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 68
From the Transformer to a Formal Semantics of Coordinated Transformations
We develop a formal semantics for a general class of coordinated systems of parameterized transformations, taking the Transformer as a paradigmatic starting point but abstracting from its particular…The Importance of Speech Genre for End-of-Turn Detector Training
End-of-turn (EoT) detection has been studied mostly through features, models, and modalities, while the speech genre of the training data has drawn far less attention.PAPERBENCHX: Can AI Reproduce Science Across Disciplines?
Before AI can be trusted to discover new science, it should be able to reproduce what science already knows.Multi-output LSTM soft sensors for industrial anaerobic co-digestion using LSTM-beta-VAE generative data augmentation
Anaerobic digestion (AD) requires close monitoring of key process variables, including pH, the ratio of volatile organic acids to alkaline buffer capacity and biogas production, to maintain…Finding Is Not Returning, Returning Is Not Reconstructing: A Multi-Dimensional Evaluation of Agent Social Re-entry
As Large Language Model (LLM) agents increasingly participate in persistent social environments, understanding their long-term social interaction capabilities becomes increasingly important.Long-Term Agent Memory through Complementary Curation and Consolidation
Long-horizon dialogue agents require effective memory systems to recall facts, events, and relationships from interaction histories.The Role of Self-Concept In Language Models
When a language model says "I," does it refer to its parameter weights, its currently running process, its stable preferences, its model lineage, or the goals it pursues?Revisiting Over-refusal in Safety-aligned Large Language Models: A Representation and Optimization Perspective
Over-refusal is a critical side effect of safety alignment in large language models (LLMs), causing harmless but superficially risky queries to be mistakenly rejected.InferD: A Honeypot Framework for AI Inference Infrastructure
Large language model serving stacks, commercial inference APIs, vector databases, and agent orchestration gateways are now ex- posed on the public internet and indexed by commodity scanners within…EvoTeam: Multi-Agent Collaboration via Evolving Teams
LLM-based multi-agent systems (MAS) rely on collaboration among specialized agents to solve complex tasks.AegisFlow: Training-Free Non-Myopic Path-Safe Guided Flow Matching
Pretrained generative models, such as diffusion and flow matching models, have demonstrated exceptional capabilities in synthesizing high-quality data.Aligning Medical Vision Language Models at the Capability Boundary with Entropy Adaptive Rubric Grounding
Medical diagnostic reasoning requires verifiable links between visual evidence and clinical knowledge, yet current medical vision language models are aligned with objectives that reward the wrong…SHPRR: An Algorithm for Pure Exploration under Constrained Regret
Best Arm Identification (BAI) conventionally measures the cost of experimentation by the number of samples collected.Exploring the Potential of Symbolic Regression in Precision Livestock Farming
The ability to predict farrowing time in pigs is crucial for optimal farm management and ensuring piglet survival.LiDAR-SHIELD: A Fully Dockerized Adversarial Robustness Assessment Framework for LiDAR-Based 3D Perception Models
LiDAR-based 3D perception underpins autonomous driving and robotics, yet deploying these models safely requires stress-testing them against adversarial inputs before real-world use.Incorporating Machine-Others: An Enactive Exploration of Human-LLM Interactions
New concepts based in interactional thinking are required to understand how humans are changed by relating to LLMs.Turing LLM Post-Training: Calibrated Distillation and Efficient Dynamic-MoE Execution
We study post-training for dynamically routed mixture-of-experts (MoE) models, focusing on integrating independently optimized specialists while keeping consolidation efficient.KARMA: Training-Free Knowledge-Atomic Harness Evolution for Industrial Advertising Agents
Industrial advertising agents must continually adapt their decision knowledge to changing product offerings, traffic patterns, and operating policies.CovFedAvg: Local Low-Loss Curves and Federated Model Selection
Clients observing different parts of a prediction task may learn locally equivalent models with incompatible behavior outside their observed support.Prompts and probabilities in the linguistic assessment of language models: Evidence from semantic focus-sensitivity
As language models (LMs) have improved, an increasingly common means of evaluating their linguistic competence is through explicit prompting (e.g., "Which sentence is more acceptable?").