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Artificial Intelligence
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The newest 5,000 papers, from 12 October 2026 to 6 November 2026: 4,992 arXiv preprints and 8 OpenAlex records.
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Newest papers, page 218
When Is Smaller Greener? Right-Sizing Language Models for Agentic AI
AI agents use large language models (LLMs) to plan and act over multiple steps, yet models are usually compared per call rather than per completed task.Asymptotic Behavior of Transformers
In this paper we study the behavior of transformer networks generating text over long periods of time.Steering Vectors for Competing Ethical Frameworks Are Interchangeable: A Competing-Target Test for Activation Steering
Activation steering adds a vector to a model's hidden states to control a named behaviour.Tail Competition Explains Scaling in Best-of-N Sampling for Verifiable Problems
Best-of-N sampling is a simple inference-time scaling strategy for large language models, yet its behavior under incomplete reward models remains poorly understood.DANGERLENS: Probing Dangerous Representations Hidden by Safety Alignment
Safety alignment teaches language models to refuse harmful requests, yet the internal accessibility of hazardous concepts can retain traces of earlier training.Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory
Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities.Beyond Corrected Actions: Training LLM Agents on Textual Critiques
Large language models (LLMs) are increasingly used for multi-turn agentic tasks that require interactions with users and environments over extended sequences of actions.Policy-Verified Self-Training: Learning Agent Compliance from Verifiable Policy Rewards
Runtime policy verifiers can prevent agents from executing disallowed actions, but their decisions are typically used only for enforcement and discarded afterward.Evaluating Compressed Memory: Dependence, Utility, and Content Benefit
Does a language model answer better because its compressed memory contains useful document information?DeLoop: Repeated Actions as Self-Supervision for Long-Horizon Agents
Language-model agents that run long tasks often redo work they have already done: they re-issue a tool call they made earlier and get back exactly the same observation, a step that adds no new…EvoWeave: Composing Reusable Agent Experience through Operation Level Graphs
Different tasks often require interactive agents to perform similar operations at intermediate steps.ixi-sLM: On-Device sLM for AI Call Agent Service
Running an AI call agent on-device makes it private, network-independent, and free of per-query cloud cost, but shipping one as a real-world service imposes constraints a cloud deployment never…STRATA: Self-Maintaining Structured Experience Graphs for Self-Evolving Language Agents
Large language model (LLM) agents can self-evolve at test time by accumulating experience: retrieving relevant memories before acting and writing new lessons back afterwards.ELARSpeech: A 30K-Hour Dataset Across 675 Underrepresented Languages for Long-Tail Multilingual Speech Technologies
Massively multilingual speech modeling has made remarkable progress in recent years, yet existing training resources and benchmarks still underrepresent much of the linguistic long tail.EpiGuard: An Autonomous Infectious-Disease Early Warning Agent Platform
Infectious disease outbreaks often spread rapidly before signs of widespread transmission become apparent, leaving a big challenge for rapid response and effective intervention.SERO: Learning from Executable Oracle Continuations for Long-Horizon Agentic Reinforcement Learning
Long-horizon agentic RL is constrained by the futures visited by the current policy.KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms?
Korean neologisms arise through diverse mechanisms, a demanding test of language models beyond lexical memorization.Scaling LLM Inference via Parallelism: Throughput-Latency Trade-off
Large Language Model (LLM) serving systems must balance latency and throughput while efficiently utilizing distributed GPU resources.Vision-Language Models Integrate Conflicting Evidence Like Biased Bayesian Observers
Vision-language models must reconcile conflicting text and images.Recursive Formation and First-Class Reuse of Compositional Capabilities
We introduce FormCraft, a framework for representing acquired operations as first-class capabilities that can be recursively composed, directly invoked, and selectively retained in a persistent…