AI & Machine Learning · Computer Science & AI
Artificial Intelligence
The newest papers in Artificial Intelligence that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 12 October 2026 to 6 November 2026: 4,992 arXiv preprints and 8 OpenAlex records.
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Newest papers, page 220
VLRLA: Deployment-Time Adaptation of Vision-Language-Action Models through In-Context Reinforcement Learning
Vision-language-action (VLA) models connect visual perception and language understanding with robot control, providing a foundation for instruction-conditioned manipulation.Revisiting Triplet Contrastive Learning for 3D Shape Understanding
In multi-modal 3D pre-training, aligning 3D shapes, their 2D image counterparts, and language descriptions has made tremendous progress.Retrieve What Helps, Only When It Helps: Agentic Reinforcement Learning for Selective Retrieval-Augmented Machine Translation
Retrieving external knowledge can improve large language model (LLM) translation by providing supplementary information such as idiom interpretations and terminology.Typed Decision Models on Tabular Data
Typed decision models are language models that answer a question with a fixed set of possible answers by returning a probability for each answer instead of generating text.LabCompiler: A Framework for Verifiable Human-Machine Laboratory Execution from Imperfect Protocols
Closing the loop of AI-driven science requires reliably translating natural-language protocols into executable laboratory workflows despite protocol defects and heterogeneous hardware.Evidence-Anchored Overdose Death Prediction from Death Investigation Reports
Confirmation of overdose deaths in the United States is often delayed due to the complexities of certifying drug-involved deaths.TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series
Contrastive learning for time series depends on deciding which pairs of inputs should be treated as similar and dissimilar.$τ$-Emotion-Bench: Exposing the Interaction Fragility of LLM Agents through Real-World User Emotions
In long-horizon tool-calling tasks, LLM agents can fail not only because of tool-use errors, but also because of ineffective interaction with users.REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning
An early mistake in the generated reasoning can constrain all subsequent reasoning.Frame Evidence:A Benchmark for Reference Frames and Visual Evidence in Multi-Image Spatial Reasoning
A spatial answer depends on the reference frame specified in language and its supporting visual evidence.Divergence-Routed On-Policy Distillation for Language Models
On-policy distillation (OPD) transfers knowledge from a teacher model to a student along student-generated trajectories, typically by minimizing the reverse Kullback-Leibler (RKL) divergence.Learning Across Temporal Graph Representation Silos
Temporal graph data are recorded in different representations, but whether these differences also determine the scope of learned knowledge reuse remains unclear.XferAgent: Cross-Agent Experience Transfer for Self-Improving LLM Agents
Large language model (LLM) agents can reuse experience from prior task executions to improve performance on subsequent tasks.Who's Behind the Text? Robust Model Authorship Attribution
As large language models (LLMs) are increasingly used to generate text, their misuse has become a growing societal problem, with implications for academic misconduct, model distillation, and…JockeyBench: Evaluating LLM Agents on Horse-Racing Decision-Making
Repeated event forecasting requires extracting predictive signals from noisy evidence and evaluating their value across successive decisions.L3M: A Verifiable Harness and Runtime for Self-Extending and Self-Improving Agents
The lasting gains in AI have come from methods that absorb more computation, and language models already scale computation within a call.JevCoder: When Does Decision Delegation Pay Off in Code Repair?
A coding agent repeatedly makes decisions as it repairs code.Beyond Static Positional Encoding: Bayesian Time-aware Attention for Non-Stationary Long-term Time Series Forecasting
Transformers have demonstrated strong performance in long-term time series forecasting (LTSF), yet modeling non-stationary temporal dependencies remains challenging.Divergent Paths: Learning an Outcome-Predictive Representation of LLM Agent Trajectories
In this work, we reframe agentic trajectories as paths through a shared embedding space of actions and environmental observations.Instruction-Conditioned Motion Control for Robotic Arms: Language-to-Code Decomposition and Reinforcement Learning
Instruction-Conditioned Motion Control (ICMC) separates parameter specification, instruction generation, and code synthesis for language-driven robot control, making execution outcomes traceable to…