The Archive

Introducing North Mini Code: Cohere's first open-source agentic coding model. Built for sovereign developers, this efficient 30B MoE model delivers strong software development performance with minimal hardware requirements.

Generating coherent and controllable long-form content remains a persistent challenge for Large Language Models (LLMs). While reasoning-enhanced models have demonstrated success in logic-intensive domains, our evaluation reveals that they suffer from a severe length collapse in open-ended writing, where performance degrades sharply as target lengths exceed 2,000 words. We attribute this failure to the limitation of static hierarchical planning, which struggles to provide dynamic guidance over extended contexts. To bridge this gap, we introduce the Interleaved Structural Chain-of-Thought (IS-C...

Whisper, a widely adopted ASR model, is known to suffer from hallucinations - coherent transcriptions generated for non-speech audio entirely disconnected from the input. We investigate whether hallucinations can be detected and mitigated through Whisper's internal representations. We extract audio encoder activations and evaluate two representation spaces: raw Whisper activations and Sparse AutoEncoder (SAE) latents. We show that both spaces encode linearly separable hallucination-related information, with discriminative power concentrated in a sparse feature subset and increasing toward dee...

Indoor scene generation is crucial for robot simulation and modern interior design. However, complex layouts together with scarce 3D scene data make learning-based generation challenging. Existing methods often rely on hand-crafted rules or focus on isolated sub-tasks (e.g., floorplan synthesis or single-room furnishing), producing whole-home scenes that lack global coherence, realism, and simulation readiness. To mitigate these limitations, we propose a unified hierarchical framework that decomposes indoor scene synthesis into controllable stages. First, we curate a large-scale dataset of 30...

A blog about how Cohere Labs built coplot, a data visualization tool that not only helps their releases, but also their research process.

Vision language models (VLMs) excel at many tasks but still struggle with spatial reasoning when critical information is not directly observable. Many such problems require imaginative perception: inferring what would be seen from an unseen viewpoint, tracing paths through occluded spaces, or integrating partial observations into a coherent spatial representation. We introduce Imaginative Perception Tokens (IPT), intermediate perceptual representations that externalize what a VLM would perceive under alternative spatial configurations while remaining consistent with the observed input. To stu...

Conversational AI agents require memory systems that are both scalable and semantically coherent across long interaction horizons. Existing approaches rely predominantly on large language model (LLM)-based summarisation at write time, which introduces non-determinism, escalating token costs, and opacity in pruning decisions. We present the Deterministic Memory Framework (DMF), a CPU-first approach that replaces generative memory compression with a fully deterministic pipeline grounded in classical NLP analysis, vector geometry, and mathematical scoring. DMF assigns each conversational interac...

Effective "all-team" summarization in high-complexity settings like the Neonatal Intensive Care Unit (NICU) requires aggregating insights from diverse disciplines (physicians, nurses, therapists) spread across hundreds of clinical free-text notes. Simply pooling heterogeneous text often leads to incoherent outputs. Structured summarization therefore first requires accurate categorization of sentence-level provenance across multi-source notes. This pilot study introduces a clinical provenance categorization pipeline using supervised fine-tuning (SFT) of large language models (LLMs). We adapted...

Order-agnostic language models (OALMs), including discrete diffusion language models (dLLMs), are trained to predict masked tokens under arbitrary conditioning sets, allowing sequences to be generated or scored under arbitrary reveal orders at inference time. In LLaDA-2.1, we report three findings. First, the learned conditionals are not exact factorizations of a coherent joint distribution: changing only the reveal order shifts target log-likelihood by up to 0.49 nats/token, so likelihood alone mixes content difficulty with path-dependent artifacts. Second, although confidence-first (CF) dec...

Unsupervised skill discovery (USD) aims to learn diverse behaviors without reward functions, but often results in task-irrelevant or hazardous behaviors due to uniform exploration. Guided skill discovery (GSD) addresses this issue by incorporating human intent to focus exploration on meaningful regions. However, existing GSD methods typically require training additional guidance models, and rely on pre-defined rules or expert demonstration, which can be ineffective under sparse, online-collected human feedback. To overcome this, we propose COLLIE, a GSD framework that leverages dense unsuperv...

Multi-component LLM agents assemble probabilistic claims from components that each see only part of a joint problem; the composition can violate basic probability axioms even when every component is locally coherent. We formalise this locally coherent, globally incoherent failure via the compositional residual eps*, the L2 distance from the composed quote to the joint coherent polytope, computable at runtime from system output and the declared cross-component coupling constraints. A product-structure dichotomy characterises when local coherence suffices, and a Rayleigh-quotient prediction mat...

A specialized translation model leverages RWS’ global language and cultural expertise and Cohere’s Command A+ model to power the new Language Weaver Pro.

Delivers advanced reasoning with a minimal compute footprint. Command A+ offers full data sovereignty for governments and regulated industries worldwide.

Cohere and Mila announced plans for a new academic research collaboration focused on improving AI evaluation across languages and cultures, starting with French-language cultural context in Quebec.

Recent generative models have largely closed the gap on low-level artifacts - pixel fingerprints, frequency anomalies, upsampling traces - particularly in person-centric and partial-edit settings where the manipulated region is small and surrounded by photometrically authentic content. We introduce Social Gaze Consistency, a high-level semantic cue defined as the mutual coherence of gaze direction, head-eye alignment, and pupil placement between interacting individuals, and show that it constitutes a previously underutilized detection axis orthogonal to existing low-level paradigms. We instan...

Browse the new Cohere Merch Store and view the latest collection for sale, along with an archive of our merch and swag history.

Developer fine-tuned Cohere Transcribe to add diarization and timestamp support, extending open-source speech-to-text capabilities.

Cohere launches Command A+, first open-weights MoE model emphasizing efficiency and latency over peak performance.

Cohere releases Command-A-Plus-05-2026 bfloat16 model weights on Hugging Face Hub.

Cohere signs MOUs with Indra Group and Multiverse Computing to advance AI deployment with focus on sovereignty, security, and accessibility.

Cohere releases Command A+, an open-source model optimized for enterprise agent deployment with improved speed and capability.

Cohere acquires Reliant AI to strengthen sovereign AI capabilities for regulated healthcare and life sciences sectors.

PDI-Bench: Quantitative framework for auditing geometric coherence in generated video via perspective distortion and point-tracking metrics.

Value-filtered decoding selectively applies safety steering at test-time, avoiding unnecessary interventions that degrade helpfulness and coherence.

Cohere outlines governance frameworks for responsible AI development and deployment.

Cohere showcases AI agents for financial services compliance, efficiency, and customer trust.

Cohere argues private AI deployment offers banks control, security, and customization advantages.

Cohere advises financial firms on scaling generative AI from pilot programs to production with risk management.

Cohere positions LLMs as solutions for financial services knowledge work and customer service automation.