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arXiv:2605.19727v1 Announce Type: new Abstract: Existing 3D foundation models typically align point clouds to frozen vision-language spaces like CLIP, which achieve strong cross-modal retrieval by com

local-aiarxiv-cs-cv

Target-Aligned Reinforcement Learning

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DGX agent

arXiv:2603.29501v2 Announce Type: replace-cross Abstract: Many value-based deep reinforcement learning algorithms rely on target networks - lagged copies of the online network - to stabilize training.

model-releasesarxiv-cs-ai

20 May 2026

Targeted Downstream-Agnostic Attack

arXiv:2605.19446v1 Announce Type: cross Abstract: Recently, pre-trained encoders have gained widespread use due to their strong capability in representation extraction. However, they are vulnerable to

TEA-Time: Transporting Effects Across Time

arXiv:2603.07018v2 Announce Type: replace-cross Abstract: Treatment effects estimated from a randomized controlled trial are local not only to the study population but also to the time at which the tr

safetyarxiv-cs-lg

TEMPO: Temporal Enforcement via Mode-Separated Policy Optimization for Trustworthy LLM Backtesting

arXiv:2605.18843v1 Announce Type: new Abstract: Backtesting large language models on historical events requires reasoning exclusively from information available before a specified cutoff date. Yet mod

safetyarxiv-cs-lg

Applications

TERGAD: Structure-Aware Text-Enhanced Representations for Graph Anomaly Detection

arXiv:2605.19738v1 Announce Type: cross Abstract: Graph Anomaly Detection (GAD) aims to identify atypical graph entities, such as nodes, edges, or substructures, that deviate significantly from the ma

applicationsarxiv-cs-ai

Text-to-SPARQL Generation with Reinforcement Learning: A GRPO-based Approach on DBLP

arXiv:2605.20066v1 Announce Type: new Abstract: Knowledge graph question answering seeks to translate natural language questions into executable queries over knowledge graphs, but existing approaches

safetyarxiv-cs-cl

TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards

model-releasesarxiv-cs-cv

arXiv:2605.19320v1 Announce Type: new Abstract: Faithful text rendering remains a persistent weakness of large text-to-image generative models, as it requires both semantic instruction following and f

TextBoost: Boosting Text Encoder for Personalized Text-to-Image Generation

arXiv:2409.08248v2 Announce Type: replace Abstract: In this paper, we introduce TextBoost, an efficient one-shot personalization approach for text-to-image diffusion models. Traditional personalizatio

researcharxiv-cs-cv

The 99% Success Paradox: When Near-Perfect Retrieval Equals Random Selection

arXiv:2605.18857v1 Announce Type: cross Abstract: For most of the history of information retrieval (IR), search results were designed for human consumers who could scan, filter, and discard irrelevant

The Accessibility Capability Boundary: Operational Limits and Expansion Potential of AI-Generated Browser-Native Accessibility Systems

arXiv:2605.19638v1 Announce Type: cross Abstract: As large language models (LLMs) demonstrate increasing competence in synthesizing functional user interfaces, a fundamental question emerges in access

safetyarxiv-cs-ai

The Annotation Scarcity Paradox in Low-Resource NLP Evaluation: A Decade of Acceleration and Emerging Constraints

model-releasesarxiv-cs-cl

arXiv:2605.19066v1 Announce Type: new Abstract: Over the past decade, low-resource natural language processing (NLP) has experienced explosive growth, propelled by cross-lingual transfer, massively mu

The Evaluation Game: Beyond Static LLM Benchmarking

arXiv:2605.19377v1 Announce Type: cross Abstract: As jailbreaks, adversarially crafted inputs that bypass safety constraints, continue to be discovered in Large Language Models, practitioners increasi

The Extremum Stack is a Minimal Sufficient Statistic for Rate-Independent Functionals: A Kolmogorov Complexity Characterisation

arXiv:2605.18885v1 Announce Type: cross Abstract: We prove that the extremum stack of a discrete sequence is a minimal sufficient statistic for the class of all computable, causal, rate-independent fu

The Growing Pains of Frontier Models: When Leaderboards Stop Separating and What to Measure Next

arXiv:2605.18840v1 Announce Type: cross Abstract: Leaderboards rank frontier models on independent axes but do not reveal whether capabilities reinforce or trade off across releases -- and at the fron

The impact of observation density on Bayesian inversion of latent dynamics in shock-dominated flows

arXiv:2605.19076v1 Announce Type: new Abstract: Inferring unknown initial states in shock-dominated compressible flows from sparse and noisy measurements is a challenging ill-posed inverse problem due

researcharxiv-cs-lg

The Insurability Frontier of AI Risk: Mapping Threats to Affirmative Coverage, Silent Exposures, and Exclusions

arXiv:2605.18784v1 Announce Type: cross Abstract: The rapid diffusion of agentic AI has created a new coverage problem for commercial insurance: some AI-mediated losses are now affirmatively insured,

The Routing and Filtering Structure of Attention

arXiv:2605.18826v1 Announce Type: cross Abstract: The attention interaction matrix QK^{op} contains two entangled computations: a skew-symmetric component that redistributes information between positi

The Silent Hyperparameter: Quantifying the Impact of Inference Backends on LLM Reproducibility

model-releasesarxiv-cs-lg

arXiv:2605.19537v1 Announce Type: new Abstract: Progress in LLMs is increasingly measured through standardized benchmarks, where state-of-the-art improvements are often separated by fractions of a per

The Wikidata Query Logs Dataset

arXiv:2602.14594v2 Announce Type: replace Abstract: We present the Wikidata Query Logs (WDQL) dataset, a dataset consisting of 335k question-query pairs over the Wikidata knowledge graph. It is over 1

agentsarxiv-cs-cl

The World Won't Stay Still: Programmable Evolution for Agent Benchmarks

arXiv:2603.05910v2 Announce Type: replace Abstract: LLM-powered tool-calling agents fulfill user requests by interacting with environments, querying data, and invoking tools in a multi-turn process. Y

Theory-optimal Quantization Based on Flatness

arXiv:2605.18800v1 Announce Type: cross Abstract: Post-training quantization has emerged as a widely adopted technique for compressing and accelerating the inference of Large Language Models (LLMs). T

Local Ai

Thinking in Scales: Accelerating Gigapixel Pathology Image Analysis via Adaptive Continuous Reasoning

arXiv:2605.19491v1 Announce Type: new Abstract: Traditional whole slide image (WSI) analysis methods typically rely on the multiple instance learning (MIL) paradigm, which extracts patch-level feature

local-aiarxiv-cs-cv

ThoughtTrace: Understanding User Thoughts in Real-World LLM Interactions

arXiv:2605.20087v1 Announce Type: cross Abstract: Conversational AI has now reached billions of users, yet existing datasets capture only what people say, not what they think. We introduce ThoughtTrac

safetyarxiv-cs-ai

Applications

Throwing Vines at the Wall: Structure Learning via Random Search

arXiv:2510.20035v3 Announce Type: replace-cross Abstract: Vine copulas offer flexible multivariate dependence modeling and have become widely used in machine learning. Yet, structure learning remains

applicationsarxiv-cs-lg

TIDE: Asymmetric Neural Circuits for Stabilized Temporal Inhibitory-Excitatory Dynamics

arXiv:2605.19403v1 Announce Type: new Abstract: Recent Continuous Thought Machine architecture decouples internal computation from external inputs via neural dynamics, but relies on multi-layer percep

researcharxiv-cs-lg

Hardware

TIDE: Efficient and Lossless MoE Diffusion LLM Inference with I/O-aware Expert Offload

arXiv:2605.20179v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as a competitive alternative to autoregressive (AR) models, offering better hardware utilization an

hardwarearxiv-cs-cl

TideGS: Scalable Training of Over One Billion 3D Gaussian Splatting Primitives via Out-of-Core Optimization

model-releasesarxiv-cs-cv

arXiv:2605.20150v1 Announce Type: new Abstract: Training 3D Gaussian Splatting (3DGS) at billion-primitive scale is fundamentally memory-bound: each Gaussian primitive carries a large attribute vector

Tight Regret Bounds for Fixed-Price Bilateral Trade

arXiv:2504.04349v3 Announce Type: replace-cross Abstract: We examine fixed-price mechanisms in bilateral trade through the lens of regret minimization. Our main results are twofold. (i) For independen

researcharxiv-cs-lg

Time-optimal neural feedback control of nilpotent systems as a binary classification problem

model-releasesarxiv-cs-lg

arXiv:2503.17581v2 Announce Type: replace-cross Abstract: A computational method for the synthesis of time-optimal feedback control laws for linear nilpotent systems is proposed. The method is based o

Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents?

model-releasesarxiv-cs-cl

arXiv:2605.19196v1 Announce Type: new Abstract: Deep research agents increasingly automate complex information-seeking tasks, producing evidence-grounded reports via multi-step reasoning, tool use, an

To Call or Not to Call: Diagnosing Intrinsic Over-Calling Bias in LLM Agents

arXiv:2605.18882v1 Announce Type: cross Abstract: LLM agents exhibit a consistent tendency to over-call, invoking tools even in situations where none is needed. On the When2Call benchmark, six models

Token by Token, Compromised: Backdoor Vulnerabilities in Unified Autoregressive Models

arXiv:2605.19227v1 Announce Type: cross Abstract: Unified autoregressive models (UAMs) are transformer models that generate text as well as image tokens within a single autoregressive pass. Shared par

Topology-Optimized Pneumatic Soft Actuator: Design and Experimental Validation

arXiv:2605.20101v1 Announce Type: new Abstract: This paper demonstrates the computational design of soft elastomeric pneumatic actuators using nonlinear topology optimization. An existing density- and

researcharxiv-cs-ro

TORQ: Two-Level Orthogonal Rotation for MXFP4 Quantization

arXiv:2605.19561v1 Announce Type: cross Abstract: As Large Language Models (LLMs) advance toward practical deployment, the Microscaling FP4 (MXFP4) format has emerged as a cornerstone for next-generat

Toto 2.0: Time Series Forecasting Enters the Scaling Era

arXiv:2605.20119v1 Announce Type: cross Abstract: We show that time series foundation models scale: a single training recipe produces reliable forecast-quality improvements from 4M to 2.5B parameters.

Toward an AI-Powered Computational Testbed for Workforce Policy

arXiv:2605.19064v1 Announce Type: cross Abstract: Workforce transformations are difficult to forecast and costly to mismanage. In particular, the integration of artificial intelligence into knowledge

safetyarxiv-cs-ai

Toward Training Superintelligent Software Agents through Self-Play SWE-RL

arXiv:2512.18552v2 Announce Type: replace-cross Abstract: While current software agents powered by large language models (LLMs) and agentic reinforcement learning (RL) can boost programmer productivit

Toward User Comprehension Supports for LLM Agent Skill Specifications

arXiv:2605.19362v1 Announce Type: cross Abstract: Users often interpret and select agent skills through their exttt{SKILL.md} specifications. To protect users, existing audits mainly focus on maliciou

Towards Camera-Robust 3D Localization: Equation-Anchored Tool-Use for MLLMs

model-releasesarxiv-cs-cv

arXiv:2605.19528v1 Announce Type: new Abstract: 3D localization in Multimodal Large Language Models (MLLMs), including 3D object detection and 3D visual grounding, is fundamentally limited by camera i

Towards Consistent Detection of Cognitive Distortions: LLM-Based Annotation and Dataset-Agnostic Evaluation

model-releasesarxiv-cs-cl

arXiv:2511.01482v2 Announce Type: replace Abstract: Text-based automated Cognitive Distortion detection is a challenging task due to its subjective nature, with low agreement scores observed even amon

Towards Data-Efficient Video Pre-training with Frozen Image Foundation Models

arXiv:2605.19137v1 Announce Type: new Abstract: Video foundation models achieve strong performance across many video understanding tasks, but typically require large-scale pre-training on massive vide

researcharxiv-cs-cv

Towards Discovery of Polymers for Insulin Delivery via Physics-Grounded Agentic Workflows

arXiv:2605.18831v1 Announce Type: cross Abstract: Cold-chain storage limits access to insulin for hundreds of millions of people; a thermally protective patch polymer could help, but the design space

agentsarxiv-cs-lg

Towards Distillation Guarantees under Algorithmic Alignment for Combinatorial Optimization

arXiv:2605.20074v1 Announce Type: new Abstract: Distillation transfers knowledge from a large model trained on broad data to a smaller, more efficient model suitable for deployment. In structured pred

safetyarxiv-cs-lg

Towards Family-Grouped Hierarchical Federated Learning on Sub-5KB Models: A Feasibility Study of Privacy-Preserving ECG Monitoring for Ultra-Resource-Constrained Wearables

arXiv:2605.18862v1 Announce Type: cross Abstract: Cardiovascular disease remains the leading cause of death worldwide, and early detection of arrhythmias through continuous ECG monitoring on wearable

Tutorials

Towards Fine-Grained Robustness: Attention-Guided Test-Time Prompt Tuning for Vision-Language Models

arXiv:2605.19956v1 Announce Type: new Abstract: Vision-Language Models (VLMs), such as CLIP, have achieved significant zero-shot performance on downstream tasks with various fine-tuning adaptation met

tutorialsarxiv-cs-cv

Towards LLM-Assisted Architecture Recovery for Real-World ROS~2 Systems: An Agent-Based Multi-Level Approach to Hierarchical Structural Architecture Reconstruction

arXiv:2605.20055v1 Announce Type: cross Abstract: Explicit software architecture models are essential artifacts for communicating, analyzing, and evolving complex software-intensive systems. In ROS~2-

Hardware

Towards Multi-Model LLM Schedulers: Empirical Insights into Offloading and Preemption

arXiv:2605.19593v1 Announce Type: new Abstract: Modern deployments of Large Language Models (LLMs) increasingly require serving multiple models with diverse architectures, sizes, and specialization on

hardwarearxiv-cs-ai