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 11 October 2026 to 5 November 2026: 4,998 arXiv preprints and 2 OpenAlex records.
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Newest papers, page 85
PsyJev: Psychological Analysis via System-1 Judgment
Self-disclosures on the Web support diverse psychological analyses, from recognizing expressed experiences to identifying symptom-relevant information.TOPS: Reshaping Flow Source and Transport for Multi-Task Robot Manipulation
Flow-based policies for language-conditioned multi-task manipulation commonly share a single $\textit{Gaussian}$ source prior and a common linear transport path across tasks.HeteGCD: Statistics-Aware Dual-Prototype Learning for Generalized Category Discovery on Heterophilous Graphs
Heterophilous graphs are prevalent in real-world applications, where connected nodes often exhibit dissimilar attributes or belong to different classes, posing significant challenges to conventional…Conservative Dueling Bandits: Preference Learning under Baseline Constraints
Contextual dueling bandits learn from pairwise comparisons, the same feedback used to fine-tune language models from human preferences.From Voiceprints to User Entities: Online Speaker-Memory Construction for Multi-User Spoken Agents
Personalized spoken agents must identify speakers before accessing user-specific memory, yet memory benchmarks typically assume correctly attributed histories.Generative Refinement Learning for Temporal Interpolation in Scientific Machine Learning
Computationally expensive simulations and real-world observations often provide data at a lower temporal resolution than downstream tasks require.Robust LLM Data Selection via Content-Invariant Gradient Attribution
Gradient-based data attribution has become an important tool for high-quality data selection in the instruction fine-tuning of large language models (LLMs), as it estimates the contribution of…Making Self-Driving Laboratories Auditable:A Unified Framework for Hybrid Actions,Measurements, and Safe Execution
Self-driving laboratories (SDLs) are laboratory systems that couple experimental planning, automated execution, and measurement feedback in a closed loop.The Vocabulary Is Already Here: Adapting Pretrained Interpretable Features for Generative Recommendation
Generative recommendation (GR) predicts the next item through its semantic ID (SID).Beyond the Evidence Coherence Trap: Agent-Driven Evidence Selection for Temporally Disordered Fake News Detection
Fake news undermines public trust and the integrity of online information ecosystems.Can LLMs Reliably Grade Olympiad Proofs? A Study of Mathematical Verification with LLMs
Large Language Models (LLMs) have made remarkable progress on solving competition-level mathematics, yet their ability to verify natural-language mathematical proofs remains relatively underexplored.Batching the Reflective Optimization Loop: Parallel Proposals Make GEPA Faster and Better
Reflective text optimization makes agentic systems better, but the optimization loop is itself a resource-hungry workload whose sequential structure leaves parallel evaluation capacity idle.Dependency Graphs of Mathematical Arguments: Representation, Structural Measurement, and Proof Review
We propose a source-grounded framework for representing and measuring mathematical arguments with assistance from large language models.Select-TTT: Query-Conditioned Selective Fast Weight Updates for In-Place Test-Time Training
Test-time training (TTT) enables a deployed language model to internalize incoming context on-the-fly into a small set of fast-weight parameters, rather than merely treating it as transient context…An Algebra of Transformers
On the theoretic analyses on transforms, they mainly focus on the computability analysis, such the Turing-completeness of unbounded precision transformers, the advantage of parallelizability of…SetGRPO: Calibrating Grader-Induced Policy Optimization
Rubric-based reinforcement learning uses fine-grained grader feedback to train large language models on open-ended tasks, but grader randomness can alter GRPO updates in ways that score…An Incremental Knowledge-Graph Learner Digital Twin for Evidence-Grounded Early Warning
Early-warning models in learning analytics can identify learners at risk of failure or withdrawal, but the resulting alerts are often only weakly connected to inspectable learning evidence.TAPESTRY: A Single Backbone for Video Generation and Robot Control
Pretrained video generation models encode rich priors about physical interactions, but leveraging them for robot control typically requires either resource-intensive video generation at test time or…Who Knows What - and What Does It Support? Perspective-Bounded Evidence Calibration for Multimodal Theory of Mind
Theory of Mind (ToM), the ability to infer others' beliefs, goals, and intentions, is a cornerstone of social intelligence.When the Critic Falls Behind: Co-Evolving Rubrics and Harnesses for Continual Agent Self-Improvement
Autonomous agents increasingly depend on harnesses that govern interaction, memory, tools, and execution around large language models (LLMs).