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 122
Adaptive World-Models for Anomaly Recognition (AWARE): A Framework for Robot Dynamics Prediction and Introspection
Unrecognized anomalies in critical infrastructure can rapidly cascade into catastrophic physical or operational failures.ActionRAG: Evidence-Refining Graph Diffusion for Long-Term Memory in LLM Agents
LLM agents need long-term memory that can recover precise and mutually consistent evidence from growing interaction histories, particularly when answers depend on relations among facts rather than…Semantic Representation for Agentic Self-Play in Automated Laboratories
Navigating complex, high-dimensional biological search spaces necessitates scalable physical and computational intelligence to explore beyond local optima through iterative self-play across…Clean-label Poisoning Attack and Defense Game for Federated Learning in Vehicular Networks
This paper studies interactions between clean-label poisoning attack and defense in vehicular networks using federated learning (FL).On-Policy Self-Training: Data-Free Self-Improvement in Large Language Models
On-policy self-distillation enables post-training foundation models without relying on stronger external teachers.AI Alignment for Digital Democracy: Fairer Voters Representation against LLM Biases
AI representation is emerging in digital democracy, assisting in voter emulation and decision-making.Every Edge Needs a Quote: Building a Quote-Grounded Knowledge Graph of Healthcare Software Adoption with Language-Model Agents
Hospitals regularly buy software that clinicians and patients depend on.Multiplicative vs. Additive State Coupling in Learned Optimizer Schedules
We learn schedules for a continuous penalty relaxation of MAX-CUT by backpropagating through an unrolled classical solver.HarnessMM: Recursive Self-Improvement for Harness Token Efficiency
Agent harnesses repeatedly invoke reasoning after tool feedback, even when an earlier call has already fixed the next action.Reimagining Enterprise RAG for Multi-Agent Systems: A Knowledge Layer That Organizes Before It Retrieves
Enterprises are deploying agentic AI across the software development lifecycle, and they need the behavioral logic of the systems they act on, not stakeholder documentation.A Foundation Model for Policy In-Context Longitudinal Causal Effect Estimation
Longitudinal causal effect estimation is complicated by time-varying confounding and treatment-confounder feedback loops.Interpretable sparse fine-tuning with TopKLoRA enables faithful circuit discovery
Fine-tuning on datasets of unknown origin can create a backdoor in the trained model when the data includes poisoned examples.From Deliberately Constructed Systemic Representations of the World of Activity to Dialogue: A Five-Level Communication Architecture for Strong Language-Based ASI
This paper proposes a five-level organization of message formation and understanding within thinking and activity in which ASI participates.Do "True" and "False" Add Up to One? Complement Consistency in LLM-Agent Opinion Simulations
In LLM-agent opinion simulations, each agent reports the probability that a claim is true after seeing its own previous probability and the probabilities reported by its neighbors, and that number…Advancing SVD-based LLM Compression via Layer-Wise Error Model Search
Low-rank SVD-based compression offers a powerful strategy to reduce the computational costs of LLMs.NgramRL: Accelerating RL Post-Training for Structured Generation via Rollout-Aware N-gram Speculative Decoding
Reinforcement learning (RL) post-training improves the reasoning capabilities of large language models, but its cost is dominated by autoregressive rollout generation, which can exceed 70% of…Closing the Modality Gap via Simplex-Constrained Representations
While multimodal contrastive models like CLIP and CLAP successfully learn shared representations, their embeddings typically exhibit a persistent \emph{modality gap}.Green Code or Costly Code? Evaluating the Energy Costs of Iterative LLM-Based Code Optimizations
Large Language Models (LLMs) are increasingly used to optimize code for performance.SemantEq: Faithful Bidirectional Equivalence of Formal Statements
Statement autoformalization translates mathematics from natural language into a proof assistant such as Lean 4, and models now do it at scale.Yalda-Embedding: Open Development of a Competitive Persian Retrieval Model
Despite advances in multilingual text representations, effective dense retrieval for Persian remains constrained by the limited availability of language-specific training resources and compact…