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Artificial Intelligence
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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 183
A Cross-Domain Evaluation of Prompting Techniques for Large Language Models
Prompt engineering has become a central mechanism for guiding the behavior of Large Language Models (LLMs) across a wide range of reasoning and language understanding tasks [1], [2].Arctic-SWE: Revisiting the Long Horizon Agent Training by Modeling-System Co-Design
Long-horizon agent training, including software engineering, is advancing rapidly, but the recipe and infrastructure needed for a strong reinforcement-learning baseline remain difficult to reproduce.Same Data, Different Bytes: Serialization-Invariant Integrity Checks for Agent-Published Datasets
Agent networks require reliable ways for requesting agents to verify where data is located, how it can be accessed, and whether the data received matches what the data owner made available.How Well Can LLMs Verbalize Mathematical Formulas? A Multi-Domain Evaluation
Large Language Models (LLMs) have shown strong capabilities across natural language tasks.Uncertainty Gates the Gradient: Adaptive Updates for Generalized Few-Shot 3D Segmentation
In generalized few-shot 3D point cloud segmentation, support annotations are often compromised by sparsity and occlusion.MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring
The adoption of Large Language Models for radiology report structuring has been largely dominated by either proprietary cloud-based models or large open models requiring high-end GPUs for…Steerable Pluralistic Policy Alignment from Anonymized Crowd Feedback
Pluralistic alignment requires AI systems to learn from diverse populations whose preferences may reflect heterogeneous or even conflicting objectives, and to personalize their behavior to…Enhancing analytical photon transport in turbid media through physics-guided neural network corrections
Analytical methods provide rapid and computationally efficient predictions of light transport in turbid media, making them attractive for real-time and inverse applications.DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution
General-purpose agents are increasingly expected to perform professional analytics work, which requires sustained reasoning, iterative investigation, and reliable evidence.Multilingual Brain Decoding Reveals Shared Cortical Semantic Geometry
Human languages differ markedly in lexical and syntactic form yet can convey similar semantic content.Expert-Guided Rubrics as Dense Reward Signals for Reinforcement Learning in Non-Verifiable Domains
Large language models (LLMs) are increasingly deployed as autonomous agents in non-verifiable domains such as financial services, where they must interpret user intent, rewrite queries, and retrieve…How Intervention History Changes AI Agent Sabotage on Subsequent Tasks
AI control evaluations often reset agents' memory between tasks, although deployed agents may retain records of prior interventions.GRaSP: Latency-Aware Optimization for Geo-Distributed Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is increasingly served across geo-distributed cloud and edge infrastructures, where wide-area latency, cross-region egress cost, and data-residency rules compete…Between Spoilers and Guesswork: Avoiding Over-Specification in Coding Benchmarks
Benchmarks drive AI progress, but are hampered by task under-specification and over-specification.StoBasket: Joint Upsell and Cross-Sell Optimization for Contribution Margin
Retail recommenders rank relevance, but retailers earn contribution margin: an upsell earns the margin difference over the item it replaces, whereas a cross-sell earns its full margin.When Can You Trust an Agent's Own Verifier? Stress-Testing Counterfactual Rationale Verification Under Search Pressure and Oracle Access
Automated agent design increasingly trusts a single score to decide whether a change is an improvement -- but a score optimized aggressively enough ceases to measure what it was meant to.Anytime Contracts for AI Services
AI services are prone to moral hazard, as model providers can silently route requests to cheaper, lower-quality models, and the inherent stochasticity of model outputs makes this degradation hard to…Next Token or Vector? Learning How Far to Decode
Standard autoregressive decoding advances one token at a time, even when several upcoming tokens can be predicted together.Self-Evolving Coding Agents via Harness-Policy Optimization
Large language model (LLM) agents have shown promising potential in coding tasks, and their performance is jointly determined by the policy model and the harness that mediates their interaction with…Prune the Leaky Neurons: Mitigating Private Data Leakage in Fine-Tuned LLMs via Joint-Embedding-Guided Pruning
Fine-tuning large language models (LLMs) on proprietary records enables domain adaptation but introduces two challenges: (i) privacy leakage of memorized private data, such as personally…