Knowledge catalogue
arXiv:2603.06798v2 Announce Type: replace Abstract: The growing scale of deep learning demands distributed training frameworks that jointly reason about parallelism, memory, and network topology. Prio
arXiv:2605.25985v1 Announce Type: new Abstract: Complex Query Answering (CQA) is a fundamental knowledge representation and reasoning task over incomplete knowledge graphs (KGs). Answering existential
arXiv:2601.20539v3 Announce Type: replace Abstract: Large Language Models (LLMs) have enabled automated heuristic design (AHD) for combinatorial optimization problems (COPs), but existing frameworks'
arXiv:2605.24834v1 Announce Type: cross Abstract: Large language model (LLM) safety classifiers such as Llama Guard are effective at detecting overtly harmful prompts but remain vulnerable to adversar
arXiv:2407.05682v2 Announce Type: replace Abstract: In-context learning (ICL) has been instrumental in adapting Large Language Models (LLMs) to downstream tasks using correct input-output examples. Re
arXiv:2602.07666v3 Announce Type: replace-cross Abstract: DARPA's AI Cyber Challenge (AIxCC, 2023--2025) is the largest competition to date for building fully autonomous cyber reasoning systems (CRSs)
this logic from 2024 held up pretty well considering all that’s changed. 9 reasons that OpenAI could someday be seen as the WeWork of AI: 👉 Lots of competitors are catching up. 👉 OpenAI has been force
arXiv:2603.06687v2 Announce Type: replace-cross Abstract: Geo-temporal understanding, the ability to infer location, time, and contextual properties from visual input alone, underpins applications suc
arXiv:2605.25864v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable advancements in reasoning capabilities empowered by Reinforcement Learning with Verifiable Rewar
arXiv:2605.24806v1 Announce Type: cross Abstract: Large audio and language models have recently demonstrated zero-shot reasoning capabilities across various domains. However, it remains unclear how th
arXiv:2605.22964v1 Announce Type: new Abstract: As state-of-the-art neural networks are deployed on reasoning and algorithmic tasks, exactness guarantees become increasingly important. However, high a
arXiv:2605.23270v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems are fundamentally limited by a mismatch between temporal causal reasoning and global trajectory consiste
arXiv:2605.23344v1 Announce Type: cross Abstract: Large Vision-Language Models have shown strong multimodal reasoning capabilities, yet they remain susceptible to object hallucinations when language p
arXiv:2509.26383v5 Announce Type: replace-cross Abstract: Knowledge-graph retrieval-augmented generation (KG-RAG) couples large language models (LLMs) with structured, verifiable knowledge graphs (KGs
arXiv:2605.22826v1 Announce Type: cross Abstract: Quantifying the deceptive potential of Large Language Models (LLMs) is critical for AI safety, yet difficult to achieve in uncontrolled environments.
arXiv:2505.13893v2 Announce Type: replace Abstract: Recent advances in large language models (LLMs) have intensified efforts to fuse heterogeneous open-source models into a unified system that inherit
MiniCPM5-1B is an impressive release in the 1B class! @OpenBMB https://huggingface.co/collections/openbmb/minicpm5 ✨ 1B - Apache 2.0 ✨ Hybrid reasoning with Think / No-Think modes ✨ 128K context ✨ Run
arXiv:2605.05704v2 Announce Type: replace-cross Abstract: Recent advances in foundation models have transformed LLMs from passive conversational systems into autonomous agents capable of reasoning and
arXiv:2601.12805v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown growing promise in biomedical research, particularly for knowledge-driven interpretation tasks. Howeve
arXiv:2605.23091v1 Announce Type: cross Abstract: The majority of software developers use or are planning to use Artificial Intelligence (AI) tools in their development processes. Their top reasons in
arXiv:2605.22902v1 Announce Type: cross Abstract: Generative Vision-Language Models (VLMs) perform well on multimodal reasoning, but how visual inputs are transformed to text remains poorly understood
arXiv:2602.07399v2 Announce Type: replace Abstract: Vision--Language--Action (VLA) models bridge multimodal reasoning with physical control, but adapting them to new tasks with scarce demonstrations r
arXiv:2510.16590v2 Announce Type: replace Abstract: Applications of machine learning in chemistry are often limited by the scarcity and expense of labeled data, restricting traditional supervised meth
arXiv:2605.22703v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a central paradigm for scaling LLM reasoning, yet its optimization often suffers fr
arXiv:2605.22566v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents demonstrate strong reasoning and execution capabilities on complex tasks when guided by structured instructions,
arXiv:2602.18600v3 Announce Type: replace Abstract: Systematic evaluation of Multimodal Large Language Models (MLLMs) is crucial for advancing Artificial General Intelligence (AGI). However, existing
arXiv:2603.19310v3 Announce Type: replace Abstract: Reinforcement learning has emerged as a powerful paradigm for improving large language model (LLM) reasoning, where rollouts are sampled from the po
arXiv:2605.22156v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a promising paradigm for scaling reasoning capabilities of Large Language Models (LLMs)
World models have existed for years (though not in LLMs); I take them to be explicit representation of objects, places, events, mechanisms etc you can reason over. Chess computers have them (board, pi
arXiv:2605.21642v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly augmented with continuous or latent non-textual tokens intended to support 'visual thinking.' Despite imp
arXiv:2605.22819v1 Announce Type: new Abstract: Camera pose matters. The position and orientation of each viewpoint define a shared spatial coordinate frame that relates observations across video fram
Cohere Command A+ is now available in Microsoft Foundry as a Managed Compute offer. Cohere’s latest open-source model is built for enterprise-grade agentic AI workloads, bringing together reasoning, m
DeepSeek released two advanced AI models—R1 and V3—with R1 specializing in complex reasoning and V3 designed for large-scale language processing. Notably, DeepSeek developed these high-performing mode
arXiv:2605.22228v1 Announce Type: new Abstract: Aspect-based sentiment analysis (ABSA) requires models to bind sentiment evidence to the correct aspect, making it a natural testbed for fine-grained st
Introducing Qwen3.7-Max from @Alibaba_Qwen, Qwen’s flagship model for the agent era with 1M context and leading performance across agentic coding, reasoning, and long-horizon autonomy. AI natives can
arXiv:2605.12321v2 Announce Type: replace Abstract: Large language models (LLMs) show strong potential for Intelligent Transportation Systems (ITS), particularly in tasks requiring situational reasoni
arXiv:2601.04537v3 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven significant performance gains in reasoning-oriented large language models (LL
arXiv:2605.22269v1 Announce Type: new Abstract: Long streaming video QA remains challenging due to growing visual tokens and limited reasoning length of large language models (LLMs). KV-caching stores
arXiv:2605.21788v1 Announce Type: new Abstract: Zero-shot 3D visual grounding requires localizing objects in unstructured environments from free-form natural language. Recent vision-language model (VL
arXiv:2512.10719v2 Announce Type: replace Abstract: End-to-end autonomous driving methods built on vision language models (VLMs) have undergone rapid development driven by their universal visual under
arXiv:2605.21800v1 Announce Type: cross Abstract: World models are central to building agents that can reason, plan, and generalize beyond their training data. However, research on world models is cur
arXiv:2605.21801v1 Announce Type: cross Abstract: Post-training has become central to improving reasoning and alignment in large language models, where critic-free models enable scalable learning from
arXiv:2512.23292v3 Announce Type: replace-cross Abstract: The prevailing paradigm in AI for physical systems (scaling general-purpose foundation models toward universal multimodal reasoning) confronts
arXiv:2605.20722v1 Announce Type: new Abstract: Reinforcement learning improves LLM reasoning, but PPO/GRPO typically use fixed clipping and decoding temperature, which makes training brittle and tuni
arXiv:2605.21083v1 Announce Type: cross Abstract: Materials discovery and biomedical translation increasingly require models that can reason across composition, processing, structure, biological respo
arXiv:2602.19320v2 Announce Type: replace Abstract: Agentic memory systems enable large language model (LLM) agents to maintain state across long interactions, supporting long-horizon reasoning and pe
Release: datasette-agent 0.1a3 'View SQL query' buttons for both visible tables and collapsed SQL result tool calls. Don't display empty reasoning chunks Improved handling of truncated responses - tab
arXiv:2605.21467v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards (RLVR) has emerged as a central technique for improving the reasoning capabilities of large language mo
arXiv:2605.20256v1 Announce Type: new Abstract: Reinforcement learning has become a cornerstone for aligning and unlocking the reasoning capabilities of large-scale models. At its core, the training l
arXiv:2605.20815v1 Announce Type: new Abstract: Graph-based Retrieval Augmented Generation (GraphRAG) extends retrieval-augmented generation to support structured reasoning over complex corpora, but i
arXiv:2605.21266v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for reasoning in language models, with GRPO as its primary exam
Marc Andreessen (@pmarca) on what it’s like to work at SpaceX: “It’s like being dropped into a zone of shocking competence.” “Everybody is ultra competent, and the reason everybody’s ultra competent i
arXiv:2605.20863v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has recently unlocked strong reasoning capabilities in large language models (LLMs), triggering
arXiv:2605.20531v1 Announce Type: cross Abstract: Reliable verification of proofs remains a bottleneck for training and evaluating AI systems on hard mathematical reasoning. Fully formal proofs, in la
🚀Qwen3.7-Max just landed at 56.6 on the Artificial Analysis Intelligence Index — a solid 4.8pt jump over Qwen3.6-Max-Preview. @ArtificialAnlys ⚡️Sharper sci reasoning, stronger agentic chops, better c
arXiv:2508.09001v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed in long-context tasks such as reasoning, code generation, and multi-turn dialogue. However, i
arXiv:2605.20924v1 Announce Type: new Abstract: Designing effective task-level prompts is crucial for improving the performance of Large Language Models (LLMs). While prior work on instruction inducti
arXiv:2605.20676v1 Announce Type: new Abstract: Establishing a clear link between model predictions and the visual evidence that supports them is critical for transparency and reliability in multimoda
arXiv:2605.21468v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving reasoning in large language models (LLMs), yet the
arXiv:2605.18984v1 Announce Type: new Abstract: Recent video generative models have greatly improved the realism of AI-generated videos, yet their outputs still exhibit artifacts such as temporal inco