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Artificial Intelligence
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The newest 5,000 papers, from 11 October 2026 to 5 November 2026: 4,998 arXiv preprints and 2 OpenAlex records.
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Newest papers, page 186
Path to Prior-Data Fitted Relational Models via Learned Latent Multi-table Representations
Prior-data fitted networks (PFNs) show that pretraining on large populations of synthetic tasks can enable generalization to unseen datasets with little or no downstream fine-tuning.Rethinking GRPO for Large Reasoning Model Safety Alignment
Large reasoning models (LRMs) exhibit strong reasoning capabilities but remain vulnerable to unsafe outputs.WebCoder: Scaling General Frontend Development with Artifact-Grounded Agentic Reinforcement Learning
Real-world frontend development requires agents to compose reproduction, generation, modification, and repair over an evolving application state.LexPilot: Benchmarking the Proactive Interaction Ability of Legal LLMs with Laypeople in Chinese Civil Case Consultation
Large Language Models (LLMs) have greatly assisted legal professionals.Local proxies for memory-efficient training
Local learning algorithms can reshape resource requirements of artificial intelligence.CIIR - Class-Indexed Interface Representation Learning
Multimodal learning commonly assumes paired or synchronized observations, yet independently collected datasets rarely provide such correspondence.QuAD: Quantitative Assessment via Distribution-Alignment for Qualitative Data Quality Enabled by a Content-Aware Multi-Agent System
Assessing whether qualitative responses collected from semi-structured questions are ready for a particular analysis goal is a persistent challenge.Algorithm Modelization: Transforming Optimization Algorithms into Evolvable Intelligence
Can algorithms be learned and evolved as models, rather than repeatedly redesigned as programs?Explaining Chess and Go in Natural Language
Modern chess engines play far beyond human ability, often choosing moves whose underlying ideas are difficult for humans to understand.LongForge: Measuring and Improving Long-Context Instruction Data via Evidence-Assertion Reasoning Graphs
Existing quality control for long-context instruction-tuning data is a one-shot judgment inside the generation pipeline that leaves only a scalar, revealing neither which document parts an answer…Tree-Aware Posterior Distillation for Long-Horizon Agentic Reinforcement Learning
Reinforcement learning with verifiable rewards has become a scalable post-training paradigm for large language models.A Stronger Teacher Within: Strengthening MoE Models via On-Policy Bidirectional Distillation
Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved Mixture-of-Experts (MoE) LLMs, but its outcome-level rewards provide only sparse supervision.LangNCO: Compiling Natural-Language Constraints into Inference-Time Interventions for Neural Combinatorial Optimization
Neural combinatorial optimization (NCO) rapidly produces high-quality solutions for problem structures encountered during training, but operational requirements may change after deployment and be…OfficeTown: Generative Organizations for Evolving Agent Intelligence
AI agents increasingly use files and software tools to produce professional deliverables.FuzzInsight: Navigating the Efficiency-Effectiveness Trade-off in Coverage-Guided DNN Fuzzing
Collecting and labeling diverse test datasets for evaluating Deep Neural Networks (DNNs) is costly and time-consuming.Framing Spatial Hearing within the Free Energy Principle: A Hierarchical Model of Perceptual Learning
Spatial hearing relies on dynamic internal representations to decode spatial information from ever-changing acoustic conditions.Validating LLM-generated political networks with survey-reported partisan exposure
Large language models (LLMs) are increasingly used to simulate human behavior in social science research, including decisions, interactions, and social relationships.Not All Test-Time Compute Is Evidence: Model-Specific Bias in LLM Reasoning
Test-time scaling methods implicitly assume that additional computation from the same model provides independent evidence.qGAMMA: A Framework for Optimal DNN Mapping on Multi-Precision Accelerators
This work describes qGAMMA, a flexible framework for exploring and optimizing the mapping of mixed-precision Deep Neural Network (DNN) models onto general-purpose spatial accelerators that support a…Characterising and Analysing Retrieval-Augmented Generation Techniques
The advent of Large Language Models (LLMs) across many application domains has increased the need for Retrieval-Augmented Generation (RAG) to reduce hallucinations and improve LLM performance.