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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 129
Co-Recursive Semantic Self-Extension: A Mathematical Foundation for Open-Ended Intelligence
Artificial intelligence has advanced through scale, data, and computation, yet remains bounded by its representational framework.Solving Flood-It Puzzles: A Comparison of Tailored A* Search with Declarative Solver Approaches
We present an optimized approach for finding minimal move sequences to solve Flood-It puzzles using A* search and compare it with approaches using SAT, Integer Programming, and Constraint…DeepOpen: Decision-Oriented Intent Classification on BANKING77 and CLINC150
We present DeepOpen, a decision-Oriented intent-classification framework with dataset- specific systems for BANKING77 and CLINC150.Who Is the Influencer? Consensus Dynamics in Multi-Agent Systems
Multi-agent LLM systems are increasingly used in practice, but their behavior is complex and hard to understand.Can AI Co-Design Structures? An AI Agent for Early-Stage Structural Design
Recent advances in large language models and agentic workflows offer a new paradigm to early-stage structural design translating design intent into structural models.A Grammar of French Civil Cassation: A Ground-Level Dataset and Tool
This paper presents a dataset of 121,536 ground-reasons pairs across 86,464 rulings of the five civil chambers of the French Court of Cassation (2016-2025).ProductWebBench: Evaluating Website Continuity Beyond Task Completion
Frontend coding agents are usually graded on one question: did the requested target get built.Constraint Reasoning and Large Language Models Join Forces: Providing Contrastive Explanations for Real-World Optimisation-Based Applications
We focus on optimisation-based Decision Support Systems in real-world applications.SetCommit: Coordinated Token Commitments for Few-Step Diffusion Language Model Decoding
Diffusion language models generate multiple token positions in parallel, but few-step decoding makes parallel commitment difficult: tokens that appear reliable individually may be jointly…Post-Audit Mirage: Identifiability Limits of Offline Agent Verification
Offline tests can fail to reveal whether an agent update will remain safe after deployment.Mixing Matters: Evidence Position Bias Across Sequence Mixers in Long-Context Question Answering
Long-context models often use answer-bearing evidence less effectively when it appears away from a prompt boundary.Rethinking Fisher Vectors for Second-order Multi-scale Deep Feature Aggregation in Scalable Scene Retrieval
Aggregating CNN responses into a compact discriminative vector, given their lack of geometric invariance, is a challenge.PriMa-Causa: Causal Prescriptive Maintenance
Automated manufacturing requires data-driven decisions under uncertainty and sparse observational records, and unplanned downtime is a dominant cost of getting them wrong.Prompt Sensitivity in Tool-Augmented Model Comparisons
Language models are compared on question-answering benchmarks that pose each question in one wording, and a reworded question can change which of two models scores higher.Network topology shapes consensus and vulnerability in LLM populations
As large language models (LLMs) are deployed in interacting populations, their collective behaviour depends not only on the models but also on who interacts with whom.Prompting Strategy Effects Depend on the Judge: A Multilingual Study of LLM-as-a-Judge Robustness
LLM-as-a-Judge evaluation is increasingly applied to multilingual tasks, yet prompting strategies such as Chain-of-Thought and Role Conditioning are recommended based solely on English benchmarks.When More Metadata Misleads: Probing Field-Level Grounding in LLM-Generated Museum Descriptions
Metadata-conditioned generation is often framed as a data-to-text task in which additional input fields should improve grounding.Different Tasks, Different Timesteps: Temporal Task-Aware Quantization for Diffusion Language Models
Diffusion Language Models (DLMs) introduce unique quantization challenges due to their iterative denoising process.Predicting Music Valence and Arousal via Textual and Multimodal In-Context Learning in Large Language Models
While arousal can be reliably predicted from musical audio, emotional valence remains difficult to model due to its subjective and context-dependent nature.Trion-1: Unifying Language, Protein Sequence, and Structure with Diffusion Language Models
We envision a general-purpose protein foundation model as a unified, interactive model of sequence, structure, and function, capable of understanding, generation, and cross-modal translation while…