Search: “models”

Interpretable Deep Learning for Stock Returns: A Consensus-Bottleneck Asset Pricing Model

arXiv:2512.16251v5 Announce Type: replace-cross Abstract: We introduce the Consensus-Bottleneck Asset Pricing Model (CB-APM), which embeds aggregate analyst consensus as a structural bottleneck, treat

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Learning Reactive Human Motion Generation from Paired Interaction Data Using Transformer-Based Models

DGX agent

arXiv:2604.22164v1 Announce Type: new Abstract: Recent advances in deep learning have enabled the generation of videos from textual descriptions as well as the prediction of future sequences from inpu

agentsarxiv-cs-cv

27 Apr 2026

Reliability Auditing for Downstream LLM tasks in Psychiatry: LLM-Generated Hospitalization Risk Scores

arXiv:2604.22063v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly utilized in clinical reasoning and risk assessment. However, their interpretive reliability in critical

UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents

arXiv:2602.07038v2 Announce Type: replace-cross Abstract: Key Information Extraction (KIE) from real-world documents remains challenging due to substantial variations in layout structures, visual qual

Voice Under Revision: Large Language Models and the Normalization of Personal Narrative

arXiv:2604.22142v1 Announce Type: new Abstract: This study examines how large language model rewriting alters the style and narrative texture of personal narratives. It analyzes 300 personal narrative

When AI Speaks, Whose Values Does It Express? A Cross-Cultural Audit of Individualism-Collectivism Bias in Large Language Models

arXiv:2604.22153v1 Announce Type: cross Abstract: When you ask an AI assistant for advice about your career, your marriage, or a conflict with your family, does it give you the same answer regardless

CaST-POI: Candidate-Conditioned Spatiotemporal Modeling for Next POI Recommendation

arXiv:2604.20845v1 Announce Type: cross Abstract: Next Point-of-Interest (POI) recommendation plays a crucial role in location-based services by predicting users' future mobility patterns. Existing me

HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering

arXiv:2604.21027v1 Announce Type: new Abstract: Electronic health record (EHR) question answering is often handled by LLM-based pipelines that are costly to deploy and do not explicitly leverage the h

applicationsarxiv-cs-ai

Learning Reasoning Reward Models from Expert Demonstration via Inverse Reinforcement Learning

arXiv:2510.01857v3 Announce Type: replace Abstract: Current approaches to improving reasoning in large language models (LLMs) primarily rely on either supervised fine-tuning (SFT) over expert traces o

local-aiarxiv-cs-ai

MathDuels: Evaluating LLMs as Problem Posers and Solvers

arXiv:2604.21916v1 Announce Type: new Abstract: As frontier language models attain near-ceiling performance on static mathematical benchmarks, existing evaluations are increasingly unable to different

Serialisation Strategy Matters: How FHIR Data Format Affects LLM Medication Reconciliation

arXiv:2604.21076v1 Announce Type: cross Abstract: Medication reconciliation at clinical handoffs is a high-stakes, error-prone process. Large language models are increasingly proposed to assist with t

The Feedback Hamiltonian is the Score Function: A Diffusion-Model Framework for Quantum Trajectory Reversal

model-releasesarxiv-cs-lg

arXiv:2604.21210v1 Announce Type: cross Abstract: In continuously monitored quantum systems, the feedback protocol of Garcia-Pintos, Liu, and Gorshkov reshapes the arrow of time: a Hamiltonian H_{meas

When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors

arXiv:2604.21255v1 Announce Type: new Abstract: Model distillation is a primary driver behind the rapid progress of LLM agents, yet it often leads to behavioral homogenization. Many emerging agents sh

A Vision-Language-Action Model for Adaptive Ultrasound-Guided Needle Insertion and Needle Tracking

arXiv:2604.20347v1 Announce Type: cross Abstract: Ultrasound (US)-guided needle insertion is a critical yet challenging procedure due to dynamic imaging conditions and difficulties in needle visualiza

Accelerating PayPal's Commerce Agent with Speculative Decoding: An Empirical Study on EAGLE3 with Fine-Tuned Nemotron Models

arXiv:2604.19767v1 Announce Type: cross Abstract: We evaluate speculative decoding with EAGLE3 as an inference-time optimization for PayPal's Commerce Agent, powered by a fine-tuned llama3.1-nemotron-

Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring

arXiv:2604.20122v1 Announce Type: cross Abstract: We propose a post-hoc adaptive conformal anomaly detection method for monitoring time series that leverages predictions from pre-trained foundation mo

applicationsarxiv-cs-ai

Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models

arXiv:2602.10386v2 Announce Type: replace Abstract: Graph problems are fundamentally challenging for large language models (LLMs). While LLMs excel at processing unstructured text, graph tasks require

local-aiarxiv-cs-lg

Hidden Reliability Risks in Large Language Models: Systematic Identification of Precision-Induced Output Disagreements

arXiv:2604.19790v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed under diverse numerical precision configurations, including standard floating-point formats (e.g.

JoyAI-RA 0.1: A Foundation Model for Robotic Autonomy

arXiv:2604.20100v1 Announce Type: new Abstract: Robotic autonomy in open-world environments is fundamentally limited by insufficient data diversity and poor cross-embodiment generalization. Existing r

applicationsarxiv-cs-ro

Agents

Lightweight LLM Agent Memory with Small Language Models

arXiv:2604.07798v3 Announce Type: replace Abstract: Although LLM agents can leverage tools for complex tasks, they still need memory to maintain cross-turn consistency and accumulate reusable informat

agentsarxiv-cs-ai

Quantum Adaptive Self-Attention for Quantum Transformer Models

arXiv:2504.05336v3 Announce Type: replace-cross Abstract: Integrating quantum computing into deep learning architectures is a promising but poorly understood endeavor: when does a quantum layer actual

applicationsarxiv-cs-lg

Task-Stratified Knowledge Scaling Laws for Post-Training Quantized Large Language Models

arXiv:2508.18609v4 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) is a critical strategy for efficient Large Language Models (LLMs) deployment. However, existing scaling laws

Beyond One Output: Visualizing and Comparing Distributions of Language Model Generations

arXiv:2604.18724v1 Announce Type: new Abstract: Users typically interact with and evaluate language models via single outputs, but each output is just one sample from a broad distribution of possible

Cell-Based Representation of Relational Binding in Language Models

arXiv:2604.19052v1 Announce Type: new Abstract: Understanding a discourse requires tracking entities and the relations that hold between them. While Large Language Models (LLMs) perform well on relati

Chain-of-Thought as a Lens: Evaluating Structured Reasoning Alignment between Human Preferences and Large Language Models

arXiv:2511.06168v3 Announce Type: replace Abstract: This paper primarily demonstrates a method to quantitatively assess the alignment between multi-step, structured reasoning in large language models

Detecting Data Contamination in Large Language Models

arXiv:2604.19561v1 Announce Type: new Abstract: Large Language Models (LLMs) utilize large amounts of data for their training, some of which may come from copyrighted sources. Membership Inference Att

Agents

Feasibility of Indoor Frame-Wise Lidar Semantic Segmentation via Distillation from Visual Foundation Model

arXiv:2604.18831v1 Announce Type: new Abstract: Frame-wise semantic segmentation of indoor lidar scans is a fundamental step toward higher-level 3D scene understanding and mapping applications. Howeve

agentsarxiv-cs-cv

Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI

arXiv:2604.19578v1 Announce Type: cross Abstract: With the rapid advancement of Large Language Models (LLMs), the academic community has faced unprecedented disruptions, particularly in the realm of a

Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models

arXiv:2601.14152v2 Announce Type: replace-cross Abstract: Large language models exhibit surprising sensitivity to the structure of the prompt, but the mechanisms underlying this sensitivity remain poo

Owner-Harm: A Missing Threat Model for AI Agent Safety

arXiv:2604.18658v1 Announce Type: cross Abstract: Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a d

Proposing Topic Models and Evaluation Frameworks for Analyzing Associations with External Outcomes: An Application to Leadership Analysis Using Large-Scale Corporate Review Data

arXiv:2604.18919v1 Announce Type: new Abstract: Analyzing topics extracted from text data in relation to external outcomes is important across fields such as computational social science and organizat

safetyarxiv-cs-cl

Reasoning Structure Matters for Safety Alignment of Reasoning Models

arXiv:2604.18946v1 Announce Type: new Abstract: Large reasoning models (LRMs) achieve strong performance on complex reasoning tasks but often generate harmful responses to malicious user queries. This

Agents

ST-Prune: Training-Free Spatio-Temporal Token Pruning for Vision-Language Models in Autonomous Driving

arXiv:2604.19145v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have become central to autonomous driving systems, yet their deployment is severely bottlenecked by the massive computat

agentsarxiv-cs-ai

Towards Streaming Target Speaker Extraction via Chunk-wise Interleaved Splicing of Autoregressive Language Model

arXiv:2604.19635v1 Announce Type: cross Abstract: While generative models have set new benchmarks for Target Speaker Extraction (TSE), their inherent reliance on global context precludes deployment in

User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation

arXiv:2501.04410v2 Announce Type: replace Abstract: User simulation is an emerging interdisciplinary topic with multiple critical applications in the era of Generative AI. It involves creating an inte

Watch the Weights: Unsupervised monitoring and control of fine-tuned LLMs

arXiv:2508.00161v3 Announce Type: replace-cross Abstract: The releases of powerful open-weight large language models (LLMs) are often not accompanied by access to their full training data. Existing in

Attraction, Repulsion, and Friction: Introducing DMF, a Friction-Augmented Drifting Model

arXiv:2604.18194v1 Announce Type: cross Abstract: Drifting Models [Deng et al., 2026] train a one-step generator by evolving samples under a kernel-based drift field, avoiding ODE integration at infer

local-aiarxiv-cs-cv

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

Depth Adaptive Efficient Visual Autoregressive Modeling

arXiv:2604.17286v1 Announce Type: new Abstract: Visual Autoregressive (VAR) modeling inefficiently applies a fixed computational depth to each position when generating high-resolution images. While ex

Different Paths to Harmful Compliance: Behavioral Side Effects and Mechanistic Divergence Across LLM Jailbreaks

arXiv:2604.18510v1 Announce Type: cross Abstract: Open-weight language models can be rendered unsafe through several distinct interventions, but the resulting models may differ substantially in capabi

local-aiarxiv-cs-cl

EvoComp: Learning Visual Token Compression for Multimodal Large Language Models via Semantic-Guided Evolutionary Labeling

arXiv:2604.17087v1 Announce Type: new Abstract: Recent Multimodal Large Language Models (MLLMs) have demonstrated strong performance on vision-language understanding tasks, yet their inference efficie

GoCoMA: Hyperbolic Multimodal Representation Fusion for Large Language Model-Generated Code Attribution

arXiv:2604.16377v1 Announce Type: new Abstract: Large Language Models (LLMs) trained on massive code corpora are now increasingly capable of generating code that is hard to distinguish from human-writ

HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models

arXiv:2511.01066v3 Announce Type: replace Abstract: We present an ongoing initiative to provide open, very large, high-quality, and richly annotated textual datasets for almost 200 languages. At 30 tr

Improving Radio Interferometry Imaging by Explicitly Modeling Cross-Domain Consistency in Reconstruction

arXiv:2604.16794v1 Announce Type: new Abstract: Radio astronomy plays a crucial role in understanding the universe, particularly within the realm of non-thermal astrophysics. Images of celestial objec

Instinct vs. Reflection: Unifying Token and Verbalized Confidence in Multimodal Large Models

arXiv:2604.17274v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in various perception and reasoning tasks. Despite this success, ens

safetyarxiv-cs-cv

Jailbreaking Large Language Models with Morality Attacks

arXiv:2604.17053v1 Announce Type: new Abstract: Pluralism alignment with AI has the sophisticated and necessary goal of creating AI that can coexist with and serve morally multifaceted humanity. Resea

safetyarxiv-cs-cl

Latent-Compressed Variational Autoencoder for Video Diffusion Models

arXiv:2604.16479v1 Announce Type: new Abstract: Video variational autoencoders (VAEs) used in latent diffusion models typically require a sufficiently large number of latent channels to ensure high-qu

Linking Exteroception and Proprioception through Improved Contact Modeling for Soft Growing Robots

arXiv:2507.10694v2 Announce Type: replace Abstract: Passive deformation due to compliance is a commonly used benefit of soft robots, providing opportunities to achieve robust actuation with few active

researcharxiv-cs-ro