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🏛️ The Artificial Intelligence Crypt
cs.AI: Where Artificial Intelligence papers rest without their code.
17382
Total Papers
10620
No Code
277
Twilight
6485
Has Code
37.3%
Survival Rate
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Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning
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On the Generalization and Adaptation Ability of Machine-Generated Text Detectors in Academic Writing
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GAS: Generative Auto-bidding with Post-training Search
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System-2 Mathematical Reasoning via Enriched Instruction Tuning
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The Cartographer
Mathematics and Machine Creativity: A Survey on Bridging Mathematics with AI
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A Proposal for Extending the Common Model of Cognition to Emotion
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How Different AI Chatbots Behave? Benchmarking Large Language Models in Behavioral Economics Games
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Systematic Analysis of LLM Contributions to Planning: Solver, Verifier, Heuristic
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Technology as uncharted territory: Contextual integrity and the notion of AI as new ethical ground
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TelOps: AI-driven Operations and Maintenance for Telecommunication Networks
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ProtDAT: A Unified Framework for Protein Sequence Design from Any Protein Text Description
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Automated Multi-Label Annotation for Mental Health Illnesses Using Large Language Models
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A Contemporary Overview: Trends and Applications of Large Language Models on Mobile Devices
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CredID: Credible Multi-Bit Watermark for Large Language Models Identification
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STDCformer: A Transformer-Based Model with a Spatial-Temporal Causal De-Confounding Strategy for Crowd Flow Prediction
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Improving Multimodal LLMs Ability In Geometry Problem Solving, Reasoning, And Multistep Scoring
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A Comparative Study of LLM-based ASR and Whisper in Low Resource and Code Switching Scenario
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Fairness at Every Intersection: Uncovering and Mitigating Intersectional Biases in Multimodal Clinical Predictions
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Dspy-based Neural-Symbolic Pipeline to Enhance Spatial Reasoning in LLMs
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Probing for Consciousness in Machines
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Enhancing Multi-Agent Consensus through Third-Party LLM Integration: Analyzing Uncertainty and Mitigating Hallucinations in Large Language Models
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Learning Lifted STRIPS Models from Action Traces Alone: A Simple, General, and Scalable Solution
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