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Token Predictors Are Not Planners: Building Physically Grounded Causal Reasoners

arXiv:2606.01810v1 Announce Type: new Abstract: Current benchmarks for embodied vision-language planning often favor linguistic next-token prediction over physically grounded next-state reasoning. Thi

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ToMAP: Training Opponent-Aware LLM Persuaders with Theory of Mind

DGX agent

arXiv:2505.22961v3 Announce Type: replace Abstract: Large language models (LLMs) have shown promising potential in persuasion, but existing works on training LLM persuaders are still preliminary. Nota

tutorialsarxiv-cs-cl

2 Jun 2026

ToolFG: Towards Well-Grounded Fine-Grained Image Classification

arXiv:2606.02518v1 Announce Type: new Abstract: Fine-grained image classification (FGIC) has broad applications and has attracted significant research attention. In this paper, we explore a novel para

safetyarxiv-cs-cv

ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Emergent Adaptation

arXiv:2602.07883v3 Announce Type: replace Abstract: LLM-powered agentic systems excel at complex long-horizon tasks, but remain constrained by static configurations fixed before execution. Such rigidi

Topological Ignorability for Structural Causal Effects Beyond Means

arXiv:2606.01184v1 Announce Type: cross Abstract: Many interventions alter the structure of an outcome distribution rather than its mean: they can split a population into disconnected regimes, create

Topological texture analysis of microscopy images of dynamic casein gelation and its relation to rheological properties

arXiv:2606.02048v1 Announce Type: new Abstract: We propose a novel computational toolbox that integrates Topological Data Analysis (TDA), Differential Box Counting (DBC), Multifractal Partition (MFP),

researcharxiv-cs-ai

Topology-Aware State Abstraction with Tangle Cores for Markov Decision Processes

arXiv:2606.00427v1 Announce Type: new Abstract: State abstraction in reinforcement learning is usually formulated as a partition of states based on reward and transition similarity. This excludes a co

researcharxiv-cs-lg

Local Ai

Torus Graphs for Large Scale Neural Phase Analysis

arXiv:2606.00496v1 Announce Type: new Abstract: Oscillatory neural signals such as electroencephalography (EEG) and local field potentials (LFPs) show phase relationships that coordinate communication

local-aiarxiv-cs-lg

Toward accurate RUL and SoH estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights

arXiv:2507.09766v2 Announce Type: replace-cross Abstract: Accurate estimation of Remaining Useful Life (RUL) and State of Health (SoH) is essential for reliable Prognostics and Health Management (PHM)

Toward Responsible and Epistemically Grounded Multilingual LLMs for Computational Social Science and Humanities

arXiv:2606.00596v1 Announce Type: new Abstract: Large language models have rapidly evolved in multilingual competence and reasoning capacity, enabling their integration into Social Sciences and Humani

safetyarxiv-cs-cl

Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval

arXiv:2606.00014v1 Announce Type: cross Abstract: Although studies have demonstrated that Large Language Models (LLMs) can perform well on Out-of-Distribution (OOD) tasks, their advantage tends to dim

tutorialsarxiv-cs-ai

Towards 3D-Aware Video Diffusion Models: Render-Free Human Motion Control with Mesh Tokenization

arXiv:2606.02000v1 Announce Type: cross Abstract: Diffusion models have shown remarkable success in video generation. However, whether such models are truly aware of the 3D structure underlying visual

researcharxiv-cs-ai

Towards a General Intelligence and Interface for Wearable Health Data

arXiv:2605.22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personal

agentsarxiv-cs-ai

Towards a Physics Foundation Model

arXiv:2509.13805v4 Announce Type: replace-cross Abstract: Foundation models have revolutionized natural language processing through a ``train once, deploy anywhere'' paradigm, where a single pre-train

tutorialsarxiv-cs-ai

Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification

model-releasesarxiv-cs-cv

arXiv:2509.16635v2 Announce Type: replace Abstract: In real applications, person re-identification (ReID) is expected to retrieve the target person at any time, including both daytime and nighttime, r

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design

arXiv:2606.02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under phys

Towards Interactive Video World Modeling: Frontiers, Challenges, Benchmarks, and Future Trends

arXiv:2606.01164v1 Announce Type: new Abstract: With rapid development of large language models and diffusion-based content generation, world modeling has attracted increasing research attention, bene

agentsarxiv-cs-cv

Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2606.00919v1 Announce Type: new Abstract: Large language models (LLMs) have seen widespread adoption across various domains, yet their reliability is frequently undermined by hallucinations - re

Towards Multidisciplinary Summarization of Hospital Stays: Efficient Sentence-Level Clinical Provenance Categorization

model-releasesarxiv-cs-cl

arXiv:2606.02487v1 Announce Type: new Abstract: Effective 'all-team' summarization in high-complexity settings like the Neonatal Intensive Care Unit (NICU) requires aggregating insights from diverse d

Towards Optimal Robustness in Learning-Augmented Paging

arXiv:2606.01342v1 Announce Type: cross Abstract: Learning-augmented paging has been extensively studied in recent years. A key advantage over naive ML-based approaches is bounded robustness, which gu

Towards Precise Intent-Aligned VLA Aerial Navigation via Expert-Guided GRPO

arXiv:2606.02313v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models offer a promising end-to-end paradigm for unmanned aerial vehicles (UAVs) to accomplish complex tasks specified by f

safetyarxiv-cs-ro

Towards Resolving Optimization Conflicts Between Image- and Text-Based Person Re-Identification

arXiv:2606.02242v1 Announce Type: cross Abstract: The joint optimization of image-based (I2I) and text-based (T2I) person re-identification (ReID) is hindered by modality discrepancies and conflicting

researcharxiv-cs-ai

Towards Simple and Provable Parameter-Free Adaptive Gradient Methods

model-releasesarxiv-cs-lg

arXiv:2412.19444v2 Announce Type: replace Abstract: Optimization algorithms such as AdaGrad and Adam have significantly advanced the training of deep models by dynamically adjusting the learning rate

Towards Sparse Video Understanding and Reasoning

arXiv:2602.13602v2 Announce Type: replace Abstract: We present revise (nderline{Re}asoning with nderline{Vi}deo nderline{S}parsity), a multi-round agent for video question answering (VQA). Instead of

agentsarxiv-cs-cv

Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors

arXiv:2410.09737v2 Announce Type: replace Abstract: A popular way to improve the expressive power of graph neural networks (GNNs) is to use Laplacian eigenvectors as additional node features, since th

Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition

arXiv:2606.00959v1 Announce Type: new Abstract: Understanding modality interaction in multimodal large language models (MLLMs) is central to reliable deployment. We introduce Partial Information Decom

TRACE: Trajectory Risk-Aware Compression for Long-Horizon Agent Safety

arXiv:2606.00611v1 Announce Type: new Abstract: Long-horizon LLM agents produce safety evidence across long trajectories, where sparse, delayed, and compositional risk signals often escape local moder

Tracing GenAI Literacy: Uncovering Student-AI Interaction Patterns in Academic Writing through Epistemic Network Analysis

arXiv:2606.00040v1 Announce Type: cross Abstract: As Generative AI (GenAI) becomes integral to education, fostering GenAI literacy is critical. However, current assessments largely rely on self-report

applicationsarxiv-cs-ai

Tracking the Behavioral Trajectories of Adapting Agents

arXiv:2606.02536v1 Announce Type: new Abstract: Text files such as skill files, memory files, and behavioral configuration files play a central role in defining how modern agents act. Through edits by

agentsarxiv-cs-ai

Tractable Shapley Values and Interactions via Tensor Networks

arXiv:2510.22138v3 Announce Type: replace Abstract: We show how to replace the O(2^n) coalition enumeration over n features behind Shapley values and Shapley-style interaction indices with a few-evalu

Local Ai

TrafficClaw: A Generalizable LLM Agent in the Unified Physical Environment for Urban Traffic Control

arXiv:2604.17456v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown strong capabilities in long-horizon reasoning, tool use, and decision-making in digital environments, y

local-aiarxiv-cs-ai

TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination

arXiv:2606.01737v1 Announce Type: new Abstract: Traffic accident liability analysis is a critical yet challenging task in intelligent transportation and legal assistance. Existing methods often suffer

applicationsarxiv-cs-ai

Train, Test, Re-evaluate: Schedule-Sensitive Evaluation of Generative Data for Hand Detection

arXiv:2606.01896v1 Announce Type: cross Abstract: Generated (or synthetic) image data is increasingly used to augment or replace real training datasets when target imagery is scarce, expensive, or bia

Training-Free Composed Video Retrieval via Visual Representation-Guided Video-LLM Reasoning

arXiv:2606.02321v1 Announce Type: new Abstract: Recent advances in large vision-language models have expanded video retrieval from simple text-based search to more flexible scenarios, where users may

researcharxiv-cs-cv

Training-Free Continuous Bitrate Control for Scalable Image Coding for Humans and Machines

arXiv:2606.00158v1 Announce Type: cross Abstract: Continuous variable-rate compression is highly demanded in real-world applications, but remains underexplored in scalable image coding for humans and

applicationsarxiv-cs-cv

Training-Free Coverless Multi-Image Steganography with Access Control

arXiv:2603.09390v2 Announce Type: replace Abstract: Coverless Image Steganography (CIS) hides information without explicitly modifying a cover image, providing strong imperceptibility and inherent rob

researcharxiv-cs-cv

Training-free image inversion for one-step diffusion models

arXiv:2606.01380v1 Announce Type: new Abstract: In this work, we introduce a novel training-free inversion (TFinv) framework for one-step diffusion models,addressing key challenges in real image inver

safetyarxiv-cs-cv

Training-Free Imitation Learning with Closed-Form Diffusion Policies

arXiv:2606.01238v1 Announce Type: cross Abstract: While diffusion-based policies have impressive performance and expressivity, their long offline training slows down the data collection and policy dep

safetyarxiv-cs-lg

Training-Free Object-Agnostic Jam Detection in Fulfillment Centers

arXiv:2606.00321v1 Announce Type: new Abstract: In fulfillment centers, diverse objects move continuously from inbound to outbound operations and can become jammed due to excessive conveyor friction,

researcharxiv-cs-cv

Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning

arXiv:2606.01967v1 Announce Type: new Abstract: While prompt engineering is instrumental in maximizing the capabilities of Large Language Models (LLMs) during inference, the role of prompts during tra

researcharxiv-cs-cl

Trajectory Data Suffices for Statistically Efficient Policy Evaluation in Fixed-Horizon Offline RL with Linear q^pi-Realizability and Concentrability

arXiv:2510.03494v2 Announce Type: replace Abstract: We study finite-horizon offline reinforcement learning (RL) with function approximation for both policy evaluation and policy optimization. Prior wo

safetyarxiv-cs-lg

TRANS: Terrain-aware Reinforcement Learning for Agile Navigation of Quadruped Robots under Social Interactions

arXiv:2602.12724v3 Announce Type: replace Abstract: This study introduces TRANS: Terrain-aware Reinforcement learning for Agile Navigation under Social interactions, a deep reinforcement learning (DRL

agentsarxiv-cs-ro

Trans2Occ: Voxel Occupancy Estimation and Grasp for Transparent Objects from Simulation to Reality

arXiv:2606.01777v1 Announce Type: new Abstract: Transparent objects remain challenging for robotic perception due to unreliable depth sensing caused by refraction and reflection. While prior approache

applicationsarxiv-cs-ro

Transferable Self-Harm Surveillance from Emergency Department Triage Notes Using an Evidence-Augmented Machine Learning Approach

arXiv:2606.02545v1 Announce Type: new Abstract: Self-harm is a major public health concern, but current surveillance relying on hospital presentations is inadequate due to the low sensitivity of diagn

researcharxiv-cs-cl

Transferring Information Across Interventions in Causal Bayesian Optimization

arXiv:2606.01457v1 Announce Type: new Abstract: Bayesian optimization is a popular way to optimize expensive systems, where every experiment, simulation, or intervention costs time or money. In its st

tutorialsarxiv-cs-ai

TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents

arXiv:2606.01046v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has significantly improved travel planning applications, yet evaluating such models is limited by existi

Tree-Guided Identify-Then-Exploit: A Unified Framework of Best Arm Identification and Regret Minimization for Dueling Bandits

arXiv:2606.01799v1 Announce Type: new Abstract: We study N-armed stochastic dueling bandits under the Condorcet-winner assumption, where three widely adopted objectives are considered: best-arm identi

researcharxiv-cs-lg

Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance

arXiv:2304.11127v5 Announce Type: replace-cross Abstract: Recent scientific advances require complex experiment design, necessitating the meticulous tuning of many experiment parameters. Tree-structur