Prépublication
· 2026 arXiv
EN
Modern language models undergo reinforcement learning with verifiable rewards (RLVR) on top of already-trained checkpoints. A key promise of RLVR is the discovery of new reasoning strategies. In principle, a model can sample novel ideas absent from its prior t...
Saif Punjwani, Micah Goldblum
#cs.LG
Prépublication
· 2026 arXiv
EN
Conditional memory architectures such as DeepSeek Engram use input n-grams to look up learned embeddings, expanding the capacity of large language models (LLMs) with limited additional computation. Beyond model scaling, this architecture has demonstrated the p...
Hongru Cai, Ran Wei et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Real-time robot control demands enough visual history to infer motion and task progress, but processing that history can delay action. We present Long-WAM, a model-system framework for scaling the context of causal world-action models under real-time control c...
Wei Huang, Bohan Zhang et al.
#cs.RO
Prépublication
· 2026 arXiv
EN
Vision-language-action models (VLAs) are strikingly sensitive to instruction phrasing and do not inherit the language robustness of the vision-language models they are built on. A one-word edit can move success by tens of points: $π_{0.5}$ turns on a LIBERO st...
Mikey Watts, Yuchen Cui
#cs.RO
Prépublication
· 2026 arXiv
EN
Latent world models have shown a remarkable ability to predict future states and to plan in the real world. In practice, however, we lack a principled way to estimate how their capabilities scale with model size, data, and compute, an open problem that slows p...
Artem Zholus, Nicolas Beltran-Velez et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Language-model agents are increasingly asked to carry out open-ended scientific research, yet their results are usually graded against a known answer, a rubric, or a language-model reviewer, none of which can tell whether a new scientific model is valid. The A...
Yinling Zhang, Langchen Liu et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided as part of the retrieval prompt. Several new datasets and studies have examined this setting, showing that the...
Amanda Myntti, Jenna Kanerva et al.
#cs.CL
Prépublication
· 2026 arXiv
EN
Large language models are increasingly applied to tasks grounded in long, heterogeneous information sources. Conventional Retrieval-Augmented Generation (RAG) relies on fixed similarity-based retrieval, while agentic variants adapt queries and tool use but rem...
Yilun Hao, Krishna Sayana et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Many of the questions now put to large language models have no correct answer to score against: what a policy is worth, which option a user should choose, how to weigh competing values. Stated-preference economics has faced this problem for decades. It judges...
Daniel Robert Kling Alexander, Catherine Louise Kling
#cs.AI
Prépublication
· 2026 arXiv
EN
Robot foundation models provide strong visuomotor control, yet their performance can degrade when object positions or task instructions change. Further improvements often require post-training on substantial robot data, which can be costly to collect through m...
Python Song, Zhixuan Liang et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
How can we predict which base checkpoint is worth an expensive round of agentic post-training? End-to-end pass@$K$ tests whether successful behavior already appears in a base model's distribution, but it is a poor fit for agentic coding: many base checkpoints...
Tan Yu, Alexander Bukharin et al.
#cs.AI
Prépublication
· 2026 arXiv
EN
Deployments of research agents are moving to populations of thousands that share one pool of compute, while most current systems organize one project at a time or leave the population unorganized. We argue that such a population will acquire an organization wh...
Ali Asaria, Deep Gandhi et al.
#cs.MA