Search: “models”

Solving Inverse Parametrized Problems via Finite Elements and Extreme Learning Networks

arXiv:2602.14757v2 Announce Type: replace-cross Abstract: We develop an interpolation-based modeling framework for parameter-dependent partial differential equations arising in control, inverse proble

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Targeted Exploration via Unified Entropy Control for Reinforcement Learning

DGX agent

arXiv:2604.14646v2 Announce Type: replace Abstract: Recent advances in reinforcement learning (RL) have improved the reasoning capabilities of large language models (LLMs) and vision-language models (

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20 Apr 2026

The Amazing Stability of Flow Matching

arXiv:2604.16079v1 Announce Type: new Abstract: The success of deep generative models in generating high-quality and diverse samples is often attributed to particular architectures and large training

researcharxiv-cs-cv

VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects

arXiv:2604.16272v1 Announce Type: cross Abstract: As AI-assisted video creation becomes increasingly practical, instruction-guided video editing has become essential for refining generated or captured

When Cultures Meet: Multicultural Text-to-Image Generation

arXiv:2502.15972v2 Announce Type: replace-cross Abstract: Text-to-image generation models have achieved strong performance in culturally homogeneous settings, yet their ability to generate multicultur

Where does output diversity collapse in post-training?

arXiv:2604.16027v1 Announce Type: cross Abstract: Post-trained language models produce less varied outputs than their base counterparts. This output diversity collapse undermines inference-time scalin

researcharxiv-cs-ai

Wisdom is Knowing What not to Say: Hallucination-Free LLMs Unlearning via Attention Shifting

arXiv:2510.17210v3 Announce Type: replace Abstract: The increase in computing power and the necessity of AI-assisted decision-making boost the growing application of large language models (LLMs). Alon

A Linguistics-Aware LLM Watermarking via Syntactic Predictability

arXiv:2510.13829v3 Announce Type: replace Abstract: As large language models (LLMs) continue to advance rapidly, reliable governance tools have become critical. Publicly verifiable watermarking is par

researcharxiv-cs-cl

AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization

arXiv:2511.15915v2 Announce Type: replace-cross Abstract: We present AccelOpt, a self-improving large language model (LLM) agentic system that autonomously optimizes kernels for emerging AI acclerator

Anomaly Detection in IEC-61850 GOOSE Networks: Evaluating Unsupervised and Temporal Learning for Real-Time Intrusion Detection

arXiv:2604.14233v1 Announce Type: cross Abstract: The IEC-61850 GOOSE protocol underpins time-critical communication in modern digital substations but lacks native security mechanisms, leaving it vuln

researcharxiv-cs-lg

Benchmarking Optimizers for MLPs in Tabular Deep Learning

arXiv:2604.15297v1 Announce Type: new Abstract: MLP is a heavily used backbone in modern deep learning (DL) architectures for supervised learning on tabular data, and AdamW is the go-to optimizer used

CaptionQA: Is Your Caption as Useful as the Image Itself?

model-releasesarxiv-cs-cv

arXiv:2511.21025v2 Announce Type: replace Abstract: Image captions serve as efficient surrogates for visual content in multimodal systems such as retrieval, recommendation, and multi-step agentic infe

Chinese Language Is Not More Efficient Than English in Vibe Coding: A Preliminary Study on Token Cost and Problem-Solving Rate

arXiv:2604.14210v1 Announce Type: new Abstract: A claim has been circulating on social media and practitioner forums that Chinese prompts are more token-efficient than English for LLM coding tasks, po

Context Over Content: Exposing Evaluation Faking in Automated Judges

arXiv:2604.15224v1 Announce Type: cross Abstract: The extit{LLM-as-a-judge} paradigm has become the operational backbone of automated AI evaluation pipelines, yet rests on an unverified assumption: th

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Data Synthesis Improves 3D Myotube Instance Segmentation

arXiv:2604.14720v1 Announce Type: new Abstract: Myotubes are multinucleated muscle fibers serving as key model systems for studying muscle physiology, disease mechanisms, and drug responses. Mechanist

researcharxiv-cs-cv

Faithfulness Serum: Mitigating the Faithfulness Gap in Textual Explanations of LLM Decisions via Attribution Guidance

arXiv:2604.14325v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance and have revolutionized NLP, but their lack of explainability keeps them treated as black boxes,

researcharxiv-cs-cl

GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis

arXiv:2604.13888v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into Geographic Information Systems (GIS) marks a paradigm shift toward autonomous spatial analysis. How

Knowing When Not to Answer: Evaluating Abstention in Multimodal Reasoning Systems

arXiv:2604.14799v1 Announce Type: new Abstract: Effective abstention (EA), recognizing evidence insufficiency and refraining from answering, is critical for reliable multimodal systems. Yet existing e

MADE: A Living Benchmark for Multi-Label Text Classification with Uncertainty Quantification of Medical Device Adverse Events

arXiv:2604.15203v1 Announce Type: new Abstract: Machine learning in high-stakes domains such as healthcare requires not only strong predictive performance but also reliable uncertainty quantification

Local Ai

One RL to See Them All: Visual Triple Unified Reinforcement Learning

arXiv:2505.18129v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) is becoming an important direction for post-training vision-language models (VLMs), but public training methodolog

local-aiarxiv-cs-cl

Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games

arXiv:2506.03610v3 Announce Type: replace Abstract: Large Language Model (LLM) agents are reshaping the game industry, by enabling more intelligent and human-preferable characters. Yet, current game b

PeerPrism: Peer Evaluation Expertise vs Review-writing AI

arXiv:2604.14513v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used in scientific peer review, assisting with drafting, rewriting, expansion, and refinement. However, ex

PixelDiT: Pixel Diffusion Transformers for Image Generation

arXiv:2511.20645v2 Announce Type: replace Abstract: Latent-space modeling has been the standard for Diffusion Transformers (DiTs). However, it relies on a two-stage pipeline where the pretrained autoe

researcharxiv-cs-cv

Pushing the Boundaries of Multiple Choice Evaluation to One Hundred Options

arXiv:2604.14634v1 Announce Type: new Abstract: Multiple choice evaluation is widely used for benchmarking large language models, yet near ceiling accuracy in low option settings can be sustained by s

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SAGE Celer 2.6 Technical Card

arXiv:2604.14168v1 Announce Type: new Abstract: We introduce SAGE Celer 2.6, the latest in our line of general-purpose Celer models from SAGEA. Celer 2.6 is available in 5B, 10B, and 27B parameter siz

Applications

Standard-to-Dialect Transfer Trends Differ across Text and Speech: A Case Study on Intent and Topic Classification in German Dialects

arXiv:2510.07890v3 Announce Type: replace Abstract: Research on cross-dialectal transfer from a standard to a non-standard dialect variety has typically focused on text data. However, dialects are pri

applicationsarxiv-cs-cl

Local Ai

The Courtroom Trial of Pixels: Robust Image Manipulation Localization via Adversarial Evidence and Reinforcement Learning Judgment

arXiv:2604.14703v1 Announce Type: new Abstract: Although some existing image manipulation localization (IML) methods incorporate authenticity-related supervision, this information is typically utilize

local-aiarxiv-cs-cv

To See or To Please: Uncovering Visual Sycophancy and Split Beliefs in VLMs

arXiv:2603.18373v2 Announce Type: replace Abstract: When VLMs answer correctly, do they genuinely rely on visual information or exploit language shortcuts? We introduce the Tri-Layer Diagnostic Framew

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VeruSAGE: A Study of Agent-Based Verification for Rust Systems

arXiv:2512.18436v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown impressive capability to understand and develop code. However, their capability to rigorously reason a

xFODE+: Explainable Type-2 Fuzzy Additive ODEs for Uncertainty Quantification

arXiv:2604.14880v1 Announce Type: new Abstract: Recent advances in Deep Learning (DL) have boosted data-driven System Identification (SysID), but reliable use requires Uncertainty Quantification (UQ)

Your LLM Agents are Temporally Blind: The Misalignment Between Tool Use Decisions and Human Time Perception

arXiv:2510.23853v3 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly used to interact with and execute tasks in dynamic environments. However, a critical yet overlook

safetyarxiv-cs-cl

An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning

model-releasesarxiv-cs-cv

arXiv:2211.16780v3 Announce Type: replace-cross Abstract: In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because pre

Agents

Bi-Predictability: A Real-Time Signal for Monitoring LLM Interaction Integrity

arXiv:2604.13061v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in high-stakes autonomous and interactive workflows, where reliability demands continuous, multi-

agentsarxiv-cs-cl

Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection

arXiv:2604.13692v1 Announce Type: new Abstract: As large language models (LLMs) generate text that increasingly resembles human writing, the subtle cues that distinguish AI-generated content from huma

Counterfactual Peptide Editing for Causal TCR--pMHC Binding Inference

arXiv:2604.13256v1 Announce Type: new Abstract: Neural models for TCR-pMHC binding prediction are susceptible to shortcut learning: they exploit spurious correlations in training data -- such as pepti

English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training

arXiv:2604.13286v1 Announce Type: new Abstract: Despite the widespread multilingual deployment of large language models, post-training pipelines remain predominantly English-centric, contributing to p

researcharxiv-cs-cl

Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression

arXiv:2604.13410v1 Announce Type: cross Abstract: We study the problem of estimating the effect function for a continuous treatment, which maps each treatment value to a population-averaged outcome. A

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Exposia: Teaching and Assessment of Academic Writing Skills for Research Project Proposals and Peer Feedback

arXiv:2601.06536v2 Announce Type: replace Abstract: We present Exposia, the first public dataset that connects writing and feedback in higher education, enabling research on educationally grounded com

FiLM-Nav: Efficient and Generalizable Navigation via VLM Fine-tuning

model-releasesarxiv-cs-ro

arXiv:2509.16445v2 Announce Type: replace Abstract: Enabling robotic assistants to navigate complex environments and locate objects described in free-form language is a critical capability for real-wo

FlexGuard: Continuous Risk Scoring for Strictness-Adaptive LLM Content Moderation

arXiv:2602.23636v3 Announce Type: replace Abstract: Ensuring the safety of LLM-generated content is essential for real-world deployment. Most existing guardrail models formulate moderation as a fixed

Functional Emotions or Situational Contexts? A Discriminating Test from the Mythos Preview System Card

arXiv:2604.13466v1 Announce Type: cross Abstract: The Claude Mythos Preview system card deploys emotion vectors, sparse autoencoder (SAE) features, and activation verbalisers to study model internals

KMMMU: Evaluation of Massive Multi-discipline Multimodal Understanding in Korean Language and Context

arXiv:2604.13058v1 Announce Type: new Abstract: We introduce KMMMU, a native Korean benchmark for evaluating multimodal understanding in Korean cultural and institutional settings. KMMMU contains 3,46

KV Packet: Recomputation-Free Context-Independent KV Caching for LLMs

arXiv:2604.13226v1 Announce Type: new Abstract: Large Language Models (LLMs) rely heavily on Key-Value (KV) caching to minimize inference latency. However, standard KV caches are context-dependent: re

Language steering in latent space to mitigate unintended code-switching

arXiv:2510.13849v3 Announce Type: replace Abstract: Multilingual Large Language Models (LLMs) often exhibit hallucinations such as unintended code-switching, reducing reliability in downstream tasks.

On an L^2 norm for stationary ARMA processes

arXiv:2408.10610v5 Announce Type: replace Abstract: We propose an L^2 norm for stationary Autoregressive Moving Average (ARMA) models. We look at ARMA models within the Hilbert space of the past with

researcharxiv-cs-lg

Online learning with noisy side observations

arXiv:2604.13740v1 Announce Type: new Abstract: We propose a new partial-observability model for online learning problems where the learner, besides its own loss, also observes some noisy feedback abo

Optimization with SpotOptim

arXiv:2604.13672v1 Announce Type: new Abstract: The `spotoptim` package implements surrogate-model-based optimization of expensive black-box functions in Python. Building on two decades of Sequential

Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning

arXiv:2604.14010v1 Announce Type: cross Abstract: Supervised Fine-Tuning (SFT) of large language models often suffers from task interference and catastrophic forgetting. Recent approaches alleviate th