Search: “models”

Mechanistic Interpretability of Antibody Language Models Using SAEs

arXiv:2512.05794v2 Announce Type: replace-cross Abstract: Sparse autoencoders (SAEs) are a mechanistic interpretability technique that have been used to provide insight into learned concepts within la

27 Apr 2026

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The Bitter Lesson of Diffusion Language Models for Agentic Workflows: A Comprehensive Reality Check

DGX agent

arXiv:2601.12979v3 Announce Type: replace Abstract: The pursuit of real-time agentic interaction has driven interest in Diffusion-based Large Language Models (dLLMs) as alternatives to auto-regressive

agentsarxiv-cs-cl

27 Apr 2026

Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models

arXiv:2604.20847v1 Announce Type: cross Abstract: Music Recommendation Systems (MRSs) are a cornerstone of modern streaming platforms. Existing recommendation models, spanning both recall and ranking

24 Apr 2026

Composition-RL: Compose Your Verifiable Prompts for Reinforcement Learning of Large Language Models

arXiv:2602.12036v2 Announce Type: replace Abstract: Large-scale verifiable prompts underpin the success of Reinforcement Learning with Verifiable Rewards (RLVR), but they contain many uninformative ex

23 Apr 2026

SSL-R1: Self-Supervised Visual Reinforcement Post-Training for Multimodal Large Language Models

arXiv:2604.20705v1 Announce Type: new Abstract: Reinforcement learning (RL) with verifiable rewards (RLVR) has demonstrated the great potential of enhancing the reasoning abilities in multimodal large

researcharxiv-cs-cv

23 Apr 2026

Attend what matters: Leveraging vision foundational models for breast cancer classification using mammograms

arXiv:2604.19350v1 Announce Type: new Abstract: Vision Transformers (exttt{ViT}) have become the architecture of choice for many computer vision tasks, yet their performance in computer-aided diagnost

local-aiarxiv-cs-cv

EssayCBM: Rubric-Aligned Concept Bottleneck Models for Transparent Essay Grading

arXiv:2512.20817v2 Announce Type: replace Abstract: Automated essay scoring (AES) has advanced significantly with neural language models, yet most systems remain opaque, offering little visibility int

Experiments or Outcomes? Probing Scientific Feasibility in Large Language Models

arXiv:2604.18786v1 Announce Type: cross Abstract: Scientific feasibility assessment asks whether a claim is consistent with established knowledge and whether experimental evidence could support or ref

Fine-Tuning Code Language Models to Detect Cross-Language Bugs

arXiv:2507.21954v2 Announce Type: replace-cross Abstract: Multilingual programming, which involves using multiple programming languages (PLs) in a single project, is increasingly common due to its ben

Large Language Models Exhibit Normative Conformity

arXiv:2604.19301v1 Announce Type: new Abstract: The conformity bias exhibited by large language models (LLMs) can pose a significant challenge to decision-making in LLM-based multi-agent systems (LLM-

safetyarxiv-cs-ai

An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisal

arXiv:2604.18293v1 Announce Type: new Abstract: Surprisal theory hypothesizes that the difficulty of human sentence processing increases linearly with surprisal, the negative log-probability of a word

AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models

arXiv:2604.17787v1 Announce Type: new Abstract: Precision-critical manipulation requires both global trajectory organization and local execution correction, yet most vision-language-action (VLA) polic

applicationsarxiv-cs-ro

How Creative Are Large Language Models in Generating Molecules?

arXiv:2604.18031v1 Announce Type: new Abstract: Molecule generation requires satisfying multiple chemical and biological constraints while searching a large and structured chemical space. This makes i

local-aiarxiv-cs-cl

Revisiting Entropy in Reinforcement Learning for Large Reasoning Models

arXiv:2511.05993v3 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a prominent paradigm for enhancing the reasoning capabilities of large language

local-aiarxiv-cs-cl

Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation

arXiv:2510.09671v2 Announce Type: replace Abstract: Table Question Answering (TQA) aims to answer natural language questions about tabular data, often accompanied by additional contexts such as text p

ToMMeR -- Efficient Entity Mention Detection from Large Language Models

arXiv:2510.19410v2 Announce Type: replace Abstract: Identifying which text spans refer to entities - mention detection - is both foundational for information extraction and a known performance bottlen

UniGeo: Unifying Geometric Guidance for Camera-Controllable Image Editing via Video Models

arXiv:2604.17565v1 Announce Type: new Abstract: Camera-controllable image editing aims to synthesize novel views of a given scene under varying camera poses while strictly preserving cross-view geomet

researcharxiv-cs-cv

Improving the Throughput of Diffusion-based Large Language Models via a Training-Free Confidence-Aware Calibration

arXiv:2512.07173v4 Announce Type: replace Abstract: We present CadLLM, a training-free method to accelerate the inference throughput of diffusion-based LLMs (dLLMs). We first investigate the dynamic n

20 Apr 2026

Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval

arXiv:2604.15951v1 Announce Type: new Abstract: Generative AI, particularly Large Language Models, increasingly integrates graph-based representations to enhance reasoning, retrieval, and structured d

agentsarxiv-cs-ai

20 Apr 2026

StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models

arXiv:2604.15416v1 Announce Type: cross Abstract: Sign-based optimization algorithms, such as SignSGD, have garnered significant attention for their remarkable performance in distributed learning and

20 Apr 2026

An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review

arXiv:2604.14179v1 Announce Type: new Abstract: Rare diseases affect over 300 million people worldwide and are characterized by complex care pathways, limited clinical expertise, and substantial unmet

applicationsarxiv-cs-cl

Cosine-Similarity Routing with Semantic Anchors for Interpretable Mixture-of-Experts Language Models

arXiv:2509.14255v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models improve efficiency through sparse activation, but their learned gating functions provide limited insight into routin

CROP: Token-Efficient Reasoning in Large Language Models via Regularized Prompt Optimization

arXiv:2604.14214v1 Announce Type: new Abstract: Large Language Models utilizing reasoning techniques improve task performance but incur significant latency and token costs due to verbose generation. E

agentsarxiv-cs-cl

Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks

arXiv:2601.03448v2 Announce Type: replace Abstract: Language models (LMs) are pre-trained on raw text datasets to generate text sequences token-by-token. While this approach facilitates the learning o

Why Do Vision Language Models Struggle To Recognize Human Emotions?

arXiv:2604.15280v1 Announce Type: new Abstract: Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans. Vision-language models (VLMs) have made trem

safetyarxiv-cs-cv

A Comprehensive Survey on Network Traffic Synthesis: From Statistical Models to Deep Learning

arXiv:2507.01976v2 Announce Type: replace-cross Abstract: Synthetic network traffic generation has emerged as a promising alternative for various data-driven applications in the networking domain. It

applicationsarxiv-cs-lg

16 Apr 2026

Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment

arXiv:2604.13462v1 Announce Type: cross Abstract: Effective IT change management is important for businesses that depend on software and services, particularly in highly regulated sectors such as fina

applicationsarxiv-cs-lg

16 Apr 2026

Detecting and refurbishing ground truth errors during training of deep learning-based echocardiography segmentation models

arXiv:2604.12832v1 Announce Type: cross Abstract: Deep learning-based medical image segmentation typically relies on ground truth (GT) labels obtained through manual annotation, but these can be prone

Does RLVR Extend Reasoning Boundaries? Investigating Capability Expansion in Vision-Language Models

arXiv:2511.00710v4 Announce Type: replace Abstract: Recent studies posit that Reinforcement Learning with Verifiable Rewards (RLVR) primarily amplifies behaviors inherent to the pre-training distribut

safetyarxiv-cs-ai

Loop Corrections to the Training and Generalization Errors of Random Feature Models

arXiv:2604.12827v1 Announce Type: cross Abstract: We investigate random feature models in which neural networks sampled from a prescribed initialization ensemble are frozen and used as random features

Safe-FedLLM: Delving into the Safety of Federated Large Language Models

arXiv:2601.07177v2 Announce Type: replace-cross Abstract: Federated learning (FL) addresses privacy and data-silo issues in the training of large language models (LLMs). Most prior work focuses on imp

local-aiarxiv-cs-ai

SCRIPT: A Subcharacter Compositional Representation Injection Module for Korean Pre-Trained Language Models

arXiv:2604.12377v1 Announce Type: cross Abstract: Korean is a morphologically rich language with a featural writing system in which each character is systematically composed of subcharacter units know

Beyond A Fixed Seal: Adaptive Stealing Watermark in Large Language Models

arXiv:2604.10893v1 Announce Type: cross Abstract: Watermarking provides a critical safeguard for large language model (LLM) services by facilitating the detection of LLM-generated text. Correspondingl

applicationsarxiv-cs-ai

Hardware

Characterizing Performance-Energy Trade-offs of Large Language Models in Multi-Request Workflows

arXiv:2604.09611v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in applications forming multi-request workflows like document summarization, search-based copilots,

hardwarearxiv-cs-ai

Differentiable free energy surface: a variational approach to directly observing rare events using generative deep-learning models

arXiv:2604.09769v1 Announce Type: cross Abstract: Rare events are central to the evolution of complex many-body systems, characterized as key transitional configurations on the free energy surface (FE

safetyarxiv-cs-lg

Rein3D: Reinforced 3D Indoor Scene Generation with Panoramic Video Diffusion Models

arXiv:2604.10578v1 Announce Type: new Abstract: The growing demand for Embodied AI and VR applications has highlighted the need for synthesizing high-quality 3D indoor scenes from sparse inputs. Howev

local-aiarxiv-cs-cv

TInR: Exploring Tool-Internalized Reasoning in Large Language Models

arXiv:2604.10788v1 Announce Type: cross Abstract: Tool-Integrated Reasoning (TIR) has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools durin

safetyarxiv-cs-ai

Towards Mitigating Modality Bias in Vision-Language Models for Temporal Action Localization

arXiv:2601.21078v3 Announce Type: replace Abstract: Temporal Action Localization (TAL) requires identifying both the boundaries and categories of actions in untrimmed videos. While vision-language mod

local-aiarxiv-cs-cv

Investigating Multimodal Large Language Models to Support Usability Evaluation

arXiv:2508.16165v2 Announce Type: replace-cross Abstract: Usability evaluation is an essential method to support the design of effective and intuitive user interfaces (UIs). However, it commonly relie

applicationsarxiv-cs-ai

13 Apr 2026

Online Quantile Regression for Nonparametric Additive Models

arXiv:2604.08969v1 Announce Type: cross Abstract: This paper introduces a projected functional gradient descent algorithm (P-FGD) for training nonparametric additive quantile regression models in onli

13 Apr 2026

SynDocDis: A Metadata-Driven Framework for Generating Synthetic Physician Discussions Using Large Language Models

arXiv:2604.08555v1 Announce Type: new Abstract: Physician-physician discussions of patient cases represent a rich source of clinical knowledge and reasoning that could feed AI agents to enrich and eve

applicationsarxiv-cs-cl

13 Apr 2026

Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach

arXiv:2604.06727v1 Announce Type: new Abstract: Heterogeneity in time series data is more pronounced than in vision or language, as temporal dynamics vary substantially across domains and tasks. Exist

DisCEdge: Distributed Context Management for Large Language Models at the Edge

arXiv:2511.22599v2 Announce Type: replace-cross Abstract: Deploying Large Language Model (LLM) services at the edge benefits latency-sensitive and privacy-aware applications. However, the stateless na

Event-Level Detection of Surgical Instrument Handovers in Videos with Interpretable Vision Models

arXiv:2604.07577v1 Announce Type: new Abstract: Reliable monitoring of surgical instrument exchanges is essential for maintaining procedural efficiency and patient safety in the operating room. Automa

safetyarxiv-cs-cv

Resource-constrained Amazons chess decision framework integrating large language models and graph attention

arXiv:2603.10512v2 Announce Type: replace Abstract: Artificial intelligence has advanced significantly through the development of intelligent game-playing systems, providing rigorous testbeds for deci

Tutorials

Transforming the Voice of the Customer: Large Language Models for Identifying Customer Needs

arXiv:2503.01870v2 Announce Type: replace Abstract: Identifying customer needs (CNs) is fundamental to product innovation and marketing strategy. Yet for over thirty years, Voice-of-the-Customer (VOC)

tutorialsarxiv-cs-cl

VLMShield: Efficient and Robust Defense of Vision-Language Models against Malicious Prompts

arXiv:2604.06502v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face significant safety vulnerabilities from malicious prompt attacks due to weakened alignment during visual integration.

safetyarxiv-cs-lg

Agentic-Priors: Using Large Language Models to Address the Cold Start Problem in Preference Elicitation