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Artificial Intelligence
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The newest 5,000 papers, from 11 October 2026 to 5 November 2026: 4,993 arXiv preprints and 7 OpenAlex records.
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Newest papers, page 9
DARE: Difficulty Analysis for Repair Enhancement
LLM-based coding agents are primarily evaluated by their solve rates.Consistency Is Not Understanding: Orbit Audits for Neural Field Weight Readers
Recognizing visual content from neural-network weights requires separating functional information from arbitrary parameter coordinates.GREP: Grounding Generated Room Evaluation in Principles
Recent LLM-centric advances can generate attractive furnished rooms by selecting and arranging assets.Matching the Training-Time Weight Rule in Inference-Time Expert Deactivation
Expert deactivation at inference time changes both token routing and expert weights, and its effect on performance depends on how these changes are implemented.When File Ingestion Misleads Language Models: Measuring and Mitigating Divergences in Office Documents
Office documents are now routinely uploaded to large language models (LLMs) for processing.Learning When Human Decisions Matter: Executor-Relative Consultation Value for LLM Agents
Large language models increasingly automate complex tasks, but human decision capacity does not scale with their capabilities.A Hybrid Approach for Automated Security Pattern Recovery
Security-by-design emphasises integrating security into software design and development.Autonomy and Coordination in Intelligent Manufacturing: A Recursive Autonomous Agent Framework
Intelligent production requires systems to make decisions and organize work according to production goals and operating conditions.RACE: Calibrated Cumulative Scoring for Rollout-Free Robot Policy Checkpoint Selection
Assessing the deployment performance of learned robot policies during training is challenging, as directly measuring task success requires costly real-world trials for each candidate checkpoint.Vision Paper: Why Distributed LLM Post-Training and Decentralized Alignment Are Now Essential
This vision paper argues that distributed learning, including federated and decentralized, should become a first-class paradigm in LLM post-training, not merely a privacy add-on.PEARL: Prediction Error-Adaptive Regularized Learning for Selective Forgetting under Partial Preference Drift
User preferences shift as needs, context, and tastes evolve.Risk Control Certifies Rates, Not Magnitudes: An Audit of Selective Fetal Biometry
Prenatal ultrasound biometry must return a head circumference (HC) and refuse when the image cannot support one.Generative AI for urban planning: synthesizing land-use maps with satellite imagery via DualControlNet
Various land-use models characterize land-use patterns and facilitate scenario planning.Auditable Unlearning for LLMs
Machine unlearning aims to remove a user's data from a trained model after a deletion request.AI-Assisted Classification under Correlation Neglect and Trust
When does an AI assistant improve human decision-making, and when does it hurt?CellCanon: An Agentic Cellular World Model for Prediction, Explanation, and Observation-Driven Updating
Biological discovery requires using molecular knowledge and new measurements to infer responses beyond the conditions already observed.RECAP: Context-Aware Prediction Recycling with Request-Local and Persistent Memories for Multimodal Speculative Decoding
Recycling a target model's own predictions offers a training-free source of proposals for speculative decoding.EchoKV: Self-Corrective KV Cache Eviction via Iterative Query Feedback
KV cache eviction is not merely a token-ranking problem: the workspace retained for generating eviction queries also determines the quality of those queries.CHARM+: Cross-Hardware Attention with Re-Merge Multistream Mechanism
As sequence lengths continue to grow, distributed attention based on sequence parallel (e.g., Ring-Attention) has become a mainstream solution for large language models (LLMs).Does Your Trial Simulator Know What the Drug Does? An Intervention-Blind Test of Interventional Validity on 7,207 Registered Trials
Patient world models are increasingly proposed as simulators of clinical trials, and recent surveys rank their capabilities on a ladder from temporal prediction to counterfactual rollouts.