8093 papers
This paper introduces two shortfall-aware reinforcement learning frameworks, RLOP and QLBS, which outperform traditional parametric models in reducing tail risk and hedging shortfalls for SPY and XOP options, thereby offering a more robust approach to autonomous derivatives risk management.
This paper introduces the Isotonic Layer, a novel differentiable framework that integrates piecewise linear fitting and learnable embeddings into neural architectures to enforce global monotonicity, thereby enabling granular, context-aware debiasing and improved calibration for large-scale recommendation systems.
This paper presents a transformer-based system that significantly advances ARC performance by integrating a compact task encoding, symmetry-based data augmentation, test-time LoRA adaptation, and multi-perspective decoding to enable efficient neural inference and human-level generalization from few examples.
This paper demonstrates that current LLM-as-a-Judge frameworks fail to reliably measure adversarial robustness due to unaccounted distribution shifts that degrade performance to near-random levels, often leading to inflated attack success rates, and proposes new benchmarks to address these evaluation flaws.
This paper introduces a two-time scale neurodynamic duplex approach utilizing projection equations to solve distributionally robust geometric joint chance-constrained optimization problems with unknown distributions, demonstrating convergence to the global optimum through neural networks in applications such as shape optimization and telecommunications.
This paper proposes an ethics-by-design control architecture that operationalizes AI governance across the entire lifecycle by embedding philosophical reasoning into a triple-gate enforcement structure (Metric, Governance, and Eco) with measurable triggers and audit trails, thereby translating normative commitments into testable controls compatible with existing MLOps pipelines and major regulatory frameworks like the EU AI Act and NIST RMF.
The paper introduces FuzzingRL, a framework that combines vision-language fuzzing with adversarial reinforcement fine-tuning to automatically generate diverse, challenging queries that systematically expose and degrade the performance of Vision Language Models.
This paper demonstrates that data duplication is scale-dependent, revealing that as model capability and corpus size increase, semantically equivalent documents behave like exact duplicates by producing aligned gradients and causing accelerated semantic collisions, which leads to rapidly increasing training losses for larger models and necessitates new scaling laws to accurately predict performance.
This paper addresses the lack of systematic evaluation in Multi-Agent Deep Reinforcement Learning for C-V2X resource allocation by introducing a disentangled benchmark suite of interference games and diverse datasets to isolate specific challenges, ultimately identifying policy robustness and generalization across vehicular topologies as the primary hurdle and demonstrating the superiority of actor-critic methods over value-based approaches.
To address the complexity gap between StarCraft II's full game and its mini-games, this paper introduces the Two-Bridge Map Suite, an open-source, lightweight benchmark that isolates tactical navigation and combat skills to enable accessible reinforcement learning research under realistic compute budgets.
The paper demonstrates that unlike in domains with external verifiers, scaling inference compute through crowd wisdom strategies fails to improve LLM truthfulness in unverified settings because correlated model errors and the inability to distinguish social prediction from truth verification cause aggregation to reinforce shared misconceptions rather than identify correct answers.
The paper introduces OptiRoulette, a stochastic meta-optimizer that dynamically selects update rules from a pool during training, demonstrating significantly faster convergence and higher test accuracy across multiple image-classification benchmarks compared to a standard AdamW baseline.
This paper proposes Annealed Co-Generation (ACG), a framework that replaces high-dimensional joint diffusion modeling with a low-dimensional, pairwise approach coupled through a three-stage annealing process to achieve efficient and consistent multivariate co-generation for scientific applications like flow-field completion and antibody generation.
RACER is a novel, model-agnostic routing framework that addresses misrouting risks in multi-LLM systems by formulating the problem as -VOR to generate calibrated, variable-sized model sets with rigorous distribution-free risk control, thereby enhancing downstream accuracy and cost-performance trade-offs.
The paper introduces Evo, a novel large language model that unifies autoregressive and diffusion-based generation within a continuous evolutionary latent framework, enabling adaptive balancing of planning and refinement to achieve state-of-the-art performance across diverse benchmarks while maintaining fast inference speeds.
This paper proposes a novel topology-aware framework that leverages domain-specific foundation models, a graph tokenizer for multiplex connectivity, and knowledge distillation to achieve robust zero-shot interaction prediction in multiplex biological networks, outperforming state-of-the-art methods.
The paper introduces NAT (Not All Tokens Are Needed), a token-efficient reinforcement learning framework that utilizes unbiased partial-token gradient estimation via Horvitz-Thompson reweighting to achieve full-sequence performance with significantly reduced compute and memory costs by updating policies on only a subset of generated tokens.
GraphSkill is an agentic framework that improves complex graph reasoning by leveraging hierarchical document retrieval and self-debugging with generated test cases, validated on a new comprehensive dataset.
This paper empirically demonstrates that a Transformer model utilizing self-attention mechanisms outperforms traditional ARIMA and recurrent neural network approaches (LSTM, BiLSTM) in short-term power load forecasting on PJM data, achieving a superior 3.8% MAPE and highlighting the effectiveness of attention-based architectures for capturing complex temporal patterns.
This paper introduces Exploration Space Theory (EST), a formal lattice-theoretic framework that adapts Knowledge Space Theory to location-based recommendation by modeling prerequisite dependencies among points of interest, thereby providing structural guarantees for validity, optimality, and explainability in the Exploration Space Recommender System (ESRS).