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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 75
Do Language Models Know What Compelling Scientific Evidence Looks Like?
Expert scientists excel at weighing empirical findings from multiple sources that vary in both statistical reliability and epistemic relevance.Violet Teaming in Practice: Lessons from a Pilot Expert Workshop on Governing Complex AI Systems
Violet teaming has been proposed as a method to integrate technical, policy, and societal considerations into the evaluation of emerging technologies.ReplayRCA: Measuring Post-hoc Diagnosis and Live Oversight of Agent Failures by Counterfactual Replay
When a language-model agent fails a multi-step task, can another model find the decisive mistake and fix it with a single action?Dreams Are Cheaper Than Flights: Curated World-Model Data Improves Aerial Detection
Robot perception is increasingly data driven, yet field data is slow, costly, and sometimes disruptive to collect.WebOverlay: High-Throughput Vision-Only Distillation for Extreme Aspect-Ratio Web Overlay Detection
Web overlays, such as cookie consent banners, newsletter prompts, and age gates, obstruct automated web crawlers and degrade downstream document representations.Characterizing Recoverable Expert-Utilization Signatures in Fine-Tuned Mixture-of-Experts LLMs
As Mixture-of-Experts (MoE) large language models are increasingly deployed in distributed environments, from federated learning to edge-cloud systems, their architecture harbors an underexplored…Capability-Grounded Agentic Planning
LLM agents serve natural-language requests through tools, data sources, and other agents, and even simple requests often need several interdependent calls.Consensus Within, Competition Between: Decentralized LLM Teams for Perimeter Defense
The use of large language models (LLMs) to coordinate multi-agent teams raises a fundamental architectural question: whether task allocation should be centralized in a single LLM controller or…Accept Changes, Reject Attacks: Robust Memory Management for LLM Agents
Since large language model (LLM) agents rely on long-term memory, what they store now shapes what they do later.LanG 2.0: Energy-Efficient Agentic AI for Semi-Automated Security Operations at Scale
Security operations centers (SOCs) need to turn terabytes of alerts, logs and flows into trusted, affordable decisions to unveil hidden threats.bf16 Rounding Silently Discards Most RL Policy Updates: A Hidden Training-Inference Mismatch and an Unbiased One-Line Fix
Reinforcement learning (RL) post-training of language models keeps fp32 master weights in the optimizer and serves rollouts from a bf16 copy produced by round-to-nearest conversion.Where, When, and How Many Bits? Scaling Laws for SGD with Stochastic Rounding
Low-precision training reduces computation but introduces additional rounding error.BABA-Bench: Evaluating Long-Horizon Patient-Advocacy Agents
An LLM agent that helps a patient get care must do two things.Optimization Techniques for Solving the Minimum Red--Blue Separating Set Problem with an Application to Human--AI Text Separation
We study the so-called Minimum Red''Blue Separating Set Problem (MRBSSP) which arises from computational geometry.Payroll Fraud Detection in Synthetic HR/ERP Graphs: An Inductive Comparison of Rules, Path Features, and Graph Neural Networks
Payroll-fraud screening requires finding hidden employee--payee links while distinguishing suspicious relationships from legitimate ones.DTP-OPSD: Reinforcement Learning with Distractor-Aware Temporal-Privilege On-Policy Self-Distillation for Temporal Video Grounding
Temporal Video Grounding (TVG) aims to localize the start and end times of an event described by a language query in a video.SPARQLcirc: Probabilistic Query Evaluation with Provenance Circuits
Probabilistic query evaluation (PQE) computes the probability of every answer of a query over a tuple-independent probabilistic knowledge graph.FB-MOAC: A Reinforcement Learning Algorithm for Forward-Backward Markov Decision Processes
Reinforcement learning (RL) algorithms are effective in solving problems that can be modeled as Markov decision processes (MDPs).Kerkoporta: Injecting Structured-Output Backdoors in Reasoning Coding Agents
Existing large language model (LLM) backdoor attacks operate primarily in chat settings, where the risk is bounded by free-form text outputs.Training LLM Agents to Automate LLM Post-Training with RSI-Dojo
Recursive Self-Improvement (RSI) aims to enable AI systems to autonomously build and improve AI systems themselves, including models, harnesses, and underlying infrastructure.