The Archive

Cohere overview of AI applications in manufacturing: use cases and implementation considerations for industrial operators.

Hierarchical spatio-temporal transformer for multi-level emergency department demand forecasting; healthcare application, not core AI frontier.

MDTransformer uses mode-division photonic hardware with inverse-designed crossbars to accelerate Transformer inference with reduced phase-shifter overhead.

Cohere publishes explainer on agentic AI definitions, applications, and enterprise deployment considerations.

Case study: Cohere's North AI agents reduce friction for wealth managers through task automation and time savings.

Cohere launches North Automations for orchestrating multi-agent enterprise workflows with monitoring and control.

Cohere partners with Government of Canada to develop sovereign AI capabilities for public-sector services.

Cohere releases W4A8 quantization with vLLM integration; compares W4A16 and W8A8 schemes on NVIDIA Hopper.

Spatial normalization technique for cross-domain retinal OCT layer segmentation in clinical neurodegenerative disease analysis.

Cohere partners with University of Toronto on multi-year AI adoption and responsibility initiative.

Proposes structure-aware dance generation via atomic movements to improve choreographic coherence and controllability.

Cohere outlines enterprise AI total cost of ownership framework covering token pricing, model selection, private deployment, and vendor infrastructure.

ESFP benchmark measures whether LLMs distinguish and coherently shift between neutral attribution and self-stance epistemic registers in contested claims.

Cohere releases Tiny Aya Expedition, a multilingual model supporting 70+ languages for on-device and educational AI applications.

Cohere guide on multi-agent system architectures, patterns, enterprise use cases, and adoption challenges.

Cohere introduces Dynamic Speculative Decoding (DSD) that optimizes K parameter selection based on hardware constraints to improve inference efficiency.

Hierarchical Acoustic-Semantic Modeling addresses modality interference in full-duplex Spoken Language Models via separation and semantic coherence techniques.

Cohere releases open-source Arabic speech recognition model for enterprise transcription across Arabic dialect variants.

Quantum algorithm study on stabilizer state testing with limited coherent memory; theoretical contribution tangential to LLM frontier.

CheckRLM detects and corrects factual inconsistencies in reasoning chains via retrieval-augmented claim checking during LLM inference.

LuckyStar 111B hybrid reasoning model from Cohere and LG CNS enables efficient multilingual tool-using agents with Korean-English support.

LeVo 2 combines LLM and diffusion models to generate full-length songs with coherent vocals, accompaniment, and lyric adherence via hierarchical track modeling.

Cohere publishes practical guide on building AI agents for enterprise automation, covering reliability, security, and deployment patterns.

Learn how the Cohere team uses North, Wiz, and a custom MCP server to automate incident response workflows with AI.

This theoretical note studies the finite axiomatizability of strict majority reasoning in finite social decision frames. Moss and Pedersen (2026) introduce a coherence criterion that characterizes exactly when qualitative majority judgments are representable by a finitely additive measure. The question addressed here is whether that coherence criterion can be replaced, in the finite setting, by any bounded finite fragment. We prove that it cannot. For every $k\ge 1$, we construct a maximal standard frame whose shortest coherence violation has length exactly $2k+2$. Hence there is no uniform f...

Cohere partners with Aston Martin Aramco Formula One™ as official Generative AI provider, bringing enterprise AI solutions to enhance performance and innovation across the racing team. Starting Australian GP 2026.

In open-ended generation, LLMs frequently fall into the "likelihood trap", marked by repetitive degeneration and vocabulary dullness, creating a discrepancy between machine-generated and human-written text. While post-hoc tail truncation (e.g., Top-$p$, Min-$p$) avoids sampling from the unreliable tail, it can over-sample from the uncalibrated head and misalign generation with human lexical preferences; fixed scalar repetition penalties likewise ignore variation in logit scale across inference steps, potentially disrupting semantic coherence. To address both limitations, we propose Variance-C...

Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes. Wall-to-wall, annually updated maps are a persistent need for effective forest management. Many planning systems and data collections combine disparate data sources with different purposes, vintages, and prediction quality, which leads to confounding behavior in operational planning systems. We introduce the VibrantForests framework, developed and applied to map forest attributes and provide a coherent foundation for effective forest and wildfire planning. ...

Enhancing the formal math reasoning capabilities of Large Language Models (LLMs) has become a key focus in both mathematical and computer science communities in recent years. While significant progress has been made in using state-of-the-art Auto-Regressive (AR) LLMs for formal theorem proving, these models suffer from inherent limitations. Their next-token prediction generation methods may yield suboptimal performance due to the challenges of long-range coherence and the compounding of errors over long sequences. Recent advancements in diffusion LLMs (dLLMs), which generate text through iter...

The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex world, but accessing this knowledge in a probabilistically coherent manner poses a difficult inference problem. We propose Large Language Gibbs, a scheme for structured probabilistic inference that uses conditional distributions of an LLM as transition operators. Rather than sampling structured objects through single-pass autoregressive generation, we iteratively resample individual variables conditioned on others using an LLM's next-token conditionals. T...