Search: “models”

Beijing, China-based Meituan Inc. today debuted its next-generation LongCat-2.0 open-source large language model, stating that the company trained the 1.6-trillion-parameter model on domestic Chinese

arXiv:2606.28670v1 Announce Type: cross Abstract: We introduce MACROCAST, a lightweight Time Series Foundation Model (TSFM) for real-time macroeconomic forecasting. Existing TSFMs suffer from data lea

Ornith is a model available through the Ollama library, a platform for running large language models locally. The specific capabilities and parameters of this model can be found on its dedicated model

arXiv:2606.26429v1 Announce Type: cross Abstract: Current LLM evaluation relies on two complementary but often disconnected signals: static benchmarks with objective correctness labels and arena-style

arXiv:2606.26902v1 Announce Type: new Abstract: Multi-task model merging aims to consolidate several task-specific experts into a unified model, yet static merging consistently suffers from parameter

arXiv:2606.27247v1 Announce Type: new Abstract: In NLP, mental health conditions are often modeled as isolated phenomena, without interpersonal context. We use Reddit posts about long-distance relatio

arXiv:2606.26389v1 Announce Type: cross Abstract: Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain com

arXiv:2605.25661v2 Announce Type: replace Abstract: Current mainstream methods of aligning diffusion models with human preferences typically employ VLM-based reward models. However, these reward model

arXiv:2606.26071v1 Announce Type: new Abstract: A central goal of safety research is determining whether a model is misaligned. Prior work has largely focused on detecting concerning behavior. But beh

arXiv:2503.12902v4 Announce Type: replace Abstract: Model trees provide an appealing way to perform interpretable machine learning for both classification and regression problems. In contrast to ``cla

arXiv:2606.24046v1 Announce Type: cross Abstract: This work presents a machine learning framework that leverages an autoencoder (AE) for the efficient modeling of FinFET. We first calibrated a BSIM-CM

I ran GLM 5.2 with OpenCode harness against Claude Opus this week deployed locally. Bottom line: It is a real frontier coding model and insanely good for the price (free). Open source model + open sou

Deepagents code is sick cuz you can just use the best model as it comes out it is indeed quite good! don't try it in claude code/codex - those harnesses are overly tuned for their proprietary models d

arXiv:2606.11187v1 Announce Type: new Abstract: Autoregressive video generation has emerged as a powerful paradigm for World Action Models (WAMs). However, existing approaches suffer from slow trainin

arXiv:2603.22473v2 Announce Type: replace-cross Abstract: Hybrid language models combine softmax attention with linear-time sequence mechanisms such as state-space or linear-attention layers, but the

I'm really not interested in testing a new closed-weight model in the expensive tier. What's the point? I know what will happen: 1. They present the model. 2. The model beats the competition on benchm

arXiv:2606.08578v1 Announce Type: new Abstract: Recently, large time series models (LTSMs) have gained increasing attention due to their similarities to large language models, including flexible conte

arXiv:2606.07289v1 Announce Type: cross Abstract: Model merging combines several independently fine-tuned experts into a single multi-task model without any training data, reducing the storage, servin

arXiv:2602.16864v2 Announce Type: replace-cross Abstract: Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in TS foundation models.

arXiv:2606.03827v1 Announce Type: cross Abstract: In-silico trials of medical devices require the generation of virtual populations of anatomies. In cardiovascular applications, virtual anatomy is typ

arXiv:2606.02958v1 Announce Type: cross Abstract: Cross-organization language-model adaptation increasingly faces hard governance constraints: in many deployments, device-level model state-parameters,

arXiv:2606.00047v1 Announce Type: cross Abstract: Frontier AI governance often centres on the model-level governance paradigm, which assumes that a model's capability profile is primarily a function o

arXiv:2606.00005v1 Announce Type: new Abstract: We present the Consilium Protocol, a Byzantine Fault Tolerance-derived architecture for structured multi-model AI deliberation that treats inter-model d

arXiv:2606.02198v1 Announce Type: new Abstract: Prediction tasks over individual futures, which are inherently noisy, often admit multiple similarly accurate models. When these models produce differen

arXiv:2602.07083v2 Announce Type: replace-cross Abstract: Structural modeling is a fundamental component of computational engineering science, in which even minor physical inconsistencies or specifica

arXiv:2606.01689v1 Announce Type: cross Abstract: The detection and segmentation of infrared small targets have important application significance in the fields of surveillance and security, maritime

arXiv:2605.30857v1 Announce Type: new Abstract: Instruction fine-tuning is employed to enhance the instruction-following ability of large language models (LLMs). As the amount of instruction fine-tuni

arXiv:2605.31408v1 Announce Type: cross Abstract: Skill documents provide procedural knowledge to large-language-model agents at inference time. This article studies whether the presentation granulari

arXiv:2605.30898v1 Announce Type: new Abstract: In real-world deployments of large language models (LLMs), balancing inference quality and computational cost has become a central challenge. Existing a

arXiv:2605.30472v1 Announce Type: new Abstract: As large neural models have become better at language tasks, researchers are increasingly building multi- and omnimodal models that handle more modaliti

arXiv:2605.29009v1 Announce Type: cross Abstract: Post-training large language models with reinforcement learning is bottlenecked by the reward signal. Existing approaches require either ground-truth

arXiv:2605.29556v1 Announce Type: new Abstract: Building mathematical optimization models is critical in operations research (OR), while it requires substantial human expertise. Recent advancements ha

arXiv:2603.05488v4 Announce Type: replace-cross Abstract: We provide evidence of performative chain-of-thought (CoT) in reasoning models, where a model becomes strongly confident in its final answer,

arXiv:2605.27406v1 Announce Type: new Abstract: Structured state space models (SSMs) have recently emerged as a promising foundation for sequence modeling, with Mamba-based architectures demonstrating

arXiv:2605.27354v1 Announce Type: cross Abstract: Model internals encode rich information about how a large language model (LLM) processes its training data; however, post-training data engineering la

arXiv:2605.26969v1 Announce Type: cross Abstract: User modeling aims to use language models (LMs) to mimic an individual's behavior from a corpus of past context-action pairs (e.g., conversation turns

arXiv:2605.26132v1 Announce Type: new Abstract: Can post-trained large language models (LLMs) further improve themselves using only unlabeled prompts, without external teachers or feedback from tools?

arXiv:2605.27367v1 Announce Type: new Abstract: While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round pla

arXiv:2603.16105v3 Announce Type: replace-cross Abstract: Post-training model compression is essential for enhancing the portability of Large Language Models (LLMs) while preserving their performance.

An open source model has returned to #1 on the 3D Design leaderboard by Design Arena. Kimi K2.6 has reached the top of the leaderboard for 3D Design, ahead of models 10X more expensive like Opus 4.7 b

arXiv:2605.20127v1 Announce Type: cross Abstract: Artificial vision models are often evaluated against the human visual cortex by measuring how accurately their internal representations predict brain

arXiv:2605.19373v1 Announce Type: cross Abstract: All 26 neural network merge strategies we tested including weight averaging, SLERP, TIES, DARE, Fisher merging, and evolutionary approaches -- fail th

arXiv:2605.18904v1 Announce Type: cross Abstract: Model merging enables the reuse of fine-tuned models without joint training or access to original data. Dynamic merging further improves flexibility b

arXiv:2508.08501v3 Announce Type: replace Abstract: We introduce GVGAI-LLM, a video game benchmark for evaluating the reasoning and problem-solving capabilities of large language models (LLMs). Built

arXiv:2410.13846v3 Announce Type: replace-cross Abstract: Scaling language models to handle longer contexts introduces substantial memory challenges due to the growing cost of key-value (KV) caches. M

arXiv:2605.16317v1 Announce Type: new Abstract: Accurate, unified models for event cameras (ECs) remain elusive, hampering calibration and algorithm design. We develop a foundational probabilistic mod