncertain ones to human review, improving reliability and practicality in large-scale assessments.
Lagrange introduces an open-vocabulary, energy-based sparse framework for generalized end-to-end driving. It uses Vision-Language Models to generate class-agnostic object proposals and encodes them into continuous semantic tokens, enabling robust generalization to anomalous scenarios while adhering to vehicle kinematics through Lagrangian action minimization.
A new deep transfer learning method leverages systems' non-linearities to generate diagnostic data under severe data scarcity. This approach uses a periodic multi-excitation procedure and a novel data visualization technique to augment limited vibration data, enabling effective fault diagnosis via pre-trained CNNs. Experimental results on a railway pantograph validate the method's effectiveness.