Companies & labs
Straight from the people who built it
Every post here is an organisation publishing about its own work: a lab's newsroom, a research blog, a paper. No intermediary, no summary of a summary, and nothing this deck had to interpret. It is the shortest distance between you and the source.
01Newest first
- PrimaryNVIDIA5 Nov, 3:29 PM CST
How NVIDIA OpenShell Reference Architecture Secures and Governs AI Agent Workloads at Scale
Autonomous agents can work for hours on complex tasks, accessing files, tools, the internet, and spawning other agents to collaborate with. Executing complex...
- PrimaryAWS Machine Learning5 Nov, 3:04 PM CST
Getting started with Web Answer on Amazon Bedrock AgentCore
Web Answer on Amazon Bedrock AgentCore returns a synthesized, source-cited answer to a natural-language question in a single tool call, with no search, retrieve, synthesize, and citation pipeline to build. Learn how it works and how to add it to an AgentCore G
- PrimaryHugging Face5 Nov, 1:35 PM CST
New NVIDIA Cosmos 3 Open Models for Interactive World Simulation
- PrimaryAWS Machine Learning5 Nov, 12:06 PM CST
How Amazon Marketplace evaluates content quality with Amazon SageMaker AI
Learn how Amazon Marketplace evaluates A+ Content quality at scale on Amazon SageMaker AI by rendering each product page as a single snapshot, scoring it with a vision-language model fused with OCR readability signals in a CatBoost classifier, and using SHAP t
- PrimaryNVIDIA5 Nov, 11:14 AM CST
Five Ways NVIDIA Co-Design Maximizes the Value of Every Byte in the AI Factory
In an AI factory, high bandwidth memory (HBM)is the fastest, most expensive, and most contested memory tier. Every byte spent on a redundant weight copy, an...
- PrimaryGoogle AI5 Nov, 11:00 AM CST
Gemini Omni 1.2 Flash: Fast and cost-effective with longer clips
Text "Gemini Introducing Gemini Omni 1.2 Flash" surroudned by images of a beehive, animals in conversation, a squid, and a woman walking down a street
- PrimaryMicrosoft Research5 Nov, 10:00 AM CST
Formally verifying agent-written full-stack web apps with PDD Kit
AI agents can generate code at a scale that’s hard for humans to review line-by-line. PDD Kit helps ensure the code is doing what the developer intended by creating formal specifications and testing against them. It converts developer intent into a Verus speci
- PrimaryMistral AI5 Nov, 6:00 AM CST
Le Chonk brings the brains. Studio puts it to work.
Build and deploy enterprise agents, workflows and apps faster with Mistral Studio and Le Chonk full control over your models, data and infrastructure.
- PrimaryOpenAI5 Nov, 3:00 AM CST
GPT‑6 Cyber: Advancing the frontier of aligned cybersecurity agents
Introducing GPT‑6 Cyber, OpenAI’s cybersecurity model for finding exploitable vulnerabilities and developing fixes, trained to stay within authorized scope.
- PrimaryOpenAI4 Nov, 8:00 PM CST
Setting a new standard for responsible AI infrastructure
OpenAI introduces its National Community Compact and Compute Campus Policy Blueprint to protect local resources, create jobs, and guide responsible AI infrastructure development.
- PrimaryApple Machine Learning4 Nov, 6:00 PM CST
Faster Retrieval for Better, More Efficient RAG
Maximum inner product search (MIPS) sits at the core of recommendation systems, search, and retrieval-augmented generation (RAG). Given a query vector, MIPS systems search within a database of millions of candidate key vectors for the one that best matches the
- PrimaryHugging Face4 Nov, 6:00 PM CST
A Closer Look at Phonon TTS. Launching High-Quality On-Device Speech That Tops the Benchmarks.
- PrimaryNVIDIA4 Nov, 4:13 PM CST
How NVIDIA Uses AI Agents to Streamline AI Factory Operations
As GPU clusters scale, operations span compute, network, hardware, and scheduling. Site reliability engineers (SREs) often have to examine multiple dashboards...
- PrimaryNVIDIA4 Nov, 2:26 PM CST
Use NVIDIA NeMo to Turn Agent Failures into Tests and Harness Fixes
Turn production evidence into prioritized issues and custom evaluations that help you improve prompts, tools, models, memory, skills, and harness code. When you...
- PrimaryarXiv4 Nov, 12:58 PM CST
A Framework for Context Enrichment and Selective Robustness in LLM Social Agents
Researchers use large language models as agents in social simulations, where agents are prompted with observations and background information intended to shape their actions. Yet LLM agents have been shown to substantially change their behavior under meaning-p
- PrimaryarXiv4 Nov, 12:57 PM CST
BEAKER: An Expert-Curated Benchmark for Embodied Brains in Self-Driving Chemical Laboratories
Self-Driving Chemical Laboratories (SDCLs) are moving chemical experimentation toward embodied automation, where machines must perceive laboratory scenes, reason about experimental states, and act under physical, procedural, and safety constraints. Although Mu
- PrimaryarXiv4 Nov, 12:56 PM CST
FrontierPhysics: Evaluating Agents for End-to-End Physics Research
AI agents are playing an increasingly important role in physics research. However, existing physics benchmarks only evaluate fixed questions, isolated sub-tasks, or paper reproduction with given solutions, and rarely check research quality alongside correctnes
- PrimaryarXiv4 Nov, 12:52 PM CST
Reinforcement Learning Enables Scaling Generalist Robot Policy Improvement
The introduction of generalist robot policies has enabled a single, large model to learn to perform a variety of tasks from a vast pre-training corpus. While these models often exhibit powerful zero-shot generalization capabilities, they typically require an a
- PrimaryAWS Machine Learning4 Nov, 12:46 PM CST
Use decision models with Amazon Bedrock AgentCore Evaluations
Some agent-evaluation questions are narrow and repeatable, and decision models answer exactly that kind of question. This post shows how to connect two decision models, Jev and Strands Decider, to Amazon Bedrock AgentCore Evaluations through code-based evaluat
- PrimaryNVIDIA4 Nov, 12:46 PM CST
NVIDIA CUDA 13.5 Brings NVIDIA Vera Rubin GA Support
As artificial intelligence and accelerated computing continue to push the boundaries of what's possible, the latest NVIDIA CUDA release arrives to unlock...
- PrimaryarXiv4 Nov, 12:41 PM CST
Dull, Dirty, Dangerous? Working Conditions and the Limits of Occupational AI Exposure
Automation is often justified as relief from dull, dirty, and dangerous work. I examine how these conditions relate to occupational AI exposure using 21 O*NET items across 754 US occupations. Higher exposure is associated with lower condition scores under a la
- PrimaryarXiv4 Nov, 12:35 PM CST
Adaptive Multi Mode: Budget-Aware Routing of Optimizers By Online Landscape Probes
The best classical optimizer for a continuous black-box problem depends on its budget and landscape. Existing routers decide from the dimension and budget, past improvement or features fitted to one benchmark, and few test an optimizer's implicit assumptions o
- PrimaryarXiv4 Nov, 12:34 PM CST
LLM agents use context compaction to manage growing interaction histories, but summarizing long histories with large models can introduce substantial delays. Compaction therefore requires balancing information retention against latency. We observe that structu
- PrimaryarXiv4 Nov, 12:29 PM CST
Learning to Decode Linear Codes with Generalized Belief Propagation Network
Error correcting codes are a fundamental component of communication systems, enabling reliable data transmission over noisy channels. Recently, neural decoders have demonstrated notable performance gains over classical iterative algorithms. In this paper, we p
- PrimaryarXiv4 Nov, 12:23 PM CST
On Turn-Level Best-of-N Sampling for Scaling LLM Agents
In this work, we evaluate a simple inference-time scaling approach based on \textit{turn-level best-of-N sampling} (\method) for large language model (LLM) agents on complex, long-horizon tasks. We instantiate this using a \textit{generator-selector} framework
- PrimaryarXiv4 Nov, 12:19 PM CST
CounterFactual Retrieval for Evidence-Aware Individual Animal Identification
Automated identification of individual animals is moving from small curated studies toward shared benchmarks and reusable foundation models. This progress is useful, but current evaluation leaves the evidence behind a match unresolved, i.e., high retrieval acc
- PrimaryarXiv4 Nov, 12:15 PM CST
Balancing hierarchical representation and quantization: A performance trade-off in text embeddings
Hierarchical text embedding models offer variable-length representations, while quantization reduces numerical precision; both aim to improve computational efficiency. This work is an empirical benchmark study of how these two orthogonal, already-established t
- PrimaryarXiv4 Nov, 12:14 PM CST
CompressionBench: How can your coding agents compress LLMs?
Recursive self-improvement requires AI systems to improve the models and infrastructure used by subsequent systems. LLM compression offers a concrete efficiency objective: retain a pretrained model's behavior within a smaller model storage budget. We study whe
- PrimaryarXiv4 Nov, 12:11 PM CST
LongBanana: An Expert-Verified Benchmark for Long-Context Multi-Reference Image Synthesis
We introduce LongBanana, to our knowledge the first expert-designed and expert-verified benchmark for long-context multi-reference image synthesis in the age of agent-driven image generation. The task requires retrieving dispersed visual evidence, rejecting di
- PrimaryarXiv4 Nov, 12:10 PM CST
Visual Token Routing: Learning to keep tokens that matter
Computer-use agents (CUAs) have recently achieved superhuman performance on several long-horizon benchmarks. However, their computational cost limits their use in long-horizon tasks and real-time domains such as video games. A key bottleneck is representing ea
- PrimaryGoogle Research4 Nov, 12:07 PM CST
Agile Deliberation: How AI helps humans define the gray areas
Data Mining & Modeling
- PrimaryarXiv4 Nov, 12:05 PM CST
Persona Perspectives: Adopting Viewpoints Reorganizes Representations in LLMs
Common methods for monitoring features in large language models (LLMs) such as linear probes and sparse autoencoders (SAEs) assume that features maintain a consistent representation across contexts. LLMs, however, can flexibly adopt different perspectives or "
- PrimaryarXiv4 Nov, 11:59 AM CST
Thermally Conditioned Time-Series Forecasting of Grain Growth in Polycrystalline Materials
Grain growth during heat treatment strongly affects the microstructure of polycrystalline materials and, consequently, their mechanical properties. Although numerical methods such as front-tracking or front-capturing simulations can accurately describe grain g
- PrimaryarXiv4 Nov, 11:59 AM CST
ServerBench: How can your coding agents design specialized LLM serving systems?
Large language model (LLM) coding agents develop software through experimentation, offering an opportunity to design the serving systems supporting their own operation. Effective serving requires coordinating implementation and operating decisions under sessio
- PrimaryarXiv4 Nov, 11:54 AM CST
Before You Continue: Evaluating Time-Sensitive Safety Awareness in Conversational Assistants
Large language model (LLM) assistants increasingly help users with everyday activities that involve time-dependent constraints. However, users may not mention these constraints in later requests, requiring assistants to recognize and adjust their advice accord
- PrimaryarXiv4 Nov, 11:53 AM CST
Context--Answer Metamodels: Forecasting LLM Activations Before the First Answer Token
Under stochastic decoding, a single context can elicit many different answers from a language model. Characterizing this variation typically requires many expensive autoregressive rollouts, each revealing only one realization. We ask whether the activations pr
- PrimaryarXiv4 Nov, 11:52 AM CST
LooM: Loop-Invariant Memory for Causal Encoder-Decoder Language Models
Looped Transformers increase effective depth by re-executing shared layers, but three limitations hinder their adoption: memory grows with the loop count, compute can only be adjusted in whole-block steps, and the loop count is hard to change after training. W
- PrimaryarXiv cs.LG4 Nov, 11:44 AM CST
Alignment Myopia in Generative AI
Current generative AI alignment relies on a myopic paradigm that prioritizes immediate conversational satisfaction. By relying on short-horizon feedback, this approach ignores the profound, longitudinal externalities of persistent human-AI interaction, which c
- PrimaryarXiv cs.LG4 Nov, 11:44 AM CST
Enabling AWS Cloud FPGA Development Using High-Level Vitis HLS
We present a toolchain for development of ML models on AWS EC2 F2 cloud FPGA infrastructure from high-level language, implementing a complete Vitis HLS to HDK bridge to AFI workflow that has been absent from the academic literature despite eight years of AWS F
- PrimaryarXiv cs.LG4 Nov, 11:42 AM CST
Systolic Linear Attention: Co-Designing Tile-Grouped Gated Delta Attention for TPUs
Gated-delta linear attention layers such as Gated DeltaNet (GDN), Kimi Delta Attention (KDA), and GDN-2 are tuned for GPUs, where small tensor-core tiles and fast vector registers make per-channel decay rescaling and sequential in-block solves cheap. On Google
- PrimaryNVIDIA4 Nov, 11:42 AM CST
Configure, Certify, and Operate Kubernetes GPU Clusters with NVIDIA DSX OS
One underperforming GPU can slow an entire distributed training job. The node may pass basic health checks and show normal utilization, leaving operators to...
- PrimaryarXiv cs.CL4 Nov, 11:25 AM CST
SAUCE: Summary Analysis Using Conversation Entailment
With the growing need for evaluating Large Language Models (LLMs) and their applications to speech, challenges persist in summarizing and evaluating conversations that lack a clear end goal. We introduce SAUCE -- a reference-free, fact-based evaluation pipelin
- PrimaryarXiv cs.LG4 Nov, 11:25 AM CST
Structure property prediction is a classical task in machine learning, ranging from molecular activity and materials properties to local functional responses measured in microscopy. For image-based regression, two choices define the information available to th
- PrimaryarXiv cs.CL4 Nov, 11:24 AM CST
LLMs Don't Know What They Know: Benchmarking Temporal Self-Knowledge for Financial Backtesting
Financial backtests of large language models (LLMs) are meaningful only if the model did not already know the outcomes it is asked to predict. Practitioners guard against this by trusting a vendor's stated knowledge cutoff, by asking the model what it knows, o
- PrimaryarXiv cs.CL4 Nov, 11:22 AM CST
Measuring Risk, Not Harm: Detecting Psychological Harm Risk Factors in LLM Conversations
Psychological harms from interactions with large language models cannot be monitored directly because they may emerge over repeated interactions and depend strongly on subjective individual user context. We propose measuring risk, not harm: detecting observabl
- PrimaryarXiv cs.LG4 Nov, 11:18 AM CST
ConDR: Conservative Reachability through Neural Hamilton-Jacobi Subsolutions
Hamilton-Jacobi (HJ) Reachability identifies the unsafe states of a dynamical system from which it can be forced into failure under worst-case disturbance. However, classical grid-based solvers scale exponentially with state dimension. DeepReach improves scala
- PrimaryarXiv cs.LG4 Nov, 11:18 AM CST
Message-passing graph persistence
Message-passing graph neural networks (GNNs) are limited in expressive power by the one-dimensional Weisfeiler-Leman test. Persistent homology (PH) provides topological descriptors that can complement GNN representations, but existing approaches typically comp
- PrimaryarXiv cs.LG4 Nov, 11:11 AM CST
Transforming Integer Grids to Spatial Intuition: Perception, Not Planning, Governs ARC-AGI-3
Sample-efficient fluid intelligence requires agents that rapidly form working models of unfamiliar interactive environments. ARC-AGI-3 tests this across uninstructed visual games scored by Relative Human Action Efficiency, which penalizes exploratory waste qua
- PrimaryarXiv cs.CL4 Nov, 11:03 AM CST
A fast acceptance classifier could select revisions for a slower evaluator. We ask whether agreement on existing papers supports that use. We compare a classifier of word and document features with two released text-only acceptance predictors fine-tuned from t
- PrimaryarXiv cs.LG4 Nov, 10:38 AM CST
We study logistic regression for continual learning with linear predictors in a high-dimensional statistical setting. Existing theoretical work has largely focused on continual linear regression with variants of L2 regularization, motivated by their analytical
- PrimaryAWS Machine Learning4 Nov, 10:33 AM CST
Smart Approvals in Amazon Quick: Human oversight where it matters
Smart Approvals in Amazon Quick reviews eligible agent actions within your existing controls, reducing unnecessary approval prompts while keeping consequential decisions with a person. See how policy sets the boundary, how a classifier reviews actions, and how
- PrimaryAWS Machine Learning4 Nov, 10:30 AM CST
In production AI agents, context management is the real bottleneck. This post presents a four-layer context engineering framework (architecture, prompts, file-based tools, and verification agents), implemented in working code from Deep Insight, an open source
- PrimaryAWS Machine Learning4 Nov, 10:25 AM CST
How Indegene is modernizing and scaling enterprise data quality in life sciences
This post explores how Indegene built a production-grade, LLM-powered Data Quality Agent in its NEXT Unified Data Platform on AWS. The agent generates context-aware data quality rules, validates them, and produces ready-to-run SQL and Python under human-in-the
- PrimaryAWS Machine Learning4 Nov, 10:23 AM CST
How U Mobile built agentic telco operations with Amazon Bedrock AgentCore
U Mobile, Malaysia's newest 5G network provider, built AiDaH, an agentic AI operations platform on Amazon Bedrock AgentCore that moves beyond AI assistance to autonomous action across telco operations. Nine months after production launch, AiDaH handles over 15
- PrimaryarXiv cs.LG4 Nov, 10:20 AM CST
Coupled Scene Graph Anticipation: When With-Constraint Recall Hides Structural Inconsistency
Scene Graph Anticipation (SGA) predicts object relationships in future video frames. Continuous-time SGA models evolve each subject-object pair with an independent Neural ODE, so relationships sharing an entity cannot influence one another during integration,
- PrimaryarXiv cs.LG4 Nov, 10:17 AM CST
ConceptARC at Scale: Studying Conceptual Understanding in Reasoning Models
Large reasoning models (LRMs) perform well on challenging benchmarks, yet it remains unclear whether they form generalizable abstractions across conceptually related reasoning tasks. The Abstraction and Reasoning Corpus (ARC) provides a controlled testbed for
- PrimaryarXiv cs.CL4 Nov, 10:17 AM CST
Template Gap Reconstruction for Shell Output Simulation with Small Language Models
Small language models offer a compact basis for shell simulation without executing submitted commands, but full-output decoding requires them to predict recurring utility structure alongside content that varies across executions. We introduce template gap reco
- PrimaryarXiv cs.CL4 Nov, 10:16 AM CST
Turns Matter: Advancing Streaming Video RL via Turn-Level Credit
Streaming video understanding requires models to repeatedly decide whether to respond or continue observing. Recently, Group Relative Policy Optimization (GRPO) has been applied to this setting, with existing approaches emphasizing reward design while retainin
- PrimaryarXiv cs.CL4 Nov, 10:11 AM CST
Retrieval-augmented generation (RAG) assumes that language models can reason over relations stated in their context rather than fall back on associations memorized during pretraining. We study this evidence-conditioned relational reasoning in isolation, with t
- PrimaryarXiv cs.CL4 Nov, 9:31 AM CST
Beyond accuracy: what AI assurance needs to establish in public services
Public bodies increasingly rely on AI-produced evidence, including classifications, summaries and assessments. These outputs can inform policy decisions and service delivery, making their reliability a matter of public accountability. Establishing whether this
- PrimaryarXiv cs.CL4 Nov, 9:20 AM CST
This chapter addresses the central reproducibility problem of research with large language models. LLM output is probabilistic and varies across models and versions, which can have significant effects on downstream statistical results. The chapter distinguishe
- PrimaryOpenAI4 Nov, 8:30 AM CST
OpenAI to acquire Pierre Computer Company, Inc.
OpenAI will acquire Pierre Computer Company, expanding Codex’s tools for building, understanding, and reviewing code.
- PrimaryarXiv cs.CL4 Nov, 8:12 AM CST
Long-running LLM agents compact their context, replacing most history with a summary. We ask whether handoff summaries state permissions or requirements the record does not support, whether such sentences change what the next instance (the successor) does, and
- PrimaryarXiv cs.CL4 Nov, 7:34 AM CST
NaijaFacts: A Framework for Disentangling Language from Knowledge in Facts about Nigeria
Multilingual benchmarks for African languages mostly translate generic questions into local languages, measuring linguistic competence but not whether models know facts about Africa. We introduce \naija, a benchmark of factual and statistical questions about N
- PrimaryarXiv cs.CL4 Nov, 7:32 AM CST
CRESiST: Self-Enhancing Explorative Policy Learning for Simultaneous Speech Translation
Simultaneous Speech Translation (SimulST) requires balancing translation quality and latency. While Large Language Models (LLMs) excel in offline translation, current LLM-based SimulST systems predominantly rely on prefix-incremental generation. This paradigm
- PrimaryarXiv cs.CL4 Nov, 7:30 AM CST
LibriSpeech-Real-QA: A Benchmark for Streaming Question Answering
Most speech large language models (SLMs) still process speech in an offline manner. They only start responding after they receive the complete speech segment. However, many real-world applications require streaming speech interaction, for which the audio conti
- PrimaryarXiv cs.CL4 Nov, 7:24 AM CST
What Does Multi-Turn Reinforcement Learning Really Improve in LLM Agents?
While multi-turn agentic reinforcement learning (RL) excels on long-horizon tasks, its performance gains remain highly sensitive to interaction constraints. Evaluating across sampling breadth $K$ and interaction depth $T$ via $Pass@(K,T)$, we show that traject
- PrimaryarXiv cs.CL4 Nov, 7:20 AM CST
ArcRAG: Agentic Compositional Retrieval over Knowledge Graphs
Graph-based retrieval-augmented generation (GraphRAG) answers questions by retrieving over a knowledge graph built from a corpus. Yet retrieval strategies are designed around the structure their own construction pipeline produces, and whether they survive a ch
- PrimaryarXiv cs.CL4 Nov, 6:47 AM CST
EvoGuard: Mitigating Degradation in Online Skill Evolution
Skills let language agents reuse experience across tasks as natural-language documents. Recent work evolves skills from interaction, but existing methods have limitations when skills evolve online. Prior methods update a skill offline from a batch of trajector
- PrimaryAllen Institute for AI4 Nov, 2:00 AM CST
Ai2 and Google collaborate on a new generation of climate models
With funding from Google.org and technical support from Google Research, Ai2 is developing NeuralGCM+, an open-source model designed to combine physics and AI to simulate the atmosphere and ocean together for faster, more reliable long-term climate projections
- PrimaryOpenAI4 Nov, 2:00 AM CST
A thousand fewer paths to root
OpenAI built an autonomous pipeline that found, validated, and generated patches for 948 local privilege-escalation vulnerabilities in the Linux kernel.
- PrimaryOpenAI4 Nov, 1:00 AM CST
Daybreak Red is now available in AWS GovCloud (US-East)
Daybreak Red brings GPT-5.6 Cyber to approved customers in AWS GovCloud (US-East) through Amazon Bedrock for authorized cybersecurity work.
- PrimaryTogether AI3 Nov, 6:00 PM CST
Together Compiler: Continual Compilation for AI inference
Together Compiler uses agents and persistent engines to optimize open models in under 24 hours, delivering up to 3.7x higher throughput as traffic changes.
- PrimaryTogether AI3 Nov, 6:00 PM CST
Polymorphic Inference: self-tuning inference for open models
Models, traffic and chips are constantly changing. Polymorphic Inference adapts to each, and Together Compiler uses AI agents to keep production inference tuned.
- PrimaryOpenAI3 Nov, 6:00 PM CST
Images 2.6 brings faster image generation, GIF creation, more precise editing, and new API controls across ChatGPT, Codex, and the API.
- PrimaryOpenAI3 Nov, 6:00 PM CST
DevRev delivers autonomous self-service with GPT-6.1 Sol
DevRev uses GPT-6.1 Sol to power Computer and Operator, balancing reasoning, speed, and cost for enterprise service.
- PrimaryNVIDIA3 Nov, 2:37 PM CST
Run AI Models for Edge Application Development with Just a Few Commands
AI is expanding beyond the data center as developers build a new generation of agents to act at the edge: monitoring driver attention and cabin conditions,...
- PrimaryNVIDIA3 Nov, 12:04 PM CST
Keep Distributed Training Running with Automated Workload Recovery
As distributed AI training scales, failures across hardware and software dependencies increasingly disrupt training progress, making fast, reliable recovery...
- PrimaryNVIDIA3 Nov, 11:47 AM CST
Diagnose Distributed AI Workload Failures with NVIDIA Attribution
In distributed AI workloads, a single error can produce a cascade of symptoms. A GPU or fabric event may surface as NCCL errors on other ranks, scheduler state...
- PrimaryHugging Face3 Nov, 11:15 AM CST
StateBench: Testing how language models track state changes over long documents
02How a post gets here
Evidence tier 1, and nothing else. This deck defines that tier as the organisation said it itself, or this is the paper, and the tier is set per source from what the source IS rather than from how good it is. So a lab's own newsroom qualifies and an outlet reporting on that lab does not, however good the reporting. 21 of the deck's sources carry that tier.
Every link went through the reader's door first, the same as every article link on this site: 0 post(s) were dropped because a reader could not open them. And this page has its own feed at companies.xml, carrying the same items under the same permission law as the main feed.
Publishing here: AWS Machine Learning · Allen Institute for AI · Apple Machine Learning · Berkeley BAIR · EleutherAI · Google AI · Google Research · Hugging Face · IBM Research · Microsoft Research · Mistral AI · NIST · NVIDIA · OpenAI · Qwen (Alibaba) · Stability AI · Together AI · arXiv · arXiv cs.CL · arXiv cs.LG