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Towards Trust Calibration in Socially Interactive Agents: Investigating Gendered Multimodal Behaviors Generation with LLMs

model-releasesarxiv-cs-cl

arXiv:2605.19798v1 Announce Type: new Abstract: As Socially Interactive Agents (SIAs) become increasingly integrated into daily life, the ability to calibrate user trust to an agent's actual capabilit

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Research

Training-Free Bayesian Filtering with Generative Emulators

DGX agent

arXiv:2605.20028v1 Announce Type: new Abstract: Bayesian filtering is a well-known problem that aims to estimate plausible states of a dynamical system from observations. Among existing approaches to

researcharxiv-cs-lg

20 May 2026

Training Neural Networks with Optimal Double-Bayesian Learning

arXiv:2605.20009v1 Announce Type: cross Abstract: Backpropagation with gradient descent is a common optimization strategy employed by most neural network architectures in machine learning. However, fi

Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform

model-releasesarxiv-cs-ro

arXiv:2605.19881v1 Announce Type: new Abstract: We present a modular framework to benchmark new and existing methods for trajectory planning and control in high-acceleration maneuvers that push autono

TrajectoryMover: Generative Movement of Object Trajectories in Videos

arXiv:2603.29092v3 Announce Type: replace Abstract: Generative video editing has enabled several intuitive editing operations for short video clips that would previously have been difficult to achieve

TrajTok: Adaptive Spatial Tokenization for Trajectory Representation Learning

arXiv:2605.20134v1 Announce Type: new Abstract: Learning generalizable trajectory representations from raw GPS traces remains difficult because the data is continuous, noisy, and irregularly sampled.

Transformers Linearly Represent Highly Structured World Models

arXiv:2605.18847v1 Announce Type: cross Abstract: Do transformers, when trained on sequential reasoning traces, build internal models of the underlying task? And if so, does the structure of those int

researcharxiv-cs-ai

Transforming Constraint Programs to Input for Local Search

arXiv:2605.19671v1 Announce Type: new Abstract: Applying local search algorithms to combinatorial optimization problems is not an easy feat. Typically, human intervention is required to compile the co

researcharxiv-cs-ai

TravExplorer: Cross-Floor Embodied Exploration via Traversability-Aware 3-D Planning

model-releasesarxiv-cs-ro

arXiv:2605.19958v1 Announce Type: new Abstract: Zero-shot Object Navigation (ZSON) has shown promise for open-vocabulary target search in unseen environments, yet most existing systems remain tied to

Trust It or Not: Evidential Uncertainty for Feed-Forward 3D Reconstruction with Trust3R

arXiv:2605.19539v1 Announce Type: new Abstract: Geometric foundation models hold promise for unconstrained dense geometry prediction from uncalibrated images. However, in current feed-forward designs,

Trust or Abstain? A Self-Aware RAG Approach

model-releasesarxiv-cs-cl

arXiv:2605.18792v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves large language models (LLMs) by incorporating external evidence, but it also introduces knowledge confli

Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On

arXiv:2605.19035v1 Announce Type: new Abstract: The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution. As these agents

TSR: Trajectory-Search Rollouts for Multi-Turn RL of LLM Agents

arXiv:2602.11767v3 Announce Type: replace Abstract: Advances in large language models (LLMs) are driving a shift toward using reinforcement learning (RL) to train agents from iterative, multi-turn int

Tutorials

Tweedie's Formulae and Diffusion Generative Models Beyond Gaussian

arXiv:2605.19391v1 Announce Type: cross Abstract: Diffusion models have achieved remarkable success in generating samples from unknown data distributions. Most popular stochastic differential equation

tutorialsarxiv-cs-lg

Tutorials

TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation

arXiv:2602.09023v4 Announce Type: replace Abstract: Despite strong generalization capabilities, Vision-Language-Action (VLA) models remain constrained by the high cost of expert demonstrations and lim

tutorialsarxiv-cs-ro

TwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing

arXiv:2605.18859v1 Announce Type: cross Abstract: LLM routing matters most in long-horizon applications such as coding agents, deep research systems, and computer-use agents, where a single user reque

Hardware

UCCI: Calibrated Uncertainty for Cost-Optimal LLM Cascade Routing

arXiv:2605.18796v1 Announce Type: cross Abstract: LLM cascades and model routing promise lower inference cost by sending easy queries to a small model and escalating hard ones to a large model, but mo

hardwarearxiv-cs-cl

Local Ai

Understanding and Exploiting Weight Update Sparsity for Communication-Efficient Distributed RL

arXiv:2602.03839v2 Announce Type: replace Abstract: Bandwidth-constrained distributed reinforcement learning (RL) post-training of large language models is bottlenecked by two channels: weight synchro

local-aiarxiv-cs-lg

Understanding Dynamics of Adam in Zero-Sum Games: An ODE Approach

arXiv:2605.19392v1 Announce Type: new Abstract: The remarkable success of the Adam in training neural networks has naturally led to the widespread use of its descent-ascent counterpart, Adam-DA, for s

Unified Deployment-Aware Evaluation of Open Reasoning Language Models

model-releasesarxiv-cs-cl

arXiv:2604.07035v2 Announce Type: replace Abstract: Open reasoning language models are often compared under mixed sample sizes, partially standardized prompts, and accuracy-centered summaries, which m

UniRefiner: Teaching Pre-trained ViTs to Self-Dispose Dross via Contrastive Register

arXiv:2605.19622v1 Announce Type: new Abstract: Representation learning with Vision Transformers (ViTs) has advanced rapidly, yet the utility of large-scale models in spatially sensitive tasks is hind

Universal Skeleton Understanding via Differentiable Rendering and MLLMs

arXiv:2603.18003v4 Announce Type: replace Abstract: Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet cannot process structured, non-visual data such as human skel

safetyarxiv-cs-cv

Unlocking the Potential of Continual Model Merging: An ODE Perspective

arXiv:2605.19409v1 Announce Type: cross Abstract: Continual Model Merging (CMM) enables rapid customization of foundation models across sequentially arriving tasks, offering a scalable alternative to

Unsupervised Unfolded rPCA (U2-rPCA): Deep Interpretable Clutter Filtering for Ultrasound Microvascular Imaging

arXiv:2510.00660v2 Announce Type: replace Abstract: High-sensitivity clutter filtering is a fundamental step in ultrasound microvascular imaging. Singular value decomposition (SVD) and robust principa

Local Ai

Using Aristotle API for AI-Assisted Theorem Proving in Lean 4: A Formalisation Case Study of the Grasshopper Problem

arXiv:2605.20120v1 Announce Type: new Abstract: AI-assisted theorem proving can now generate substantial Lean developments for olympiad-level mathematics, but the evidential status of such development

local-aiarxiv-cs-ai

Variance-Reduced Manifold Sampling via Polynomial-Maximization Density Estimation

arXiv:2605.19938v1 Announce Type: cross Abstract: Uniform sampling on implicitly defined manifolds is a core primitive in motion planning, constrained simulation, and probabilistic machine learning. M

Applications

Variational Diffusion Channel Decoder

arXiv:2605.18902v1 Announce Type: cross Abstract: Neural channel decoder, as a data-driven channel decoding strategy, has shown very promising improvement on error-correcting capability over the class

applicationsarxiv-cs-lg

Applications

VCR: Learning Valid Contextual Representation for Incomplete Wearable Signals

arXiv:2605.18837v1 Announce Type: cross Abstract: Wearable devices enable continuous health monitoring from multimodal signals, but real-world deployment is hindered by limited labeled data and pervas

applicationsarxiv-cs-ai

ViroGym: Realistic Large-Scale Benchmarks for Evaluating Viral Proteins

arXiv:2603.06740v2 Announce Type: replace-cross Abstract: Protein language models (pLMs) have shown strong potential for zero-shot prediction of missense variant effects, yet systematic benchmarking o

Vision Harnessing Agent for Open Ad-hoc Segmentation

model-releasesarxiv-cs-cv

arXiv:2605.19410v1 Announce Type: new Abstract: Segmentation has become easy when the concept is known, requiring retrieval of a learned visual grounding from text. It remains hard for open ad-hoc con

VL-DPO: Vision-Language-Guided Finetuning for Preference-Aligned Autonomous Driving

arXiv:2605.20082v1 Announce Type: cross Abstract: The rapid growth of autonomous driving datasets has enabled the scaling of powerful motion forecasting models. While large-scale pretraining provides

Walsh-Hadamard Neural Operators for Solving PDEs with Discontinuous Coefficients

arXiv:2511.07347v2 Announce Type: replace-cross Abstract: Neural operators have emerged as powerful tools for learning solution operators of partial differential equations (PDEs). However, standard sp

WARC-Bench: Web Archive Based Benchmark for GUI Subtask Executions

arXiv:2510.09872v2 Announce Type: replace-cross Abstract: Training web agents to navigate complex, real-world websites requires them to master extit{subtasks} - short-horizon interactions on multiple

Warm-starting active-set solvers using graph neural networks

arXiv:2511.13174v2 Announce Type: replace Abstract: Quadratic programming (QP) solvers are widely used in real-time control and optimization, but their computational cost often limits applicability in

WBCAtt+: Fine-Grained Pixel-Level Morphological Annotations for White Blood Cell Images

arXiv:2605.19692v1 Announce Type: new Abstract: The microscopic examination of white blood cells (WBCs) plays a fundamental role in pathology and is essential for diagnosing blood disorders such as le

What and When to Distill: Selective Hindsight Distillation for Multi-Turn Agents

arXiv:2605.19447v1 Announce Type: new Abstract: Reinforcement learning can train LLM agents from sparse task rewards, but long-horizon credit assignment remains challenging: a single success-or-failur

What Are LLMs Doing to Scientific Communication? Measuring Changes in Writing Practices and Reading Experience

arXiv:2605.19936v1 Announce Type: new Abstract: Has the style of scientific communication changed due to the growing use of large language models in the writing process? We address this question in th

researcharxiv-cs-cl

What Do Evolutionary Coding Agents Evolve?

arXiv:2605.20086v1 Announce Type: cross Abstract: Recent work pairs LLMs with evolutionary search to iteratively generate, modify, and select code using task-specific feedback. These systems have prod

What Machine Learning Can Do for Focusing Aerogel Detectors

arXiv:2312.02652v2 Announce Type: replace-cross Abstract: Particle identification at the Super Charm-Tau factory experiment will be provided by a Focusing Aerogel Ring Imaging CHerenkov detector (FARI

What Makes a Representation Good for Single-Cell Perturbation Prediction?

model-releasesarxiv-cs-lg

arXiv:2605.19343v1 Announce Type: new Abstract: Single-cell perturbation modeling is fundamental for understanding and predicting cellular responses to genetic perturbations. However, existing approac

What Makes Synthetic Data Effective in Image Segmentation

model-releasesarxiv-cs-cv

arXiv:2605.19289v1 Announce Type: new Abstract: Driven by rapid advances in large-scale generative models, synthetic data has emerged as a promising solution for visual understanding. While modern dif

What Really Improves Mathematical Reasoning: Structured Reasoning Signals Beyond Pure Code

arXiv:2605.19762v1 Announce Type: new Abstract: Code has become a standard component of modern foundation language model (LM) training, yet its role beyond programming remains unclear. We revisit the

researcharxiv-cs-ai

When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control System

arXiv:2605.20037v1 Announce Type: cross Abstract: Reward-poisoning attacks present a significant risk to learning-based wireless control systems. Given this, we propose a Disagreement-Guided Reward Po

When Does Model Collapse Occur in Structured Interactive Learning?

arXiv:2605.20151v1 Announce Type: new Abstract: The proliferation of generative artificial intelligence has given rise to an interactive learning environment, where model parameters are continuously u

When Individually Calibrated Models Become Collectively Miscalibrated

arXiv:2605.18858v1 Announce Type: cross Abstract: Probabilistic prediction systems often aggregate probability estimates from multiple models into a single decision. A common assumption is that if eac

When Preference Labels Fall Short: Aligning Diffusion Models from Real Data

arXiv:2605.19839v1 Announce Type: new Abstract: Preference alignment aims to guide generative models by learning from comparisons between preferred and non-preferred samples. In practice, most existin

safetyarxiv-cs-cv

When Skills Don't Help: A Negative Result on Procedural Knowledge for Tool-Grounded Agents in Offensive Cybersecurity

arXiv:2605.20023v1 Announce Type: new Abstract: Agent Skills, structured packages of procedural knowledge loaded into an LLM agent at inference time, are widely reported to improve task pass rates by

When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach

arXiv:2605.19662v1 Announce Type: new Abstract: Tabular foundation models based on pretrained prior-data fitted networks~(PFNs) have shown strong generalization on diverse tabular tasks, but they are