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: 4,993 arXiv preprints and 7 OpenAlex records.
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Newest papers, page 23
Replica-Aware Split Inference for Edge IoT Systems
Split Inference (SI) addresses the hardware resource limitations of Internet of Things (IoT) devices by distributing tiny Machine Learning (TinyML) model segments across multiple IoT devices.Reducing Logprob Without Causal Verification: Diagnosing SAE-Based PII Suppression in LLMs
SAE-based feature-level interventions to suppress PII leakage in LLMs are primarily evaluated by log-probability reduction.Kierston & KierstonBench: Simulation of Allentown, PA and Evaluation of Language Models in Crime Investigation and Surveillance Tasks
Large language models are candidates for collecting and analyzing records in criminal investigations and mass surveillance.ReSI-Math: Exploring Recoverable Capability Frontiers for Efficient Reasoning Improvement
Reinforcement learning with verifiable rewards (RLVR) has become an effective approach for improving mathematical reasoning in large language models.Density-Aware Reward Scaling: A Reinforcement Learning Framework for Reward-Proportional Sampling
Reinforcement learning objectives that maximize expected return can concentrate probability mass on a small subset of high-reward outcomes, limiting diversity when multiple distinct solutions are…Where Is the Truth When AI Agents Disagree? Dependence-Aware Belief Propagation for Unsupervised Truth Discovery (Extended Version)
LLM agents often give conflicting answers to the same question, and many deployed agents are variants of one base model, so their errors are correlated.Asymptotic Optimality of $cμ$-Rule for LLM Inference Scheduling under Memory Constraint
Large language model (LLM) inference, the process by which a pre-trained model receives an input prompt and generates output tokens sequentially, has become increasingly important with the…RAHC: Rule-Augmented Hyper-Relational Knowledge Graph Completion for Materials Synthesis
Scientific discovery depends on reusing experimental knowledge, yet records scattered across papers and repositories are often incomplete.AgenticFinBench: Benchmarking Financial Agents on Diverse Tasks from Real-World Requests
Financial agents are becoming an important class of real-world AI systems.Think Less, Locally: Local Preference Learning for Reasoning Compression
Large reasoning models achieve strong performance on challenging tasks, but their long chain-of-thought often incurs substantial inference costs by continuing to deliberate after sufficient…The Pen is Mightier than the Pixel: Natural Textual Unlearnable Example for Large Vision Language Models
The proliferation of Large Vision Language Models (LVLMs), trained on vast and often sensitive datasets, raised concerns regarding data privacy and unauthorized usage.Reconstructing Fraudulent Strategies in Online Payments via Interpretable Inverse Reinforcement Learning
Payment fraud causes financial losses and undermines users' trust in web payment services.UniFlow: Unified Multi-Scale Flow Matching for Time Series Generation
Real-world time series exhibit complex dynamics across multiple temporal resolutions, yet effectively incorporating multi-scale information into generative modeling remains challenging without…Feeling of Knowing in Large Language Models
Post-training updates change what large language models (LLMs) can answer, but may leave their answerability judgments misaligned.HTCE: Homeomorphism-inspired Topological Cross-Scene Experience Canonicalization and Reuse for Object Navigation
As embodied agents perform object-goal navigation across residential environments, they accumulate successful experiences whose semantic structure recurs across scenes even when geometric…Found by Search, Broken by a Shortcut: Transfer and Tampering of Searched Intrinsic Rewards
Can a search over innate drives find intrinsic rewards that help a reinforcement-learning agent learn, transfer to a task it never saw, and resist an exploit that was absent during the search?DGC-Net: Cross-Modal Dynamic Routing with Global-Guided Compensation in Multimodal Sentiment Analysis
User-generated videos, including opinion vlogs and video reviews, provide a rich source of people's attitudes toward products, services, and social topics.Amortized Nonlinear Variable Selection with Prior-Data Fitted Networks
Variable selection is an important task for interpreting potentially complex systems in fields such as biology, business, and sociology.One Assistant, Many Memories: Benchmarking Real-World Group Assistance When Recall Is Not Enough
The value of memory lies not only in recall but in using it to provide appropriate assistance.ScamWorld: An Interactive Environment for Evaluating Web Agents Against Online Fraud
Web agents increasingly browse, shop, and manage accounts on behalf of their users.