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Generating High Quality Synthetic Data for Dutch Medical Conversations GIANTS: Generative Insight Anticipation from Scientific Literature Should We be Pedantic About Reasoning Errors in Machine Translation? Computational Implementation of a Model of Category-Theoretic Metaphor Comprehension CoSToM:Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models ASPIRin: Action Space Projection for Interactivity-Optimized Reinforcement Learning in Full-Duplex Speech Language Models CircuitSynth: Reliable Synthetic Data Generation Think in Sentences: Explicit Sentence Boundaries Enhance Language Model's Capabilities CodaRAG: Connecting the Dots with Associativity Inspired by Complementary Learning From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation ReFEree: Reference-Free and Fine-Grained Method for Evaluating Factual Consistency in Real-World Code Summarization LLMs Should Incorporate Explicit Mechanisms for Human Empathy Early Decisions Matter: Proximity Bias and Initial Trajectory Shaping in Non-Autoregressive Diffusion Language Models Bridging Linguistic Gaps: Cross-Lingual Mapping in Pre-Training and Dataset for Enhanced Multilingual LLM Performance Computational Lesions in Multilingual Language Models Separate Shared and Language-specific Brain Alignment Efficient Process Reward Modeling via Contrastive Mutual Information Learning and Enforcing Context-Sensitive Control for LLMs Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models Deep-Reporter: Deep Research for Grounded Multimodal Long-Form Generation Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models Do BERT Embeddings Encode Narrative Dimensions? 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Iterative Deepening Branch and Bound
1999-09-03 · via cs.AI updates on arXiv.org

In tree search problem the best-first search algorithm needs too much of space . To remove such drawbacks of these algorithms the IDA* was developed which is both space and time cost efficient. But again IDA* can give an optimal solution for real valued problems like Flow shop scheduling, Travelling Salesman and 0/1 Knapsack due to their real valued cost estimates. Thus further modifications are done on it and the Iterative Deepening Branch and Bound Search Algorithms is developed which meets the requirements. We have tried using this algorithm for the Flow Shop Scheduling Problem and have found that it is quite effective.

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