Knowledge catalogue
This release is absolutely stacked. I want to call out a sleeper feature: Interpreters Imagine your cloud agent gets a 10k-row CSV of support tickets. Without a code runtime, the model is stuck reason
arXiv:2510.14466v3 Announce Type: replace-cross Abstract: Large language models (LLMs) continue to struggle with low-resource languages, primarily due to limited training data, translation noise, and
arXiv:2605.17247v1 Announce Type: new Abstract: Argumentative essays serve as a vital medium for assessing critical thinking and reasoning skills, yet there is limited works on accurately understandin
arXiv:2605.16740v1 Announce Type: new Abstract: Multi-video event understanding demands models that can locate and attribute query-relevant evidence scattered across long, heterogeneous video corpora.
two years ago we started building agents to automate work. turns out these are really useful, so there’s a LOT of runs and long traces that are hard to reason about now, use an ambient agent (engine)
arXiv:2605.17912v1 Announce Type: cross Abstract: World models have emerged as a central paradigm for embodied intelligence, enabling agents to predict action-conditioned future and reason about envir
arXiv:2605.15565v1 Announce Type: cross Abstract: Reinforcement learning (RL) is increasingly used to improve the reasoning, coding, and tool-use capabilities of large language models, but agentic RL
arXiv:2605.15588v1 Announce Type: new Abstract: As large language models (LLMs) are deployed in consequential settings such as medical question answering and legal reasoning, the ability to estimate w
arXiv:2605.15445v1 Announce Type: new Abstract: Automated proving of polynomial inequalities is a fundamental challenge in automated mathematical reasoning, where rich algebraic structure and a rapidl
arXiv:2605.15464v1 Announce Type: cross Abstract: Post-training has become a crucial step for unlocking the capabilities of large language models, with reinforcement learning (RL) emerging as a critic
arXiv:2605.15626v1 Announce Type: new Abstract: Large language models deliver strong performance across language and reasoning tasks, but their storage and compute costs remain major barriers to deplo
Large language models demonstrate a specific and persistent theory-of-mind failure despite excelling at other theory-of-mind tasks, suggesting a particular gap in their reasoning about beliefs, intent
arXiv:2605.15341v1 Announce Type: cross Abstract: LLMs are increasingly deployed in autonomous laboratories, under the assumption that their domain priors and reasoning over iterative feedback let the
arXiv:2509.21663v2 Announce Type: replace-cross Abstract: Neurosymbolic integration (NeSy) blends neural-network learning with symbolic reasoning. The field can be split between methods injecting hand
arXiv:2605.15777v1 Announce Type: new Abstract: Computer-Using Agents (CUAs) are rapidly extending large language models (LLMs) beyond text-based reasoning toward action execution in more complex envi
arXiv:2503.02597v3 Announce Type: replace-cross Abstract: Recent Multimodal Large Language Models (MLLMs) have demonstrated significant progress in perceiving and reasoning over multimodal inquiries,
arXiv:2605.15215v1 Announce Type: new Abstract: Recently, skills have been widely adopted in large language model (LLM)-based agent systems across various domains. In existing frameworks, skills are t
arXiv:2605.16137v1 Announce Type: new Abstract: Generating simulation-ready tabletop scenes from task instructions is an intriguing and promising research direction in the field of Embodied AI. Howeve
arXiv:2605.15207v1 Announce Type: new Abstract: Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single-model baselines. We ident
arXiv:2605.15257v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is one of the most promising tools we have for detecting model misbehavior, but its effectiveness depends on models fa
arXiv:2605.16079v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown significant progress in video understanding, yet they face substantial challenges in tasks requiring p
Recently @pinecone introduced Nexus – a new knowledge-engine layer for AI agents that reduces token use by up to 90%. It’s built on top of a vector database, but shifts reasoning earlier in the pipeli
arXiv:2603.27517v3 Announce Type: replace-cross Abstract: AI agent frameworks connecting large language model (LLM) reasoning to host execution surfaces -- shell, filesystem, containers, and messaging
arXiv:2605.14892v1 Announce Type: new Abstract: LLM-based autonomous agents have demonstrated strong capabilities in reasoning, planning, and tool use, yet remain limited when tasks require sustained
arXiv:2605.14537v1 Announce Type: new Abstract: We introduce extsc{Cattle Trade, a multi-agent benchmark for evaluating large language models (LLMs) as agents in strategic reasoning under imperfect in
arXiv:2605.14495v1 Announce Type: cross Abstract: Multimedia verification requires not only accurate conclusions but also transparent and contestable reasoning. We propose a contestable multi-agent fr
arXiv:2605.14310v1 Announce Type: new Abstract: Streaming video understanding with large vision-language models (VLMs) requires a compact memory that can support future reasoning over an ever-growing
arXiv:2602.02711v2 Announce Type: replace Abstract: Large language models (LLMs) achieve strong performance in long-horizon decision-making tasks through multi-step interaction and reasoning at test t
arXiv:2604.06757v2 Announce Type: replace Abstract: Multimodal generation has long been dominated by text-driven pipelines where language dictates vision but cannot reason or create within it. We chal
Gary Marcus critiques the claim that token prediction in large language models constitutes genuine thought or reasoning, using the analogy of smartphone autocomplete to illustrate that predictive text
arXiv:2605.14215v1 Announce Type: new Abstract: Genetic circuit design remains a laborious, expert-driven process despite decades of progress in synthetic biology. We study this problem through code g
arXiv:2605.14498v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly serve as personal assistants and workplace collaborators, where their utility depends on memory systems t
arXiv:2605.14906v1 Announce Type: new Abstract: Memory is essential for large vision-language models (LVLMs) to handle long, multimodal interactions, with two method directions providing this capabili
arXiv:2605.14120v1 Announce Type: cross Abstract: Geospatial foundation models compress multispectral observations into dense embeddings increasingly used in natural-language environmental reasoning s
arXiv:2605.14721v1 Announce Type: new Abstract: Strong equivalence between knowledge bases ensures the possibility of replacing one with the other without affecting reasoning outcomes, in any given co
arXiv:2507.21023v2 Announce Type: replace Abstract: Recent publications have suggested using the Shapley value for anomaly localization for sensor data systems. Using a reasonable mathematical anomaly
arXiv:2605.15040v1 Announce Type: new Abstract: Agentic modeling aims to transform LLMs into autonomous agents capable of solving complex tasks through planning, reasoning, tool use, and multi-turn in
Paper of the day! https://huggingface.co/papers/2605.13301 We’re releasing a 30B-A3B reasoning model that reaches gold-medal level across both physics and math Olympiad evaluations: IPhO directly, and
arXiv:2605.13894v1 Announce Type: cross Abstract: In this work, we introduce a Tropical Axial Attention neural reasoning architecture that replaces vanilla softmax dot-product attention with max-plus
arXiv:2605.14002v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) embedded in agentic frameworks have transformed information retrieval from static, long context question answering into op
arXiv:2605.14113v1 Announce Type: cross Abstract: While interpretable prototype networks offer compelling case-based reasoning for clinical diagnostics, their raw continuous outputs lack the semantic
arXiv:2605.14126v1 Announce Type: cross Abstract: Fast Healthcare Interoperability Resources (FHIR) is the dominant standard for interoperable exchange of healthcare data. In FHIR, electronic health r
arXiv:2605.14937v1 Announce Type: cross Abstract: Predictive world models enable agents to model scene dynamics and reason about the consequences of their actions. Inspired by human perception, object
SuperGrok has been integrated into the Hermes Agent, representing an advancement in Nous Research's AI agent capabilities. This integration likely combines SuperGrok's reasoning or processing features
Three new open-source models just landed in ComfyUI natively: → Gemma 4 (Google DeepMind) - multimodal LLM handling text, image, audio, and video input with built-in step-by-step reasoning mode → VOID
arXiv:2605.14504v1 Announce Type: new Abstract: Long-horizon household tasks demand robust high-level planning and sustained reasoning capabilities, which are largely overlooked by existing embodied A
arXiv:2605.13382v1 Announce Type: new Abstract: While autoregressive (AR) Vision-Language-Action (VLA) models have demonstrated formidable reasoning capabilities in robotic tasks, their sequential dec
arXiv:2605.13062v1 Announce Type: new Abstract: Recent image editing models have achieved remarkable progress in instruction following, multimodal understanding, and complex visual editing. However, e
The NVIDIA Vera Rubin Platform is a rack-scale AI supercomputer designed to power agentic AI and reasoning models at scale by eliminating bottlenecks in communication and memory movement for efficient
arXiv:2505.12741v2 Announce Type: replace Abstract: Language models are increasingly used not only as standalone predictors but also as components in larger inference systems, from test-time reasoning
arXiv:2605.13137v1 Announce Type: cross Abstract: Proving theorems in Lean 4 often requires identifying a scattered set of library lemmas whose joint use enables a concise proof -- a task we call glob
arXiv:2605.12652v1 Announce Type: cross Abstract: Large language models are often post-trained with sparse verifier rewards, which indicate whether a sampled trajectory succeeds but provide limited gu
arXiv:2512.07775v2 Announce Type: replace Abstract: Autonomous robots rely on geometric maps to inform a diverse set of perception and decision-making algorithms. As autonomy requires reasoning and pl
arXiv:2603.20334v2 Announce Type: replace-cross Abstract: In high-complexity abstract reasoning, a system must infer a latent rule from a few examples or structured observations and apply it to unseen
arXiv:2605.12674v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are increasingly used in safety-critical applications because of their broad reasoning capabilities and ability to general
arXiv:2602.07342v2 Announce Type: replace Abstract: Large language models (LLMs) have shown promise in complex reasoning and tool-based decision making, motivating their application to real-world supp
arXiv:2605.12620v1 Announce Type: new Abstract: Building generalist embodied agents capable of solving complex real-world tasks remains a fundamental challenge in AI. Multimodal Large Language Models
arXiv:2605.13034v1 Announce Type: new Abstract: Recent deep research systems have improved the ability of large language models to produce long, grounded reports through iterative retrieval and reason
arXiv:2605.13172v1 Announce Type: cross Abstract: Recent advances in agent and multi-agent systems have shown strong performance on tool use, reasoning, and collaborative tasks. However, existing benc
arXiv:2605.11378v1 Announce Type: new Abstract: Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive