Search: “models”

arXiv:2609.05381v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on molecular property benchmarks, but accuracy cannot distinguish a model that predicts a proper

Thariq Shihipar (@trq212) is an engineer on Anthropic’s Claude Code team I asked him how Anthropic makes the most out of the models for engineering and how the industry will change soon. In this episo

arXiv:2609.03565v1 Announce Type: cross Abstract: Action-conditioned JEPA world models enable planning toward visually specified goals without reconstructing future pixels, yet latent prediction alone

arXiv:2609.02215v1 Announce Type: new Abstract: Safety alignment trains large language models to refuse harmful requests stated plainly, but that training is applied mostly to surface form. Requests t

arXiv:2607.10110v2 Announce Type: replace Abstract: Recent work on looped language models suggests that many reasoning problems benefit from greater computational depth rather than from additional ind

Congrats @ClementDelangue @Thom_Wolf!!!!!!!! Big day for sovereign ai. excited to see open weight models, open science, (and most importantly, micro ducks) find their forever home 🤗🦆 Exciting day for

arXiv:2506.00290v2 Announce Type: replace Abstract: This paper introduces DLM-One, a score-distillation-based framework for one-step sequence generation with continuous diffusion language models (DLMs

arXiv:2606.09435v2 Announce Type: replace Abstract: Multilingual dictionaries are among the most valuable documentary resources for low-resource and endangered languages, yet many remain available onl

arXiv:2609.02638v1 Announce Type: new Abstract: Knowledge graphs have become an important source of structured knowledge for Web applications, including search, question answering, and recommender sys

arXiv:2603.16749v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in applications with societal impact, raising concerns about the cultural biases they encode.

arXiv:2609.02438v1 Announce Type: new Abstract: Large language models can look capable of logical reasoning, but correct or incorrect answers alone tell us little about what the model represents inter

arXiv:2609.00005v1 Announce Type: new Abstract: Financial scams targeting older adults increasingly occur through text and voice channels such as email, SMS, and phone calls, unfolding over multiple c

arXiv:2603.03517v2 Announce Type: replace-cross Abstract: General-purpose large language models (LLMs) that rely on in-context learning do not reliably deliver the scientific understanding and perform

arXiv:2608.28725v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as graders, verifiers, and process auditors, but most mathematical evaluations still emphasize final-

arXiv:2608.30884v1 Announce Type: cross Abstract: While gender and racial biases in language models have been widely studied, anti-LGBTQ biases remain underexplored, particularly beyond English. Exist

arXiv:2608.30210v1 Announce Type: cross Abstract: AI image generators now create face portraits that are hard to tell from real photographs. Vision-language models (VLMs) are increasingly proposed to

arXiv:2608.30609v1 Announce Type: cross Abstract: Large language models are increasingly capable in general, but their utility can remain modest in niche or understudied areas. One approach to address

arXiv:2608.30396v1 Announce Type: new Abstract: Long-horizon physical-world agents must reason over distant goals while grounding decisions in reliable closed-loop behavior. Today's foundation models

arXiv:2608.29070v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning traces are increasingly proposed as a mechanism for AI oversight: a monitor inspecting a model's reasoning can, in prin

arXiv:2608.27512v1 Announce Type: cross Abstract: Post-training quantization is often treated as a semantically neutral optimization for edge deployment of Large Language Models. When a full-precision

arXiv:2608.26291v1 Announce Type: new Abstract: Mentalization - the ability to infer others' beliefs and intentions to guide one's own choices - is a key cognitive function underlying human social int

arXiv:2608.26375v1 Announce Type: cross Abstract: Standard machine-learning training minimizes a loss function over a dataset, but does not guarantee that the resulting model will satisfy predefined r

arXiv:2608.26317v1 Announce Type: cross Abstract: Frontier language models are increasingly marketed as omni systems that can perceive and respond across modalities. Existing evaluation frameworks, ho

Neat paper suggesting that human augmentation and task automation are not necessarily related. Models that are really good at doing work are not always good at helping humans do work better. Given the

arXiv:2608.23664v1 Announce Type: new Abstract: Reward fine-tuning is becoming an important tool for adapting diffusion models to human preferences and task-specific objectives, but existing methods l

arXiv:2511.08873v3 Announce Type: replace Abstract: Large language models (LLMs) are shifting from answer providers to intelligent tutors in educational settings, yet current supervised fine-tuning me

arXiv:2608.21664v1 Announce Type: new Abstract: Safe deployment of increasingly capable models will likely come to rely on latent-space monitoring as a complement to behavioral evaluations, especially

arXiv:2608.20364v1 Announce Type: cross Abstract: We examine how hadith computational science is being reshaped by transformer models, retrieval-grounded pipelines, and large language models (LLMs). R

arXiv:2608.20359v1 Announce Type: new Abstract: Large language models (LLMs) are deployed for increasingly complex tasks involving planning and multi-step decision making, but high-quality performance

arXiv:2608.20347v1 Announce Type: cross Abstract: Language models (LMs) often pass behavioral bias evaluations, but it remains unclear whether they no longer represent the underlying associations that

arXiv:2608.19680v1 Announce Type: new Abstract: Smart contract vulnerability detection with Large Language Models (LLMs) faces three causally linked challenges. First, new vulnerability categories dem

arXiv:2608.19981v1 Announce Type: new Abstract: We present HealMed, an expert-reviewed benchmark for multilingual evaluation of large language models in medicine. HealMed contains 1,000 examples in ea

arXiv:2608.18938v1 Announce Type: new Abstract: Vision Language Models (VLMs) have recently emerged as a critical component of multimodal AI systems, enabling joint reasoning over visual and textual i

arXiv:2608.19067v1 Announce Type: cross Abstract: The empirical success of diffusion models in generative modelling has motivated theoretical work, including quantitative error bounds and qualitative

arXiv:2608.18289v1 Announce Type: new Abstract: The extraction of structured information from unstructured documents represents a critical component of digital transformations in all sectors. While pr

arXiv:2608.18669v1 Announce Type: new Abstract: Humans solve complex problems by constructing plans and mentally simulating their outcomes with an internal model of the world. Machine learning has pro

arXiv:2604.00547v2 Announce Type: replace Abstract: Unified Multimodal Large Models (UMLMs) integrate understanding and generation capabilities within a single architecture. While unified architecture

arXiv:2608.17567v1 Announce Type: cross Abstract: Pretrained molecular language models are increasingly used as molecular encoders for learning structure-property relationships. However, their practic

arXiv:2608.17202v1 Announce Type: new Abstract: Safety alignment in open-weight language models is trivially removable: abliteration projects a refusal-mediating direction out of the weights in minute

Top 15 models in Agent Arena vs. median task cost: who's delivering the most for their price? Key findings: Even within the top 5, median cost per task ranges from 0.62 for Kimi K3 (Max) to 3.37 for C

arXiv:2510.08713v3 Announce Type: replace Abstract: Enabling embodied agents to imagine future states is essential for robust and generalizable visual navigation. Yet, state-of-the-art systems typical

arXiv:2608.15798v1 Announce Type: new Abstract: Language models are compared by their held-out per-token cross-entropy risk---the quantity scaling laws are fitted to. We show that it cannot be consist

arXiv:2608.14615v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well on established code-generation and mathematical-reasoning benchmarks, but their capabilities in mechanics and

arXiv:2601.03391v3 Announce Type: replace-cross Abstract: Image restoration has traditionally required training specialized models on thousands of paired examples per degradation type. Large pre-train

arXiv:2602.12498v2 Announce Type: replace Abstract: Negation is a fundamental linguistic operation in clinical reporting, yet vision-language models (VLMs) frequently fail to distinguish affirmative f

We currently have an ancient qwen 3.5 27b on the cloud probably and that sucks compared to later versions , why doesnt ollama update automatically . We have practically ancient models right now on the