Search: “papers”

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

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An LLM-Guided Query-Aware Inference System for GNN Models on Large Knowledge Graphs

DGX agent

arXiv:2603.04545v2 Announce Type: replace Abstract: Efficient inference for graph neural networks (GNNs) on large knowledge graphs (KGs) is essential for many real-world applications. GNN inference qu

applicationsarxiv-cs-lg

21 Apr 2026

ArgBench: Benchmarking LLMs on Computational Argumentation Tasks

arXiv:2604.17366v1 Announce Type: new Abstract: Argumentation skills are an essential toolkit for large language models (LLMs). These skills are crucial in various use cases, including self-reflection

ARMove: Learning to Predict Human Mobility through Agentic Reasoning

arXiv:2604.17419v1 Announce Type: cross Abstract: Human mobility prediction is a critical task but remains challenging due to its complexity and variability across populations and regions. Recently, l

agentsarxiv-cs-lg

AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data

arXiv:2604.16793v1 Announce Type: cross Abstract: In astronomical imaging, the low photon count of exposures necessitates extensive post-processing steps, including contamination removal and denoising

Attention Is not Everything: Efficient Alternatives for Vision

arXiv:2604.17439v1 Announce Type: new Abstract: Recently computer vision has seen advancements mainly thanks to Transformer-based models. However many non-Transformer methods are still doing well bein

Attention-ResUNet for Automated Fetal Head Segmentation

arXiv:2604.18148v1 Announce Type: new Abstract: Automated fetal head segmentation in ultrasound images is critical for accurate biometric measurements in prenatal care. While existing deep learning ap

Autonomous Unmanned Aircraft Systems for Enhanced Search and Rescue of Drowning Swimmers: Image-Based Localization and Mission Simulation

arXiv:2604.18088v1 Announce Type: new Abstract: Drowning is an omnipresent risk associated with any activity on or in the water, and rescuing a drowning person is particularly challenging because of t

agentsarxiv-cs-cv

Autonomous Vehicle Collision Avoidance With Racing Parameterized Deep Reinforcement Learning

arXiv:2604.16702v1 Announce Type: new Abstract: Road traffic accidents are a leading cause of fatalities worldwide. In the US, human error causes 94% of crashes, resulting in excess of 7,000 pedestria

safetyarxiv-cs-ro

AutoPPA: Automated Circuit PPA Optimization via Contrastive Code-based Rule Library Learning

arXiv:2604.18445v1 Announce Type: new Abstract: Performance, power, and area (PPA) optimization is a fundamental task in RTL design, requiring a precise understanding of circuit functionality and the

researcharxiv-cs-lg

Balanced Co-Clustering of Users and Items for Embedding Table Compression in Recommender Systems

arXiv:2604.18351v1 Announce Type: cross Abstract: Recommender systems have advanced markedly over the past decade by transforming each user/item into a dense embedding vector with deep learning models

BASIS: Balanced Activation Sketching with Invariant Scalars for 'Ghost Backpropagation'

arXiv:2604.16324v1 Announce Type: new Abstract: The activation memory required for exact backpropagation scales linearly with network depth, context length, and feature dimensionality, forming an O(L

Before You Interpret the Profile: Validity Scaling for LLM Metacognitive Self-Report

arXiv:2604.17707v1 Announce Type: new Abstract: Clinical personality assessment screens response validity before interpreting substantive scales. LLM evaluation does not. We apply the validity scaling

BEFT: Bias-Efficient Fine-Tuning of Language Models in Low-Data Regimes

arXiv:2509.15974v2 Announce Type: replace Abstract: Fine-tuning the bias terms of large language models (LLMs) has the potential to achieve unprecedented parameter efficiency while maintaining competi

Benchmarking System Dynamics AI Assistants: Cloud Versus Local LLMs on CLD Extraction and Discussion

arXiv:2604.18566v1 Announce Type: cross Abstract: We present a systematic evaluation of large language model families -- spanning both proprietary cloud APIs and locally-hosted open-source models -- o

Beyond Fine-Tuning: In-Context Learning and Chain-of-Thought for Reasoned Distractor Generation

arXiv:2604.17574v1 Announce Type: new Abstract: Distractor generation (DG) remains a labor-intensive task that still significantly depends on domain experts. The task focuses on generating plausible y

Tutorials

Block-encodings as programming abstractions: The Eclipse Qrisp BlockEncoding Interface

arXiv:2604.18276v1 Announce Type: cross Abstract: Block-encoding is a foundational technique in modern quantum algorithms, enabling the implementation of non-unitary operations by embedding them into

tutorialsarxiv-cs-lg

BOIL: Learning Environment Personalized Information

arXiv:2604.17137v1 Announce Type: new Abstract: Navigating complex environments poses challenges for multi-agent systems, requiring efficient extraction of insights from limited information. In this p

agentsarxiv-cs-lg

Bounded Ratio Reinforcement Learning

arXiv:2604.18578v1 Announce Type: new Abstract: Proximal Policy Optimization (PPO) has become the predominant algorithm for on-policy reinforcement learning due to its scalability and empirical robust

Bridging the Reasoning Gap in Vietnamese with Small Language Models via Test-Time Scaling

arXiv:2604.17794v1 Announce Type: new Abstract: The democratization of ubiquitous AI hinges on deploying sophisticated reasoning capabilities on resource-constrained devices. However, Small Language M

Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA

arXiv:2604.17316v1 Announce Type: new Abstract: Safe clinical deployment of Large Language Models (LLMs) requires not only high accuracy but also robust uncertainty calibration to ensure models defer

applicationsarxiv-cs-cl

Can Explicit Physical Feasibility Benefit VLA Learning? An Empirical Study

arXiv:2604.17896v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models map multimodal inputs directly to robot actions and are typically trained through large-scale imitation learning. Wh

Channel Attention-Guided Cross-Modal Knowledge Distillation for Referring Image Segmentation

model-releasesarxiv-cs-cv

arXiv:2604.16806v1 Announce Type: new Abstract: Referring image segmentation (RIS) requires accurate segmentation of target regions in images according to language descriptions, which is a cross-modal

Clusterability-Based Assessment of Potentially Noisy Views for Multi-View Clustering

arXiv:2604.18024v1 Announce Type: new Abstract: In multi-view clustering, the quality of different views may vary substantially, and low-quality or degraded views can impair overall clustering perform

applicationsarxiv-cs-lg

CoAct: Co-Active LLM Preference Learning with Human-AI Synergy

arXiv:2604.17501v1 Announce Type: new Abstract: Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks. However, high-quality human-annotated p

Combined Hyperbolic and Euclidean Soft Triple Loss Beyond the Single Space Deep Metric Learning

model-releasesarxiv-cs-cv

arXiv:2510.05643v2 Announce Type: replace Abstract: Deep metric learning (DML) aims to learn a neural network mapping data to an embedding space, which can represent semantic similarity between data p

Compressing then Matching: An Efficient Pre-training Paradigm for Multimodal Embedding

arXiv:2511.08480v3 Announce Type: replace Abstract: Multimodal Large Language Models advance multimodal representation learning by acquiring transferable semantic embeddings, thereby substantially enh

Hardware

Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning

arXiv:2412.00069v3 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) has garnered significant attention for its ability to scale up neural networks while utilizing the same or even fewer

hardwarearxiv-cs-cl

Conformal Risk Control under Non-Monotone Losses: Theory and Finite-Sample Guarantees

arXiv:2604.01502v2 Announce Type: replace-cross Abstract: Conformal risk control (CRC) provides distribution-free guarantees for controlling the expected loss at a user-specified level. Existing theor

Contact-Rich Robotic Assembly in Construction via Diffusion Policy Learning

arXiv:2511.17774v3 Announce Type: replace Abstract: Fabrication uncertainty arising from tolerance accumulation, material imperfection, and positioning errors remains a critical barrier to automated r

safetyarxiv-cs-ro

Creating ConLangs to Probe the Metalinguistic Grammatical Knowledge of LLMs

arXiv:2510.07591v3 Announce Type: replace Abstract: We present a system that uses LLMs as a tool in the development of Constructed Languages -- ConLangs, which we call IASC (Interactive Agentic System

CRISP: Compressing Redundancy in Chain-of-Thought via Intrinsic Saliency Pruning

arXiv:2604.17297v1 Announce Type: new Abstract: Long Chain-of-Thought (CoT) reasoning is pivotal for the success of recent reasoning models but suffers from high computational overhead and latency. Wh

CT Open: An Open-Access, Uncontaminated, Live Platform for the Open Challenge of Clinical Trial Outcome Prediction

arXiv:2604.16742v1 Announce Type: cross Abstract: Scientists have long sought to accurately predict outcomes of real-world events before they happen. Can AI systems do so more reliably? We study this

applicationsarxiv-cs-cl

Data Mixing for Large Language Models Pretraining: A Survey and Outlook

arXiv:2604.16380v1 Announce Type: new Abstract: Large language models (LLMs) rely on pretraining on massive and heterogeneous corpora, where training data composition has a decisive impact on training

Decoding RWA Tokenized U.S. Treasuries: Functional Dissection and Address Role Inference

arXiv:2507.14808v3 Announce Type: replace-cross Abstract: Tokenized U.S. Treasuries have emerged as a prominent subclass of real-world assets (RWAs), offering cryptographically secured, yield-bearing

applicationsarxiv-cs-lg

Deep Hierarchical Knowledge Loss for Fault Intensity Diagnosis

arXiv:2604.16459v1 Announce Type: cross Abstract: Fault intensity diagnosis (FID) plays a pivotal role in intelligent manufacturing while neglecting dependencies among target classes hinders its pract

applicationsarxiv-cs-cv

Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework

arXiv:2510.24074v2 Announce Type: replace-cross Abstract: The Heston stochastic volatility model is a widely used tool in financial mathematics for pricing European options. However, its calibration r

DeInfer: Efficient Parallel Inferencing for Decomposed Large Language Models

arXiv:2604.17709v1 Announce Type: new Abstract: Existing works on large language model (LLM) decomposition mainly focus on improving performance on downstream tasks, but they ignore the poor parallel

Demystifying the unreasonable effectiveness of online alignment methods

arXiv:2604.17207v1 Announce Type: cross Abstract: Iterative alignment methods based on purely greedy updates are remarkably effective in practice, yet existing theoretical guarantees of (O(log T)) KL-

Designing Explainable Conversational Agentic Systems for Guarani Speakers

arXiv:2603.05743v3 Announce Type: replace Abstract: Although artificial intelligence (AI) and Human-Computer Interaction (HCI) systems are often presented as universal solutions, their design remains

agentsarxiv-cs-cl

Diagnosing LLM-based Rerankers in Cold-Start Recommender Systems: Coverage, Exposure and Practical Mitigations

arXiv:2604.16318v1 Announce Type: cross Abstract: Large language models (LLMs) and cross-encoder rerankers have gained attention for improving recommender systems, particularly in cold-start scenarios

Differential Privacy in Two-Layer Networks: How DP-SGD Harms Fairness and Robustness

arXiv:2603.04881v2 Announce Type: replace Abstract: Differentially private learning is essential for training models on sensitive data, but empirical studies consistently show that it can degrade perf

DisCa: Accelerating Video Diffusion Transformers with Distillation-Compatible Learnable Feature Caching

arXiv:2602.05449v3 Announce Type: replace Abstract: While diffusion models have achieved great success in the field of video generation, this progress is accompanied by a rapidly escalating computatio

Document-as-Image Representations Fall Short for Scientific Retrieval

arXiv:2604.18508v1 Announce Type: cross Abstract: Many recent document embedding models are trained on document-as-image representations, embedding rendered pages as images rather than the underlying

Does 'Do Differentiable Simulators Give Better Policy Gradients?'' Give Better Policy Gradients?

arXiv:2604.18161v1 Announce Type: new Abstract: In policy gradient reinforcement learning, access to a differentiable model enables 1st-order gradient estimation that accelerates learning compared to

DreamShot: Personalized Storyboard Synthesis with Video Diffusion Prior

arXiv:2604.17195v1 Announce Type: new Abstract: Storyboard synthesis plays a crucial role in visual storytelling, aiming to generate coherent shot sequences that visually narrate cinematic events with

safetyarxiv-cs-cv

Dual Alignment Between Language Model Layers and Human Sentence Processing

arXiv:2604.18563v1 Announce Type: new Abstract: A recent study (Kuribayashi et al., 2025) has shown that human sentence processing behavior, typically measured on syntactically unchallenging construct

Dual-Anchoring: Addressing State Drift in Vision-Language Navigation

arXiv:2604.17473v1 Announce Type: new Abstract: Vision-Language Navigation(VLN) requires an agent to navigate through 3D environments by following natural language instructions. While recent Video Lar

agentsarxiv-cs-cv