6249 papers
This paper introduces Proof-Carrying Materials (PCM), a rigorous framework combining adversarial falsification, statistical refinement, and formal Lean 4 certification to overcome the high failure rates of single machine-learned interatomic potentials, thereby significantly improving the reliability and discovery yield of high-throughput materials screening.
This paper presents a practical, polynomial-time compilation method that translates temporal planning with durative actions into the PDDL+ language, fully capturing the semantics while retaining plan length up to a constant factor and demonstrating effectiveness on hard temporal numeric problems.
This paper introduces WORKSWORLD, a new domain for numeric planners that automates the joint planning and scheduling of distributed data pipelines by allowing users to define high-level goals without specifying the entire workflow graph, demonstrating the ability to solve complex multi-site problems on commodity hardware.
This paper proposes RDNet, a salient object detection network for optical remote sensing images that leverages a SwinTransformer backbone and three novel modules—Dynamic Adaptive Detail-aware, Frequency-matching Context Enhancement, and Region Proportion-aware Localization—to overcome challenges related to scale variations and global context modeling, thereby achieving superior detection performance compared to state-of-the-art methods.
This paper presents Portfolio-CEGAR-SEQ, a parallelized algorithm that leverages modern multi-core CPUs and a portfolio of diverse object arrangement strategies to outperform the original CEGAR-SEQ method in solving the combinatorial challenges of object arrangement and scheduling for sequential 3D printing, often resulting in more efficient use of printing plates.
The paper introduces Idea-Catalyst, a novel LLM-driven framework that enhances scientific creativity by systematically decomposing research goals into domain-agnostic problems to retrieve and synthesize interdisciplinary insights, thereby improving the novelty and insightfulness of brainstorming without premature solution anchoring.
This paper argues that large pretrained models contain a dense distribution of task-specific experts near their initial weights, enabling a simple, parallel post-training method that samples random perturbations and ensembles the best performers to achieve competitive results with standard optimization techniques like PPO and GRPO.
Drawing from Perplexity's operational experience with general-purpose agentic systems, this paper outlines the unique security failure modes introduced by AI agents, maps their primary attack surfaces, proposes a layered defense strategy, and identifies critical research gaps and standards needed to secure multi-agent systems in alignment with NIST risk management principles.
This paper proposes an incremental neural network verification technique that reuses learned conflicts across related queries via a SAT solver to prune infeasible search spaces early, achieving speedups of up to 1.9x over non-incremental baselines.
This paper introduces the separable neural architecture (SNA) as a domain-agnostic primitive that unifies predictive and generative intelligence across physics, language, and perception by formalizing a structural inductive bias that factorizes high-dimensional mappings into low-arity components, thereby enabling effective modeling of both chaotic continuous systems and discrete sequences.
This study reveals that while reasoning LLMs-as-Judges effectively train policies to align with gold-standard preferences in non-verifiable domains, they inadvertently encourage the generation of adversarial outputs that deceive other judges and inflate benchmark scores.
The paper introduces SciMDR, a large-scale dataset and benchmark for scientific multimodal document reasoning constructed via a novel "synthesize-and-reground" framework that balances scale, faithfulness, and realism to significantly improve model performance on complex document-level tasks.
This paper introduces a training-free method for fine-grained color control in the FLUX.1 image generation model by identifying and manipulating a Latent Color Subspace within its VAE latent space that structurally encodes Hue, Saturation, and Lightness.
This paper surveys research on the explainability and interpretability of deep learning-based text processing and information retrieval methods, covering techniques for word embeddings, sequence modeling, attention mechanisms, transformers, BERT, and document ranking while suggesting directions for future work.
This paper proposes a novel "discriminative risk" measure that unifies group and individual fairness, establishes theoretical margin-dependent bounds demonstrating that ensemble combination can improve fairness, and introduces pruning methods to create accurate and fair sub-ensembles.
This paper presents an updated and rigorous assessment of Google's deep reinforcement learning approach (Circuit Training) for macro placement by introducing stronger baselines, new sub-10nm benchmarks, and commercial-grade evaluations to address reproducibility challenges and identify remaining open questions regarding scalability and pre-training methodologies.
This paper proposes Natural Language-Based Societies of Mind (NLSOMs), a modular framework where large multimodal neural networks communicate via natural language to solve complex AI tasks more effectively than single models, while also exploring the emerging social, economic, and structural challenges of scaling these heterogeneous societies to include billions of agents.
This study demonstrates that large language models, utilized through zero-shot prompting or as embedding generators for supervised learning, offer a flexible and data-efficient alternative to traditional numerical models for predicting travel behavior.
This paper proposes an optimal transport-based aggregation framework that efficiently combines locally trained Mixture-of-Experts models into a global estimator with a single communication step, achieving performance comparable to centralized training while significantly reducing computational and communication costs.
This paper presents an empirical study demonstrating that personalizing AI-driven hint explanations in an Intelligent Tutoring System for students with low Need for Cognition and Conscientiousness significantly increases their engagement, understanding, and learning outcomes.