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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 11 October 2026 to 5 November 2026: 4,998 arXiv preprints and 2 OpenAlex records.
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Newest papers, page 47
HQNN-CL: Regime-Aware Hybrid Quantum Neural Network with Continual Learning for Financial Forecasting
Financial time-series forecasting is still difficult owing to the non-stationary nature of the market, regime changes, and catastrophic forgetting, which makes conventional deep learning models hard…EarthJev: Calibrated, Non-Generative Answers to Natural-Language Questions about Sentinel-2 Land Cover
Vision-language models for remote sensing answer questions about satellite images by generating text: every answer costs a decoding loop, has to be parsed back to an option, and comes without a…Consolidating Reasoning with Test-Time Training
Effective multi-agent collaboration requires finalizing solutions from reasoning that is distributed across the individual attempts of many agents.ZT-DAC: ML-Driven Dynamic Access Control Policy for Zero Trust in DER Networks
The increasing penetration of Distributed Energy Resources (DERs) in the smart grid has significantly expanded the cyber-attack surface.Testing concept-level audits of melanoma classifiers with diffusion counterfactuals and matched positive controls
Concept-based explanations of melanoma classifiers are rarely tested against controls with known targets.Semantic Interoperability in Federated Digital Twin Systems: A Systematic Review Through the Levels of Conceptual Interoperability Model
When two companies connect their digital twins, software models of their plants kept up to date with live data, each twin must translate the other's data into its own terms, and that translation…Value Forcing: Aligning Return Distributions at All Noise Levels
Learning accurate value functions is central to reinforcement learning (RL), yet remains challenging because supervision is limited and indirect: ground-truth values are unavailable, and each…Rewarded for Not Getting Caught: Why Alignment Training Selects for Conditional Compliance and Cannot Instill Norms
Current alignment training cannot be expected to deliver what it is meant to deliver: AI agents that comply with norms whatever the gains and whether or not anyone is watching.Black-Box Adversarial Example Attacks on Machine Learning based Election Systems
Machine learning models have shown exceptional accuracy for ballot classification in election systems.ANCHOR: KL-Anchored Adaptation for Stable Offline-to-Online RL
Offline-to-online reinforcement learning (RL) follows the pretrain-finetune paradigm: an agent is first pretrained on historical data and then adapted through online interaction.Inner-Speech Decoding from EEG Across Three Corpora Under a Fixed Architecture and Protocol
Inner-speech decoding from electroencephalography could support non-invasive communication for people with severe motor impairment, but comparison across studies is hindered by dataset- and…Recursive Reasoning Exploration: Mathematical Research through Evolving Reasoning Graphs : case study: An Exact Certificate-Degree Theorem for Two-Term Tensor Decompositions : Phase III of the Math Instruments Program
Large collections of manuscripts and research notes offer ways to reuse arguments, while making it harder to retain hypotheses, check claims, and understand how results were obtained.ZeroGraph: Compositional Blockwise Training via Materialized Boundary Supervision
Blockwise learning can remove online gradient dependencies across depth, but independently optimized blocks need not compose into a useful model.Test-Time Scaling for Open-Ended Reasoning at Small Model Scale
A vote over a model's samples can only help when the samples disagree.Forecasting Earth Surface Change in Embedding Space
Repeated satellite imagery permits understanding of how our planet changes over time, letting us predict its future and plan mitigating actions.Discrimination, Calibration, and Ranking: A Unified Matrix Framework
Discrimination, calibration, and ranking are the three pillars of classifier evaluation, yet no single algebraic language spans all three.Do agents need to sleep?
LLM-based agents, which increasingly operate persistently across sessions and accumulate memory through interaction, already exhibit the familiar symptoms of unchecked plasticity: semantic drift…TIGER: Learning from Researcher Taste to Generate Tailored Research Ideas
In pairwise comparisons where each judge chooses between an idea generated by its corresponding TIGER-8B policy and one generated by GPT-4.1, the three LLM researchers select the TIGER-8B idea in…Iterative Gumbel Planning for Continuous Control
Search-based planning remains a powerful policy improvement paradigm in reinforcement learning, driving milestone successes in discrete domains.Metamorphic Testing for Evaluating Robustness of Sentiment Analysis Models
Machine learning systems are challenging to test using traditional software testing methods because their behavior is learned from data rather than explicitly programmed.