Knowledge catalogue
arXiv:2602.17251v2 Announce Type: replace Abstract: Electroencephalography (EEG) foundation models (EFMs) have shown strong potential for transferable representation learning, yet their adaptation in
Swap models & view their capabilities! Try out in Deep Agents CLI: https://docs.langchain.com/oss/python/deepagents/cli/ here's model profile details look like in practice, using @NVIDIAAIDev's Nemotr
arXiv:2605.07288v1 Announce Type: cross Abstract: The integration of Vision-Language-Action (VLA) models with World Models has gained increasing attention. One representative approach treats learned W
arXiv:2602.04939v2 Announce Type: replace Abstract: Modern T2V/I2V generators synthesize people increasingly hard to distinguish from authentic footage, while current evaluation suites lag: legacy ben
arXiv:2510.04839v2 Announce Type: replace Abstract: Accurate online inertial parameter estimation is essential for adaptive robotic control, enabling real-time adjustment to payload changes, environme
arXiv:2605.06886v1 Announce Type: new Abstract: This work introduces TajPersLexon, a curated Tajik--Persian parallel lexical resource of 40,112 word and short-phrase pairs for cross-script lexical ret
arXiv:2605.06931v1 Announce Type: new Abstract: Learning-based approaches to NP-hard problems have shown increasing promise, but their progress is fundamentally constrained by the high cost of generat
arXiv:2605.07256v1 Announce Type: new Abstract: Transformer architecture search (TAS) discovers optimal vision transformer (ViT) architectures automatically, reducing human effort to manually design V
arXiv:2605.07943v1 Announce Type: cross Abstract: Active vision -- where a policy controls its own gaze during manipulation -- has emerged as a key capability for imitation learning, with multiple ind
arXiv:2601.18700v2 Announce Type: replace Abstract: Emotional Support Conversation requires not only affective expression but also grounded instrumental support to provide trustworthy guidance. Howeve
arXiv:2605.07237v1 Announce Type: new Abstract: Tool-integrated reasoning (TIR) has emerged as a dominant paradigm for mathematical problem solving in language models, combining natural language (NL)
arXiv:2605.07073v1 Announce Type: new Abstract: Agent systems often decompose a task across multiple roles, but these roles are typically specified by prompts rather than enforced by access controls.
arXiv:2601.21839v2 Announce Type: replace-cross Abstract: Test-time compute has emerged as a promising strategy to enhance the reasoning abilities of large language models (LLMs). However, this strate
arXiv:2605.07807v1 Announce Type: cross Abstract: Text-to-CAD has recently emerged as an important task with the potential to substantially accelerate design workflows. Despite its significance, there
This post discusses strategies for scaling from managing a single AI agent to coordinating multiple agents efficiently, likely addressing workflow challenges and tooling improvements that eliminate th
arXiv:2605.07282v1 Announce Type: new Abstract: Final outputs hide when a checkpoint commits to its next-token prediction. We introduce the convergence gap, a model-diffing diagnostic that decodes eac
arXiv:2605.07686v1 Announce Type: new Abstract: Chain-of-thought reasoning is often treated as a monotone way to improve language-model accuracy by letting a model think longer. We identify a counterv
arXiv:2605.06729v1 Announce Type: cross Abstract: We present the EDelta-MHC-Geo Transformer, a novel architecture that unifies Manifold-Constrained Hyper-Connections (mHC), Deep Delta Learning (DDL),
arXiv:2605.08060v1 Announce Type: cross Abstract: Context window expansion is often treated as a straightforward capability upgrade for LLMs, but we find it systematically fails in multi-agent social
arXiv:2605.07127v1 Announce Type: cross Abstract: Modern large language models (LLMs) can find a needle in a haystack (locating a single relevant fact buried among hundreds of thousands of irrelevant
arXiv:2605.06707v1 Announce Type: cross Abstract: This paper presents an eight-week observational comparison of 68 single-file HTML generations collected across 17 public experiments in the 'HTML AI B
arXiv:2605.07186v1 Announce Type: cross Abstract: Existing Large Language Model (LLM) benchmarks primarily focus on syntactically correct inputs, leaving a significant gap in evaluation on imperfect t
arXiv:2605.07093v1 Announce Type: cross Abstract: The Translation Tax is often treated as a scalar: translated benchmarks are assumed to inflate scores by preserving English-source cues. We audit this
arXiv:2602.04774v2 Announce Type: replace-cross Abstract: Setting the learning rate (LR) for a deep learning model is a critical part of successful training. Choosing LRs is often done empirically wit
arXiv:2601.23143v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieve remarkable performance by leveraging reinforcement learning (RL) on reasoning tasks to generate long chain-of-
arXiv:2510.01290v2 Announce Type: replace Abstract: The long-output context generation of large reasoning models enables extended chain of thought (CoT) but also drives rapid growth of the key-value (
Today we’re launching the OpenAI Deployment Company to help businesses build and deploy AI. It's majority-owned and controlled by OpenAI. It brings together 19 leading investment firms, consultancies,
arXiv:2605.06683v1 Announce Type: cross Abstract: Transformer-based large language models are in some respects limited by the quadratic time and space computational complexity of attention. We introdu
arXiv:2605.07990v1 Announce Type: cross Abstract: When a tool-calling agent picks the wrong tool, the failure is invisible until execution: the email gets sent, the meeting gets missed. Probing 12 ins
arXiv:2605.07339v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in orchestrating tools for reasoning tasks. However, existing methods rely on a s
arXiv:2605.07158v1 Announce Type: cross Abstract: Vector search and retrieval-augmented generation (RAG) rest on the assumption that cosine similarity between text embeddings reflects conceptual relat
arXiv:2605.07013v1 Announce Type: new Abstract: Diffusion language models (DLMs) promise parallel, order-agnostic generation, but on standard benchmarks they have historically lagged behind autoregres
arXiv:2605.07593v1 Announce Type: new Abstract: Real-world audio-visual understanding requires chaining evidence that is sparse, temporally dispersed, and split across the visual and auditory streams,
arXiv:2605.07776v1 Announce Type: cross Abstract: Language model (LM) 'reasoning', commonly described as Chain-of-Thought or test-time scaling, often improves benchmark performance, but the dynamics u
arXiv:2605.06966v1 Announce Type: new Abstract: Autonomous vehicles (AVs) require extensive testing in simulation, but test case generation for driving scenarios is laborious. The desired scenarios ar
arXiv:2605.07772v1 Announce Type: new Abstract: Transformers perform inference by iteratively transforming token representations across layers. This layerwise computation has been studied empirically,
arXiv:2605.07924v1 Announce Type: cross Abstract: Discrete flow matching generates text by iteratively transforming noise tokens into coherent language, but may require hundreds of forward passes. Dis
arXiv:2601.18744v2 Announce Type: replace Abstract: Time series are ubiquitous in real-world scenarios and crucial for applications ranging from energy management to traffic control. Consequently, the
arXiv:2410.06355v3 Announce Type: replace-cross Abstract: This paper presents UNCOM, a novel hybrid framework for interpreting natural human commands in tabletop scenarios. The system integrates multi
arXiv:2511.03182v2 Announce Type: replace-cross Abstract: Large language models (LLMs) for code are increasingly used in software development, but they remain static after pretraining while APIs and s
arXiv:2605.06815v1 Announce Type: new Abstract: The pursuit of artificial general intelligence necessitates robust methods for evaluating the cognitive capabilities of models beyond narrow task perfor
arXiv:2605.07395v1 Announce Type: cross Abstract: Efficient routing across multiple LLMs enables cost-quality tradeoffs by directing queries to the cheapest capable model. Prior work attributes routin
TIL: Using LLM in the shebang line of a script Kim_Bruning on Hacker News: But seriously, you can put a shebang on an english text file now (if you're sufficiently brave) [...] This inspired me to loo
arXiv:2602.16571v2 Announce Type: replace Abstract: Large-scale sharing of dialogue data is key to advancing the science of teaching and learning, yet rigorous de-identification remains a major barrie
This is a release candidate version of Ollama that merges updates from the main development branch into the llama-runner-phase-0 branch, likely incorporating bug fixes and features in preparation for
arXiv:2605.07872v1 Announce Type: cross Abstract: Multimodal reward models have advanced substantially in text and image domains, yet progress in video understanding reward modeling remains severely l
arXiv:2605.06708v1 Announce Type: cross Abstract: Visual text compression (VTC) promises efficient long-context processing by rendering text into an image and re-encoding it with a vision-language mod
arXiv:2605.06175v2 Announce Type: replace Abstract: Vision-language-action (VLA) models inherit rich visual-semantic priors from pre-trained vision-language backbones, but adapting them to robotic con
📣We're calling for ambassadors! Whether you're a developer with great technical taste or a local community leader who loves bringing people together, we'd love to have you join us. Visit the website b
OpenAI announced the acquisition of Tomoro, a company that will add 150 experienced Forward Deployed Engineers and Deployment Specialists to OpenAI's team. This acquisition appears focused on expandin
arXiv:2605.07260v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) language models route each token to a small subset of experts, but whether the routes selected by a trained top-k router are
arXiv:2605.06772v1 Announce Type: new Abstract: As large language models (LLMs) show increasing promise on research-level physics reasoning tasks and agentic AI becomes more common, a practical questi
arXiv:2605.06731v1 Announce Type: cross Abstract: Personalized LLM agents maintain persistent cross-session state to support long-horizon collaboration. Yet, this persistence introduces a subtle but c
arXiv:2605.07114v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a central paradigm for improving the reasoning capabilities of large language model
arXiv:2605.07984v1 Announce Type: cross Abstract: We study planning site formation in language models -- where internal representations of structurally-constrained future tokens form during the forwar
arXiv:2605.07527v1 Announce Type: cross Abstract: Recent work has observed that explanations produced by Self-Interpretable Graph Neural Networks (SI-GNNs) can be self-inconsistent: when the model is
arXiv:2605.07068v1 Announce Type: cross Abstract: The LLM Wiki pattern, to compile and provide domain knowledge into a persistent artifact and serve it to LLMs via KV cache inference, promises context
This post from Elon Musk's X account likely recommends or highlights content that Musk finds valuable or important, though without access to the specific tweet content, the exact subject matter cannot
would you call it a superapp? After being a Claude Code devotee for a year, I finally tried Codex on a new project this weekend. Once again, in the matter of a few months, it feels like the world chan
arXiv:2605.07579v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) for Large Reasoning Models hinges on baseline estimation for variance reduction, but existing ap