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Think Less, Know More: State-Aware Reasoning Compression with Knowledge Guidance for Efficient Reasoning

arXiv:2604.09150v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex tasks by leveraging long Chain-of-Thought (CoT), but often suffer from overthinking,

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Through Their Eyes: Fixation-aligned Tuning for Personalized User Emulation

DGX agent

arXiv:2604.09368v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed as scalable user simulators for recommender system evaluation. Yet existing simulators per

safetyarxiv-cs-cv

13 Apr 2026

TME-PSR: Time-aware, Multi-interest, and Explanation Personalization for Sequential Recommendation

arXiv:2604.09439v1 Announce Type: cross Abstract: In this paper, we propose a sequential recommendation model that integrates Time-aware personalization, Multi-interest personalization, and Explanatio

Tora3: Trajectory-Guided Audio-Video Generation with Physical Coherence

arXiv:2604.09057v1 Announce Type: new Abstract: Audio-video (AV) generation has recently made strong progress in perceptual quality and multimodal coherence, yet generating content with plausible moti

Toward World Models for Epidemiology

arXiv:2604.09519v1 Announce Type: new Abstract: World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting planning under uncertai

Training event-based neural networks with exact gradients via Differentiable ODE Solving in JAX

arXiv:2603.08146v3 Announce Type: replace Abstract: Existing frameworks for gradient-based training of spiking neural networks face a trade-off: discrete-time methods using surrogate gradients support

Traj2Action: A Co-Denoising Framework for Trajectory-Guided Human-to-Robot Skill Transfer

arXiv:2510.00491v3 Announce Type: replace-cross Abstract: Learning diverse manipulation skills for real-world robots is severely bottlenecked by the reliance on costly and hard-to-scale teleoperated d

Truncated Rectified Flow Policy for Reinforcement Learning with One-Step Sampling

arXiv:2604.09159v1 Announce Type: new Abstract: Maximum entropy reinforcement learning (MaxEnt RL) has become a standard framework for sequential decision making, yet its standard Gaussian policy para

Unbiased Rectification for Sequential Recommender Systems Under Fake Orders

arXiv:2604.08550v1 Announce Type: cross Abstract: Fake orders pose increasing threats to sequential recommender systems by misleading recommendation results through artificially manipulated interactio

UniSemAlign: Text-Prototype Alignment with a Foundation Encoder for Semi-Supervised Histopathology Segmentation

arXiv:2604.09169v1 Announce Type: new Abstract: Semi-supervised semantic segmentation in computational pathology remains challenging due to scarce pixel-level annotations and unreliable pseudo-label s

VAG: Dual-Stream Video-Action Generation for Embodied Data Synthesis

arXiv:2604.09330v1 Announce Type: cross Abstract: Recent advances in robot foundation models trained on large-scale human teleoperation data have enabled robots to perform increasingly complex real-wo

VISOR: Agentic Visual Retrieval-Augmented Generation via Iterative Search and Over-horizon Reasoning

arXiv:2604.09508v1 Announce Type: cross Abstract: Visual Retrieval-Augmented Generation (VRAG) empowers Vision-Language Models to retrieve and reason over visually rich documents. To tackle complex qu

Visually-Guided Policy Optimization for Multimodal Reasoning

arXiv:2604.09349v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has significantly advanced the reasoning ability of vision-language models (VLMs). However, the

When & How to Write for Personalized Demand-aware Query Rewriting in Video Search

arXiv:2602.17667v2 Announce Type: replace-cross Abstract: In video search systems, user historical behaviors provide rich context for identifying search intent and resolving ambiguity. However, tradit

Wireless Communication Enhanced Value Decomposition for Multi-Agent Reinforcement Learning

arXiv:2604.08728v1 Announce Type: new Abstract: Cooperation in multi-agent reinforcement learning (MARL) benefits from inter-agent communication, yet most approaches assume idealized channels and exis

You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector

arXiv:2603.15757v2 Announce Type: replace-cross Abstract: What happens when a pretrained generative robot policy is provided a constant initial noise as input, rather than repeatedly sampling it from

A Clinical Point Cloud Paradigm for In-Hospital Mortality Prediction from Multi-Level Incomplete Multimodal EHRs

arXiv:2604.04614v2 Announce Type: replace-cross Abstract: Deep learning-based modeling of multimodal Electronic Health Records (EHRs) has become an important approach for clinical diagnosis and risk p

A First Guess is Rarely the Final Answer: Learning to Search in the Travelling Salesperson Problem

arXiv:2604.06940v1 Announce Type: cross Abstract: Most neural solvers for the Traveling Salesperson Problem (TSP) are trained to output a single solution, even though practitioners rarely stop there:

A Physical Agentic Loop for Language-Guided Grasping with Execution-State Monitoring

arXiv:2604.07395v1 Announce Type: cross Abstract: Robotic manipulation systems that follow language instructions often execute grasp primitives in a largely single-shot manner: a model proposes an act

A systematic framework for generating novel experimental hypotheses from language models

arXiv:2408.05086v3 Announce Type: replace Abstract: Neural language models (LMs) have been shown to capture complex linguistic patterns, yet their utility in understanding human language and more broa

Active Reward Machine Inference From Raw State Trajectories

arXiv:2604.07480v1 Announce Type: new Abstract: Reward machines are automaton-like structures that capture the memory required to accomplish a multi-stage task. When combined with reinforcement learni

safetyarxiv-cs-ro

ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration

arXiv:2604.08534v1 Announce Type: new Abstract: Large-scale real-world robot data collection is a prerequisite for bringing robots into everyday deployment. However, existing pipelines often rely on s

safetyarxiv-cs-ro

Adaptive Replay Buffer for Offline-to-Online Reinforcement Learning

arXiv:2512.10510v2 Announce Type: replace-cross Abstract: Offline-to-Online Reinforcement Learning (O2O RL) faces a critical dilemma in balancing the use of a fixed offline dataset with newly collecte

AgentCity: Constitutional Governance for Autonomous Agent Economies via Separation of Power

arXiv:2604.07007v1 Announce Type: cross Abstract: Autonomous AI agents are beginning to operate across organizational boundaries on the open internet -- discovering, transacting with, and delegating t

AI-Driven Research for Databases

arXiv:2604.06566v1 Announce Type: cross Abstract: As the complexity of modern workloads and hardware increasingly outpaces human research and engineering capacity, existing methods for database perfor

Alternatives to the Laplacian for Scalable Spectral Clustering with Group Fairness Constraints

arXiv:2510.20220v3 Announce Type: replace Abstract: Recent research has focused on mitigating algorithmic bias in clustering by incorporating fairness constraints into algorithmic design. Notions such

An Agentic Evaluation Architecture for Historical Bias Detection in Educational Textbooks

arXiv:2604.07883v1 Announce Type: cross Abstract: History textbooks often contain implicit biases, nationalist framing, and selective omissions that are difficult to audit at scale. We propose an agen

Android Coach: Improve Online Agentic Training Efficiency with Single State Multiple Actions

arXiv:2604.07277v1 Announce Type: cross Abstract: Online reinforcement learning (RL) serves as an effective method for enhancing the capabilities of Android agents. However, guiding agents to learn th

Are Face Embeddings Compatible Across Deep Neural Network Models?

arXiv:2604.07282v1 Announce Type: cross Abstract: Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be t

Attention Flows: Tracing LLM Conceptual Engagement via Story Summaries

arXiv:2604.06416v1 Announce Type: cross Abstract: Although LLM context lengths have grown, there is evidence that their ability to integrate information across long-form texts has not kept pace. We ev

AudioRole: An Audio Dataset for Character Role-Playing in Large Language Models

arXiv:2509.23435v2 Announce Type: replace-cross Abstract: The creation of high-quality multimodal datasets remains fundamental for advancing role-playing capabilities in large language models (LLMs).

Beyond Loss Values: Robust Dynamic Pruning via Loss Trajectory Alignment

arXiv:2604.07306v1 Announce Type: cross Abstract: Existing dynamic data pruning methods often fail under noisy-label settings, as they typically rely on per-sample loss as the ranking criterion. This

Beyond Pessimism: Offline Learning in KL-regularized Games

arXiv:2604.06738v1 Announce Type: cross Abstract: We study offline learning in KL-regularized two-player zero-sum games, where policies are optimized under a KL constraint to a fixed reference policy.

Beyond Surface Judgments: Human-Grounded Risk Evaluation of LLM-Generated Disinformation

arXiv:2604.06820v1 Announce Type: new Abstract: Large language models (LLMs) can generate persuasive narratives at scale, raising concerns about their potential use in disinformation campaigns. Assess

Bias Redistribution in Visual Machine Unlearning: Does Forgetting One Group Harm Another?

arXiv:2604.08111v1 Announce Type: cross Abstract: Machine unlearning enables models to selectively forget training data, driven by privacy regulations such as GDPR and CCPA. However, its fairness impl

Model Releases

Blind Refusal: Language Models Refuse to Help Users Evade Unjust, Absurd, and Illegitimate Rules

model-releasesarxiv-cs-ai

arXiv:2604.06233v1 Announce Type: new Abstract: Safety-trained language models routinely refuse requests for help circumventing rules. But not all rules deserve compliance. When users ask for help eva

Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning

arXiv:2604.08068v1 Announce Type: new Abstract: Decoding visual information from electroencephalography (EEG) has recently achieved promising results, primarily focusing on reconstructing two-dimensio

CAFP: A Post-Processing Framework for Group Fairness via Counterfactual Model Averaging

arXiv:2604.07009v1 Announce Type: new Abstract: Ensuring fairness in machine learning predictions is a critical challenge, especially when models are deployed in sensitive domains such as credit scori

CNN-based Surface Temperature Forecasts with Ensemble Numerical Weather Prediction

arXiv:2507.18937v3 Announce Type: replace-cross Abstract: Due to limited computational resources, medium-range temperature forecasts typically rely on low-resolution numerical weather prediction (NWP)

Contextualising (Im)plausible Events Triggers Figurative Language

arXiv:2604.07885v1 Announce Type: new Abstract: This work explores the connection between (non-)literalness and plausibility at the example of subject-verb-object events in English. We design a system

Decompose, Look, and Reason: Reinforced Latent Reasoning for VLMs

arXiv:2604.07518v1 Announce Type: new Abstract: Vision-Language Models often struggle with complex visual reasoning due to the visual information loss in textual CoT. Existing methods either add the c

Demystifying OPD: Length Inflation and Stabilization Strategies for Large Language Models

arXiv:2604.08527v1 Announce Type: new Abstract: On-policy distillation (OPD) trains student models under their own induced distribution while leveraging supervision from stronger teachers. We identify

Discrete Flow Matching Policy Optimization

arXiv:2604.06491v1 Announce Type: cross Abstract: We introduce Discrete flow Matching policy Optimization (DoMinO), a unified framework for Reinforcement Learning (RL) fine-tuning Discrete Flow Matchi

Drift-Based Policy Optimization: Native One-Step Policy Learning for Online Robot Control

arXiv:2604.03540v2 Announce Type: replace Abstract: Although multi-step generative policies achieve strong performance in robotic manipulation by modeling multimodal action distributions, they require

safetyarxiv-cs-ro

EchoTorrent: Towards Swift, Sustained, and Streaming Multi-Modal Video Generation

arXiv:2602.13669v4 Announce Type: replace Abstract: Recent multi-modal video generation models have achieved high visual quality, but their prohibitive latency and limited temporal stability hinder re

EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World

arXiv:2604.07607v1 Announce Type: cross Abstract: Robot learning increasingly depends on large and diverse data, yet robot data collection remains expensive and difficult to scale. Egocentric human da

Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems

arXiv:2604.06914v1 Announce Type: new Abstract: In this paper, we study a vehicle-to-infrastructure (V2I) system where distributed base stations (BSs) acting as road-side units (RSUs) collect multimod

Faithful GRPO: Improving Visual Spatial Reasoning in Multimodal Language Models via Constrained Policy Optimization

arXiv:2604.08476v1 Announce Type: new Abstract: Multimodal reasoning models (MRMs) trained with reinforcement learning with verifiable rewards (RLVR) show improved accuracy on visual reasoning benchma