All entries

TopoGeoScore: A Self-Supervised Source-Only Geometric Framework for OOD Checkpoint Selection

arXiv:2605.08870v1 Announce Type: new Abstract: Out-of-distribution (OOD) robustness is difficult to diagnose when target-domain labels are unavailable. We consider a more restrictive source-only vari

Content type

All Blog X Post Paper YouTube Reddit GitHub

Topological Data Analysis Applications in Natural Language Processing: A Survey

DGX agent

arXiv:2411.10298v5 Announce Type: replace Abstract: The surge of data available on the Internet has driven the adoption of a wide range of computational methods for analyzing and extracting insights f

applicationsarxiv-cs-cl

12 May 2026

TopoU-Net: a U-Net architecture for topological domains

arXiv:2605.10091v1 Announce Type: new Abstract: Many modern datasets mix points, edges, regions, groups, objects, events, hyperedges, and relations. Yet neural architectures often force such data into

Total Generalized Variation regularization closes the gap between neural-eld and classical methods in seismic travel-time tomography

arXiv:2605.09960v1 Announce Type: cross Abstract: Travel-time tomography forces a trade-off between mesh resolution and stability in which the regularizer choice dominates what can be recovered. We in

safetyarxiv-cs-lg

Toward an Engineering of Science: Rebalancing Generation and Verification in the Age of AI

arXiv:2605.10425v1 Announce Type: cross Abstract: AI systems can now cheaply generate plausible scientific artifacts such as papers, reviews, and surveys. This creates a risk of epistemic pollution in

Toward General and Robust LLM-enhanced Text-attributed Graph Learning

arXiv:2504.02343v2 Announce Type: replace Abstract: Recent advancements in Large Language Models (LLMs) and the proliferation of Text-Attributed Graphs (TAGs) across various domains have positioned LL

applicationsarxiv-cs-lg

Tutorials

Toward Multi-Database Query Reasoning for Text2Cypher

arXiv:2605.10373v1 Announce Type: cross Abstract: Large language models have significantly improved natural language interfaces to databases by translating user questions into executable queries. In p

tutorialsarxiv-cs-cl

Toward Optimal Regret in Robust Pricing: Decoupling Corruption and Time

arXiv:2605.08290v1 Announce Type: cross Abstract: We design the first regret guarantees for robust dynamic pricing that decouple the dependence on the corruption C and the time horizon T. In dynamic p

Toward Reliable Sim-to-Real Predictability for MoE-based Robust Quadrupedal Locomotion

arXiv:2602.00678v4 Announce Type: replace Abstract: Reinforcement learning has shown strong promise for quadrupedal agile locomotion, even with proprioception-only sensing. In practice, however, sim-t

safetyarxiv-cs-ro

Towards a Certificate of Trust: Task-Aware OOD Detection for Scientific AI

arXiv:2509.25080v3 Announce Type: replace Abstract: Data-driven models are increasingly adopted in critical scientific fields like weather forecasting and fluid dynamics. These methods can fail on out

Towards a Large Language-Vision Question Answering Model for MSTAR Automatic Target Recognition

arXiv:2605.10772v1 Announce Type: cross Abstract: Large language-vision models (LLVM), such as OpenAI's ChatGPT and GPT-4, have gained prominence as powerful tools for analyzing text and imagery. The

Agents

Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration

arXiv:2605.09366v1 Announce Type: new Abstract: Transforming neuroimaging data into clinically actionable biomarkers is a knowledge-intensive and labor-intensive process. Standardized workflows such a

agentsarxiv-cs-ai

Agents

Towards Autonomous Railway Operations: A Semi-Hierarchical Deep Reinforcement Learning Approach to the Vehicle Rescheduling Problem

arXiv:2605.10257v1 Announce Type: new Abstract: Managing disruptions in railway traffic management is a major challenge. Rising traffic density and infrastructure limits increase complexity, making th

agentsarxiv-cs-ai

Towards Backdoor-Based Ownership Verification for Vision-Language-Action Models

arXiv:2605.09005v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs) support generalist robotic control by enabling end-to-end decision policies directly from multi-modal inputs. As

Local Ai

Towards Batch-to-Streaming Deep Reinforcement Learning for Continuous Control

arXiv:2603.08588v2 Announce Type: replace-cross Abstract: State-of-the-art deep reinforcement learning (RL) methods have achieved remarkable performance in continuous control tasks, yet their computat

local-aiarxiv-cs-ai

Towards Compact Sign Language Translation: Frame Rate and Model Size Trade-offs

model-releasesarxiv-cs-cl

arXiv:2605.09554v1 Announce Type: new Abstract: Sign Language Translation (SLT) converts sign language videos into spoken-language text, bridging communication between Deaf and hearing communities. Cu

Towards Conversational Medical AI with Eyes, Ears and a Voice

arXiv:2605.09272v1 Announce Type: new Abstract: The practice of medicine relies not only upon skillful dialogue but also on the nuanced exchange and interpretation of rich auditory and visual cues bet

Towards Cross-lingual Values Judgment: A Consensus-Pluralism Perspective

arXiv:2602.17283v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are employed worldwide, existing evaluation paradigms for their multilingual capabilities primarily focus on f

Towards Customized Multimodal Role-Play

arXiv:2605.08129v1 Announce Type: new Abstract: Unified multimodal understanding and generation models enable richer human-AI interaction. Yet jointly customizing a character's persona, dialogue style

safetyarxiv-cs-lg

Towards Effective Theory of LLMs: A Representation Learning Approach

arXiv:2605.09294v1 Announce Type: cross Abstract: We propose Representational Effective Theory (RET), a framework for describing large language model computation in terms of learned macrostates rather

Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse

model-releasesarxiv-cs-cv

arXiv:2605.09965v1 Announce Type: new Abstract: The real world unfolds along a single set of physics laws, yet human intelligence demonstrates a remarkable capacity to generalize experiences from this

Towards Generative Predictive Display for Vision-Based Teleoperation: A Zero-Shot Benchmark of Off-the-Shelf Video Models

model-releasesarxiv-cs-cv

arXiv:2605.09670v1 Announce Type: cross Abstract: Teleoperation systems are fundamentally limited by communication latency, which degrades situational awareness and control performance. Predictive dis

Towards Interpretable Damage Detection based on Aerodynamic Pressure Measurements

arXiv:2605.08187v1 Announce Type: cross Abstract: The increasing flexibility of modern large wind turbine blades necessitates cost-efficient and reliable structural monitoring solutions. For this purp

Towards On-Policy Data Evolution for Visual-Native Multimodal Deep Search Agents

model-releasesarxiv-cs-cl

arXiv:2605.10832v1 Announce Type: new Abstract: Multimodal deep search requires an agent to solve open-world problems by chaining search, tool use, and visual reasoning over evolving textual and visua

Towards Robust Sequential Decomposition for Complex Image Editing

arXiv:2605.09233v1 Announce Type: cross Abstract: Recent advances in visual generative models have enabled high-fidelity image editing guided by human instructions. However, these models often struggl

applicationsarxiv-cs-ai

Agents

Towards Robust Surgical Automation via Digital Twin Representations from Foundation Models

arXiv:2409.13107v3 Announce Type: replace Abstract: Large language model-based (LLM) agents are emerging as a powerful enabler of robust embodied intelligence due to their capability of planning compl

agentsarxiv-cs-ro

Towards Shutdownable Agents: Generalizing Stochastic Choice in RL Agents and LLMs

arXiv:2604.17502v2 Announce Type: replace Abstract: Misaligned artificial agents might resist shutdown. One proposed solution is to train agents to lack preferences between different-length trajectori

Towards Trustworthy Audio Deepfake Detection: A Systematic Framework for Diagnosing and Mitigating Gender Bias

model-releasesarxiv-cs-lg

arXiv:2605.09087v1 Announce Type: cross Abstract: Audio deepfake detection systems are increasingly deployed in high-stakes security applications, yet their fairness across demographic groups remains

Tutorials

Towards Understanding Continual Factual Knowledge Acquisition of Language Models: From Theory to Algorithm

arXiv:2605.10640v1 Announce Type: cross Abstract: Continual Pre-Training (CPT) is essential for enabling Language Models (LMs) to integrate new knowledge without erasing old. While classical CPT techn

tutorialsarxiv-cs-ai

Towards Universal Gene Regulatory Network Inference: Unlocking Generalizable Regulatory Knowledge in Single-cell Foundation Models

arXiv:2605.08128v1 Announce Type: cross Abstract: Gene Regulatory Network (GRN) inference is essential for understanding complex cellular mechanisms, rendered tractable through single-cell transcripto

TRACE: Distilling Where It Matters via Token-Routed Self On-Policy Alignment

arXiv:2605.10194v1 Announce Type: new Abstract: On-policy self-distillation (self-OPD) densifies reinforcement learning with verifiable rewards (RLVR) by letting a policy teach itself under privileged

safetyarxiv-cs-ai

TRACER: Verifiable Generative Provenance for Multimodal Tool-Using Agents

model-releasesarxiv-cs-cl

arXiv:2605.09934v1 Announce Type: new Abstract: Multimodal large language models increasingly solve vision-centric tasks by calling external tools for visual inspection, OCR, retrieval, calculation, a

Tracing Moral Foundations in Large Language Models

arXiv:2601.05437v2 Announce Type: replace-cross Abstract: Large language models often produce human-like moral judgments, but it is unclear whether this reflects an internal conceptual structure or su

Tracking the Truth: Object-Centric Spatio-Temporal Monitoring for Video Large Language Models

arXiv:2605.08974v1 Announce Type: cross Abstract: While multimodal large language models (MLLMs) have advanced video understanding, they remain highly prone to hallucinations in dynamic scenes. We arg

Training-Free Cultural Alignment of Large Language Models via Persona Disagreement

arXiv:2605.10843v1 Announce Type: cross Abstract: Large language models increasingly mediate decisions that turn on moral judgement, yet a growing body of evidence shows that their implicit preference

safetyarxiv-cs-ai

Training Reasoning Models on Saturated Problems via Failure-Prefix Conditioning

arXiv:2601.20829v2 Announce Type: replace-cross Abstract: As Reinforcement Learning with Verifiable Rewards (RLVR) substantially improves the reasoning abilities of large language models (LLMs), a new

Training with Harnesses: On-Policy Harness Self-Distillation for Complex Reasoning

arXiv:2605.08741v1 Announce Type: new Abstract: Inference-time harnesses substantially improve large language models on complex reasoning tasks. However, the intrinsic capabilities of the underlying m

safetyarxiv-cs-cl

TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation

arXiv:2605.10020v1 Announce Type: new Abstract: Generating high-fidelity synthetic GPS trajectories is increasingly important for applications in transportation, urban planning, and what-if scenario s

applicationsarxiv-cs-lg

Trajectory-Consistent Flow Matching for Robust Visuomotor Policy Learning

arXiv:2605.08511v1 Announce Type: new Abstract: Flow matching policies learn continuous velocity fields that transport noise to actions, enabling fast deterministic inference for robot manipulation. H

safetyarxiv-cs-ro

Trajectory Supervision for Continual Tool-Use Learning in LLMs

arXiv:2605.09734v1 Announce Type: cross Abstract: Most language-model training data shows final artifacts, not the process that produced them. We study a tractable version of this question in tool use

TrajPrism: A Multi-Task Benchmark for Language-Grounded Urban Trajectory Understanding

arXiv:2605.10782v1 Announce Type: new Abstract: Urban mobility is naturally expressed both as trajectories in space and as natural-language descriptions of travel intent, constraints, and preferences.

TrajTok: Learning Trajectory Tokens enables better Video Understanding

arXiv:2602.22779v2 Announce Type: replace Abstract: Tokenization in video models, typically through patchification, generates an excessive and redundant number of tokens. This severely limits video ef

safetyarxiv-cs-cv

TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators

arXiv:2605.08231v1 Announce Type: cross Abstract: Reducing power consumption in AI accelerators is increasingly important. Approximate computing can reduce power consumption while keeping the accuracy

Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training

model-releasesarxiv-cs-cv

arXiv:2605.10835v1 Announce Type: new Abstract: Optical Music Recognition (OMR), the task of transcribing sheet music into a structured textual representation, is currently bottlenecked by a lack of l

Transfer Learning for Dead Fuel Moisture Prediction Using Time-Warping Recurrent Neural Networks

arXiv:2605.08379v1 Announce Type: cross Abstract: This paper proposes a time-warping transfer learning method, a technique for temporally rescaling the learned dynamics of a recurrent neural network (

Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo

model-releasesarxiv-cs-lg

arXiv:2605.09125v1 Announce Type: cross Abstract: Preliminary low-thrust spacecraft mission design is a global search problem characterized by a complex solution landscape, multiple objectives, and nu

Transformation-Augmented GRPO for Enhancing Exploration in Reasoning of Large Language Models

model-releasesarxiv-cs-lg

arXiv:2601.22478v3 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) has become the dominant method for reinforcement learning with verifiable rewards in large language models

Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimation

arXiv:2605.08914v1 Announce Type: cross Abstract: This paper introduces a framework specifically designed for sparse and irregular time series {risk estimation}. It is based on a Transformer Autoencod

applicationsarxiv-cs-ai