preprint

A preprint estimates that by the end of 2025, 89% of open-access biomedical papers show signs of LLM-assisted writing, using a new word-frequency-based method.

French researchers connected a robot to brain cortex tissue donated from deceased individuals, demonstrating that isolated human brain tissue can still form new memories, learn new movements, and even learn to play new songs, potentially changing our understanding of brain death and viability.

A new preprint called TEPA treats memory validity as a first-class state, revoking outdated precedents when new evidence conflicts while keeping audit trails. It outperforms append-only and last-write-wins in a complete-reversal experiment, though results are not yet independently reproduced.

This preliminary technical report proposes RIG-RoPE, a relation- and instance-gated rotary positional encoding with duration-aware temporal coordinates, aiming to address spatial interference and improper temporal scaling in multimodal LLMs. It introduces a gating mechanism for height/width rotations and duration-aware temporal coordinates, but leaves large-scale empirical validation to future work.

Arc Institute announces that their popular 2025 preprint on a framework for generating and evaluating AI-generated genomes is now published in Science. The work, from Brian Hie's lab, used Evo 1 and Evo 2 to design viable bacteriophage genomes.

Yohei Nakajima announces his first SSRN paper, sharing a link to it.

Alexander Kalian argues that biology lacks sufficient high-quality data for AI, while César de la Fuente counters that data exists but needs better organization, highlighting the AllTheBacteria preprint as an open platform for bacterial genomes.

This paper proposes Multi-level Context Fusion MOE (MCF-MOE), a framework that improves routing consistency in Mixture-of-Experts models by integrating cross-layer semantic aggregation and local token-level interactions, outperforming strong baselines on language modeling and understanding benchmarks.

The author is seeking collaborators for scaling and independent evaluation of a new recurrent language model architecture, with a preprint and code available.

This preprint reveals that under certain data symmetries, LLM training dynamics can be reduced to a low-dimensional subspace, making analysis more tractable and interpretable, with each coordinate corresponding to a clear mechanism.

This paper studies norm enforcement mechanisms to shape behavior of language model agents in multi-agent systems. The authors propose robust mechanisms that estimate agent reliability over time and apply escalating penalties to resist exploitation.

Introduces in-span learning, a method to adapt reduced-order models by streaming the model's own predictions through an incremental singular-value decomposition, reweighting and realigning the basis without changing the subspace. The approach is demonstrated on several dynamical systems and proposed as a computational-science analogue of in-context learning.

The author announces a preprint on building an agentic system with composable domains, a verification ratchet, and careful tool naming, sharing practical lessons and an open-source Common Lisp implementation.

Researchers at the University of Minnesota developed SpudCells, artificial cells that can undergo a few rounds of cell division by importing materials from the environment, using components from viruses and purified translation machinery.

arXiv has become independent from Cornell University, changing its logo from Cornell red to black, marking the end of an era.

arXiv will spin out from Cornell University to become an independent nonprofit organization, with major funding from the Simons Foundation and Schmidt Sciences, as announced in a blog post.

ArXiv discusses future developments and improvements to its preprint repository.

A preprint finds that large language models spontaneously develop specialized modular brain regions for language, math, physics, and social reasoning, similar to the human brain, suggesting convergence in intelligent system design.

A new preprint introduces auto-psych, a framework that uses AI agents to propose cognitive models and design/conduct human experiments, extending AI scientific automation to psychology research.

The Violation Situation Pattern (VSP) reifies compliance violations as persistent graph nodes with lifecycle states and audit history, enabling durable, queryable violation records that can evolve detection logic without invalidating accumulated history.