Knowledge catalogue
arXiv:2607.13220v1 Announce Type: new Abstract: Most AI-for-science systems focus on scaling a single reasoning process through better models, larger context windows, long-horizon agentic execution, o
arXiv:2607.13926v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated remarkable potential for high-level reasoning in autonomous driving, yet they fundamentally struggle to
A model drop by Thinky 🚨🚨 Have been doing some early testing on the model for the past couple of days. Here are some of my findings 1. The reasoning is sharp and concise! Always love to see models tha
arXiv:2603.25112v2 Announce Type: replace-cross Abstract: Standard evaluation of LLM confidence relies on calibration metrics (ECE, Brier score) that conflate how much a model knows (Type-1 accuracy)
arXiv:2607.12982v1 Announce Type: new Abstract: Math reasoning has achieved significant progress with the rapid advancement of Multimodal Large Language Models (MLLMs), however analytic geometry remai
Deploying the Gemini Enterprise app across an organization marks a transformative leap forward in workforce productivity, providing employees with an amazing, high-performance suite of agentic AI tool
arXiv:2607.11921v1 Announce Type: cross Abstract: A key question in operator learning is how to design surrogate operators with provable approximation guarantees in reasonable computational time. Wher
arXiv:2603.16307v2 Announce Type: replace Abstract: Remote sensing underpins crucial applications such as disaster relief and ecological field surveys, where systems must understand complex scenes and
arXiv:2506.12339v2 Announce Type: replace-cross Abstract: We present SheetMind, a modular multi-agent framework powered by large language models (LLMs) for spreadsheet automation via natural language
arXiv:2607.12447v1 Announce Type: cross Abstract: Reliable confidence -- the probability that a model's own answer is correct -- is essential for the trustworthy deployment of language models. Existin
arXiv:2607.12520v1 Announce Type: new Abstract: What a frontier model recalls about a person or tool from its own weights -- before any retrieval step -- often shapes the first description a human see
arXiv:2607.10744v2 Announce Type: replace Abstract: Benefiting from the powerful priors embedded in large-scale pre-training data and the emerging commonsense reasoning ability, large language models
arXiv:2512.15748v2 Announce Type: replace-cross Abstract: Visual Species Recognition (VSR) is a fundamental task in scientific disciplines that require species-level identification, including ecology,
Running frontier AI in production is demanding — accelerators to manage, latency to hold steady across continents, regulated data to keep in-region, and long-context requests to serve reliably. Claude
NVIDIA Cosmos 3 was post‑trained in under a day using TAO agent skills and LoRA adapters, raising accuracy on the Woven Traffic Safety video QA dataset from 54.41 % to 93.35 %. The mixture‑of‑transfor
arXiv:2602.17686v4 Announce Type: replace-cross Abstract: Distilling Chain-of-Thought (CoT) reasoning from large language models into compact student models presents a fundamental challenge: teacher r
Editor’s note: Some of the most interesting questions in AI are being asked by information theoreticians, around how to provide context to an emerging class of AI agents. A few weeks ago, we waded int
arXiv:2607.08448v1 Announce Type: new Abstract: Language-conditioned manipulation requires both precise contact-rich control and robust reasoning over language, scenes, and long horizons. End-to-end V
arXiv:2607.08066v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is a promising safety mechanism for AI agents, based on the premise that visible reasoning traces can surface misalign
arXiv:2607.08285v1 Announce Type: new Abstract: Current AI evaluation frameworks focus primarily on technical performance, including accuracy, robustness, reasoning ability, and policy compliance. The
arXiv:2607.07976v1 Announce Type: cross Abstract: Reinforcement learning (RL) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, widely u
arXiv:2607.06990v1 Announce Type: new Abstract: Multi-robot systems provide the parallelism and redundancy necessary for long-horizon tasks, while Large Language Models (LLMs) offer the reasoning capa
arXiv:2607.07103v1 Announce Type: new Abstract: Safe autonomous driving requires both rapid responses to common high-risk events and deeper reasoning over rare, extreme long-tail scenarios in traffic
arXiv:2607.07608v1 Announce Type: cross Abstract: Mainstream Vision-Language-Action (VLA) models predict actions primarily from the current observation under a Markovian assumption, thus struggling wi
Hermes Agent is a project from Nous Research focused on developing agentic AI systems capable of autonomous reasoning and task execution. The initiative likely explores methods for creating AI agents
Multimodal large language models (MLLMs) have recently shown strong performance in visual understanding, yet they often lack temporal awareness, particularly in egocentric settings where reasoning dep
Lilian Weng provides a comprehensive summary of 35 research papers focusing on Harness Engineering approaches for Reasoning and System Integration (RSI), covering recent developments in this specializ
arXiv:2607.06176v1 Announce Type: new Abstract: Scene graph generation (SGG) approaches can be broadly classified into detector-based and query-based methods according to their underlying reasoning me
arXiv:2607.05734v1 Announce Type: cross Abstract: Chain-of-thought (CoT) distillation in the recommendation domain is a necessary precursor to RL training, but raw teacher traces are ill-suited to thi
arXiv:2607.06124v1 Announce Type: cross Abstract: The increasing energy demand of software systems is raising concerns about their environmental impact and associated costs. Reasoning on energy usage
If you’re an IT leader, you might be getting a lot of questions about how to build and deploy agents. The pressure to move fast is intense, but the engineering reality is incredibly complex. Where do
arXiv:2607.03126v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has substantially improved the reasoning ability of large language models (LLMs), but sparse outcome rewards still make to
arXiv:2607.03182v1 Announce Type: cross Abstract: Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable con
arXiv:2607.04854v1 Announce Type: new Abstract: Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task cons
arXiv:2607.04179v1 Announce Type: cross Abstract: End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving. However, standard Supervised Fine-Tuning (SFT) often suffers fr
imo this is the most impt part of anthropic's J-space paper today. it's a two-parter: 1) ant proved that they can do 'brain surgery' interventions into reasoning to change topics midstream* 2) THE MOD
arXiv:2607.02680v1 Announce Type: cross Abstract: MLLMs have shown strong zero-shot capabilities across diverse inputs such as across images, video, audio, and text. A crucial, yet underexplored, appl
arXiv:2607.02549v1 Announce Type: new Abstract: Many manipulation tasks require reasoning about free-space affordances: discovering volumes where an extended rigid tool can safely navigate, complement
arXiv:2607.04637v1 Announce Type: new Abstract: Vision-Language-Action Models (VLAs), which leverage the advanced reasoning capabilities of Vision-Language Models (VLMs), show promising generalization
arXiv:2607.03502v1 Announce Type: cross Abstract: Frontier LLMs can perform multi-step reasoning over content-free filler tokens like dots or counting sequences, producing correct answers with no visi
arXiv:2507.16331v4 Announce Type: replace Abstract: Existing informal language-based (e.g., human language) Large Language Models (LLMs) trained with Reinforcement Learning (RL) face a significant cha
arXiv:2607.05184v1 Announce Type: new Abstract: Self-distillation is a promising recipe for self-improvement in language models. In this setting, a model can serve as its own teacher when given privil
arXiv:2607.02689v1 Announce Type: cross Abstract: As wearable devices enable continuous first-person recording, AI assistants must reason across long time horizons to recall past experiences-a capabil
arXiv:2603.23483v2 Announce Type: replace-cross Abstract: Agentic multimodal large language models (MLLMs) (e.g., OpenAI o3 and Gemini Agentic Vision) achieve remarkable reasoning capabilities through
arXiv:2607.05339v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) is effective when the current policy already samples useful reasoning trajectories, but it stalls on hard pr
arXiv:2607.05196v1 Announce Type: cross Abstract: Audio intelligence involves understanding, reasoning about, and generating both audio and speech. In this work, we introduce Nemotron-Labs-Audex-30B-A
arXiv:2607.04222v1 Announce Type: new Abstract: Interpretability methods aim to reveal the features represented inside large language models (LLMs). Many existing methods begin with labeled examples o
McGovern neuroscientists have discovered that you don't need language to think logically. People with severe language impairments solve complex reasoning puzzles perfectly and brain scans show languag
arXiv:2607.01838v1 Announce Type: new Abstract: Counterfactual explanations (CEs) for multivariate time-series classifiers are often difficult to interpret in domains where experts reason in terms of
arXiv:2607.02182v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit remarkable reasoning capabilities, but their task-specific fine-tuning is notoriously plagued by overconfidence,
arXiv:2607.02502v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) has emerged as a practical method for training large language models (LLMs) to reason, where a single model acts as
arXiv:2607.01661v1 Announce Type: new Abstract: Multi-agent systems are increasingly used for forecasting future events, as deliberation among multiple LLMs is believed to improve reasoning and calibr
arXiv:2607.02396v1 Announce Type: new Abstract: Steering and monitoring activations in Large Language Models (LLMs) are increasingly used for both safety and interpretability. Early work assumed behav
arXiv:2607.01247v1 Announce Type: cross Abstract: Open-ended mathematics exams are valuable because they assess reasoning, proof construction, algorithmic thinking, and communication of intermediate s
arXiv:2607.02490v1 Announce Type: new Abstract: Large vision-language models can reason over multimodal inputs by generating textual chains of thought (CoT). A key capability exhibited in CoT reasonin
arXiv:2607.00972v1 Announce Type: new Abstract: Trustworthy deployment of Agentic Retrieval-Augmented Generation (RAG) systems requires mechanisms for estimating when multi-stage reasoning pipelines m
Francois Chollet discusses how current workflows are increasingly incorporating Large Reasoning Models (LRMs) as control systems that direct symbolic programs and tools, representing a pragmatic but i
arXiv:2607.00776v1 Announce Type: new Abstract: Safe motion planning in dynamic environments requires reasoning about the uncertainty in predicted obstacle motion without sacrificing real-time perform
arXiv:2607.00457v1 Announce Type: new Abstract: Embodied agents operating in the real world require multi-scale reasoning and knowledge adaptation as conditions change. We identify two challenges in a
Reinforcement learning (RL) finetuning has become a key technique for enhancing large language models (LLMs) on reasoning-intensive tasks, motivating its extension to vision language models (VLMs). Wh