Search: “models”

arXiv:2509.24517v2 Announce Type: replace Abstract: Development of modern deep learning methods has been driven primarily by the push for improving model efficacy (accuracy metrics). This sole focus o

arXiv:2605.21856v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive reasoning abilities across a wide range of tasks, but data contamination undermines the object

arXiv:2512.14896v2 Announce Type: replace Abstract: In our study, we evaluated large language model (LLM) performance on pharmacy licensure-style question-answering tasks and developed an external kno

arXiv:2502.12120v3 Announce Type: replace-cross Abstract: Scaling laws guide the development of large language models (LLMs) by offering estimates for the optimal balance of model size, tokens, and co

arXiv:2605.21090v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) and diffusion-based generative models have substantially improved prompt-driven image editin

arXiv:2605.19645v1 Announce Type: new Abstract: Recent advancements in large language models (LLMs) have shown their remarkable capacities in many NLP tasks. However, their substantial size often pres

arXiv:2605.19568v1 Announce Type: new Abstract: Embedding models are pivotal in industrial information retrieval systems like search and advertising. However, existing pretrained models often exhibit

arXiv:2605.18956v1 Announce Type: new Abstract: Recent motion-language models unify tasks like comprehension and generation but operate at a coarse granularity, lacking fine-grained understanding and

arXiv:2605.16795v1 Announce Type: cross Abstract: Video generative models have made remarkable progress, yet they often yield visual artifacts that violate grounding in physical dynamics. Recent works

arXiv:2605.16882v1 Announce Type: new Abstract: Low-resource deployment constraints have made model quantization essential for deploying neural networks while preserving performance. Meanwhile, model

Google’s new Gemini 3.5 Flash is the clear leader on the Intelligence vs Speed Pareto frontier and makes large gains on GDPval-AA (real-world agentic tasks), but is 5x the cost of Gemini 3 Flash @Goog

arXiv:2605.17106v1 Announce Type: new Abstract: Production LLM deployments increasingly maintain heterogeneous model pools spanning order-of-magnitude cost differences. Existing routers make binary st

arXiv:2605.17949v1 Announce Type: new Abstract: Remote sensing vision-language models commonly rely on pretrained visual encoders to convert images into semantic features before language-model reasoni

arXiv:2601.12696v2 Announce Type: replace Abstract: Current guardian models are predominantly Western-centric and optimized for high-resource languages, leaving low-resource African languages vulnerab

arXiv:2605.15708v1 Announce Type: new Abstract: Recent advances in 3D datasets and multimodal models have greatly improved natural language 3D scene understanding. However, most 3D referring segmentat

arXiv:2602.14896v2 Announce Type: replace Abstract: Large-scale deep learning models are well-suited for compression. Across a variety of tasks, methods like pruning, quantization, and knowledge disti

arXiv:2605.14885v1 Announce Type: new Abstract: Scene Text Recognition requires modeling visual structures that evolve from coarse layouts to fine-grained character strokes. Training such models relie

arXiv:2605.06132v2 Announce Type: replace Abstract: In agent memory systems, the reranking model serves as the critical bridge connecting user queries with long-term memory. Most systems adopt the 're

arXiv:2605.14055v1 Announce Type: cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) is widely used for adapting Large Language Models (LLMs) for various tasks. Recently, there has been an increas

arXiv:2605.14152v1 Announce Type: cross Abstract: Safety evaluations for large language models (LLMs) increasingly target high-stakes National Security and Public Safety (NSPS) risks, yet multilingual

arXiv:2605.14457v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning has become a foundation for eliciting multi-step reasoning in large language models, but recent studies show that its b

arXiv:2605.13986v1 Announce Type: new Abstract: Tabular data underpins most high-value prediction problems in science and industry, and TabPFN has driven the foundation model revolution for this modal

A lot of people have been wondering about Mythos, Glasswing, and the vulns we / our partners are fixing. Today, I’m excited for us to start sharing more. (For context, I lead Glasswing @AnthropicAI.)

arXiv:2605.11857v1 Announce Type: new Abstract: Federated fine-tuning of large language models is commonly formulated as a parameter aggregation problem. However, even parameter-efficient methods requ

arXiv:2605.10528v1 Announce Type: cross Abstract: We investigate the emergent collective dynamics of LLM-based multi-agent systems on a 2D square lattice and present a model-agnostic statistical-physi

arXiv:2605.08665v1 Announce Type: new Abstract: Large reasoning models achieve high accuracy through extended chain-of-thought but generate 5--8 more tokens than necessary, applying verbose reasoning

arXiv:2605.09384v1 Announce Type: cross Abstract: The reasoning gap between large and compact vision-language models (VLMs) limits the deployment of medical AI on portable clinical devices. Compact VL

arXiv:2605.10777v1 Announce Type: new Abstract: The quality of open-weight language models has dramatically improved in recent years. Sharing weights greatly facilitates model adoption by enabling the

arXiv:2605.08904v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and tool use. However, the fundamental cognitive faculties essential

arXiv:2605.06241v2 Announce Type: replace Abstract: Reinforcement learning has become the standard for improving reasoning in large language models, yet evidence increasingly suggests that RL does not

arXiv:2605.08151v1 Announce Type: cross Abstract: LLM serving platforms are increasingly deployed as multi-model cloud systems, where user demand is often long-tailed: a few popular large models recei

arXiv:2605.08412v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have made rapid progress in single-video understanding, yet their ability to reason across multiple independent

arXiv:2605.09329v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by using a fast draft model to generate tokens and a more accurate target model to verify them. Its perfo

arXiv:2605.09195v1 Announce Type: new Abstract: Large language models confidently produce outdated answers, and no existing method can detect them. We show this is not an engineering failure but a str

arXiv:2605.06895v1 Announce Type: new Abstract: How can we make models robust to even imperfect human feedback? In reinforcement learning from human feedback (RLHF), human preferences over model outpu

arXiv:2605.07647v1 Announce Type: cross Abstract: Automated short answer scoring (ASAS) is shifting from discriminative, fine-tuned models to large language models (LLMs) used in few-shot settings. Th

arXiv:2605.07820v1 Announce Type: new Abstract: Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they u

The agricultural and crop protection supply chain is one of the most intricate networks in the world. It takes up to two years to turn active ingredients into the final products farmers need, and a si

arXiv:2602.00933v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) is rapidly becoming the standard interface for Large Language Models (LLMs) to discover and invoke external t

arXiv:2605.01630v1 Announce Type: new Abstract: Rankings produced by holistic LLM-as-a-judge scoring are sensitive to the bias of the chosen judge model. We show that switching to binary rubric scorin

arXiv:2605.00674v1 Announce Type: new Abstract: Large language models (LLMs) are becoming increasingly capable mathematical collaborators, but static benchmarks are no longer sufficient for evaluating

arXiv:2605.00300v1 Announce Type: cross Abstract: Public inference benchmarks compare AI systems at the model and provider level, but the unit at which deployment decisions are actually made is the en

arXiv:2604.27487v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA), which leverages the insight that model updates typically reside in a low-dimensional space, has significantly improved the t

arXiv:2604.26954v1 Announce Type: cross Abstract: Strategic model selection and reasoning settings are more effective than ensembling for optimizing automated scoring with large language models (LLMs)

arXiv:2604.26173v1 Announce Type: cross Abstract: An effective way to scale up test-time compute of large language models is to sample multiple responses and then select the best one, as in Grok Heavy

arXiv:2604.25926v1 Announce Type: new Abstract: The use of large language models (LLMs) for complex mathematical reasoning is an emergent area of research, with fast progress in methods, models, and b