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Stable Language Guidance for Vision-Language-Action Models

arXiv:2601.04052v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have demonstrated impressive capabilities in generalized robotic control; however, they remain notoriously

Stable On-Policy Distillation through Adaptive Target Reformulation

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DGX agent

arXiv:2601.07155v2 Announce Type: replace Abstract: Knowledge distillation (KD) is a widely adopted technique for transferring knowledge from large language models to smaller student models; however,

model-releasesarxiv-cs-lg

21 Apr 2026

StableIDM: Stabilizing Inverse Dynamics Model against Manipulator Truncation via Spatio-Temporal Refinement

model-releasesarxiv-cs-ro

arXiv:2604.17887v1 Announce Type: new Abstract: Inverse Dynamics Models (IDMs) map visual observations to low-level action commands, serving as central components for data labeling and policy executio

StableMTL: Repurposing Latent Diffusion Models for Multi-Task Learning from Partially Annotated Synthetic Datasets

arXiv:2506.08013v2 Announce Type: replace Abstract: Multi-task learning for dense prediction is limited by the need for extensive annotation for every task, though recent works have explored training

researcharxiv-cs-cv

STaD: Scaffolded Task Design for Identifying Compositional Skill Gaps in LLMs

model-releasesarxiv-cs-cl

arXiv:2604.18177v1 Announce Type: new Abstract: Benchmarks are often used as a standard to understand LLM capabilities in different domains. However, aggregate benchmark scores provide limited insight

StageMem: Lifecycle-Managed Memory for Language Models

arXiv:2604.16774v1 Announce Type: new Abstract: Long-horizon language model systems increasingly rely on persistent memory, yet many current designs still treat memory primarily as a static store: wri

StealthGraph: Exposing Domain-Specific Risks in LLMs through Knowledge-Graph-Guided Harmful Prompt Generation

arXiv:2601.04740v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied in specialized domains such as finance and healthcare, where they introduce unique safety risk

STEP-PD: Stage-Aware and Explainable Parkinson's Disease Severity Classification Using Multimodal Clinical Assessments

arXiv:2604.17611v1 Announce Type: new Abstract: Parkinson's disease (PD) is a progressive disorder in which symptom burden and functional impairment evolve over time, making severity staging essential

StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning

model-releasesarxiv-cs-cl

arXiv:2604.18401v1 Announce Type: new Abstract: General agents have given rise to phenomenal applications such as OpenClaw and Claude Code. As these agent systems (a.k.a. Harnesses) strive for bolder

Still Between Us? Evaluating and Improving Voice Assistant Robustness to Third-Party Interruptions

arXiv:2604.17358v1 Announce Type: new Abstract: While recent Spoken Language Models (SLMs) have been actively deployed in real-world scenarios, they lack the capability to discern Third-Party Interrup

applicationsarxiv-cs-cl

STL-Based Motion Planning and Uncertainty-Aware Risk Analysis for Human-Robot Collaboration with a Multi-Rotor Aerial Vehicle

arXiv:2509.10692v3 Announce Type: replace Abstract: This paper presents a motion planning and risk analysis framework for enhancing human-robot collaboration with a Multi-Rotor Aerial Vehicle. The pro

safetyarxiv-cs-ro

Stochastic Control Methods for Optimization

arXiv:2601.01248v4 Announce Type: replace-cross Abstract: In this work, we investigate a stochastic control framework for global optimization over both Euclidean spaces and the Wasserstein space of pr

Stop Tracking Me! Proactive Defense Against Attribute Inference Attack in LLMs

arXiv:2602.11528v2 Announce Type: replace-cross Abstract: Recent studies have shown that large language models (LLMs) can infer private user attributes (e.g., age, location, gender) from user-generate

StrEBM: A Structured Latent Energy-Based Model for Blind Source Separation

arXiv:2604.17381v1 Announce Type: cross Abstract: This paper proposes StrEBM, a structured latent energy-based model for source-wise structured representation learning. The framework is motivated by a

STRIKE: Additive Feature-Group-Aware Stacking Framework for Credit Default Prediction

arXiv:2604.17622v1 Announce Type: new Abstract: Credit risk default prediction remains a cornerstone of risk management in the financial industry. The task involves estimating the likelihood that a bo

applicationsarxiv-cs-lg

Local Ai

Structure-Adaptive Sparse Diffusion in Voxel Space for 3D Medical Image Enhancement

arXiv:2604.17773v1 Announce Type: new Abstract: Three-dimensional (3D) medical image enhancement, including denoising and super-resolution, is critical for clinical diagnosis in CT, PET, and MRI. Alth

local-aiarxiv-cs-cv

Structure-Aware Diversity Pursuit as an AI Safety Strategy against Homogenization

arXiv:2601.06116v2 Announce Type: replace-cross Abstract: Generative AI models reproduce the biases in the training data and can further amplify them through mode collapse. We refer to the resulting h

Structured 3D-SVD: A Practical Framework for the Compression and Reconstruction of Biological Volumetric Images

arXiv:2604.16947v1 Announce Type: cross Abstract: This work introduces Structured 3D-SVD as a practical framework for the reconstruction, compression, and analysis of biological volumetric data. Inspi

researcharxiv-cs-cv

Style-Based Neural Architectures for Real-Time Weather Classification

arXiv:2604.18251v1 Announce Type: new Abstract: In this paper, we present three neural network architectures designed for real-time classification of weather conditions (sunny, rain, snow, fog) from i

researcharxiv-cs-cv

Style over Story: Measuring LLM Narrative Preferences via Structured Selection

arXiv:2510.02025v4 Announce Type: replace Abstract: We introduce a constraint-selection-based experiment design for measuring narrative preferences of Large Language Models (LLMs). This design offers

Sub-metre Lunar DEM Generation and Validation from Chandrayaan-2 OHRC Multi-View Imagery Using an Open-Source Pipeline

arXiv:2604.01032v3 Announce Type: replace Abstract: High-resolution digital elevation models (DEMs) of the lunar surface are essential for surface mobility planning, landing site characterization, and

safetyarxiv-cs-cv

Subject-Aware Multi-Granularity Alignment for Zero-Shot EEG-to-Image Retrieval

model-releasesarxiv-cs-cv

arXiv:2604.17782v1 Announce Type: new Abstract: Zero-shot EEG-to-image retrieval aims to decode perceived visual content from electroencephalography (EEG) by aligning neural responses with pretrained

Support Sufficiency as Consequence-Sensitive Compression in Belief Arbitration

arXiv:2604.16434v1 Announce Type: cross Abstract: When a system commits to a hypothesis, much of the evidential structure behind that commitment is lost to compression. Standard accounts assume that s

safetyarxiv-cs-lg

Surgical Repair of Insecure Code Generation in LLMs

arXiv:2604.16697v1 Announce Type: cross Abstract: Large language models write production code, and yet they routinely introduce well-known vulnerabilities. We show that this is not a knowledge deficit

applicationsarxiv-cs-lg

SVL: Goal-Conditioned Reinforcement Learning as Survival Learning

arXiv:2604.17551v1 Announce Type: new Abstract: Standard approaches to goal-conditioned reinforcement learning (GCRL) that rely on temporal-difference learning can be unstable and sample-inefficient d

SYMBOLIZER: Symbolic Model-free Task Planning with VLMs

arXiv:2604.17830v1 Announce Type: new Abstract: Traditional Task and Motion Planning (TAMP) systems depend on physics models for motion planning and discrete symbolic models for task planning. Althoug

researcharxiv-cs-ro

Symmetry Guarantees Statistic Recovery in Variational Inference

arXiv:2604.18310v1 Announce Type: cross Abstract: Variational inference (VI) is a central tool in modern machine learning, used to approximate an intractable target density by optimising over a tracta

SynAgent: Generalizable Cooperative Humanoid Manipulation via Solo-to-Cooperative Agent Synergy

arXiv:2604.18557v1 Announce Type: new Abstract: Controllable cooperative humanoid manipulation is a fundamental yet challenging problem for embodied intelligence, due to severe data scarcity, complexi

safetyarxiv-cs-cv

SynopticBench: Evaluating Vision-Language Models on Generating Weather Forecast Discussions of the Future

arXiv:2604.16451v1 Announce Type: new Abstract: Recent advances in visual-language models (VLMs) have led to significant improvements in a plethora of complex multimodal tasks like image captioning, r

Synthetic Data Generation for Training Diversified Commonsense Reasoning Models

arXiv:2603.18361v2 Announce Type: replace Abstract: Conversational agents are required to respond to their users not only with high quality (i.e. commonsense bearing) responses, but also considering m

Tutorials

SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair

arXiv:2604.17184v1 Announce Type: cross Abstract: Large Language Models (LLMs) show promise for automated code repair but often struggle with the complex semantic and structural correctness required.

tutorialsarxiv-cs-lg

Synthia: Scalable Grounded Persona Generation from Social Media Data

arXiv:2507.14922v2 Announce Type: replace Abstract: Persona-driven simulations are increasingly used in computational social science, yet their validity critically depends on the fidelity of the under

SynthPID: P&ID digitization from Topology-Preserving Synthetic Data

model-releasesarxiv-cs-cv

arXiv:2604.16513v1 Announce Type: new Abstract: Automating the digitization of Piping and Instrumentation Diagrams (P&IDs) into structured process graphs would unlock significant value in plant operat

Systematic Capability Benchmarking of Frontier Large Language Models for Offensive Cyber Tasks

model-releasesarxiv-cs-cl

arXiv:2604.17159v1 Announce Type: cross Abstract: We present, to our knowledge, the most comprehensive cross-model evaluation of LLM agents on offensive cybersecurity tasks, benchmarking 10 frontier m

Local Ai

T-REN: Learning Text-Aligned Region Tokens Improves Dense Vision-Language Alignment and Scalability

arXiv:2604.18573v1 Announce Type: new Abstract: Despite recent progress, vision-language encoders struggle with two core limitations: (1) weak alignment between language and dense vision features, whi

local-aiarxiv-cs-cv

Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation

arXiv:2510.09671v2 Announce Type: replace Abstract: Table Question Answering (TQA) aims to answer natural language questions about tabular data, often accompanied by additional contexts such as text p

Agents

TacticGen: Grounding Adaptable and Scalable Generation of Football Tactics

arXiv:2604.18210v1 Announce Type: cross Abstract: Success in association football relies on both individual skill and coordinated tactics. While recent advancements in spatio-temporal data and deep le

agentsarxiv-cs-lg

TagaVLM: Topology-Aware Global Action Reasoning for Vision-Language Navigation

model-releasesarxiv-cs-cv

arXiv:2603.02972v2 Announce Type: replace Abstract: Vision-Language Navigation (VLN) presents a unique challenge for Large Vision-Language Models (VLMs) due to their inherent architectural mismatch: V

Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction

arXiv:2508.11184v2 Announce Type: replace Abstract: Distractors-incorrect yet plausible answer choices in multiple-choice questions (MCQs)-are vital in educational assessments, as they help identify s

Tape: A Cellular Automata Benchmark for Evaluating Rule-Shift Generalization in Reinforcement Learning

arXiv:2601.04695v2 Announce Type: replace-cross Abstract: Out-of-distribution generalization in reinforcement learning is hard to diagnose when benchmark shifts mix dynamics, observations, goals, and

Target Parameterization in Diffusion Models for Nonlinear Spatiotemporal System Identification

arXiv:2604.17566v1 Announce Type: cross Abstract: Machine learning is becoming increasingly important for nonlinear system identification, including dynamical systems with spatially distributed output

Task Matters: Knowledge Requirements Shape LLM Responses to Context-Memory Conflict

arXiv:2506.06485v4 Announce Type: replace Abstract: Large language models (LLMs) draw on both contextual information and parametric memory, yet these sources can conflict. Prior studies have largely e

TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunications

arXiv:2604.17778v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in the telecommunications domain for critical tasks, relying heavily on Retrieval-Augmented Gener

Agents

Temporal Representations for Exploration: Learning Complex Exploratory Behavior without Extrinsic Rewards

arXiv:2603.02008v2 Announce Type: replace Abstract: Effective exploration in reinforcement learning requires not only tracking where an agent has been, but also understanding how the agent perceives a

agentsarxiv-cs-lg

TeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation

arXiv:2604.17005v1 Announce Type: new Abstract: Existing music-driven dance generation approaches have achieved strong realism and effective audio-motion alignment. However, they generally lack semant

safetyarxiv-cs-cv

TensorHub: Rethinking AI Model Hub with Tensor-Centric Compression

arXiv:2604.17104v1 Announce Type: cross Abstract: Modern AI models are growing rapidly in size and redundancy, leading to significant storage and distribution challenges in model hubs. We present Tens

applicationsarxiv-cs-lg

Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts

arXiv:2604.16926v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet

Test-Time Alignment via Hypothesis Reweighting

arXiv:2412.08812v2 Announce Type: replace Abstract: Reward models trained on aggregate preferences often fail to capture individual users' values, but existing adaptation methods such as fine-tuning o

safetyarxiv-cs-lg