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Traces of Social Competence in Large Language Models

arXiv:2603.04161v2 Announce Type: replace Abstract: The False Belief Test (FBT) has been the main method for assessing Theory of Mind (ToM) and related socio-cognitive competencies. For Large Language

applicationsarxiv-cs-cl

Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning

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DGX agent

arXiv:2601.19624v2 Announce Type: replace-cross Abstract: Real-world reinforcement learning often faces environment drift, but most existing methods rely on static entropy coefficients/target entropy,

applicationsarxiv-cs-ai

19 May 2026

Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments

arXiv:2605.16269v1 Announce Type: cross Abstract: Modern military operations expose soldiers to sustained psychological stress, leading to acute reactions, post-traumatic stress symptoms, and other me

agentsarxiv-cs-ai

Training data attribution in diffusion models via mirrored unlearning and noise-consistent skew

arXiv:2605.17938v1 Announce Type: cross Abstract: Training data attribution (TDA) should enable generative model interpretability and foster a variety of related downstream tasks. Nonetheless, current

Local Ai

Training-Free Occluded Text Rendering via Glyph Priors and Attention-Guided Semantic Blending

arXiv:2605.16810v1 Announce Type: new Abstract: We present a training-free framework for occluded text rendering with a pretrained FLUX.1-dev backbone. The task requires a model to render recognizable

local-aiarxiv-cs-cv

Training Infinitely Deep and Wide Transformers

arXiv:2605.17660v1 Announce Type: cross Abstract: Transformers have become the dominant architecture in modern machine learning, yet the theoretical understanding of their training dynamics remains li

researcharxiv-cs-ai

Trajectory-Aware Adaptive Inference in Object Detection Models

arXiv:2605.16397v1 Announce Type: cross Abstract: The increasing integration of sensors in autonomous maritime navigation has led to large-scale multimodal datasets, raising challenges in achieving ef

agentsarxiv-cs-ai

Transfer Learning for Customized Car Racing Environments

arXiv:2605.17928v1 Announce Type: cross Abstract: Transfer Learning, a technique where a model/agent can use the knowledge/expertise that it gained from one task and exploit that to solve another clos

agentsarxiv-cs-lg

Transformer-Based MCS Prediction for 5G Multicast-Broadcast Services (MBS)

arXiv:2605.16735v1 Announce Type: cross Abstract: The deployment of 5G Multicast-Broadcast Services (MBS) is emerging as a critical technology for spectral-efficient UHD content delivery and serving a

safetyarxiv-cs-lg

Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights

arXiv:2505.03205v3 Announce Type: replace Abstract: Transformers serve as the foundational architecture for large language and video generation models, such as GPT, BERT, SORA and their successors. Em

applicationsarxiv-cs-lg

Transitivity Meets Cyclicity: Explicit Preference Decomposition for Dynamic Large Language Model Alignment

arXiv:2605.17342v1 Announce Type: cross Abstract: Standard RLHF relies on transitive scalar rewards, failing to capture the cyclic nature of human preferences. While some approaches like the General P

TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT

model-releasesarxiv-cs-cv

arXiv:2605.16572v1 Announce Type: new Abstract: Automated segmentation of liver lesions on non-contrast computed tomography (NCCT) is clinically important but fundamentally challenging, particularly i

Hardware

TriAxialKV: Toward Extreme Low-Precision KV-Cache Quantization for Agentic Inference Tasks

arXiv:2605.17170v1 Announce Type: new Abstract: Agentic workloads have emerged as a major workload for LLM inference. They differ significantly from chat-only workloads, requiring long-context process

hardwarearxiv-cs-lg

TriOpt: A Scalable Algorithm for Linear Causal Discovery

arXiv:2605.17465v1 Announce Type: new Abstract: Learning causal relations from observational data is challenging because the graph search space grows super-exponentially with the number of variables.

applicationsarxiv-cs-lg

Industry

Trump signs an EO calling on regulators and the Fed to review policies that could support fintech growth, including expanding fintech access to payment accounts (Reuters)

Reuters: Trump signs an EO calling on regulators and the Fed to review policies that could support fintech growth, including expanding fintech access to payment accounts — U.S. President Donald Trump

industrytechmeme

Trump’s “Great” Economy: -7% mortgage rates -6% inflation -$5 gas -More unemployed than job openings for 1st time since pandemic -Lowest Con…

Trump’s “Great” Economy: -7% mortgage rates -6% inflation -$5 gas -More unemployed than job openings for 1st time since pandemic -Lowest Consumer Sentiment in 74 years -CC Delinquencies highest since

researchyann-lecun--x

Trust No Tool: Evaluating and Defending LLM Agents under Untrusted Tool Feedback

model-releasesarxiv-cs-cl

arXiv:2605.17453v1 Announce Type: cross Abstract: Tool-using LLM agents increasingly rely on external tools to make consequential decisions, yet most existing agent-security benchmarks and defenses im

Tutorials

Trust the uncertain teacher: distilling dark knowledge via calibrated uncertainty

arXiv:2602.12687v2 Announce Type: replace-cross Abstract: The core of knowledge distillation lies in transferring the teacher's rich 'dark knowledge'-subtle probabilistic patterns that reveal how clas

tutorialsarxiv-cs-ai

Trustworthiness in Retrieval-Augmented Generation Systems: A Survey

arXiv:2409.10102v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs). Although ex

Industry

Truth via Humor

This post likely discusses how humor can be an effective vehicle for communicating truth or revealing reality in ways that straightforward statements cannot. Musk may be commenting on how comedic fram

industryelon-musk--x

Hardware

Truthful Calibration Errors for Multi-Class Prediction

arXiv:2510.06388v2 Announce Type: replace Abstract: Calibrated predictions are useful because their numerical values can be interpreted as probabilities. Calibration errors are therefore widely used t

hardwarearxiv-cs-lg

Try Anthropic Claude in Comfy today: https://links.comfy.org/4eWCvFk

ComfyUI announced the availability of Anthropic's Claude AI model integrated into their platform, allowing users to utilize Claude's capabilities within the ComfyUI interface. The announcement include

model-releasescomfyui--x

Industry

Try Composer 2.5 on Cursor!

Elon Musk posted a promotional message encouraging users to try Composer 2.5 on the Cursor code editor platform. The post likely highlights new features or improvements in version 2.5 of Composer, whi

industryelon-musk--x

TTE-Flash: Accelerating Reasoning-based Multimodal Representations via Think-Then-Embed Tokens

arXiv:2605.16638v1 Announce Type: new Abstract: Recent research has demonstrated that Universal Multimodal Embedding (UME) benefits significantly from Chain-of-Thought (CoT) reasoning. In this paradig

TusoAI: Agentic Optimization for Scientific Methods

arXiv:2509.23986v2 Announce Type: replace Abstract: Scientific discovery is often slowed by the manual development of computational tools needed to analyze complex experimental data. Building such too

Two-Valued Symmetric Circulant Matrices: Applications in Deep Learning

arXiv:2605.16443v1 Announce Type: cross Abstract: Despite the success of deep neural networks in vision, medical diagnosis, and IoT scenarios, their deployment on resource-limited platforms poses seri

researcharxiv-cs-ai

two years ago we started building agents to automate work. turns out these are really useful, so there’s a LOT of runs and long traces that …

two years ago we started building agents to automate work. turns out these are really useful, so there’s a LOT of runs and long traces that are hard to reason about now, use an ambient agent (engine)

agentsharrison-chase--x

UAVFF3D: A Geometry-Aware Benchmark for Feed-Forward UAV 3D Reconstruction

model-releasesarxiv-cs-cv

arXiv:2605.17942v1 Announce Type: new Abstract: Feed-forward 3D reconstruction has recently demonstrated strong generalization across diverse scenes, yet its performance in UAV imagery remains underex

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models

arXiv:2605.16690v1 Announce Type: new Abstract: Heterogeneous LoRA-rank methods address system heterogeneity in federated fine-tuning of foundation models by assigning client-specific ranks based on c

researcharxiv-cs-lg

UbuntuGuard: A Culturally-Grounded Policy Benchmark for Equitable AI Safety in African Languages

model-releasesarxiv-cs-cl

arXiv:2601.12696v2 Announce Type: replace Abstract: Current guardian models are predominantly Western-centric and optimized for high-resource languages, leaving low-resource African languages vulnerab

UCSF-PDGM-VQA: Visual Question Answering dataset for brain tumor MRI interpretation

arXiv:2605.17140v1 Announce Type: cross Abstract: Brain tumor diagnosis is largely dependent on Magnetic Resonance Imaging (MRI) evaluation, which requires radiologists to synthesize thousands of imag

Uncertainty-Calibrated Recommendations for Low-Active Users

arXiv:2605.17788v1 Announce Type: cross Abstract: A fundamental challenge in recommender systems is balancing reliability for Low-Active Users (LAUs) with diversity for High-Active Users (HAUs). The k

safetyarxiv-cs-lg

Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations

arXiv:2502.17007v2 Announce Type: replace-cross Abstract: In this paper we argue that, to its detriment, transparency research overlooks many foundational concepts of artificial intelligence. As an il

Uncertainty Reliability Under Domain Shift: An Investigation for Data-Driven Blood Pressure Estimation in Photoplethysmography

arXiv:2605.18008v1 Announce Type: new Abstract: Uncertainty quantification (UQ) is critical for safety-critical domains like healthcare, yet it is rarely evaluated under realistic out-of-distribution

safetyarxiv-cs-lg

Understanding In-Context Learning on Structured Manifolds: Bridging Attention to Kernel Methods

arXiv:2506.10959v3 Announce Type: replace-cross Abstract: While in-context learning (ICL) has achieved remarkable success in natural language and vision domains, its theoretical understanding-particul

researcharxiv-cs-ai

Understanding the modern cybercrime landscape

applicationsmit-tech-review

Throughout 2025, HPE observed significant changes in how cybercriminals operate. Analyzing real-world threats, our HPE Threat Labs highlighted an industrialization of the cyber criminals’ methods in i

UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts

arXiv:2605.17575v1 Announce Type: cross Abstract: Network traffic classification (NTC) models often suffer severe performance degradation when deployed in real-world environments due to distribution s

safetyarxiv-cs-ai

UniER: A Unified Benchmark for Item-level and Path-level Exercise Recommendation

arXiv:2605.16750v1 Announce Type: cross Abstract: Personalized exercise recommendation dynamically aligns pedagogical resources with individual knowledge mastery, which is crucial for satisfying stude

Unified Walking, Running, and Recovery for Humanoids via State-Dependent Adversarial Motion Priors

arXiv:2605.18611v1 Announce Type: new Abstract: We propose a unified reinforcement learning framework that enables a single policy to perform walking, running, and fall recovery on the Unitree G1 huma

safetyarxiv-cs-ro

Unifying Contrastive and Generative Objectives for Visual Understanding and Text-to-Image Generation

arXiv:2603.02667v2 Announce Type: replace Abstract: Unifying text-image contrastive learning and text-to-image (T2I) generation in a single end-to-end model is challenging because the two objectives d

safetyarxiv-cs-cv

UniPPTBench: A Unified Benchmark for Presentation Generation Across Diverse Input Settings

model-releasesarxiv-cs-cv

arXiv:2605.17356v1 Announce Type: new Abstract: Existing works typically focus on presentation generation under isolated input settings, whereas real-world use cases span diverse scenarios, including

Universal Adversarial Triggers

arXiv:2605.17936v1 Announce Type: new Abstract: Recent works have illustrated that modern NLP models trained for diverse tasks ranging from sentiment analysis to language generation succumb to univers

researcharxiv-cs-cl

Universal Dynamics of Punctuated Progress

arXiv:2605.16719v1 Announce Type: cross Abstract: Scientific and technological frontiers advance through punctuated dynamics, yet the principles governing these dynamics remain poorly understood. Here

Universal Graph Backdoor Defense: A Feature-based Homophily Perspective

arXiv:2605.16815v1 Announce Type: cross Abstract: Graph neural networks (GNNs) have achieved remarkable success in relational learning. However, their vulnerability to graph backdoor attacks (GBAs) po

researcharxiv-cs-lg

Universal Inverse Distillation for Matching Models with Real-Data Supervision (No GANs)

arXiv:2509.22459v4 Announce Type: replace-cross Abstract: While achieving exceptional generative quality, modern diffusion, flow, and other matching models suffer from slow inference, as they require

researcharxiv-cs-lg

Universal Pose Pretraining for Generalizable Vision-Language-Action Policies

arXiv:2602.19710v2 Announce Type: replace Abstract: Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low training efficiency because they entangle high-level percept

safetyarxiv-cs-cv

Universal Time-Series Representation Learning: A Survey

arXiv:2401.03717v4 Announce Type: replace-cross Abstract: Time-series data exists in every corner of real-world systems and services, ranging from satellites in the sky to wearable devices on human bo

UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities

arXiv:2504.20734v4 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external kn