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Kimi 2.6 Thinking seems very good for an open weights model, but many rough edges compared to closed SoTA. The Lem Test resulted in a 74 page thinking trace... and an okay-ish answer. It did an okay T

Kimi is the current open-source SOTA on Artificial Analysis Moonshot’s Kimi K2.6 is the new leading open weights model. Kimi K2.6 lands at #4 on the Artificial Analysis Intelligence Index (54) behind

📢 Kimi K2.6 API is live • Input Price (Cache Hit): 0.16 / M tokens • Input Price (Cache Miss): 0.95 / M tokens • Output: $4.00 / M tokens Kimi K2.6 is our latest + most intelligent model - stronger lo

Kimi K2.6 autonomously overhauled exchange-core, an 8-year-old open-source financial matching engine. Over a 13-hour execution, the model iterated through 12 optimization strategies, initiating over 1

Kimi K2.6 demonstrates strong long-horizon coding in complex engineering tasks: Kimi K2.6 successfully downloaded and deployed the Qwen3.5-0.8B model locally on a Mac. By implementing and optimizing m

Kimi K2.6, a language model developed by Moonshot AI, has achieved the top ranking on the open-weight category of the Vals Index benchmark, while placing 7th overall across all model categories. The V

Cognition AI has made Kimi K2.6, an advanced language model, available within their Windsurf IDE for free during a two-week promotional period. This access is offered to Pro, Teams, and Max subscripti

Kimi K2.6, an AI model from Moonshot, has been integrated into the Anything platform. This update likely enables users to access Kimi's capabilities directly within the Anything application interface.

Kimi K2.6 @Kimi_Moonshot is the new leading open-weights agent model, landing at #4 on Claw-Eval (Pass^3: 62.3%). Key takeaways: - 👑 Best open-source agent, period: Pass^3 of 62.3% is the highest of a

This appears to be a social media post from Ollama's X account regarding a model run command for 'kimi-k2.6:cloud,' likely announcing or demonstrating how to execute this specific AI model variant usi

arXiv:2604.16915v1 Announce Type: new Abstract: Retrieval augmented generation (RAG) has transformed text based question answering, yet its extension to visual domains remains hindered by fundamental

arXiv:2604.18419v1 Announce Type: cross Abstract: Large language models (LLMs) using chain-of-thought reasoning often waste substantial compute by producing long, incorrect responses. Abstention can m

arXiv:2603.00883v2 Announce Type: replace Abstract: LLMs increasingly excel on AI benchmarks, but doing so does not guarantee validity for downstream tasks. This study contrasts LLM alignment on bench

arXiv:2604.16949v1 Announce Type: new Abstract: The paper considers the computation of L1 regularization paths in a state space setting, which includes L1 regularized Kalman smoothing, linear SVM, LAS

arXiv:2604.16910v1 Announce Type: new Abstract: Low-altitude Gaussian splatting (LAGS) facilitates 3D scene reconstruction by aggregating aerial images from distributed drones. However, as LAGS priori

LangChain received Google Cloud's 2026 Partner of the Year Award in the Marketplace: Agent Platform category, recognizing its contributions to the cloud ecosystem. The award highlights LangChain's pro

arXiv:2603.16120v2 Announce Type: replace Abstract: Deep Research (DR) systems help researchers cope with ballooning publishing counts. Such tools synthesize scientific papers to answer research queri

arXiv:2601.05414v2 Announce Type: replace Abstract: As large language models (LLMs) transition from chat interfaces to integral components of stochastic pipelines and systems approaching general intel

arXiv:2505.16522v3 Announce Type: replace Abstract: With the evolution of large language models (LLMs), their robustness against individual simple biases has been enhanced. However, we observe that th

arXiv:2604.17224v1 Announce Type: new Abstract: Recursive architectures such as Tiny Recursive Models (TRMs) perform implicit reasoning through iterative latent computation, yet the geometric structur

Last Wednesday, our founder and scientific advisor, @Yoshua_Bengio, was officially appointed an Officer of the Order of the British Empire (OBE). This prestigious distinction recognizes his contributi

arXiv:2604.16721v1 Announce Type: new Abstract: Developing neural operators that accurately predict the behavior of systems governed by partial differential equations (PDEs) across unseen parameter re

arXiv:2604.17866v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a standard approach for enhancing large language models (LLMs) with external knowledge, mitigating hallu

arXiv:2604.16479v1 Announce Type: new Abstract: Video variational autoencoders (VAEs) used in latent diffusion models typically require a sufficiently large number of latent channels to ensure high-qu

arXiv:2604.18567v1 Announce Type: cross Abstract: Large language models frequently commit unrecoverable reasoning errors mid-generation: once a wrong step is taken, subsequent tokens compound the mist

arXiv:2604.17886v1 Announce Type: new Abstract: Users often omit essential details in their requests to LLM-based agents, resulting in under-specified inputs for tool use. This poses a fundamental cha

arXiv:2604.16440v1 Announce Type: new Abstract: Developing natural and diverse locomotion controllers for quadruped robots that can adapt to complex terrains while preserving motion style remains a si

arXiv:2604.16492v1 Announce Type: new Abstract: Flow Matching models achieve state-of-the-art image generation quality but incur substantial inference cost due to iterative denoising through large Tra

arXiv:2604.18358v1 Announce Type: new Abstract: In face recognition systems, facial templates are widely adopted for identity authentication due to their compliance with the data minimization principl

arXiv:2509.12539v2 Announce Type: replace-cross Abstract: We present LEAF ('Lightweight Embedding Alignment Framework'), a knowledge distillation framework for text embedding models. A key distinguish

arXiv:2604.17453v1 Announce Type: cross Abstract: Being one of the oldest and most basic problems in image processing, image denoising has seen a resurgence spurred by rapid advances in deep learning.

arXiv:2604.16509v1 Announce Type: cross Abstract: Many robotic exploration algorithms rely on graph structures for frontier-based exploration and dynamic path planning. However, these graphs grow rapi

arXiv:2604.18381v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) typically relies on large quantities of high-quality annotated data, or questions with well-defined ground tr

arXiv:2604.18460v1 Announce Type: new Abstract: Multimodal affective computing aims to predict humans' sentiment, emotion, intention, and opinion using language, acoustic, and visual modalities. Howev

arXiv:2509.01038v2 Announce Type: replace-cross Abstract: Many methods have been developed to predict static protein structures, however understanding the dynamics of protein structure is essential fo

arXiv:2604.05002v2 Announce Type: replace Abstract: Learning from weak, proxy, or relative supervision is common when ground-truth labels are unavailable, but robustness under distribution shift remai

arXiv:2604.18507v1 Announce Type: cross Abstract: We propose a computational framework for replacing the repeated numerical solution of differential Riccati equations in finite-horizon Linear Quadrati

arXiv:2604.17197v1 Announce Type: new Abstract: Recent advances in summarization research focus on improving summary quality across multiple criteria, such as completeness, conciseness, and faithfulne

arXiv:2604.17912v1 Announce Type: new Abstract: State-of-the-art reasoning models utilize long chain-of-thought (CoT) to solve increasingly complex problems using more test-time computation. In this w

arXiv:2601.05654v3 Announce Type: replace Abstract: Estimating the persuasiveness of messages is critical in various applications, from recommender systems to safety assessment of LLMs. While it is im

arXiv:2604.17827v1 Announce Type: new Abstract: Large language models (LLMs) offer strong capabilities but raise cost and privacy concerns, whereas small language models (SLMs) facilitate efficient an

arXiv:2604.16862v1 Announce Type: new Abstract: Recent deployments of large language models (LLMs) as autonomous trading agents raise questions about whether financial decision-making competence gener

arXiv:2604.17505v1 Announce Type: cross Abstract: Many high-stakes AI deployments proceed only if every stakeholder deems the system acceptable relative to their own minimum standard. With randomizati

arXiv:2604.17335v1 Announce Type: new Abstract: Whole-body humanoid locomotion is challenging due to high-dimensional control, morphological instability, and the need for real-time adaptation to vario

全网都在吹的LeCun新论文，90%的解读都是错的。 他们说生成式AI是死路，说过去三年花的几百亿全白费了，说15M参数的小模型就能吊打万亿大模型。 这些全是营销号的夸张， 我觉得这篇论文的真正分量比他们吹的还要重。 Yann LeCun团队这次解决了JEPA困扰了好几年的表征坍缩问题。 以前的世界模型，学着学着就会把狗车人都压成一模一样的向量，什么都学不到。 这次他们只加了一个极其优雅的数学正则

arXiv:2604.17892v1 Announce Type: new Abstract: Recently, latent reasoning has been introduced into large language models (LLMs) to leverage rich information within a continuous space. However, withou