Search: “models”

arXiv:2606.07552v1 Announce Type: cross Abstract: Large language models exhibit innate behavioral tendencies when deployed as strategic agents -- notably a risk-averse 'turtle' bias toward defensive p

arXiv:2510.16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces). Yet ML-as-a-Service reveals litt

arXiv:2606.08770v1 Announce Type: cross Abstract: The analysis of internet memes in the Nepali language is complicated by frequent code-mixing and a lack of established baseline resources. While memes

@tejalpatwardhan this directly inspired my work at @cognition frontiercode https://x.com/jai_torregrosa/status/2064100437549072593?s=20 props for including your own model on the chart. it's making a t

arXiv:2606.07898v1 Announce Type: new Abstract: High-resolution regional climate simulations provide critical information for climate impacts assessments but remain computationally expensive, motivati

arXiv:2606.08347v1 Announce Type: cross Abstract: Modern language models represent text using discrete token-level embeddings, which forces recurring multi-token patterns to be learned implicitly acro

The networking industry loves inflection points. Over the years, we have had many new compute models that require the network to evolve. For as long as I can remember, the holy war between InfiniBand

The best AI infrastructure shouldn't be reserved for the biggest companies. Together AI is partnering with @pax8 to bring powerful, cost-efficient AI and leading open-source models to small and mid-si

arXiv:2606.07916v1 Announce Type: new Abstract: The growing ability of generative models to produce realistic documents poses a direct challenge to evidentiary workflows in the justice system and the

arXiv:2606.08467v1 Announce Type: cross Abstract: While confidence calibration is essential for trustworthy decision-making in safety-critical applications, the robustness of calibrated GNNs to advers

arXiv:2606.08719v1 Announce Type: new Abstract: ''Thinking with Images'' has emerged as an effective paradigm for fine-grained visual reasoning: by explicitly zooming into relevant regions and reasoni

thought-to-image Can you believe the image on the right was generated from brain activity? We showed a person the dog image on the left for 0.5 seconds while recording brain signals. Our model, OB1, d

arXiv:2606.07697v1 Announce Type: cross Abstract: As atmospheric environmental prediction continues to improve, interpretable validation of pollution mechanisms and feedback processes has become a mai

arXiv:2606.08260v1 Announce Type: new Abstract: Recent advances in Diffusion Transformers have driven rapid progress in video generation and editing, yet these capabilities are still handled by separa

arXiv:2606.07520v1 Announce Type: cross Abstract: Instruction Following (IF) is a core capability of LLMs, requiring strict adherence to diverse constraints, ranging from verifiable ones (e.g., output

arXiv:2606.08573v1 Announce Type: new Abstract: Speech emotion recognition (SER) is commonly formulated as utterance-level classification, although conversational emotion depends on a speaker's usual

AI spending is outpacing every budgeting model enterprise finance teams have built, and the gap between what tokens cost on paper and what organizations actually owe is becoming an operational crisis.

arXiv:2606.08202v1 Announce Type: cross Abstract: Most statistical and machine learning methods for directed interactions focus on pairwise effects among variables. Even existing cyclic models represe

arXiv:2606.08437v1 Announce Type: cross Abstract: Palmprint modality offers a privacy-preserving biometric solution, yet its deployment is hindered by the domain gap between controlled enrollment and

arXiv:2606.06904v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have advanced rapidly across backbones, training recipes, and data scale, yet the action decoder, which converts t

arXiv:2606.06787v1 Announce Type: new Abstract: Large Language Models (LLMs) show promise as tool-using agents but remain limited in long-horizon tasks that require remembering, organizing, and reusin

In this post, we cover the structure of Amazon Quick ARNs and provide a practical mental model for working with them. By the end, you can look at an ARN and immediately understand what it means for yo

arXiv:2606.06879v1 Announce Type: new Abstract: Our prior work introduced COVA, a synthetically generated multi-turn conversational smishing dataset of 3,201 labeled conversations, establishing baseli

B9561 is an intermediate build release of llama.cpp, the C/C++ implementation of large language model inference. Llama.cpp releases use build identifiers (b-numbers) to track development versions betw

b9563 is a release build of llama.cpp, an open-source software library for large language model inference that is co-developed alongside the GGML tensor library. This intermediate build includes vario

arXiv:2606.06813v1 Announce Type: cross Abstract: Recent text-to-image models built on large-scale Transformer backbones and flow-based objectives deliver strong text-image alignment and high visual q

arXiv:2606.06840v1 Announce Type: cross Abstract: Modern reasoning models offer surprisingly strong zero-shot performance on challenging multi-label tasks that require selecting a small set of relevan

arXiv:2606.06842v1 Announce Type: new Abstract: Table reasoning remains challenging for large language models (LLMs), particularly in tasks that require multi-step inference over long and structured t

Does a token buy you more or less now than it did a few months ago? We built a consumer price index (CPI) for AI coding output from Anthropic's Opus 4.6 model in SWE-chat, Feb 5–Apr 15, 2026. What we

arXiv:2601.16622v2 Announce Type: replace-cross Abstract: Equivariant Graph Neural Networks (EGNNs) have become a widely used approach for modeling 3D atomistic systems. However, mainstream architectu

Epic research. Not far off from personal experience : https://tomtunguz.com/using-local-ai-to-work-faster/ Narrative violation: according to @Stanford research, local models can answer 71.3% of real-w

arXiv:2606.06976v1 Announce Type: new Abstract: Large language model (LLM)-based agents often make suboptimal tool-use decisions, including unsupported tool invocation and hallucinated direct response

arXiv:2606.07150v1 Announce Type: cross Abstract: Agent-interoperability protocols such as A2A and MCP standardize what agents say to one another, but assume address-based transport over HTTP(S). Such

FrontierCode has three task sets: Extended (150 tasks), Main (100 tasks) and Diamond (50 tasks). SOTA LLMs have significant room for improvement, with the top model earning a score of just 13.4/100 on

Gary Marcus argues that large language models based on transformers quickly reach their limitations, prompting developers to add external tools as workarounds, while neurosymbolic AI—which combines ne

arXiv:2606.06576v1 Announce Type: new Abstract: In the sciences, regression tasks often require predicting high-dimensional outputs from few training examples. Multi-output Gaussian processes excel in

arXiv:2606.07102v1 Announce Type: cross Abstract: We propose GP-Adapter, a training-free framework that augments CLIP (Contrastive Language-Image Pre-training) with Gaussian Process (GP) uncertainty m

This paper from researchers at Hazy Research (Jon Saad-Falcon and Avanika Narayan) appears to present novel research findings, likely related to machine learning, language models, or AI systems given

arXiv:2603.20967v2 Announce Type: replace-cross Abstract: One of the most common machine learning setups is logistic regression. In many classification models, including neural networks, the final pre

arXiv:2606.07381v1 Announce Type: cross Abstract: Background and Purpose: Automated detection of focal cortical dysplasia (FCD) requires large volumes of voxelwise lesion-delineated MRI data, which ar

Introducing FrontierCode: a coding eval that raises the bar for difficulty & quality. Each task took 40+ hrs of work by leading open-source maintainers. Models write sloppy code that works but isn’t m

arXiv:2601.18510v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents excel at general tasks, they inherently struggle with continual adaptation due to the frozen weights a

arXiv:2508.17821v3 Announce Type: replace-cross Abstract: This paper investigates the limitations of the normalization in attention mechanisms. We begin with a theoretical framework that enables the i

arXiv:2511.07380v2 Announce Type: replace Abstract: Adapting large language models (LLMs) to low-resource domains remains challenging due to the scarcity of domain-specific data. While in-domain data

arXiv:2603.24481v2 Announce Type: replace Abstract: Miscalibrated confidence scores are a practical obstacle to deploying AI in clinical settings. A model that is always overconfident offers no useful

arXiv:2606.07464v1 Announce Type: cross Abstract: Monolithic vision-action models represent an emerging paradigm in autonomous driving. However, this architecture produces token sequences that quickly