Research

Research papers, whitepapers, case studies, etc. 1271 public listings.

• Research Report: Modernizing Without Disruption: Unlocking AI-Ready Transformation (Mongodb)
  Discover how modernizing IT systems unlocks AI-driven innovation. This report explores key drivers, challenges, and solutions for seamless…
• State of DevSecOps (Datadoghq)
  For our 2026 State of DevSecOps report, we analyzed data from thousands of cloud environments to assess trends in application security post…
• State of Cloud Security (Datadoghq)
  For our 2025 report, we analyzed AWS, Google Cloud, and Azure data from thousands of organizations to understand the latest trends in cloud…
• Cost-Based Hybrid Query Optimization in MotherDuck - MotherDuck Research Papers (Motherduck)
  MotherDuck is the serverless data warehouse built on DuckDB. Fast SQL analytics without managing infrastructure.
• State of Cloud Native Development Q1 2026 (Cncf)
  Cloud native is reshaping how software is built worldwide.
• Query-Log-Informed Schema Descriptions and their Impact on Text-to-SQL - MotherDuck Research Papers (Motherduck)
  MotherDuck is the serverless data warehouse built on DuckDB. Fast SQL analytics without managing infrastructure.
• Towards Efficient Data Wrangling with LLMs using Code Generation - MotherDuck Research Papers (Motherduck)
  MotherDuck is the serverless data warehouse built on DuckDB. Fast SQL analytics without managing infrastructure.
• Public Sector Software Supply Chain Whitepaper (Cncf)
  The software supply chain is a vital component of successful software development organizations.
• Vibe physics: The AI grad student (Anthropic)
  A Harvard physicist guided Claude Opus 4.5 through a full quantum chromodynamics research project in two weeks.
• Towards measuring the representation of subjective global opinions in language models (Anthropic)
  GlobalOpinionQA quantifies whose opinions LLM responses reflect, finding alignment closest to the US and some European countries.
• Tracing the thoughts of a large language model (Anthropic)
  Language models like Claude aren't programmed directly by humans—instead, they‘re trained on large amounts of data.
• The Reality of AI and Biorisk (Cohere)
  Dec 02, 2024 The Reality of AI and Biorisk This paper provides an analysis of existing available research surrounding two AI and biorisk th…
• The Culture Funnel: You can’t align what isn’t in the data (Cohere)
  Current cultural alignment approaches focus on inference-time interventions, assuming models already contain sufficient cultural knowledge.
• BidirLM: From Text to Omnimodal Bidirectional Encoders by Adapting and Composing Causal LLMs (Cohere)
  Transforming causal generative language models into bidirectional encoders offers a powerful alternative to BERT-style architectures.
• Improving Policy Learning via Language Dynamics Distillation (Cohere)
  Recent work has shown that augmenting environments with language descriptions improves policy learning.
• RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs (Cohere)
  Preference optimization techniques have become a standard final stage for training state-of-art large language models (LLMs).
• Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning (Cohere)
  Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning Can we use Mixture of Experts (MoE) for i…
• When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning (Cohere)
  Feb 26, 2025 When Personalization Meets Reality: A Multi-Faceted Analysis of Personalized Preference Learning We present a multi-faceted ev…
• The Presidio Recommendations on Responsible Generative AI - World Economic Forum (Cohere)
  Generative AI has the potential to transform industries and society, but responsible design and collaboration among stakeholders are crucia…
• An off switch for dual-use knowledge (Anthropic)
  An off switch for dual-use knowledge in AI models This post describes research conducted by AE Studio in collaboration with Anthropic.
• RewardBench 2: Advancing Reward Model Evaluation (Cohere)
  Reward models are used throughout the post-training of language models to capture nuanced signals from preference data and provide a traini…
• No Need for Explanations: LLMs can implicitly learn from mistakes in-context (Cohere)
  Showing incorrect answers to Large Language Models (LLMs) is a popular strategy to improve their performance in reasoning-intensive tasks.
• The Multilingual Divide and Its Impact on Global AI Safety (Cohere)
  The Multilingual Divide and Its Impact on Global AI Safety This paper provides researchers, policymakers and governance experts with an ove…
• Here's a Free Lunch: Sanitizing Backdoored Models with Model Merge (Cohere)
  However, this openness also brings significant security risks, including backdoor attacks, where hidden malicious behaviors are triggered b…
• Diversify and Conquer: Diversity-Centric Data Selection with Iterative Refinement (Cohere)
  Finetuning large language models on instruction data is crucial for enhancing pre-trained knowledge and improving instruction-following cap…
• LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable Objectives (Cohere)
  Jul 05, 2024 LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable Objectives Our work exhaustively characterizes the impac…
• CIRCLE: A Framework for Evaluating AI from a Real-World Lens (Cohere)
  This paper proposes CIRCLE, a six-stage, lifecycle-based framework to bridge the reality gap between model-centric performance metrics and…
• Sparkles: Unlocking Chats Across Multiple Images for Multimodal Instruction-Following Models (Cohere)
  Large language models exhibit enhanced zero-shot performance on various tasks when fine-tuned with instruction-following data.
• Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt (Cohere)
  Training on web-scale data can take months. But most computation and time is wasted on redundant and noisy points that are already learnt o…
• From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions (Cohere)
  Feb 19, 2025 From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions Large Language Models (LLMs) are increasingly us…
• Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought (Cohere)
  Large Language Models (LLMs) can achieve strong performance on many tasks by producing step-by-step reasoning before giving a final output,…
• Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects (Cohere)
  Conventional AI evaluation approaches concentrated within the AI stack exhibit systemic limitations for exploring, navigating and resolving…
• Nexus: Specialization meets Adaptability for Efficiently Training Mixture of Experts (Cohere)
  Efficiency, specialization, and adaptability to new data distributions are qualities that are hard to combine in current Large Language Mod…
• OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators (Cohere)
  Offline policy evaluation (OPE) allows us to evaluate and estimate a new sequential decision-making policy's performance by leveraging hist…
• Aya 23: Open Weight Releases to Further Multilingual Progress (Cohere)
  This technical report introduces Aya 23, a family of multilingual language models.
• Red teaming language models to reduce harms (Anthropic)
  Red teaming models across scales shows RLHF models become harder to attack as they grow, with a released dataset of 38,961 attacks.
• AI agents find smart contract exploits (Anthropic)
  On a benchmark of real exploited contracts, AI agents found vulnerabilities worth a combined $4.6 million, underscoring the need for AI-pow…
• Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training (Anthropic)
  Humans are capable of strategically deceptive behavior: behaving helpfully in most situations, but then behaving very differently in order…
• Project Vend: Phase two (Anthropic)
  In June, we revealed that we’d set up a small shop in our San Francisco office lunchroom, run by an AI shopkeeper.
• Open-sourcing circuit-tracing tools (Anthropic)
  Today, we’re open-sourcing the method so that anyone can build on our research.
• Superposition, memorization, and double descent (Anthropic)
  Toy models show networks store data points in superposition when data is scarce, switching to generalizable features as data grows.
• SHADE-Arena: Evaluating Sabotage and Monitoring in LLM Agents (Anthropic)
  As AI models get smarter, we also need to become smarter in how we monitor them. More intelligent systems can take more complex actions, wh…
• Reasoning models don't always say what they think (Anthropic)
  Since late last year, “reasoning models” have been everywhere. These are AI models—such as Claude 3.7 Sonnet—that show their working: as we…
• Trustworthy agents in practice (Anthropic)
  Five principles for building AI agents that balance autonomy with safety, with defenses spanning the model, harness, tools, and environment.
• Soft-SVeRL: Self-Verified Reinforcement Learning with Soft Rewards (Cohere)
  Reinforcement Learning from Verifiable Rewards (RLVR) has improved language models in domains such as mathematics and code, where correctne…
• Agents Explore but Agents Ignore: LLMs Lack Environmental Curiosity (Cohere)
  LLM-based agents are assumed to integrate environmental observations into their reasoning: discovering highly relevant but unexpected infor…
• Locally Differentially Private Document Generation Using Zero Shot Prompting (Cohere)
  Numerous studies have highlighted the privacy risks associated with pretrained large language models.
• Kaleidoscope: Exams for Multilingual Vision Evaluation (Cohere)
  Apr 10, 2025 Kaleidoscope: Exams for Multilingual Vision Evaluation Kaleidoscope is a new benchmark for evaluating vision-language models a…
• Back to Basics (Cohere)
  Feb 23, 2024 Back to Basics – REINFORCE for Human Feedback in LLMs Reinforcement learning from human feedback has been widely adopted as a…
• Light bulbs have energy ratings (Cohere)
  The rising energy and environmental cost of the artificial-intelligence boom is fuelling concern.