Papers Decision Making
“Decision Making” 태그가 달린 논문 13,094편 · 필터 해제
Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence
In this technical report, we propose Pelican-Sim 1.0, a general world model simulator for embodied intelligence that predicts future observations from visual context and robot actions to support downstream learning and d…
Decision MakingSearching for New Physics with Reinforcement Learning
Finding new physics (NP) is the most important problem in particle physics today. Studying ``anomalies'', i.e., measurements of low-energy observables whose values disagree with the predictions of the Standard Model (SM)…
Reinforcement LearningDecision MakingCT-SAFR: Safe and Interpretable Chain-of-Thought Reasoning for Autonomous Robots: A Multi-Layered Verification Framework for Trustworthy AI-Driven Robotic Decision Making
Chain-of-Thought (CoT) prompting enables LLMs to perform explicit, step-by-step reasoning, creating opportunities for sophisticated autonomous robots. However, recent research reveals that reasoning models verbalize thei…
Decision MakingMobileVLA-R1 2.0: RL-Enhanced Reasoning for Mobile Robot Control
Grounding natural-language instructions into reliable and executable actions remains a fundamental challenge for vision-language-action (VLA) systems on mobile robots, due to the persistent gap between high-level semanti…
Reinforcement LearningInstruction FollowingMultimodal ReasoningDecision MakingWhen Does a Classifier Help an LLM? Classifier-Guided Prompting and Hybrid Classifier-LLM Models for Credit-Default Prediction
Credit-default prediction is an important task in financial decision making. Traditional methods use fitted classifiers such as logistic regression and random forests on tabular features. Large language models (LLMs) hav…
Decision MakingConformal Risk-Averse Decision Making with Optimized Certainty Equivalent Risk Control
We study risk-averse decision making, in which an agent selects actions while being uncertain about the true system state. The risk is measured via optimized certainty equivalent (OCE) metrics, which generalize popular c…
Decision MakingType predictionFrom Uncertainty to Clinical Risk: Severity-Aware Conformal Planning for Interactive Medical Diagnosis
Interactive medical diagnosis dynamically acquires patient information through multiple rounds of questioning, supporting accurate, efficient, and safe clinical decisions under incomplete evidence. Existing methods commo…
Medical DiagnosisDecision MakingTrust-Aware Sequential Decision Making and Rollout Planning for Resilient Multi-Robot Systems
Sequential decision-making in multi-robot systems typically assumes that planning information is reliable and that agents execute the actions anticipated by the planner. Compromised agents can violate both assumptions, c…
Decision MakingAdvantage-Driven Explicit Memory for Social Navigation
Robot policies are predominantly learned with classical parametric variants of imitation learning or RL, where training stores the agent's behavior exclusively in the policy's network parameters, putting a heavy burden o…
Representation LearningContinual LearningDecision MakingBVR Sim: An Open and High-Throughput Environment for Heterogeneous Air-Combat Reinforcement Learning
Beyond-visual-range (BVR) air combat is a challenging reinforcement-learning domain characterized by partial observability, long-horizon decision making, energy management, and limited weapons. We present BVR Sim, an ope…
Reinforcement LearningDecision MakingToward Effective and Reliable LLM Agents via Dynamic Ontology
Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks, this leaves important semantic connections implicit. This often resu…
Decision MakingBeyond Instance Slots: Semantically Rich World Models for Physical Interaction Planning
World models for physical interaction are typically trained to predict future observations or latent features; however, a planning-oriented model must answer a fundamentally different question: whether a candidate action…
Decision MakingA Collaborative Multi-Modality Interaction for VLA-based End-to-End Autonomous Driving
Vision-Language-Action (VLA) models have emerged as a powerful paradigm for end-to-end autonomous driving by jointly integrating perception, reasoning, and decision making within a unified multimodal framework. However, …
Visual Question AnsweringTrajectory PlanningAutonomous DrivingDecision MakingBeyond End-to-End Success: Diagnosing Failures in Long-Horizon Security LLM Agents
Long-horizon security LLM agents must carry information and decisions across many dependent interactions, where later actions often depend on services, state, or access discovered much earlier. This makes final task succ…
Decision MakingORBITER: Conflict-Aware Decision-Making for Agentic Last-Mile Delivery
Last-mile delivery aims to handle dynamically arriving orders with couriers while modeling complex spatial and temporal correlations. Recent learning-based methods model spatiotemporal dependencies among orders to predic…
Decision MakingVision-Language Models for Egocentric Video: From Hand-Object Interaction to Embodied AI
Egocentric video captures activities from the wearer's perspective, providing a direct view of human attention, hand--object interaction, and goal-directed behavior. This perspective is increasingly important for wearabl…
Representation LearningDomain GeneralizationDecision MakingThe Curious Case of Exploding DecPOMDPs: Containing the Fire through Policy Counting
Decentralised partially observable Markov decision processes (DecPOMDPs) provide a general framework for modelling multi-agent decision making under uncertainty. However, DecPOMDPs are known to suffer from exponential co…
Decision MakingQuantifying Risk Under Evolving Uncertainty: Belief-Dependent Robustness for Safe Sequential Decision Making
How cautious should an agent be while it is still learning its environment? We propose RATTL (Risk-Adversarial Total-Reward Learning), which ties caution to epistemic uncertainty: the agent holds a Bayesian posterior ove…
Decision MakingDomain-Adapted Molecular Language Models for Efficient Search of Make-on-Demand Libraries
Pretrained molecular language models are increasingly used as molecular encoders for learning structure-property relationships. However, their practical suitability for molecular discovery within and beyond their pretrai…
Domain AdaptationDecision MakingDrug DiscoveryChronocooked: A Benchmark for Implicit Interval Timing in Reinforcement Learning Agents
This paper presents Chronocooked, a reinforcement learning (RL) benchmark suite for studying implicit interval timing in RL agents. Inspired by Overcooked, the suite comprises cooking scenarios that require temporal deci…
Reinforcement LearningDecision Making