# AI Research Published articles for AI Research. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## From AI demos to real work: How Engineering and Operations learn side by side DevFeed: [From AI demos to real work: How Engineering and Operations learn side by side]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-14T16:28:23Z Content type: article Language: en Sources: [Nubank]() Topics: [Artificial Intelligence](), [Generative AI](), [AI Development](), [Software Engineering](), [Development]() Tags: [agents](), [ai](), [ai-research](), [applied-ai](), [data-science-machine-learning](), [development](), [engineering](), [mcps]() ### AI overview This article describes Nubank's Ops AI Acceleration Program and an applied AI workshop with PJ Operations. The workshop combined foundational technical concepts about generative AI, agents, models, tools, context, skills, and MCPs with a real operational challenge to connect engineering knowledge with operational expertise. ### Source excerpt Building shared technical understanding so operational expertise can turn AI into practical improvements The post From AI demos to real work: How Engineering and Operations learn side by side appeared first on Building Nubank. ## Jakub Pachocki Calls for Caution and Stronger Safeguards as AI Capabilities Advance DevFeed: [Jakub Pachocki Calls for Caution and Stronger Safeguards as AI Capabilities Advance]() Original publisher: [Read original article]() Published: 2026-09-06T09:00:00Z Content type: opinion Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [Machine Intelligence](), [Language models](), [OpenAI]() Tags: [ai](), [ai-research](), [alignment](), [language-models](), [machine-intelligence](), [openai](), [research](), [safety]() ### AI overview Jakub Pachocki reflects on the rapid growth of reasoning language models and the possibility of continued capability advances through recursive self-improvement. He urges extreme caution, stronger alignment and monitoring efforts, defensive systems, and broader international interventions. ### Source excerpt Jakub Pachocki reflects on increasingly capable AI and the challenge of keeping it aligned. He calls for stronger safeguards and international coordination. ## Research acceleration: The view inside OpenAI DevFeed: [Research acceleration: The view inside OpenAI]() Original publisher: [Read original article]() Published: 2026-09-06T08:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Research](), [AI-assisted coding](), [coding]() Tags: [agents](), [ai-research](), [coding](), [deep-learning](), [experiments](), [frontier-ai](), [openai](), [research]() ### AI overview OpenAI describes how coding agents are being used throughout its AI research workflow, with reported increases in code contribution, experiment execution, task complexity, and success rates. It frames this progress as a step toward supervised automated AI research while emphasizing human control over research priorities and deployment decisions. ### Source excerpt Inside OpenAI, coding agents are reshaping AI research. Explore early data on agent usage, experiment velocity, task complexity, and research acceleration. ## When AI skills become supply-chain dependencies DevFeed: [When AI skills become supply-chain dependencies]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-02T16:47:57Z Content type: article Language: en Sources: [Nubank]() Topics: [Security](), [supply-chain-security](), [Application Security](), [Development](), [Software]() Tags: [ai](), [ai-research](), [developer](), [development](), [product-security](), [security](), [supply-chain]() ### AI overview Nubank describes how AI skills and related components are expanding the software supply chain. Its security team reviewed more than 2,000 AI skills before distribution and argues that security controls must evolve as AI becomes part of the developer toolchain. ### Source excerpt How Nubank vetted 2,000+ AI skills before distribution, building security into the developer workflow without turning safety into a separate gate The post When AI skills become supply-chain dependencies appeared first on Building Nubank. ## An Organizational Second Brain: Building an AI That Learns From Experts DevFeed: [An Organizational Second Brain: Building an AI That Learns From Experts]() Original publisher: [Read original article]() Author: Shaurya Sengar; Jason Nawrocki; Jay Shah; Prashant Kommireddi Published: 2026-09-02T09:00:29Z Content type: article Language: en Sources: [Engineering at Meta](), [Meta AI Research](), [Meta ML Applications]() Topics: [Large Language Model](), [Architecture & Design]() Tags: [ai-agent](), [ai-research](), [architecture](), [compliance](), [llms](), [ml-applications](), [security-privacy]() ### AI overview Meta describes an AI agent for a compliance domain that preserves specialist knowledge through an auditable knowledge architecture, an expert-like reasoning layer, and a feedback-driven improvement pipeline without model retraining. ### Source excerpt We've built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon. This is not a typical domain-specific agent. Its novelty comes from integrating two layers: A structured, auditable knowledge architecture separates what [...] Read More... The post An Organizational Second Brain: Building an AI That Learns From Experts appeared first on Engineering at Meta. ## 🗓 This Week In AI Research (1-7 August 26) DevFeed: [🗓 This Week In AI Research (1-7 August 26)]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-08-13T19:29:25Z Content type: article Language: en Sources: [Into AI]() Topics: [AI Research](), [releases](), [Benchmark](), [Large Language Model](), [Mixture of Experts (MoE)](), [model architecture](), [qwen]() Tags: [ai](), [ai-research](), [algorithm](), [benchmark](), [llms](), [mixture-of-experts](), [model-architecture](), [qwen](), [reasoning](), [releases]() ### AI overview A weekly roundup of AI research and model releases. It highlights Pathway, Bielik AI, and NYU's BDH-CQ reasoning model, which uses in-context learning with recurrent memory and latent-state reasoning, reports ARC-AGI-1 cost-efficiency results, and describes Alibaba's Qwen3.8-Max release and the U-OPSD self-distillation algorithm. ### Source excerpt The top 10 AI research papers and releases that you must know about this week. ## Announcing Evals and Releases: Evaluate Fin before, during, and after you go live DevFeed: [Announcing Evals and Releases: Evaluate Fin before, during, and after you go live]() Original publisher: [Read original article]() Author: Brian Donohue Published: 2026-08-13T17:33:52Z Content type: article Language: en Sources: [The Intercom Blog]() Topics: [AI Agent](), [AI Research](), [configuration](), [releases]() Tags: [agent](), [ai-agent](), [ai-research](), [evals](), [evaluation](), [news-updates](), [release](), [releases]() ### AI overview Intercom announces Evals and Releases for Fin, paired with Monitors as an eval-driven delivery system. Teams can test Fin with simulated customer conversations, evaluate changes against defined criteria, safely release updates, and monitor live conversations for regressions. ### Source excerpt Providing a complete evaluation system for Fin, you can now test changes before they go live, roll them out with control, evaluate every live conversation, and have confidence in the experience Fin delivers. ## What We Learned by Reproducing 2,200 papers from ICML DevFeed: [What We Learned by Reproducing 2,200 papers from ICML]() Original publisher: [Read original article]() Author: Abubakar Abid Published: 2026-08-13T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [AI research agents](), [AI-assisted coding](), [Human-AI evaluation](), [Claude Code](), [codex](), [cursor](), [Open Source]() Tags: [2026](), [agents](), [ai-agents](), [ai-research](), [claude-code](), [codex](), [community](), [cursor](), [open-source](), [research](), [science]() ### AI overview The article reports lessons from the ICML 2026 Open Reproductions challenge, in which the community used coding agents to reproduce research papers at scale. It discusses how agents can read papers, write code, run experiments, and report findings, while examining the continuing role of human oversight in AI research reproducibility. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Highlights from MLSys 2026 DevFeed: [Highlights from MLSys 2026]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-08-11T15:07:08Z Content type: opinion Language: en Sources: [Capital One Tech]() Topics: [Large Language Model](), [Retrieval Augmented Generation (RAG)](), [Machine learning](), [systems](), [Optimization](), [Deployment](), [Hardware](), [Streaming](), [Cache]() Tags: [2026](), [agentic](), [agentic-ai](), [ai-research](), [deployment](), [hardware](), [inference](), [large-language-model](), [machine-learning](), [optimization](), [research](), [retrieval-augmented-generation-rag](), [science]() ### AI overview Capital One's AI research team reviews themes and selected papers from MLSys 2026, focusing on efficient LLM serving, retrieval-augmented generation, cache management, model speculation, and agentic AI. The article highlights research on inference optimization, distributed compute and communication, streaming, and vector search. ### Source excerpt Capital One's AI research team recaps MLSys 2026, including optimizing serving LLMs, RAG and agentic AI. The 9th Annual Conference on Machine Learning and Systems (MLSys) took place in May in Bellevue, Washington. MLSys is a highly selective interdisciplinary conference sitting at the intersection of machine learning (ML) and systems design. The conference highlights cutting-edge research that combines generative AI, natural language processing, computer vision and reinforcement learning with infrastructure, deployment and hardware optimizations to make AI faster, scalable and more performant. MLSys offered Capital One associates the opportunity to learn from world-class conference sessions presented by experts in the field. All the attending associates left brimming with new ideas and planned collaborations. Kel Vanee, MVP, Machine Learning Engineering, presented some of the work happening at Capital One on using AI to make AI more efficient. Takeaways and favorite papers from MLSys 2026 Some of the most prevalent topics at MLSys this year were on cache management, model speculation, retrieval augmented generation (RAG) and agentic AI. With a plethora of relevant and interesting talks, we had no shortage of papers to choose favorites from. While a complete list of the papers we loved would be far too long, here are a few standouts: Large language model inference optimization One of the leading themes this year was how to more efficiently serve LLM models. We especially liked the papers on reducing self-attention costs, such as MAC-Attention: a Match-Amend-Complete scheme for fast and accurate attention computation and BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding. We found valuable insights in papers covering how best to overlap computation with communication, such as TokenWeave: Efficient Compute-Communication Overlap for Distributed LLM Inference, Stream2LLM: Overlap Context Streaming and Prefill for Reduced Time-to-First-Token and FlashAgen ## 34 Amazon Research Awards Build on Trainium recipients announced DevFeed: [34 Amazon Research Awards Build on Trainium recipients announced]() Original publisher: [Read original article]() Author: Amazon Research Awards team Published: 2026-08-05T15:00:00Z Content type: news Language: en Sources: [Amazon Science homepage]() Topics: [Artificial Intelligence](), [responsible-ai](), [AWS AI chips](), [AI Research](), [Optimization](), [Inference](), [moe](), [Large Language Model](), [distributed-systems](), [synthetic-data]() Tags: [academic-ai-funding](), [ai](), [ai-research](), [ai-research-grants](), [ai-safety-and-alignment](), [amazon-research-awards](), [ara](), [aws-ai-chips](), [aws-trainium](), [build-on-trainium](), [data](), [deep-learning](), [distributed-systems](), [inference](), [internal-ara-program-updates](), [llm](), [machine-learning-research](), [mixture-of-experts](), [optimization](), [research](), [responsible-ai](), [synthetic](), [synthetic-data-generation]() ### AI overview Amazon announces 34 recipients of its Build on Trainium program, a $110 million credit initiative supporting AI research and university education. The awards fund work in areas including Responsible AI, language models, synthetic data, distributed systems, model architectures, libraries, and optimization on AWS Trainium. ### Source excerpt Amazon announces 34 recipients of the Build on Trainium program, a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI. ## GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model DevFeed: [GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model]() Original publisher: [Read original article]() Author: Darren Liu; Huayu Li; Raghav Boinepalli; Yuzhen Huang; Jackie (Jiaqi) Xu; Richard Qiu; Chunzhi Yang; Rich Zhu; Dev (Devashish) Shankar; Huaqing Xiong Published: 2026-08-03T18:00:17Z Content type: article Language: en Sources: [Engineering at Meta](), [Meta AI Research](), [Meta ML Applications]() Topics: [recommendation systems](), [networking]() Tags: [ads](), [ai-infrastructure](), [ai-research](), [data-infrastructure](), [gpu](), [hardware](), [kernels](), [llm](), [meta](), [ml-applications](), [networking](), [recommendation-systems](), [training]() ### AI overview Meta describes training its GEM ads recommendation foundation model at LLM scale. The article covers recommendation-specific kernels, ultra-low-precision training, and topology-aware parallelism that doubled end-to-end training efficiency to 20-25% MFU while increasing training FLOPs fourfold. ### Source excerpt Meta's Generative Ads Recommendation Model (GEM), the foundation model behind ads recommendations across Instagram and Facebook, now trains at LLM scale on several thousand of the latest-generation GPUs. This post goes into the details on how we achieved: doubling end-to-end (E2E) training efficiency to 20-25% Model FLOPs Utilization (MFU) while scaling training FLOPs 4x in [...] Read More... The post GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model appeared first on Engineering at Meta. ## 🗓 This Week In AI Research (17-24 July 26) DevFeed: [🗓 This Week In AI Research (17-24 July 26)]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-07-30T10:44:06Z Content type: article Language: en Sources: [Into AI]() Topics: [AI Research](), [Artificial Intelligence](), [releases](), [Large Language Model](), [Deep neural networks](), [parquet]() Tags: [ai](), [ai-research](), [anthropic](), [claude](), [compression](), [llms](), [neural-networks](), [releases]() ### AI overview A weekly roundup of AI research papers and releases, including Claude Opus 5, the HOPE framework for analyzing knowledge in deep neural networks through compression, and research on how large language models track evolving user intent across conversations. ### Source excerpt The top 10 AI research papers and releases this week (Claude Opus 5, Laguna S 2.1, Loopie, Nanbeige 4.2, Fugu-Cyber, and more) ## Investigate every security event with an AI agent, without the frontier bill DevFeed: [Investigate every security event with an AI agent, without the frontier bill]() Original publisher: [Read original article]() Author: Nicolas Grislain Published: 2026-07-28T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [AI Bots]() Tags: [ai](), [ai-agent](), [ai-research](), [ai-security](), [bits-ai](), [cloud-siem](), [llm](), [logs](), [security]() ### AI overview Datadog describes a two-stage security detection pipeline in which Mambark scores audit-log events and routes only the most suspicious ones to an AI agent for deeper investigation. ### Source excerpt Learn how Datadog built Mambark, a small state-space model that scores every security event and enables heavier AI agents to investigate only the events that matter. ## Announcing Capital One's 2026 UIUC AI Awardees DevFeed: [Announcing Capital One's 2026 UIUC AI Awardees]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-07-21T14:45:44Z Content type: release Language: en Sources: [Capital One Tech]() Topics: [Artificial Intelligence](), [ai safety](), [Generative AI](), [Large Language Model](), [Code generation](), [Cybersecurity]() Tags: [2026](), [academic-research](), [agentic-ai](), [agents](), [ai](), [ai-research](), [ai-safety](), [code-generation](), [cybersecurity](), [generative-ai](), [large-language-models-llms](), [research](), [science]() ### AI overview Capital One announces its 2026-2027 research and fellowship awardees from the University of Illinois. The featured projects address LLM reasoning faithfulness, code reasoning, imaginative LLM agents, and agentic AI safety. ### Source excerpt Meet the University of Illinois researchers and fellows advancing Agentic AI through our academic partnership.Announcing the Center for Generative AI Safety, Knowledge Systems, and Cybersecurity (ASKS) 2026-2027 Capital One Research Awardees from the University of Illinois As we look toward the 2026-2027 academic year, we continue to partner with institutions that lead the global conversation on the future of intelligence. We are thrilled to announce this year's cohort of research and fellowship awardees from the University of Illinois, whose pioneering work addresses the most critical frontier in technology today: Agentic AI. From the way machines "think" and "imagine" to the hardware that powers them and the safety protocols that govern them, these five projects represent Capital One's holistic push toward AI that is not only powerful but also faithful, safe and creative. The 2026-2027 research awardees1. Ensuring Intellectual Honesty Advancing LLM Reasoning Faithfulness without Faithfulness Rewards Faculty: Hao Peng Professor Peng is tackling the "hallucination" problem at its core. By developing methods to ensure large language models (LLMs) follow a logical, faithful reasoning path-without relying on traditional, often biased, reward systems-this work ensures that when an AI gives an answer, the "why" behind it is actually true, a critical component for trustworthy financial applications. TrACE-Contrast: Faithful & Consistent Code Reasoning via Trace-Aware Contrastive Learning Faculty: Talia Ringer and Reyhan Jabaarvand Writing code is one thing; understanding how it executes is another. Professor Ringer's project uses contrastive learning to align a model's code generation with its actual execution "trace." This ensures that AI-generated software is verified and logically sound, which is vital for maintaining the integrity of our core technology systems. 2. Bridging the Gap: Agency & Imagination Creative LLM Agents based on Thinking with Imagination Faculty: H ## Laguna S 2.1 is now available on AI Gateway DevFeed: [Laguna S 2.1 is now available on AI Gateway]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-21T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [AI Models](), [agentic-coding](), [Mixture of Experts (MoE)](), [context window](), [API](), [MLOps](), [AI Research](), [API keys](), [Routing (disambiguation)]() Tags: [agentic-coding](), [ai-models](), [ai-research](), [api-keys](), [context-window](), [cost](), [mixture-of-experts](), [mlops](), [routing](), [sdk](), [support]() ### AI overview Poolside's Laguna S 2.1 is now available through Vercel AI Gateway in free and paid versions, with context windows of 256K and 1M tokens. The open-weight Mixture-of-Experts model supports thinking and no-thinking modes and is designed for agentic coding, long-running tasks, browser tooling, MLOps pipelines, and AI research. ### Source excerpt Laguna S 2.1 from Poolside is now available on AI Gateway. There are 2 versions of the model available: Free version (256K context window): poolside/laguna-s-2.1-free Paid version (1M context window): poolside/laguna-s-2.1 Laguna S 2.1 is an open-weight Mixture-of-Experts model that supports a context window of up to 1M tokens and runs in thinking and no-thinking modes. The model specializes in agentic coding and long-running tasks, including writing and debugging code, running tests, building browser-based tooling, and working on MLOps pipelines and AI research. In thinking mode, Laguna S 2.1 reports 70.2% on Terminal-Bench 2.1, 78.5% on SWE-bench Multilingual, and 59.4% on SWE-Bench Pro. To use Laguna S 2.1, set model to poolside/laguna-s-2.1-free or poolside/laguna-s-2.1 in the AI SDK: AI Gateway provides a unified API for calling models, tracking usage and cost, and configuring retries, failover, and performance optimizations for higher-than-provider uptime. It includes built-in custom reporting, Zero Data Retention support, budgets for API keys, routing rules, and more. AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests. Try Laguna S 2.1 in the model playground. Read more ## Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization DevFeed: [Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization]() Original publisher: [Read original article]() Author: Yuhui Ouyang; Di Wang; Sreedal Menon; Jie Tian Published: 2026-07-15T17:00:52Z Content type: article Language: en Sources: [Engineering at Meta](), [Meta AI Research]() Topics: [Optimization](), [Embeddings](), [Graphs](), [recommendation systems](), [LLMs](), [Artificial Intelligence](), [Transformer](), [data]() Tags: [ads](), [ai](), [ai-research](), [data](), [embeddings](), [engineering](), [frontier-ai](), [generative](), [learning](), [meta](), [optimization](), [personalization](), [reasoning](), [research](), [retrieval](), [scale]() ### AI overview Meta describes Hierarchical Interest Representation, an upstream system that learns unified embeddings for users, advertisers, products, and services. It combines graph learning, multimodal content processed through LLMs, engagement signals, and self-supervised distillation to improve personalization, retrieval, ranking, and deep-funnel advertising optimization. ### Source excerpt Hierarchical Interest Representation is a research area for Meta Ads. We're exploring an upstream representation layer over the universe of Ads entities - users, advertisers, products, services - learning unified embeddings that connect users' inferred interests with the breadth of what advertisers offer in their deep funnel ads. The innovations in Hierarchical Interest Representation are [...] Read More... The post Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization appeared first on Engineering at Meta. ## This Week In AI Research (21-30 June 26) 🗓 DevFeed: [This Week In AI Research (21-30 June 26) 🗓]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-07-03T02:04:12Z Content type: article Language: en Sources: [Into AI]() Topics: [Artificial Intelligence](), [AI Research](), [Large Language Model](), [LLMs](), [OpenAI](), [Meta](), [real-time](), [coding]() Tags: [agentic](), [ai](), [ai-research](), [benchmarks](), [coding](), [gpt](), [llms](), [meta](), [model](), [openai](), [real-time](), [release](), [research]() ### AI overview A weekly roundup of AI research and releases, including Brain2Qwerty v2 for real-time decoding of typed sentences from MEG recordings, OpenAI's GPT-5.6 Sol, and Sakana Fugu orchestrator LLMs. The supplied excerpt also discusses agent architectures and performance on several benchmarks. ### Source excerpt The top 10 research papers of this week: GPT-5.6, Sonnet 5, Meta's real-time brain-to-text decoder, a 35B model that beats trillion-parameter LLMs & more! ## LLM reasoning and agentic safety at ICML 2026 DevFeed: [LLM reasoning and agentic safety at ICML 2026]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-07-02T14:14:40Z Content type: article Language: en Sources: [Capital One Tech]() Topics: [Large Language Model](), [Fine-tuning](), [Trustworthy AI](), [Artificial Intelligence]() Tags: [2026](), [ai](), [ai-research](), [fine-tuning](), [icml](), [icml-2026](), [llm](), [llm-reasoning](), [reasoning](), [research](), [safety](), [science](), [trustworthy-ai]() ### AI overview Capital One presents research for ICML 2026 on critique-guided distillation for robust LLM reasoning and on safety risks in multi-turn tool-using agents. The article says its Critique-Guided Distillation framework trains models to refine flawed responses using teacher critiques, and reports higher mathematical-reasoning benchmark performance than critique fine-tuning and standard distillation, including a 7% average improvement and gains of up to 15.0% on AMC23 and 12.2% on MATH-500. ### Source excerpt Explore our latest research in critique-guided distillation and multi-turn agent uncertainty in Seoul.Explore our latest research in critique-guided distillation and multi-turn agent uncertainty in Seoul. Capital One technologists are excited to participate in the 43rd International Conference on Machine Learning (ICML) taking place at the COEX Convention & Exhibition Center in Seoul, South Korea, July 6-11, 2026. As a premier global venue for machine learning research, ICML provides an essential forum for exploring foundational advancements, algorithmic innovations and cutting-edge deep learning systems. Capital One is excited to share advancements in large language model (LLM) scaling efficiencies, multi-turn tool-using agent safety and the development of robust, trustworthy AI frameworks. This work delivers the underlying engineering and algorithmic improvements crucial for deploying the next generation of safe financial technologies. Main conference research: Robust reasoning and agentic risk The following research, accepted to the ICML Main Conference, pushes the boundaries of how models self-correct, how trajectory-level risks can be proactively flagged, and how multi-turn agent interactions maintain reliable execution. This section features work led by Capital One researchers alongside deep collaborations with academic partners. Critique-Guided Distillation for Robust Reasoning via Refinement Capital One Authors: Berkcan Kapusuzoglu, Supriyo Chakraborty, Michael Lee, Sambit Sahu Supervised fine-tuning with expert demonstrations often produces models that imitate outputs without internalizing the reasoning processes needed for robust generalization. While critique-based approaches show promise, training models to generate critiques directly, such as Critique Fine-Tuning (CFT), can lead to output-format drift and degradation of general capabilities. We propose Critique-Guided Distillation (CGD), a training framework that decouples critique consumption from crit ## Capital One at ACL 2026 DevFeed: [Capital One at ACL 2026]() Original publisher: [Read original article]() Author: Capital One Tech Published: 2026-07-01T15:28:51Z Content type: article Language: en Sources: [Capital One Tech]() Topics: [Natural language processing](), [Large Language Model](), [LLM security](), [Machine Learning, Security Attacks](), [Reinforcement learning](), [Jailbreak](), [Security](), [Routing (disambiguation)](), [datasets]() Tags: [2026](), [ai](), [ai-ml](), [ai-research](), [conference](), [jailbreak](), [llm-security](), [natural-language-processing](), [paper](), [partners](), [red-teaming](), [reinforcement-learning](), [research](), [science]() ### AI overview Capital One describes its accepted ACL 2026 research on natural language processing, including work on adaptive LLM red teaming, query-only model routing with generated data, and language identification on web data. The article also highlights collaboration with academic partners. ### Source excerpt Discover how Capital One is advancing state-of-the-art AI/ML science through collaborative natural language processing research.Advancing AI and NLP Frontiers at ACL 2026 As language models grow more deeply integrated into technology ecosystems, pioneering robust, efficient, and reliable Natural Language Processing (NLP) techniques becomes paramount. Capital One continues to invest in state-of-the-art AI/ML science through deep multi-sector collaboration and peer-reviewed research. At the upcoming Annual Meeting of the Association for Computational Linguistics (ACL 2026), Capital One researchers and academic partners will showcase novel findings stretching from LLM security to multilingual capabilities. Through the Science & Academic Partnerships program, Capital One bridges industry needs with academic expertise, funding critical university research and engineering solutions that make technology safer and more powerful. Our accepted publications at ACL 2026 demonstrate this thriving flywheel of talent and collaborative innovation across multiple research categories. Main Conference Research Adaptive Instruction Composition for Automated LLM Red Teaming Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection Capital One Authors: Jesse Zymet, Swapnil Shinde, Sahil Wadhwa, Andy Luo Overview: Standard red teaming approaches often struggle with a limited range of jailbreak strategies or rely on ineffective, randomized crowd-sourced tactics. This paper introduces a novel framework -- Adaptive Instruction Composition -- that utilizes reinforcement learning and a neural contextual bandit to tailor attack compositions dynamically, balancing diversity and effectiveness to proactively uncover target model vulnerabilities. Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection Capital One Authors: Genta Winata, Sambit Sahu, Supriyo Chakraborty, Shixiong Zhang Overview: Emerging from our gifted research collaboration ## 10 Years of Meta's Commitment to Python DevFeed: [10 Years of Meta's Commitment to Python]() Original publisher: [Read original article]() Author: Chris Wiltz Published: 2026-06-30T16:00:46Z Content type: opinion Language: en Sources: [Meta AI Research](), [Meta ML Applications]() Topics: [Meta](), [Python](), [Open Source](), [Programming language](), [Software](), [PyTorch]() Tags: [ai-research](), [culture](), [data-infrastructure](), [devinfra](), [engineering](), [meta](), [ml-applications](), [open-source](), [production-engineering](), [programming](), [python](), [pytorch](), [software]() ### AI overview Meta reflects on its 10th consecutive year sponsoring the Python Software Foundation and explains Python's importance across its engineering stack, including products, infrastructure, and AI research. ### Source excerpt This year marks Meta's 10th consecutive year as a sponsor of the Python Software Foundation (PSF), the charitable organization dedicated to advancing, supporting, and protecting the open-source Python programming language and the community that sustains it. Python is one of the world's most influential programming languages, and we use it across our engineering stack, from [...] Read More... The post 10 Years of Meta's Commitment to Python appeared first on Engineering at Meta. ## Physics AI research that's shaping the industry. DevFeed: [Physics AI research that's shaping the industry.]() Original publisher: [Read original article]() Published: 2026-05-27T12:00:05Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [AI Research](), [Physics-guided deep learning](), [Artificial Intelligence](), [data](), [datasets](), [AI Foundation Models](), [GPU](), [Machine learning](), [Transformer]() Tags: [ai](), [ai-research](), [data](), [datasets](), [deep-learning](), [design](), [energy](), [engineering](), [gpu](), [industry](), [machine-learning](), [models](), [performance](), [physics](), [real-time](), [research]() ### AI overview Mistral describes its acquisition of Emmi AI and its focus on Physics AI for industrial engineering. The article surveys published work on CFD, neural surrogates, foundation models, datasets, plasma turbulence, and real-time industrial simulation across aerospace, automotive, semiconductors, and energy. ### Source excerpt Published breakthroughs pushing the state of the art. ## ConvApparel: Measuring and bridging the realism gap in user simulators DevFeed: [ConvApparel: Measuring and bridging the realism gap in user simulators]() Original publisher: [Read original article]() Published: 2026-04-09T11:22:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Conversational AI](), [Generative AI](), [LLMs](), [Artificial Intelligence](), [dataset](), [AI Research](), [Statistics](), [data](), [Google]() Tags: [agents](), [ai](), [ai-agents](), [ai-research](), [conversational-ai](), [data](), [evaluation](), [generative](), [generative-ai](), [llm](), [machine-intelligence](), [natural-language-processing](), [research](), [testing](), [training](), [validation]() ### AI overview Google Research introduces ConvApparel, a human-AI conversation dataset and evaluation framework for measuring the realism gap in LLM-based user simulators. It uses Good and Bad agents and validates results through population-level statistics, human-likeness scoring, and counterfactual validation. ### Source excerpt Generative AI ## Tsinghua's Multi-Agent AI Classroom, Anthropic's Context Engineering Playbook, and a 54 LLM-Architecture Gallery - 📚 The Tokenizer Edition #22 DevFeed: [Tsinghua's Multi-Agent AI Classroom, Anthropic's Context Engineering Playbook, and a 54 LLM-Architecture Gallery - 📚 The Tokenizer Edition #22]() Original publisher: [Read original article]() Author: Sairam Sundaresan Published: 2026-04-02T23:34:09Z Content type: article Language: en Sources: [Gradient Ascent]() Topics: [Artificial Intelligence](), [anthropic](), [Large Language Model](), [interactive storytelling]() Tags: [ai](), [ai-research](), [anthropic](), [benchmarks](), [claude-code](), [gallery](), [llm]() ### AI overview The Tokenizer Edition #22 curates AI and machine learning resources, including papers on real-time video generation, video world models, and task-aware sampling; videos on LLM architectures, KV-cache compression, and Claude Code; articles on AI interfaces, benchmarks, and context management; agent tools; and learning resources such as Tsinghua's multi-agent AI classroom. ### Source excerpt This week's most valuable AI resources ## Google Research at The Check Up: from healthcare innovation to real-world care settings DevFeed: [Google Research at The Check Up: from healthcare innovation to real-world care settings]() Original publisher: [Read original article]() Published: 2026-03-17T19:47:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [Artificial Intelligence](), [AI Research](), [datasets](), [data](), [Ground truth / benchmark quality]() Tags: [ai](), [ai-research](), [data](), [datasets](), [google](), [health](), [health-bioscience](), [healthcare](), [innovation](), [machine-intelligence](), [research](), [science](), [uk](), [us]() ### AI overview Google Research highlights AI applications in healthcare, including a Personal Health Agent for preventative care, multimodal analysis of wearable data, and diagnostic research for improving breast cancer detection. The article emphasizes collaboration with healthcare professionals and the use of diverse datasets and expert-validated ground truth data. ### Source excerpt Health & Bioscience [Next page]()