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GR4CIL: Gap-compensated Routing for CLIP-based Class Incremental Learning

arXiv:2604.17822v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) aims to continuously acquire new categories while preserving previously learned knowledge. Recently, Contrastive Langua

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GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling

DGX agent

arXiv:2604.18556v1 Announce Type: new Abstract: Weight quantization has become a standard tool for efficient LLM deployment, especially for local inference, where models are now routinely served at 2-

model-releasesarxiv-cs-cl

21 Apr 2026

Guardrails in Logit Space: Safety Token Regularization for LLM Alignment

arXiv:2604.17210v1 Announce Type: new Abstract: Fine-tuning well-aligned large language models (LLMs) on new domains often degrades their safety alignment, even when using benign datasets. Existing sa

HalluSAE: Detecting Hallucinations in Large Language Models via Sparse Auto-Encoders

arXiv:2604.16430v1 Announce Type: new Abstract: Large Language Models (LLMs) are powerful and widely adopted, but their practical impact is limited by the well-known hallucination phenomenon. While re

Harness as an Asset: Enforcing Determinism via the Convergent AI Agent Framework (CAAF)

arXiv:2604.17025v1 Announce Type: cross Abstract: Large Language Models (LLMs) produce a controllability gap in safety-critical engineering: even low rates of undetected constraint violations render a

HiGMem: A Hierarchical and LLM-Guided Memory System for Long-Term Conversational Agents

arXiv:2604.18349v1 Announce Type: new Abstract: Long-term conversational large language model (LLM) agents require memory systems that can recover relevant evidence from historical interactions withou

HiP-LoRA: Budgeted Spectral Plasticity for Robust Low-Rank Adaptation

arXiv:2604.17751v1 Announce Type: cross Abstract: Adapting foundation models under resource budgets relies heavily on Parameter-Efficient Fine-Tuning (PEFT), with LoRA being a standard modular solutio

HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution

arXiv:2604.17745v1 Announce Type: new Abstract: Recent advances in large language models have highlighted their potential to automate computational research, particularly reproducing experimental resu

HORIZON: A Benchmark for In-the-wild User Behaviour Modeling

arXiv:2604.17259v1 Announce Type: cross Abstract: User behavior in the real world is diverse, cross-domain, and spans long time horizons. Existing user modeling benchmarks however remain narrow, focus

HorizonBench: Long-Horizon Personalization with Evolving Preferences

arXiv:2604.17283v1 Announce Type: new Abstract: User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent lif

How Robustly do LLMs Understand Execution Semantics?

arXiv:2604.16320v1 Announce Type: cross Abstract: LLMs demonstrate remarkable reasoning capabilities, yet whether they utilize internal world models or rely on sophisticated pattern matching remains o

How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study

arXiv:2505.15404v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have achieved remarkable success on reasoning-intensive tasks such as mathematics and programming. However, their enha

HQA-VLAttack: Towards High Quality Adversarial Attack on Vision-Language Pre-Trained Models

arXiv:2604.16499v1 Announce Type: new Abstract: Black-box adversarial attack on vision-language pre-trained models is a practical and challenging task, as text and image perturbations need to be consi

Hybrid-Vector Retrieval for Visually Rich Documents: Combining Single-Vector Efficiency and Multi-Vector Accuracy

arXiv:2510.22215v2 Announce Type: replace-cross Abstract: Retrieval over visually rich documents is essential for tasks such as legal discovery, scientific search, and enterprise knowledge management.

ICAT: Incident-Case-Grounded Adaptive Testing for Physical-Risk Prediction in Embodied World Models

arXiv:2604.16405v1 Announce Type: cross Abstract: Video-generative world models are increasingly used as neural simulators for embodied planning and policy learning, yet their ability to predict physi

IDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem

arXiv:2604.18521v1 Announce Type: new Abstract: Epidemic forecasting has become an integral part of real-time infectious disease outbreak response. While collaborative ensembles composed of statistica

iDocV2: Leveraging Self-Supervision and Open-Set Detection for Improving Pattern Spotting in Historical Documents

arXiv:2604.16726v1 Announce Type: new Abstract: Considering the imminent massification of digital books, it has become critical to facilitate searching collections through graphical patterns. Current

Improving LLM Code Reasoning via Semantic Equivalence Self-Play with Formal Verification

arXiv:2604.17010v1 Announce Type: new Abstract: We introduce a self-play framework for semantic equivalence in Haskell, utilizing formal verification to guide adversarial training between a generator

In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks

arXiv:2603.15250v2 Announce Type: replace Abstract: Symbolic regression aims to replace black-box predictors with concise analytical expressions that can be inspected and validated in scientific machi

Incoherent Deformation, Not Capacity: Diagnosing and Mitigating Overfitting in Dynamic Gaussian Splatting

arXiv:2604.16747v1 Announce Type: new Abstract: Dynamic 3D Gaussian Splatting methods achieve strong training-view PSNR on monocular video but generalize poorly: on the D-NeRF benchmark we measure an

IncreFA: Breaking the Static Wall of Generative Model Attribution

arXiv:2604.17736v1 Announce Type: new Abstract: As AI generative models evolve at unprecedented speed, image attribution has become a moving target. New diffusion, adversarial and autoregressive gener

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation

arXiv:2510.09275v2 Announce Type: replace Abstract: Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLM

Injecting Structured Biomedical Knowledge into Language Models: Continual Pretraining vs. GraphRAG

arXiv:2604.16422v1 Announce Type: new Abstract: The injection of domain-specific knowledge is crucial for adapting language models (LMs) to specialized fields such as biomedicine. While most current a

INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval

arXiv:2604.18051v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) is a challenging image retrieval paradigm that enables to retrieve target images based on multimodal queries consisting o

InternScenes: A Large-scale Simulatable Indoor Scene Dataset with Realistic Layouts

arXiv:2509.10813v3 Announce Type: replace Abstract: The advancement of Embodied AI heavily relies on large-scale, simulatable 3D scene datasets characterized by scene diversity and realistic layouts.

Interpolating Discrete Diffusion Models with Controllable Resampling

arXiv:2604.17310v1 Announce Type: new Abstract: Discrete diffusion models form a powerful class of generative models across diverse domains, including text and graphs. However, existing approaches fac

Judge a Book by its Cover: Investigating Multi-Modal LLMs for Multi-Page Handwritten Document Transcription

arXiv:2502.20295v2 Announce Type: replace-cross Abstract: Handwriting text recognition (HTR) remains a challenging task. Existing approaches require fine-tuning on labeled data, which is impractical t

JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew

arXiv:2604.18041v1 Announce Type: new Abstract: Despite significant advances in large language models, personalizing them for individual decision-makers remains an open problem. Here, we introduce a s

Jupiter-N Technical Report

arXiv:2604.17429v1 Announce Type: new Abstract: We present Jupiter-N, a hybrid reasoning model post-trained from Nemotron 3 Super, a fully open-source 120 billion parameter LLM. We target three object

KIRA: Knowledge-Intensive Image Retrieval and Reasoning Architecture for Specialized Visual Domains

arXiv:2604.16915v1 Announce Type: new Abstract: Retrieval augmented generation (RAG) has transformed text based question answering, yet its extension to visual domains remains hindered by fundamental

Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning

arXiv:2604.18419v1 Announce Type: cross Abstract: Large language models (LLMs) using chain-of-thought reasoning often waste substantial compute by producing long, incorrect responses. Abstention can m

Knowledge without Wisdom: Measuring Misalignment between LLMs and Intended Impact

arXiv:2603.00883v2 Announce Type: replace Abstract: LLMs increasingly excel on AI benchmarks, but doing so does not guarantee validity for downstream tasks. This study contrasts LLM alignment on bench

Language Models Don't Know What You Want: Evaluating Personalization in Deep Research Needs Real Users

arXiv:2603.16120v2 Announce Type: replace Abstract: Deep Research (DR) systems help researchers cope with ballooning publishing counts. Such tools synthesize scientific papers to answer research queri

Large Language Models Are Still Misled by Simple Bias Ensembles

arXiv:2505.16522v3 Announce Type: replace Abstract: With the evolution of large language models (LLMs), their robustness against individual simple biases has been enhanced. However, we observe that th

Late Fusion Neural Operators for Extrapolation Across Parameter Space in Partial Differential Equations

arXiv:2604.16721v1 Announce Type: new Abstract: Developing neural operators that accurately predict the behavior of systems governed by partial differential equations (PDEs) across unseen parameter re

Latent Preference Modeling for Cross-Session Personalized Tool Calling

arXiv:2604.17886v1 Announce Type: new Abstract: Users often omit essential details in their requests to LLM-based agents, resulting in under-specified inputs for tool use. This poses a fundamental cha

LayerCache: Exploiting Layer-wise Velocity Heterogeneity for Efficient Flow Matching Inference

arXiv:2604.16492v1 Announce Type: new Abstract: Flow Matching models achieve state-of-the-art image generation quality but incur substantial inference cost due to iterative denoising through large Tra

LEAF: Knowledge Distillation of Text Embedding Models with Teacher-Aligned Representations

arXiv:2509.12539v2 Announce Type: replace-cross Abstract: We present LEAF ('Lightweight Embedding Alignment Framework'), a knowledge distillation framework for text embedding models. A key distinguish

Learned Nonlocal Feature Matching and Filtering for RAW Image Denoising

arXiv:2604.17453v1 Announce Type: cross Abstract: Being one of the oldest and most basic problems in image processing, image denoising has seen a resurgence spurred by rapid advances in deep learning.

Learning Stable Predictors from Weak Supervision under Distribution Shift

arXiv:2604.05002v2 Announce Type: replace Abstract: Learning from weak, proxy, or relative supervision is common when ground-truth labels are unavailable, but robustness under distribution shift remai

Learning to Control Summaries with Score Ranking

arXiv:2604.17197v1 Announce Type: new Abstract: Recent advances in summarization research focus on improving summary quality across multiple criteria, such as completeness, conciseness, and faithfulne

Learning to Retrieve User History and Generate User Profiles for Personalized Persuasiveness Prediction

arXiv:2601.05654v3 Announce Type: replace Abstract: Estimating the persuasiveness of messages is critical in various applications, from recommender systems to safety assessment of LLMs. While it is im

Leveraging Large Language Models for Sarcastic Speech Annotation in Sarcasm Detection

arXiv:2506.00955v2 Announce Type: replace Abstract: Sarcasm fundamentally alters meaning through tone and context, yet detecting it in speech remains a challenge due to data scarcity. In addition, exi

LexRel: Benchmarking Legal Relation Extraction for Chinese Civil Cases

arXiv:2512.12643v2 Announce Type: replace Abstract: Legal relations serve as an important analytical framework for dispute resolution in civil cases. However, legal relations in Chinese civil cases re

LiFT: Does Instruction Fine-Tuning Improve In-Context Learning for Longitudinal Modelling by Large Language Models?

arXiv:2604.16382v1 Announce Type: new Abstract: Longitudinal NLP tasks require reasoning over temporally ordered text to detect persistence and change in human behavior and opinions. However, in-conte

LIFT the Veil for the Truth: Principal Weights Emerge after Rank Reduction for Reasoning-Focused Supervised Fine-Tuning

arXiv:2506.00772v2 Announce Type: replace-cross Abstract: Recent studies have shown that supervised fine-tuning of LLMs on a small number of high-quality datasets can yield strong reasoning capabiliti

LiquidTAD: An Efficient Method for Temporal Action Detection via Liquid Neural Dynamics

arXiv:2604.18274v1 Announce Type: new Abstract: Temporal Action Detection (TAD) in untrimmed videos is currently dominated by Transformer-based architectures. While high-performing, their quadratic co

Live Avatar: Streaming Real-time Audio-Driven Avatar Generation with Infinite Length

arXiv:2512.04677v5 Announce Type: replace Abstract: Audio-driven avatar interaction demands real-time, streaming, and infinite-length generation -- capabilities fundamentally at odds with the sequenti