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STaT: Resolving Shape Distortion in Non-Stationary Time Series via Tri-Modal Synergy

arXiv:2605.25943v1 Announce Type: new Abstract: Recent research in time series forecasting frequently investigates the integration of textual and visual modalities with numerical models to better navi

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Statistical Inference for Stochastic Gradient Descent Beyond Finite Variance

DGX agent

arXiv:2605.26000v1 Announce Type: cross Abstract: Stochastic gradient descent (SGD) is a foundational algorithm for large-scale statistical learning and stochastic optimization. However, statistical i

researcharxiv-cs-lg

26 May 2026

Steering Beyond the Support: Adversarial Training on Unsupervised Jailbroken Activation Simulation

arXiv:2605.24535v1 Announce Type: cross Abstract: Jailbreak prompts can trigger harmful completions on aligned LLMs, In accordance, safety steering has been proposed: test-time activation intervention

Stein Variational Ergodic Surface Coverage with SE(3) Constraints

model-releasesarxiv-cs-ro

arXiv:2603.09458v3 Announce Type: replace Abstract: Surface manipulation tasks require robots to generate trajectories that comprehensively cover complex 3D surfaces while maintaining precise end-effe

Step-TP: A Grounded, Step-Level Dataset with Chain-of-Thought Reasoning for LLM-Guided Tensor Program Optimization

arXiv:2605.25954v1 Announce Type: cross Abstract: Despite the strong reasoning capabilities of large language models (LLMs), optimizing the execution efficiency of tensor programs remains challenging

StepGap: A Hybrid NLI-LLM Checker for Step-Level Evidence-Gap Detectionin Multi-Hop Question Answering

arXiv:2605.24733v1 Announce Type: new Abstract: We present extbf{StepGap}, a hybrid NLI-LLM decision tree that detects step-level evidence gaps in multi-hop QA and emits one of three typed labels: ext

Stiffness Optimization for Concentrated Bending in Magnetically Actuated Catheters: Maintaining Steerability under Gradient Stiffness

arXiv:2605.25005v1 Announce Type: new Abstract: Achieving both efficient pushability (propulsion transmission) and proximally concentrated bending for steerability is challenging for magnetically actu

researcharxiv-cs-ro

Stochastic Estimation of the Layer-wise Hessian Trace for Monitoring Neural-network Training

arXiv:2605.25674v1 Announce Type: new Abstract: The loss and the norm of its gradient separate the healthy and the pathological regimes of neural-network training only weakly, whilst the curvature of

Stochastic Linear Bandits with Parameter Noise

arXiv:2601.23164v2 Announce Type: replace Abstract: We study the stochastic linear bandits with parameter noise model, in which the reward of action a is a^op heta where heta is sampled i.i.d. We show

Stop Comparing LLM Agents Without Disclosing the Harness

arXiv:2605.23950v1 Announce Type: new Abstract: This position paper argues that, for long-horizon tasks evaluated across models with comparable frontier capability, the agent execution harness, namely

Agents

STORM: Internalized Modeling for Spatial-Temporal Reasoning in Video-Language Models

arXiv:2605.26014v1 Announce Type: cross Abstract: Many video reasoning tasks require tracking motion, temporal order, and evolving visual states across frames. Existing methods built on large vision-l

agentsarxiv-cs-cl

Strat-Reasoner: Reinforcing Strategic Reasoning of LLMs in Multi-Agent Games

arXiv:2605.04906v2 Announce Type: replace Abstract: While Large Language Models (LLMs) excel in certain reasoning tasks, they struggle in multi-agent games where the final outcome depends on the joint

STREAM: A Data-Centric Framework for Mining High-Value Task-Oriented Dialogues from Streaming Media

arXiv:2605.25162v1 Announce Type: cross Abstract: Large language models for vertical domains are bottlenecked by the scarcity of complex, domain-specific task-oriented dialogues. Existing data acquisi

Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning

arXiv:2605.24709v1 Announce Type: new Abstract: Streaming reinforcement learning has emerged as an online learning paradigm that conforms to the restrictions of natural learning agents that process da

StreamProfileBench: A Benchmark for Fine-Grained User Profile Inference in Real-World Streaming Scenarios

arXiv:2605.25758v1 Announce Type: new Abstract: Large Language Models (LLMs) have reshaped user profiling, yet current evaluations mainly focus on static data snapshots. This paradigm overlooks the re

Applications

StrTransformer: Source-Wise Structured Transformers for Unsupervised Blind Source Recovery

arXiv:2605.25648v1 Announce Type: cross Abstract: This paper proposes StrTransformer, a source-wise structured Transformer framework for blind source recovery and branch-wise latent modeling. Instead

applicationsarxiv-cs-lg

StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs

arXiv:2605.25534v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel at structural reasoning yet suffer from a sharp logical brittleness in structural consistency. We term th

Structural Abstraction as an Inductive Bias for Non-Stationary Language Model Training

arXiv:2603.17198v2 Announce Type: replace-cross Abstract: A foundational principle in cognitive science holds that intelligent agents do not learn by storing experiences as isolated instances, but by

Applications

Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents

arXiv:2605.24366v1 Announce Type: new Abstract: Large Language Models (LLMs) have been widely adopted in conversational applications. However, their reliance on parametric knowledge limits reliability

applicationsarxiv-cs-cl

Subspace Aggregation Query and Index Generation for Multidimensional Resource Space Model

arXiv:2505.02129v3 Announce Type: replace-cross Abstract: Organizing large-scale resources in a multidimensional semantic space is an approach to efficiently managing and querying resources from diffe

Subspace-Guided Semantic and Topological Invariant Registration for Annotation-Free Ultrasound Plane Quality Control

arXiv:2605.25396v1 Announce Type: cross Abstract: Reliable quality control (QC) of ultrasound images is essential for both real-time acquisition guidance and retrospective clinical audit, yet existing

Sum of Costs Diffusion with Dynamic Guidance for Motion Planning

arXiv:2605.24690v1 Announce Type: cross Abstract: The motion planning problem for robotic manipulation can be addressed through classical or deep learning approaches. Existing methods face significant

researcharxiv-cs-lg

Summoning the Oracle to Slay It: Mitigating Look-Ahead Bias in Financial Backtesting with Large Language Models

arXiv:2605.24564v1 Announce Type: new Abstract: Backtesting large language models (LLMs) on historical financial data is unreliable because pre-training cuts off after the events happened. An LLM trai

SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors

arXiv:2502.11167v5 Announce Type: replace-cross Abstract: Neural surrogate models are powerful and efficient tools in data mining. Meanwhile, large language models (LLMs) have demonstrated remarkable

Synheart Capacity: A Theory-Driven Physiological Representation of Cognitive Capacity Dynamics from Wearable Signals

arXiv:2605.24416v1 Announce Type: new Abstract: Human cognitive performance is constrained by limited mental resources, yet continuous computational estimation of cognitive capacity dynamics remains a

researcharxiv-cs-lg

T2S-MPC: Time-Embedded Online Adaptive Model Predictive Control for Time-Varying Dynamics

arXiv:2605.24852v1 Announce Type: new Abstract: Recent advances in learning-based model predictive control (MPC) have leveraged neural networks for online model learning, achieving strong performance

researcharxiv-cs-lg

TaBIIC2: Interactive Building of Ontological Taxonomies using Weighted Self-Organizing Maps

arXiv:2605.24899v1 Announce Type: new Abstract: Ontologies represent the conceptual knowledge of a domain. At the core of an ontology is the taxonomy of concepts and subconcepts that represent specifi

TapSampling: Inference-Time Sampling with a Task-Progress-Understanding Verifier for Robotic Manipulation

arXiv:2605.25547v1 Announce Type: new Abstract: Existing embodied control research demonstrates remarkable performance improvements by scaling training data and model size. We instead explore inferenc

safetyarxiv-cs-ro

Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Systematic Review and Practical Design Guidelines

arXiv:2605.23995v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has emerged as a promising paradigm for addressing the annotation bottleneck in medical imaging by learning representat

Teaching large language models to reason like expert diagnosticians

arXiv:2509.12194v2 Announce Type: replace Abstract: Differential diagnosis is an iterative process that integrates patient information with broader medical knowledge. Clinical case series such as the

Teaching Through Analogies: A Modular Pipeline for Educational Analogy Generation

arXiv:2605.24211v1 Announce Type: cross Abstract: Analogies help learners understand unfamiliar concepts by relating them to known concepts. Despite recent advances, large language models (LLMs) conti

Applications

Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions

arXiv:2605.24452v1 Announce Type: cross Abstract: Legal NLP benchmarks evaluate models on randomly split data, implicitly assuming that legal language is stationary. We test this assumption by fine-tu

applicationsarxiv-cs-ai

Local Ai

Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models

arXiv:2510.01184v2 Announce Type: replace Abstract: We present a mechanism to steer the sampling diversity of denoising diffusion and flow matching models, allowing users to sample from a sharper or b

local-aiarxiv-cs-lg

Terrain-Adaptive Grouser Wheel for Optimal Planetary Exploration: Design and Experimental Investigation

arXiv:2605.24311v1 Announce Type: new Abstract: Planetary rovers operating in extraterrestrial environments often encounter significant mobility challenges due to varying terrain features such as grad

researcharxiv-cs-ro

Agents

Test-Time Deep Thinking to Explore Implicit Rules

arXiv:2605.24828v1 Announce Type: new Abstract: With the continuous advancement of Large Language Models (LLMs), intelligent agents are becoming increasingly vital. However, these agents often fail in

agentsarxiv-cs-ai

Test-Time Graph Search for Goal-Conditioned Reinforcement Learning

arXiv:2510.07257v2 Announce Type: replace Abstract: Offline goal-conditioned reinforcement learning (GCRL) often struggles with long-horizon tasks, where errors in value estimation accumulate and prod

Test-Time Self-Adaptive Conditioning for Stable Audio-Driven Talking-Head Generation

arXiv:2605.25488v1 Announce Type: cross Abstract: Audio-driven talking-head generation has achieved remarkable progress with recent models such as AniTalker, FLOAT, and Sonic. Despite their success, m

Testing the Deliteralization Hypothesis in Human and Machine Translation

arXiv:2605.25686v1 Announce Type: new Abstract: The recent shift from dedicated NMT systems to general-purpose LLMs has reshaped machine translation, with LLMs reported to produce more fluent, less li

TGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism

arXiv:2605.24971v1 Announce Type: cross Abstract: The growing interest in Temporal Graph Neural Networks (TGNNs) stems from their ability to model complex dynamics and deliver superior performance. Ho

Thaka at KSAA-2026 Task 2: Regularized Fine-Tuning for Arabic Speech Diacritization

arXiv:2605.25928v1 Announce Type: new Abstract: We describe the winning system for Task 2 of the KSAA-2026 Shared Task on Arabic Speech Dictation with Automatic Diacritization. The task requires produ

The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models

arXiv:2605.25510v1 Announce Type: new Abstract: Children increasingly have access to Large Language Models (LLMs), which may expose them to responses that are developmentally inappropriate or require

The Behavioral Credibility Trilemma: When Calibrated Autonomy Becomes Impossible

arXiv:2605.25739v1 Announce Type: new Abstract: We prove that no reinforcement learning policy with confidence-gated autonomy can simultaneously achieve maximum helpfulness, optimal calibration, and f

The Concept Allocation Zone: Tracking How Concepts Form Across Transformer Depth

arXiv:2605.24856v1 Announce Type: cross Abstract: Concept formation in transformer language models is depth-extended, not a single-layer event: concepts emerge gradually across a contiguous region of

The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot

arXiv:2409.08379v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are reshaping knowledge work, yet their impact on voluntary, self-guided open innovation forums (contributors cho

The Implicit Bias of Adam and Muon on Smooth Homogeneous Neural Networks

arXiv:2602.16340v3 Announce Type: replace Abstract: We study the implicit bias of momentum-based optimizers on smooth homogeneous models. We show that extit{momentum steepest descent} algorithms like

The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks

arXiv:2404.00176v3 Announce Type: replace Abstract: Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-le

The Many Faces of On-Policy Distillation: Pitfalls, Mechanisms, and Fixes

arXiv:2605.11182v2 Announce Type: replace Abstract: On-policy distillation (OPD) and on-policy self-distillation (OPSD) have emerged as promising post-training methods for large language models, offer

The meaning of prompts and the prompts of meaning: Semiotic reflections and modelling

arXiv:2509.14250v2 Announce Type: replace Abstract: This paper explores prompts and prompting in large language models (LLMs) as dynamic semiotic phenomena, drawing on Peirce's triadic model of signs,