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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: 4,993 arXiv preprints and 7 OpenAlex records.
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Newest papers, page 2
Adversarial Robustness of Kernel Machines: A Comprehensive Analysis
Kernel machines are an important class of models in machine learning.KOSMOS: Self-scheduling Persistent Kernels for Communication-Compute Overlap
Tensor and sequence parallelism (TP+SP) and expert parallelism (EP) put collectives on the critical path of transformer layers.Localization Does Not Imply Steerability: A Mechanistic Study of Sign Errors in LLM Mathematics
When large language models do multi-step mathematics, a characteristic failure recurs: the model writes one wrong sign token mid-computation -- a cofactor sign, the minus of a recursive…SAVeR$^2$: Reasoning-based Safety Alignment for Large Reasoning Models via Verifiable Rewards
Large Reasoning Models (LRMs) have achieved impressive performance via explicit Chain-of-Thought (CoT) reasoning, yet this process introduces critical safety risks.Signed but Not Safe: Requirement Integrity in Multi-Agent LLM Handoffs
Enterprise AI agents increasingly split a user's request across subagents and merge their results, and each handoff can summarize away, alter or ignore a requirement.DelegateEval: Evaluating Platforms for Personal External Service Agents
AI agents that work for an individual over time may also help that individual serve external clients.Concertina: Embodying Manipulation and Cognition in One Diffusion Vision-Language Model
Most vision-language-action (VLA) policies decouple manipulation from cognition by bolting an external action mechanism onto a vision-language model (VLM)--either quantizing actions into discrete…Graph learning-assisted logic-based Benders decomposition for distributionally robust in-station train rescheduling under disruptions
Uncertainty in resource reopening times complicates train holding and rerouting decisions when resource closures coincide with arrival delays and processing-time extensions.Goal-Branching LaCAM: Temporary Goals as Search Branches in Dense Multi-Agent Pathfinding
LaCAM is a search-based solver for multi-agent pathfinding (MAPF) that runs a depth-first search over joint configurations, using PIBT to generate successors and lazily adding single-agent…Adaptive Experimentation Can Hide Model Error:Detection Limits and Falsification Budget
An experiment can be informative about the parameters of a working model and uninformative about whether that model is wrong.Action-Conditioned VLM Monitoring for Autonomous Vehicle Semantic Anomalies
We study action-conditioned monitoring of autonomous-vehicle behavior in semantically unusual driving scenes.CST-Bench: Benchmarking Counterfactual Spatial Transition Reasoning with Causal Transition On-Policy Self-Distillation
Spatial reasoning is a fundamental capability of multimodal large language models (MLLMs), yet existing benchmarks mainly focus on static scene understanding.FlashOffloading: Low-Latency KV Cache Offloading for Long-Context LLM Serving
In large language model (LLM) serving systems, the key-value (KV) cache grows linearly with sequence length and concurrency, so long-context inference easily hits the high-bandwidth memory (HBM)…The Memory Trap: Language Agents Overgeneralize from Benign Interactions
Language agents increasingly use persistent memory to record user preferences and procedures across sessions.SKIM: Pruning Large Language Model Agents via Selective Knowledge Informed Masking
Large Language Model (LLM) agents are increasingly deployed under structured generation, where grammar constraints enforce output format at inference time.Stress Testing Chain-of-Thought Monitoring Against Covert Misalignment
As LLM agents advance rapidly in autonomy, the risk of hidden objectives that bypass safety constraints becomes increasingly plausible.Learning and Steering in Incomplete-Information Games
In many modern multi-agent systems, a mediator seeks to influence strategic decision dynamics using only outcome-level feedback and without knowing agents' utilities.Evolver: Turning Agent Exploration into Evolving Capabilities
Agent exploration produces valuable procedural knowledge, yet much of this experience remains confined to transient execution traces and is not systematically reused.Align Before Aggregation: Basis-Consistent Federated LoRA under Heterogeneous Ranks
Federated fine-tuning of large language models (LLMs) under heterogeneous client capacities is increasingly important, and low-rank adaptation (LoRA) with heterogeneous ranks makes it practical.FisherDistill: Diffusion Distribution Distillation via Closed-Form Fisher Critics
Recent advances in diffusion distillation have enabled high-quality image generation in only one or a few network function evaluations (NFEs).