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SCOReD: Student-Aware CoT Optimization for Recommendation Distillation

arXiv:2607.05734v1 Announce Type: cross Abstract: Chain-of-thought (CoT) distillation in the recommendation domain is a necessary precursor to RL training, but raw teacher traces are ill-suited to thi

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SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation

DGX agent

arXiv:2607.05943v1 Announce Type: new Abstract: Training multimodal search agents to perform multi-hop reasoning remains challenging due to a fundamental structural disconnect: existing pipelines cons

safetyarxiv-cs-ai

8 Jul 2026

SecureCode: A Production-Grade Multi-Turn Dataset for Training Security-Aware Code Generation Models

arXiv:2512.18542v3 Announce Type: replace-cross Abstract: AI coding assistants produce vulnerable code in 45% of security-relevant scenarios~ite{veracode2025}, yet no public training dataset teaches b

Segmentation before Answering: Pixel Grounding for MLLM Visual Reasoning

arXiv:2607.05798v1 Announce Type: cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have evolved from static perception to interleaved visual-language reasoning, often re

Self-Review Reinforcement Learning (SRRL) with Cross-Episode Memory and Policy Distillation

arXiv:2607.05541v1 Announce Type: cross Abstract: Reinforcement Learning is commonly used to train large language models using environmental feedback. In applied settings, the environment usually prov

Self-Routing: Parameter-Free Expert Routing from Hidden States

arXiv:2604.00421v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token, and typically rely on a learned rout

Self-Supervised Implicit CEST Reconstruction via Physics-Informed Lorentz Encoding

arXiv:2607.06132v1 Announce Type: cross Abstract: Multi-Pool Chemical Exchange Saturation Transfer (CEST) MRI provides valuable metabolic information but is clinically limited by long acquisition time

Separation Capacity of Scattering Networks on Low-Dimensional Datasets

arXiv:2607.06048v1 Announce Type: cross Abstract: We aim to identify scattering network architectures that maximize the separation capacity on data with low intrinsic dimension. The networks we consid

researcharxiv-cs-lg

SEVRA-BENCH: Social Engineering of Vulnerabilities in Review Agents

arXiv:2606.13757v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed in automated code-review systems, where their approvals can determine which code is mer

SGD-Based Knowledge Distillation with Bayesian Teachers: Theory and Guidelines

arXiv:2601.01484v2 Announce Type: replace Abstract: Knowledge Distillation (KD) is a central paradigm for transferring knowledge from a large teacher network to a typically smaller student model, ofte

researcharxiv-cs-lg

Local Ai

SHARC: SHAP-Based Interpretability in Machine Learning Risk Models for Regulatory Capital under ICAAP and CCAR

arXiv:2607.05484v1 Announce Type: cross Abstract: The adoption of non-parametric machine learning models for regulatory capital estimation introduces a fundamental governance challenge: the inability

local-aiarxiv-cs-lg

SIEVE: Structure-Aware Data Selection for Imitation Learning with VLA Models

arXiv:2607.06442v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are typically trained by imitation learning on large-scale robot demonstration datasets, but more data does not nece

researcharxiv-cs-ro

Signed-Graph Recommendation as Structural Consistency Maximization

arXiv:2607.05952v1 Announce Type: cross Abstract: While signed social recommendation has shown great potential by modeling both trust and distrust relations, its effectiveness is often hindered by str

Social 3D Scene Graphs: Modeling Human Actions and Relations for Interactive Service Robots

model-releasesarxiv-cs-cv

arXiv:2509.24966v2 Announce Type: replace Abstract: Understanding how people interact with their surroundings and each other is essential for enabling robots to act in socially compliant and context-a

SpanUQ: Span-Level Uncertainty Quantification for Large Language Model Generation

model-releasesarxiv-cs-cl

arXiv:2607.05721v1 Announce Type: new Abstract: Uncertainty estimation is essential not only for the trustworthy deployment of large language models (LLMs) but also as a foundation for self-refinement

Sparse but Wrong: Incorrect L0 Leads to Incorrect Features in Sparse Autoencoders

arXiv:2508.16560v4 Announce Type: replace-cross Abstract: Sparse Autoencoders (SAEs) extract features from LLM internal activations, meant to correspond to interpretable concepts. A core SAE training

SparseCtrl-HOI: Sparse Temporal Control for Human-Object Interaction Video Generation

arXiv:2607.05994v1 Announce Type: new Abstract: Human-Object Interaction (HOI) video generation aims to synthesize realistic videos of humans manipulating diverse objects, serving as a promising avenu

SpatialFly: Implicit 3D Prior-Guided Visual Reparameterization for Continuous UAV Vision-and-Language Navigation

arXiv:2603.21046v2 Announce Type: replace-cross Abstract: UAVs play an important role in applications such as autonomous exploration, disaster response, and infrastructure inspection. However, UAV VLN

SpecTrack: Spectral Prompt Guided Adaptive Experts for Multispectral Object Tracking

arXiv:2607.05988v1 Announce Type: new Abstract: Multispectral image(MSI) and hyperspectral image(HSI) object tracking object tracking exploits recorded band-wise observations to improve target--backgr

Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows

arXiv:2607.06229v1 Announce Type: cross Abstract: Major cloud data platforms now expose large language model capabilities as native SQL functions, enabling analysts to perform classification, filterin

Applications

SplineNet: An Isogeometric Deep Learning Method for Complex Shells

arXiv:2607.06026v1 Announce Type: new Abstract: We present a novel isogeometric deep learning method, termed SplineNet, for the seamless design and analysis of shell structures with complex geometries

applicationsarxiv-cs-lg

Applications

SSA-3DGS: Unsupervised Removal of Screen-Space Artifacts for 3D Gaussian Splatting

arXiv:2607.05598v1 Announce Type: cross Abstract: Novel View Synthesis (NVS) methods, such as 3D Gaussian Splatting (3DGS), rely heavily on the assumption of clean, multi-view consistent, posed input

applicationsarxiv-cs-cv

Stability Annealing Selects the Implicit Bias of Smoothed Sign Descent: A Rate-Indexed Barrier Path on Separable Data

arXiv:2607.06013v1 Announce Type: new Abstract: Adaptive gradient methods can favor max-margin separators that differ from gradient descent, yet a fixed positive numerical stability constant eventuall

safetyarxiv-cs-lg

StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems

arXiv:2607.05844v1 Announce Type: new Abstract: Agent systems accumulate conflicting observations across branches, retries, and replicas, yet many practical memory layers still collapse disagreement b

Static Metrics Are Insufficient: Predicting Java Method Energy Usage with Execution Time

arXiv:2607.06124v1 Announce Type: cross Abstract: The increasing energy demand of software systems is raising concerns about their environmental impact and associated costs. Reasoning on energy usage

Statistical Adversaries: Natural Backdoor-like Features in Vision Datasets

arXiv:2607.05516v1 Announce Type: cross Abstract: Model-specific adversarial attacks have been extensively studied. We study a different failure mode: naturally occurring statistical signals in vision

Statistically Meaningful Geometry and Gauge Symmetry Breaking: A Geometric Foundation for Scientific Discovery and Intelligence Emergence

model-releasesarxiv-cs-lg

arXiv:2607.05436v1 Announce Type: new Abstract: The rapid scaling of over-parameterized machine learning architectures, particularly LLMs, raises a profound crisis: do these systems exhibit genuine in

StepShield: When, Not Whether to Intervene on Rogue Agents

arXiv:2601.22136v2 Announce Type: replace-cross Abstract: Agent safety benchmarks measure whether a monitor detects harm, not when. Yet timing is the difference between intervention and autopsy. We in

Straight-Path Flow Matching for Incomplete Multi-View Clustering

arXiv:2607.06281v1 Announce Type: new Abstract: Incomplete Multi-View Clustering addresses the problem of clustering multi-modal data when certain views are missing. Recent end-to-end generative appro

Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations

arXiv:2607.05863v1 Announce Type: new Abstract: Negotiation is a fundamental strategic interaction in management science, characterized by agents attempting to reach agreements while protecting privat

agentsarxiv-cs-lg

Structured-Condensed Prompt Tuning in Vision-Language Models for Fine-grained Image Recognition

arXiv:2607.06185v1 Announce Type: new Abstract: Fine-grained image recognition poses a significant challenge due to the substantial expertise and effort required for manual annotation. Vision-language

Structured Data Extraction from Real Estate Documents using Clustering, Classification, and Large Language Models

model-releasesarxiv-cs-cv

arXiv:2607.06012v1 Announce Type: new Abstract: Real estate property listings expose structured metadata through the API. Still, the richest property-level information (i.e., legal status, structural

Superman: Unifying Skeleton and Vision for Human Motion Perception and Generation

arXiv:2602.02401v2 Announce Type: replace Abstract: Human motion analysis tasks, such as temporal 3D pose estimation, motion prediction, and motion in-betweening, play an essential role in computer vi

Supervised Reward Inference

model-releasesarxiv-cs-lg

arXiv:2502.18447v2 Announce Type: replace Abstract: Existing approaches to reward inference typically assume that humans provide demonstrations according to specific behavior models. However, humans o

Tutorials

Synthetic Consumer Insight Generation with Large Language Models

arXiv:2607.05761v1 Announce Type: new Abstract: Modern data-driven marketing relies on large amounts of consumer data, yet collecting such data can be costly, time-consuming, and difficult to scale. T

tutorialsarxiv-cs-ai

Synthetic-to-Real Translation for Class-Agnostic Motion Prediction

arXiv:2607.06319v1 Announce Type: new Abstract: Motion understanding is critical for ensuring safety and robustness in autonomous driving systems, driving increasing interest in motion prediction. A k

Tangent classes of matroids and wonderful compactifications

arXiv:2607.05835v1 Announce Type: cross Abstract: For every loopless matroid M and every Feichtner--Yuzvinsky building set G containing the top flat, we construct an integral tangent class T_{M,G}^{Z}

Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval

arXiv:2607.06283v1 Announce Type: new Abstract: Skill usage can significantly enhance the ability of modern agent systems to complete complex tasks. However, the growing scale of skill libraries makes

Taxlifier: Leveraging Disease Taxonomy for Enhanced Multi-Label Classification in Chest Radiography

arXiv:2607.05628v1 Announce Type: new Abstract: Accurate and efficient classification of thoracic diseases in chest X-ray (CXR) images is crucial for timely diagnosis and treatment. However, the prese

Temporal Modeling of Optically Variable Devices in Identity Documents

arXiv:2607.06408v1 Announce Type: new Abstract: Robust remote verification of identity documents relies on analyzing faint, transparent security features like Optically Variable Devices (OVDs), or 'ho

Tutorials

Temporal Variational Implicit Neural Representations

arXiv:2506.01544v2 Announce Type: replace Abstract: We introduce Temporal Variational Implicit Neural Representations (TV-INRs), a probabilistic framework for modeling irregular multivariate time seri

tutorialsarxiv-cs-lg

Text Distance from Nested and Hierarchical Repetitions: A Compression-Based Perspective

arXiv:2607.05416v1 Announce Type: new Abstract: We present a new method for structural sequence analysis grounded in Algorithmic Information Theory (AIT). At its core is the Ladderpath approach, which

researcharxiv-cs-cl

The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities

arXiv:2607.05743v1 Announce Type: cross Abstract: AI coding agents now read repositories, call tools, and execute shell commands with limited human oversight, and a fast-growing body of work studies w

Applications

The GenAI Skill Bypass: Mapping Divergent Pathways of University Students and Staff AI Literacy

arXiv:2607.05411v1 Announce Type: cross Abstract: Higher education institutions are increasingly expected to ensure that both students and staff develop Generative AI (GenAI) literacies. In response,

applicationsarxiv-cs-ai

The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error

arXiv:2607.05450v1 Announce Type: cross Abstract: This paper explores the 'Granularity Paradox' in time-series forecasting, wherein finer temporal disaggregation (e.g., Monthly to Weekly/Daily) improv

The Jagged Global Economy: Frontier AI Unevenly Exposes National Economies

arXiv:2607.05404v1 Announce Type: cross Abstract: Frontier AI's labor-market effects matter to workers, firms, and policymakers, but current evidence generally comes from a handful of high-income econ

The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology

arXiv:2607.06531v1 Announce Type: new Abstract: - Objective: Multimodal deep learning models in oncology are currently limited by monolithic designs that rigidly couple data ingestion, clinical routin

The relationship between reasoning and performance in large language models--o3 (mini) thinks harder, not longer

arXiv:2502.15631v2 Announce Type: replace-cross Abstract: Large language models have demonstrated remarkable progress in mathematical reasoning, leveraging chain-of-thought and reinforcement learning.