Search: “framework”

SciLT: Long-tailed Image Classification under Scientific Image Domains

arXiv:2604.03687v2 Announce Type: replace Abstract: Long-tailed recognition has benefited from foundation models and fine-tuning paradigms, yet existing studies and benchmarks are mainly confined to n

researcharxiv-cs-cv

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SCOT: Multi-Source Cross-City Transfer with Optimal-Transport Soft-Correspondence Objective

DGX agent

arXiv:2604.07383v2 Announce Type: replace Abstract: Cross-city transfer improves prediction in label-scarce cities by leveraging labeled data from other cities, but it becomes challenging when cities

safetyarxiv-cs-lg

12 May 2026

SDFlow: Similarity-Driven Flow Matching for Time Series Generation

arXiv:2605.05736v2 Announce Type: replace Abstract: Vector quantization (VQ) with autoregressive (AR) token modeling is a widely adopted and highly competitive paradigm for time-series generation. How

SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks

arXiv:2605.09038v1 Announce Type: new Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries. This is especia

Selection of the Best Policy under Fairness Constraints for Subpopulations

arXiv:2605.09945v1 Announce Type: new Abstract: Many high-stakes decisions in health care, public policy, and clinical development require committing to a single policy that will be applied uniformly

safetyarxiv-cs-lg

Self-ReSET: Learning to Self-Recover from Unsafe Reasoning Trajectories

arXiv:2605.08936v1 Announce Type: new Abstract: Large Reasoning Models possess remarkable capabilities for self-correction in general domain; however, they frequently struggle to recover from unsafe r

Semantic Alignment in Hyperbolic Space for Open-Vocabulary Semantic Segmentation

arXiv:2605.08874v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation requires adapting image-level vision-language models such as CLIP to dense pixel-level prediction, which is challe

safetyarxiv-cs-cv

Semi-Supervised Neural Super-Resolution for Mesh-Based Simulations

arXiv:2605.09284v1 Announce Type: cross Abstract: Mesh-based simulations provide high-fidelity solutions to partial differential equations (PDEs), but achieving such accuracy typically requires fine m

Separate First, Fuse Later: Mitigating Cross-Modal Interference in Audio-Visual LLMs Reasoning with Modality-Specific Chain-of-Thought

arXiv:2605.09906v1 Announce Type: new Abstract: Audio and vision provide complementary evidence for audio-visual question answering, yet current audio-visual large language models may suffer from cros

Sequential Feature Selection for Efficient Landslide Segmentation from Multi-Spectral Data

arXiv:2605.09746v1 Announce Type: cross Abstract: Landslide detection from satellite imagery has advanced through deep learning, yet most models rely on large, highly correlated spectral-topographic i

SGC-RML: A reliable and interpretable longitudinal assessment for PD in real-world DNS

arXiv:2605.08302v1 Announce Type: cross Abstract: Real-world digital Parkinson's disease assessment faces challenges such as heterogeneous modalities, cross-device bias, and incomplete labeling. Exist

ShifaMind: A Multiplicative Concept Bottleneck for Interpretable ICD-10 Coding

arXiv:2605.08482v1 Announce Type: cross Abstract: Automated ICD-10 coding from clinical discharge summaries requires models that are both accurate on long-tailed multi-label classification tasks and i

researcharxiv-cs-cl

Signature Approach for Contextual Bandits with Nonlinear and Path-dependent Rewards

arXiv:2605.10313v1 Announce Type: new Abstract: We study contextual bandits with nonlinear and path-dependent rewards through a novel signature-transform-based approach. Leveraging the universal nonli

researcharxiv-cs-lg

Simulating Complex Multi-Turn Tool Calling Interactions in Stateless Execution Environments

arXiv:2601.19914v2 Announce Type: replace-cross Abstract: Synthetic data has proven itself to be a valuable resource for tuning smaller, cost-effective language models to handle the complexities of mu

agentsarxiv-cs-ai

Simultaneous Long-tailed Recognition and Multi-modal Fusion for Highly Imbalanced Multi-modal Data

arXiv:2605.10498v1 Announce Type: cross Abstract: Long-tailed distributions in class-imbalanced data present a fundamental challenge for deep learning models, which tend to be biased toward majority c

Simulus: Combining Improvements in Sample-Efficient World Model Agents

arXiv:2502.11537v4 Announce Type: replace-cross Abstract: World models (WMs) represent the frontier of sample-efficient reinforcement learning, but their complexity leaves many promising improvements

agentsarxiv-cs-ai

Single-Thread JPEG Decoder Benchmarks Mis-Evaluate ML Data Loaders

model-releasesarxiv-cs-lg

arXiv:2605.08731v1 Announce Type: cross Abstract: JPEG decode is routine ML infrastructure, but Python decoder choices are often justified by single-process, single-thread microbenchmarks. We audit th

Skill-R1: Agent Skill Evolution via Reinforcement Learning

arXiv:2605.09359v1 Announce Type: cross Abstract: Agentic large language models often rely on skills, reusable natural language procedures that guide planning, action, and tool use. In practice, skill

SkillLens: Adaptive Multi-Granularity Skill Reuse for Cost-Efficient LLM Agents

arXiv:2605.08386v1 Announce Type: new Abstract: Skill libraries have become a practical way for LLM agents to reuse procedural experience across tasks. However, existing systems typically treat skills

agentsarxiv-cs-ai

SkillMAS: Skill Co-Evolution with LLM-based Multi-Agent System

arXiv:2605.09341v1 Announce Type: cross Abstract: Large language model (LLM) agent systems are increasingly expected to improve after deployment, but existing work often decouples two adaptation targe

agentsarxiv-cs-cl

SLIM: Sparse Latent Steering for Interpretable and Property-Directed LLM-Based Molecular Editing

arXiv:2605.10831v1 Announce Type: cross Abstract: Large language models possess strong chemical reasoning capabilities, making them effective molecular editors. However, property-relevant information

Smoothing Out the Edges: Continuous-Time Estimation with Gaussian Process Motion Priors on Factor Graphs

arXiv:2605.09073v1 Announce Type: new Abstract: Continuous-time state estimation is gaining in popularity due to its abilities to provide smooth solutions, handle asynchronous sensors, and interpolate

researcharxiv-cs-ro

Spatial Priming Outperforms Semantic Prompting: A Grid-Based Approach to Improving LLM Accuracy on Chart Data Extraction

arXiv:2605.08220v1 Announce Type: new Abstract: The automated extraction of data from scientific charts is a critical task for large-scale literature analysis. While multimodal Large Language Models (

SpatiaLab: Can Vision-Language Models Perform Spatial Reasoning in the Wild?

model-releasesarxiv-cs-cl

arXiv:2602.03916v3 Announce Type: replace-cross Abstract: Spatial reasoning is a fundamental aspect of human cognition, yet it remains a major challenge for contemporary vision-language models (VLMs).

Spectral Characterization and Mitigation of Sequential Knowledge Editing Collapse

arXiv:2601.11042v2 Announce Type: replace-cross Abstract: Sequential knowledge editing in large language models often causes catastrophic collapse of the model's general abilities, especially for para

Applications

SPECTRE: Hybrid Ordinary-Parallel Speculative Serving for Resource-Efficient LLM Inference

arXiv:2605.08151v1 Announce Type: cross Abstract: LLM serving platforms are increasingly deployed as multi-model cloud systems, where user demand is often long-tailed: a few popular large models recei

applicationsarxiv-cs-ai

Speech-based Psychological Crisis Assessment using LLMs

arXiv:2605.10027v1 Announce Type: cross Abstract: Psychological support hotlines provide critical support for individuals experiencing mental health emergencies, yet current assessments largely rely o

Stealthy Patch-Wise Backdoor Attack in 3D Point Cloud via Curvature Awareness

arXiv:2503.09336v4 Announce Type: replace Abstract: Backdoor attacks pose a severe threat to deep neural networks (DNNs) by implanting hidden backdoors that can be activated with predefined triggers t

researcharxiv-cs-cv

Supercharging Bayesian Inference with Reliable AI-Informed Priors

arXiv:2605.09834v1 Announce Type: cross Abstract: Modern predictive systems encode beliefs that can act as useful prior information for statistical inference in data-limited settings. Using them for p

safetyarxiv-cs-lg

SWE Atlas: Benchmarking Coding Agents Beyond Issue Resolution

model-releasesarxiv-cs-lg

arXiv:2605.08366v1 Announce Type: new Abstract: We introduce SWE Atlas, a benchmark suite for coding agents spanning three professional software engineering workflows: Codebase Q&A (124 tasks), Test W

Local Ai

SWIFT: Prompt-Adaptive Memory for Efficient Interactive Long Video Generation

arXiv:2605.09442v1 Announce Type: cross Abstract: Streaming long-video generation faces a central challenge in continuous semantic switching, requiring adaptive memory to preserve coherent visual evol

local-aiarxiv-cs-ai

Hardware

SwiftI2V: Efficient High-Resolution Image-to-Video Generation via Conditional Segment-wise Generation

arXiv:2605.06356v2 Announce Type: replace Abstract: High-resolution image-to-video (I2V) generation aims to synthesize realistic temporal dynamics while preserving fine-grained appearance details of t

hardwarearxiv-cs-cv

Local Ai

SymTorch: Symbolic Distillation of Neural Networks

arXiv:2602.21307v2 Announce Type: replace Abstract: What mathematical functions do neural network components learn? Symbolic distillation addresses this question by expressing neural network component

local-aiarxiv-cs-lg

SynerMedGen: Synergizing Medical Multimodal Understanding with Generation via Task Alignment

arXiv:2605.08724v1 Announce Type: new Abstract: Unifying multimodal understanding and generation is a compelling frontier that is beginning to emerge in the medical field. However, the limited existin

safetyarxiv-cs-cv

TacoMAS: Test-Time Co-Evolution of Topology and Capability in LLM-based Multi-Agent Systems

model-releasesarxiv-cs-cl

arXiv:2605.09539v1 Announce Type: new Abstract: Multi-agent systems (MAS) have emerged as a promising paradigm for solving complex tasks. Recent work has explored self-evolving MAS that automatically

TAD: Temporal-Aware Trajectory Self-Distillation for Fast and Accurate Diffusion LLM

arXiv:2605.09536v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer a promising paradigm for parallel text generation, but in practice they face an accuracy-parallelism tra

TAH-QUANT: Effective Activation Quantization in Pipeline Parallelism over Slow Network

arXiv:2506.01352v2 Announce Type: replace Abstract: Decentralized training of large language models offers the opportunity to pool computational resources across geographically distributed participant

researcharxiv-cs-lg

Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation

arXiv:2605.10165v1 Announce Type: cross Abstract: Noisy labels are common in large-scale medical imaging datasets due to inter-observer variability and ambiguous cases. We propose a statistically grou

TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation

arXiv:2605.09810v1 Announce Type: cross Abstract: Protein function is often controlled by ligands that bias the direction of state transitions, such as agonists and antagonists, rather than stabilizin

safetyarxiv-cs-lg

Local Ai

Teacher-Aware Evolution of Heuristic Programs from Learned Optimization Policies

arXiv:2605.10634v1 Announce Type: new Abstract: LLM-based automatic heuristic design has shown promise for generating executable heuristics for combinatorial optimization, but existing methods mainly

local-aiarxiv-cs-ai

Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs

arXiv:2605.09922v1 Announce Type: cross Abstract: While recent self-training approaches have reduced reliance on human-labeled data for aligning LLMs, they still face critical limitations: (i) sensiti

Tutorials

TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer

arXiv:2601.02366v2 Announce Type: replace-cross Abstract: Graph-based recommendation has achieved great success in recent years. The classical graph recommendation model utilizes ID embedding to store

tutorialsarxiv-cs-ai

TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models

arXiv:2605.06047v2 Announce Type: replace-cross Abstract: Tabular foundation models (TFMs), such as TabPFN-2.6, TabICLv2, ConTextTab, Mitra, LimiX, and TabDPT, achieve strong zero-shot performance thr

The Differences Between Direct Alignment Algorithms are a Blur

model-releasesarxiv-cs-lg

arXiv:2502.01237v3 Announce Type: replace Abstract: Direct Alignment Algorithms (DAAs) simplify LLM alignment by directly optimizing policies, bypassing reward modeling and RL. While DAAs differ in th

The Echo Amplifies the Knowledge: Somatic Marker Analogues in Language Models via Emotion Vector Re-Injection

arXiv:2605.08611v1 Announce Type: new Abstract: Current language model memory systems store what happened but not how it felt. This distinction -- between semantic memory (knowing about a past event)

The Geometric Reasoner: Manifold-Informed Latent Foresight Search for Long-Context Reasoning

arXiv:2601.18832v3 Announce Type: replace-cross Abstract: Scaling test-time compute enhances long chain-of-thought (CoT) reasoning, yet existing approaches face a fundamental trade-off between computa

The Realignment Problem: When Right becomes Wrong in LLMs

model-releasesarxiv-cs-cl

arXiv:2511.02623v2 Announce Type: replace Abstract: Post-training alignment of large language models (LLMs) relies on large-scale human annotations guided by policy specifications that change over tim

The Safety-Aware Denoiser for Text Diffusion Models

arXiv:2605.08116v1 Announce Type: cross Abstract: Recent work on text diffusion models offers a promising alternative to autoregressive generation, but controlling their safety remains underexplored.