arXiv Machine Learning
1d ago
Temporal Logic Guided Universal Task Representations for Reinforcement Learning
arXiv:2608. 15509v1 Announce Type: cross Abstract: Task guided agents demonstrate strong performance in a wide range of complex tasks.
Reinforcement learning
Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.
arXiv Machine Learning
1d ago
Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization
arXiv:2608. 16492v1 Announce Type: cross Abstract: This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization.
arXiv AI
1d ago
Trust Is Not Enough: Influence Calibration for On-Policy Self-Distillation in Agentic RL
arXiv:2608. 14945v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) gives language agents dense token-level supervision from a privileged self-teacher on the policy's own trajectories.
arXiv AI
1d ago
Individual Disempowerment through an Advice Channel: Control Loss when Influence is Endogenous
arXiv:2608. 14795v1 Announce Type: new Abstract: An AI that can only give advice seems safe: the human is always free to ignore it.
arXiv AI
1d ago
MINT: Min-Selection Preference Distillation for Balanced Multi-Objective Alignment
arXiv:2608. 14828v1 Announce Type: new Abstract: Aligning a language agent to several objectives at once is a persistent failure mode of preference-based training: when objectives are combined additively, optimization collapses onto whichever is cheapest to improve and sacrifices the rest, so a support agent learns to sound warm while giving no real help.
arXiv AI
1d ago
RLCascadeRouter: Quality-Estimator-Free Cascade Routing via Reinforcement Learning
arXiv:2608. 15817v1 Announce Type: new Abstract: The growing ecosystem of large language models (LLMs) offers huge potential to optimize performance-cost trade-offs.
arXiv AI
1d ago
When Entropy Is Not Enough: Reclaiming Lost Semantics in LLM Output Length Prediction
arXiv:2608. 15592v1 Announce Type: new Abstract: Efficient LLM serving is often bottlenecked by the need to pad sequences to a fixed maximum length, and this wastes compute and degrades throughput.
arXiv AI
1d ago
VibeWorlding: Can Multimodal Agents Construct 3D Open Worlds End-to-End?
arXiv:2608. 15265v1 Announce Type: new Abstract: Constructing an interactive 3D open world from a user query is important.
arXiv AI
1d ago
UC-PSRO: Utility-Conditioned Policy-Space Response Oracles with a Communication-Dropout Curriculum for Game-Theoretic Course-of-Action Generation in Adversarial Swarms
arXiv:2608. 15372v1 Announce Type: new Abstract: We study generating game-theoretically optimized Courses of Action (COAs) for a Blue UAS swarm against an adaptive Red adversary in a communication-degraded environment, motivated by (but not derived from) a public U.
arXiv AI
1d ago
ReasonCast: Agentic Demand Forecasting with Selective Semantic Reasoning
arXiv:2608. 15291v1 Announce Type: new Abstract: Demand forecasting increasingly requires combining two complementary sources of information: historical sales reveal recurring numerical dynamics, while future promotions, holidays, price changes, and platform interventions provide forward-looking knowledge.
arXiv AI
1d ago
TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents
arXiv:2608. 16156v1 Announce Type: new Abstract: Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult.
arXiv AI
1d ago
When to Communicate: Belief Distributions and KL Divergence for Principled Gating in Multi-Agent RL
arXiv:2608. 14559v1 Announce Type: new Abstract: Effective communication in multi-agent reinforcement learning requires agents to decide not only \textit{what} to communicate, but when?
arXiv AI
1d ago
UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations
arXiv:2608. 15930v1 Announce Type: new Abstract: Foundation GUI agents can automate complex digital tasks, but deployment is hindered by scarce and biased training data, ambiguous prompts, and unreliable execution.
arXiv AI
1d ago
CEDAR-GRPO: Process-Aware Reinforcement Learning for General Abductive Reasoning in LLMs
arXiv:2608. 14791v1 Announce Type: new Abstract: Abductive reasoning, often characterized as inference to the best explanation, is central to explanation under uncertainty, from everyday sense-making and investigation to scientific discovery.
arXiv AI
1d ago
Offline Reinforcement Learning for Hemodynamic Management of Sepsis in the ICU: a MIMIC-IV Study with Dual Off-Policy Evaluation
arXiv:2608. 16482v1 Announce Type: new Abstract: The dosing of intravenous fluids and vasopressors in sepsis is a sequential decision made under uncertainty and guided largely by clinical judgment, which makes it a natural target for reinforcement learning from historical care.
arXiv AI
1d ago
A Shop Floor Production Scheduling Case based on RFID-supported Smart Factory
arXiv:2608. 16626v1 Announce Type: new Abstract: Radio frequency identification (RFID) technology has been widely implemented for real-time data collection in manufacturing shop floors, which, in turn, can be used to support dynamic shop floor production planning and scheduling.
arXiv AI
1d ago
Drive, Pack, Fly: The Travelling Thief Problem with Drone
arXiv:2608. 16435v1 Announce Type: new Abstract: In collection operations, accumulating payload progressively slows the vehicle, imposing a cumulative penalty on routing efficiency.
arXiv Machine Learning
1d ago
Pointer Networks with Q-Learning for Combinatorial Optimization
arXiv:2311. 02629v5 Announce Type: replace Abstract: We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets) to enhance the optimality of attention-based sequence generation, focusing on long-term outcomes.
arXiv AI
1d ago
Max-Q Selective Imitation for Human-in-the-Loop Online Robot Learning
arXiv:2608. 15088v1 Announce Type: cross Abstract: Human-in-the-loop (HIL) online reinforcement learning for real robots must absorb human interventions quickly while continuing to improve beyond the human prior.
arXiv Machine Learning
1d ago
Training and Evaluating Ethical Reinforcement Learning Agents on Per-Episode Distributions
arXiv:2608. 14642v1 Announce Type: new Abstract: Reinforcement Learning (RL) agents trained on a single reward signal exploit the gap between the designed reward and the intended behavior.