1444 matching published research entries
Computer Science · 2026-11-04
arXiv:2611.04253v1 Announce Type: cross Abstract: On-demand model loading dominates response latency for interactive on-device AI, yet unified memory architectures (UMAs), now common on edge devices, should make it cheap by placing weights in memory the GPU already sees. However, frameworks still use the loading flow built for discrete GPUs, leaving significant inefficiencies.
arXiv Computer Science ARRead article →Computer Science · 2026-11-04
arXiv:2611.04222v1 Announce Type: cross Abstract: Under expert parallelism, mixture-of-experts (MoE) layers pay all-to-all token dispatch, grouped GEMMs, and a combine step in every layer. In decode each rank holds only 1-256 tokens: dispatch latency and weight traffic dominate while tensor-core utilization is inherently low.
arXiv Computer Science ARRead article →Computer Science · 2026-11-04
arXiv:2611.04804v1 Announce Type: new Abstract: 4D Gaussian Splatting (4DGS) enables high-quality real-time novel-view synthesis for dynamic scenes, supporting free-viewpoint video, mobile XR, and autonomous systems at the edge. Methods using explicit 4D distributions, keyframe interpolation, or anchor binding share two GPU data-movement bottlenecks.
arXiv Computer Science ARRead article →Computer Science · 2026-11-04
arXiv:2611.04144v1 Announce Type: new Abstract: LLMs have rapidly expanded from simple chatbots to agentic AI, where models directly use tools to solve complex problems. To improve problem solving, most frontier LLMs adopt an MoE architecture, pushing model sizes into the trillion-parameter regime.
arXiv Computer Science ARRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.04100v1 Announce Type: new Abstract: Harnesses for large language models have converged on a single-threaded shape: one agent, one history, one machine, one sitting. That shape fails work that spans many agents, outlives any one model call, or waits on systems that are not agents. We describe the Agent Board, the coordination substrate in the Corvic harness.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.04079v1 Announce Type: new Abstract: Real-world problems span diverse domains, including program synthesis, symbolic reasoning, planning, and optimization, and often require fundamentally different solution paradigms. A central challenge for LLM-based reasoning is twofold: identifying an appropriate problem-solving approach and formulating it correctly.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.04061v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a promising approach for enhancing the reasoning capabilities of large language models. However, it still remains unclear whether RLVR can extend reasoning capabilities beyond what is already encoded in the base model.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.04059v1 Announce Type: new Abstract: As large language models are fine-tuned for increasingly specialized tasks, their performance depends more on which training examples are selected than on how many. Given a large heterogeneous training pool and a small target set specifying the desired capability, targeted data selection aims to find a compact subset that maximizes downstream performance.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.04057v1 Announce Type: new Abstract: In mixed-vendor accelerator fleets, many training jobs can run on several hardware types, and the best choice depends on each job's throughput on each type. Routing a job needs a throughput estimate for every candidate type before the job runs.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.04055v1 Announce Type: new Abstract: In speculative decoding, parallel drafters such as DFlash make drafting cheap, so target verification dominates each round. A central choice is the verification length, the number of tokens that the target processes in each round. Verifying more draft tokens can accept more of them, but work after the first rejection is wasted.
arXiv AIRead article →Artificial Intelligence · 2026-11-04
arXiv:2611.04054v1 Announce Type: new Abstract: The uncertainty of a statistical model is most commonly factorized into an aleatoric and an epistemic part. This factorization changes how predictions are interpreted in downstream decision tasks. Importantly, except for the idealistic scenario with no model mismatch, the quantification is a characteristic of the model and not the data generating process.
arXiv AIRead article →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.
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