1673 matching published research entries
Artificial Intelligence · 2026-11-04
arXiv:2611.04048v1 Announce Type: new Abstract: Large Language Models (LLMs) serve both as tools for aiding human decision-making in everyday life and as models of human agents in experimental simulation studies. Alignment on prototypical social decisions -- such as whether to cooperate on a collective action -- therefore has immediate practical consequences for researchers and for society at large.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.04035v1 Announce Type: new Abstract: Foundation models in changing environments can encounter unfamiliar domain knowledge that their original training did not cover. However, new domains rarely provide large amounts of expert-created training data or reliable supervisory signals for further training.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.04032v1 Announce Type: new Abstract: Serving an LLM efficiently depends on the engine, how the model is split across GPUs, precision, scheduler settings, speculative decoding, KV-cache management, and prefill-decode disaggregation. The best configuration depends on the model, hardware, workload, and objective. Operators usually find it by trial and error.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.04006v1 Announce Type: new Abstract: Modern learning systems increasingly combine pretrained models with downstream heads. Despite the growing importance of such modular designs, how the downstream model co-adapts to the geometry of pretrained representations remains relatively underexplored.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.04003v1 Announce Type: new Abstract: LLM agents struggle to automate complex enterprise procedures because static, pre-built harness cannot capture proprietary policies, undocumented behaviors, or state-dependent recovery paths that surface only during execution.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03985v1 Announce Type: new Abstract: Large language models (LLMs) read company news at scale, and backtests suggest that their readings predict stock returns. We ask how much of that predictability survives a test that rules out look-ahead, memorization and selection.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03983v1 Announce Type: new Abstract: Translating population-level exercise guidance into individualized prescriptions requires integrating clinical and wearable data, including diagnoses, medications, functional limitations, and baseline activity, with condition-specific recommendations, a process difficult to scale beyond specialist rehabilitation settings.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03980v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly used as synthetic participants in opinion-dynamics experiments, often assuming that an agent's behaviour is fully determined by its prompt.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03946v1 Announce Type: new Abstract: Generative AI tools for research are widely deployed, yet individual productivity gains do not guarantee better team research. We argue that how AI use is structured matters alongside access.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03930v1 Announce Type: new Abstract: A stored experience may answer one future question and offer little help with another. We measure this task-conditioned utility rather than assigning each memory a single importance score. A task-blind writer stores a memory without seeing the later task; a frozen reader then answers that task with and without the memory, using identical filler.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03882v1 Announce Type: new Abstract: Students can use large language models to complete assignments, but a correct solution does not establish whether they understand the reasoning behind it. Individualized interviews can probe that understanding but require recurring staff effort.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03861v1 Announce Type: new Abstract: Retaining predictive gains can require persistent errors in a model's response to interventions. For deterministic predictors with finite second moments under squared-error loss, any observational advantage over the best predictor using only an outcome's direct causes requires dependence on other inputs, regardless of the learning rule.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03853v1 Announce Type: new Abstract: Healthcare information systems are fragmented, and retrieving data from legacy web portals is a recurrent need. Robotic Process Automation (RPA) is deterministic but sensitive to interface changes, whereas autonomous agents based on large language models (Agent Process Automation, APA) promise adaptability at a higher computational cost.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03849v1 Announce Type: new Abstract: Identifying faulty components from sensor anomalies is difficult because fault effects can propagate through a plant and different faults can produce similar observations. Detailed component models and labeled fault examples may also be unavailable.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03834v1 Announce Type: new Abstract: Long-to-Short (L2S) model merging seeks to combine the strong reasoning capability of long chain-of-thought models with the concise style of base models, but existing methods that rely on linear interpolation and first-order activation statistics fail to capture how the loss landscape responds to parameter displacement, leading to degraded accuracy on hard tasks.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03832v1 Announce Type: new Abstract: Computational drug discovery offers a growing range of tools for proposing and evaluating molecules, but combining them and deciding what to do next still requires substantial expert effort.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03765v1 Announce Type: new Abstract: Embodied agents are increasingly deployed in open-world environments, yet current robustness evaluations remain biased toward hazardous instructions and jailbreak vulnerabilities. A more fundamental issue is largely overlooked: agents are rarely tested under semantically sufficient but contextually minimal instructions (i.e., common in real-world human communication)…
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03739v1 Announce Type: new Abstract: We formulate and investigate the hypothesis that reasoning text has simpler co-occurrence topology than non-reasoning text, and that higher-quality reasoning has simpler co-occurrence topology than lower-quality reasoning. We discuss and test applications to AI safety and AI evaluation.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03725v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) pipelines are widely used for domain-specific question answering, yet locating faults remains manual and time-consuming. Fault localization is challenging because RAG pipelines consist of interdependent stages, where faults can propagate downstream, masking their origin, or remain undetected even when the final answer is correct.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03723v1 Announce Type: new Abstract: Deep Research (DR) systems extend large language models into autonomous agents that decompose complex information needs, iteratively search the web, and synthesize evidence to answer challenging questions.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03721v1 Announce Type: new Abstract: Recent breakthroughs in artificial-intelligence-assisted mathematics show that open-ended machine search can outperform human mathematicians, yet transferring this paradigm to physics requires refactoring the research workflow. We introduce Simjecture, an open-source framework in which language-model agents pursue human-defined physical conjectures.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03713v1 Announce Type: new Abstract: Before a scientist fits any parameter, they must decide what the model contains: which quantities exist, which are hidden, how they interact, and how they are measured. Physicists call such a trial form an ansatz. Benchmarks for automated equation discovery mostly supply it, by fixing the variables, a library of candidate terms, or a law to be modified.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03698v1 Announce Type: new Abstract: Recursive Self-Improvement (RSI) aims to enable AI systems to autonomously build and improve AI systems themselves, including models, harnesses, and underlying infrastructure. Within this broader agenda, a central capability is enabling agents to train and improve base models.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.03685v1 Announce Type: new Abstract: Visual agents need to coordinate reasoning and action while their environment evolves during inference. We introduce LOLPlay, a benchmark in native \emph{League of Legends} with 36 champion-specific tasks spanning resource management, combat, cooperation, and objectives.
arXiv AIRead article →