Search: “synthesis”

Spatio-Temporal Conditional Denoising Transformer for Modality-Missing RGBT Tracking

arXiv:2607.24701v1 Announce Type: new Abstract: Missing modalities in RGBT tracking often lead to incomplete and unstable multimodal feature representations that greatly degrade the performance. Exist

28 Jul 2026

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Local Ai

Training Language Models to Cooperate with Inference-Time Controllers

DGX agent

arXiv:2607.23771v1 Announce Type: new Abstract: Large language model (LLM) performance increasingly depends not only on the base model, but also on the inference-time controller used to organize reaso

local-aiarxiv-cs-ai

28 Jul 2026

Understanding Human-like Solutions in Combinatorial Optimization via Learning and Search

arXiv:2607.23854v1 Announce Type: new Abstract: Humans often find good solutions to combinatorial optimization problems that are computationally hard even for advanced computer algorithms. In the Eucl

researcharxiv-cs-ai

28 Jul 2026

AgentHOI: Multi-Agent Reasoning for Human-Object-Interaction Video Generation via Implicit Representation Alignment

arXiv:2607.22241v1 Announce Type: new Abstract: Recent advances in video diffusion models have spurred interest in human-object interaction (HOI) video generation, which demands fine-grained control o

Local Ai

FrED: External Data Influence Estimation via Domain Knowledge Graph Grounding

arXiv:2607.21615v1 Announce Type: cross Abstract: The rapid deployment of generative AI has amplified the critical need for Training Data Attribution to ensure transparency and accountability. However

local-aiarxiv-cs-lg

Generative and multimodal AI for materials prediction and design: Progress, challenges, and perspectives

arXiv:2607.21660v1 Announce Type: cross Abstract: Artificial intelligence (AI) is accelerating materials prediction and design by enabling efficient exploration of chemical and structural spaces, with

safetyarxiv-cs-lg

Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face Detection

arXiv:2412.01101v2 Announce Type: replace Abstract: Face-swapping DeepFakes have become an escalating societal concern, attracting increasing attention in recent years. To counter this, we investigate

ISPCloak: Weaponizing ISP for Optimization-Free Physical Camouflage against Deepfake Detectors

arXiv:2607.21897v1 Announce Type: new Abstract: The rapid advancement of generative models has spurred the critical need to evaluate the worst-case robustness of deepfake detectors. In this paper, we

LeAct: Learning to Reason from Expert Actions

model-releasesarxiv-cs-cl

arXiv:2607.21856v1 Announce Type: cross Abstract: Modern reasoning models depend on reasoning data, today sourced from human annotations or distilled from stronger LLMs. However, a rich and largely un

Nanbeige4.2-3B: Unlocking Agentic Capabilities in a Compact Mode

arXiv:2607.22083v1 Announce Type: cross Abstract: We present Nanbeige4.2-3B, a compact general agentic model with 3B non-embedding parameters. It delivers strong performance across code-agent, office-

safetyarxiv-cs-cl

Physiological Signals as a Forensic Modality for Talking-Face Deepfake Detection

arXiv:2607.21776v1 Announce Type: cross Abstract: Talking-face (TF) deepfake generation synthesizes photore- alistic facial video from a static source image and an au- dio signal, producing forgeries

Probing Speaker Identity Sensitivity in Audio Deepfake Detectors

arXiv:2607.21820v1 Announce Type: cross Abstract: Audio deepfake detectors are trained to distinguish genuine speech from synthetic speech and often perform well on standard benchmarks. Yet the same d

researcharxiv-cs-lg

Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era

arXiv:2607.22068v1 Announce Type: new Abstract: Multi-branch architectures and CNN-Transformer fusion have long been regarded as effective ways to improve vehicle re-identification (Re-ID) by combinin

Teaching LLMs to Self-Evolve: Cultivating Core Meta-Skills with Reinforcement Learning

arXiv:2607.21971v1 Announce Type: cross Abstract: Test-time scaling through iterative self-evolution with environment feedback, as demonstrated by AlphaEvolve, shows remarkable performance gains. We h

researcharxiv-cs-cl

Twins: Learn to Predict Unified Representations with Focal Loss

arXiv:2607.22531v1 Announce Type: new Abstract: Unified multimodal models seek a shared visual token space that supports both multimodal understanding and image generation. Discrete methods unify the

Agentic Designer: Progressive Multi-Agent Collaboration for Structure-Aware Interior Layout Generation

arXiv:2607.20866v1 Announce Type: new Abstract: Generating realistic interior furniture layouts that strictly adhere to architectural constraints (e.g., walls, doors, and windows) remains a fundamenta

Can AI Agents Really Complete RTL-to-GDS? Lessons from Benchmarking Tool-Interactive EDA Workflows

arXiv:2607.17528v3 Announce Type: replace Abstract: Large language model (LLM) agents are extending electronic design automation (EDA) beyond static RTL generation toward long-horizon, tool-interactiv

EvoSQL: Memory-Augmented Critic-Generator Co-Evolution for Text-to-SQL

arXiv:2607.20489v1 Announce Type: new Abstract: Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generation, including

safetyarxiv-cs-ai

Future Rendering neq Future Surface: A Benchmark and Dataset for Dynamic Surface Reconstruction Beyond the Observed Window

arXiv:2607.21471v1 Announce Type: new Abstract: Dynamic-scene reconstruction is almost always evaluated inside the observed time window, yet deployment settings such as AR overlays, robot interaction,

MKEvolve: A Modular Multi-Agent Framework for Kernel Code Generation

arXiv:2607.20501v1 Announce Type: new Abstract: Despite rapid progress in LLM-based code generation, writing correct and performant kernels for hardware accelerators remains a key bottleneck in scalin

Applications

Position: Natural Language Should Not Fully Replace Formal Languages

arXiv:2607.20432v1 Announce Type: new Abstract: Recent advances in large language models and their widespread adoption have prompted claims that natural language could entirely replace formal language

applicationsarxiv-cs-cl

Applications

RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring

arXiv:2607.20628v1 Announce Type: cross Abstract: Real-world video deblurring remains challenging due to diverse motion patterns, complex degradations, and the scarcity of realistic training data, yet

applicationsarxiv-cs-ai

Semi-Supervised Text-Attributed Graph Distillation

model-releasesarxiv-cs-ai

arXiv:2607.20477v1 Announce Type: new Abstract: {em Text-Attributed Graphs} (TAGs) have emerged as an expressive data model for integrating graph topology with rich textual semantics. Existing represe

Applications

TableVerse: A Large-scale Tabletop Dataset with Real-world Grounded Layouts for Generalizable Manipulation

arXiv:2607.21017v1 Announce Type: new Abstract: The development of generalizable robotic manipulation policies is inherently bounded by the availability of large-scale, high-fidelity scene data. While

applicationsarxiv-cs-ro

Tractable Hierarchical Control of Autoregressive Language Models

arXiv:2607.20483v1 Announce Type: new Abstract: Constraining the generation of autoregressive large language models (LLMs) is an important component of integrating language models into formal systems.

researcharxiv-cs-ai

Local Ai

Appearance Pointers -- Multimodal Region Control of Diffusion Transformers

arXiv:2607.19344v1 Announce Type: new Abstract: Controllable image generation remains challenging for creative professionals, who often require precise regional control over materials, object identiti

local-aiarxiv-cs-cv

Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments

arXiv:2607.20302v1 Announce Type: new Abstract: Quantum machine learning (QML) algorithms in high energy physics (HEP) can efficiently represent and leverage long-range, high-order correlations in hig

researcharxiv-cs-lg

Local Ai

Development of an automated, reliable, and clinically meaningful artificial intelligence (AI) tool for diagnosing cardiac disease from conventional cardiovascular magnetic resonance (CMR) images

arXiv:2607.20087v1 Announce Type: new Abstract: Aims: Cardiovascular magnetic resonance (CMR) imaging enables non-invasive assessment of myocardial structure, function, and pathology, but requires sub

local-aiarxiv-cs-cv

PoTRE: Test-Time Reasoning inspired by Cognitive Heterogeneity

arXiv:2607.20268v1 Announce Type: new Abstract: While Large Language Models (LLMs) excel at many tasks, they frequently struggle with complex reasoning that requires long-horizon planning and iterativ

REGEN: Replay-recycling for Expert-to-Generalist distillation with Offline Reinforcement Learning

arXiv:2607.19450v1 Announce Type: cross Abstract: Large-scale online reinforcement learning (RL) is the predominant means of eliciting advanced abilities including long-term reasoning and agentic tool

safetyarxiv-cs-ai

STEREOFLOW: Progressive Stereo Matching with StereoDiT and Transition Flow Matching

arXiv:2607.19986v1 Announce Type: new Abstract: Stereo matching is a fundamental task in 3D reconstruction. Despite remarkable advances, the prevailing paradigms formulate stereo matching as a determi

TAP-RAG: Task-Aware Policy Control for Long-Document Multimodal Question Answering

arXiv:2607.18917v1 Announce Type: new Abstract: Long-document multimodal question answering requires more than retrieving relevant chunks from a large document. Different queries require different evi

Text Template Tokens Are Implicit Semantic Registers in Diffusion Transformers

arXiv:2607.19139v1 Announce Type: new Abstract: Text-to-image diffusion transformers (DiTs) jointly process text and image tokens, yet their internal computation during denoising remains poorly unders

Autonomous UAV Route Planning for Coverage Maximization in Environmental Monitoring: A Systematic Literature Review

arXiv:2607.13054v1 Announce Type: cross Abstract: Environmental monitoring with unmanned aerial vehicles (UAVs) requires route planning methods that maximize covered area while handling energy limits,

Faithful Autoformalization of Natural Language Assertions

arXiv:2607.13303v1 Announce Type: cross Abstract: Formal contracts are essential for software testing and verification, yet writing them remains labor-intensive and error-prone. LLMs offer a promising

researcharxiv-cs-ai

FreeLit: Paired-Free Indoor Relighting via Physics-Guided Diffusion

arXiv:2607.13656v1 Announce Type: new Abstract: Image-based indoor scene relighting remains challenging due to the complex interplay between cluttered geometry and local illumination, requiring precis

Improving Medical Image Generative Models with Frechet Distance Loss

arXiv:2607.13300v1 Announce Type: new Abstract: Diffusion generative models have demonstrated immense potential for synthetic medical image generation. However, these models often struggle to capture

NeMo: Needle in a Montage for Video-Language Understanding

arXiv:2509.24563v3 Announce Type: replace Abstract: Recent advances in video large language models (VideoLLMs) call for new evaluation protocols and benchmarks for video-language understanding. Inspir

Tutorials

Reflecting Process Expertise in Procedural Material Generation

arXiv:2607.13318v1 Announce Type: new Abstract: Procedural material creation underpins applications in digital content creation, visual effects, and 3D asset design. Achieving high-quality results req

tutorialsarxiv-cs-cv

The Cafe in Amsterdam: When the Incumbent Becomes the Oracle

arXiv:2607.13393v1 Announce Type: cross Abstract: A field can reformulate its computations freely exactly where its demand is stated independently of any incumbent implementation, and finds itself una

safetyarxiv-cs-ai

ThinkBLOX: 3D Indoor Scene Generation with Progressive Reasoning

arXiv:2607.13539v1 Announce Type: new Abstract: While traditional graphics methods often synthesize 3D indoor scenes autoregressively or hierarchically, recent vision-language model (VLM)-based genera

Towards Enhancing 3D Spatial Reasoning in Medical Multimodal Large Language Models

arXiv:2607.13860v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in 2D medical image understanding, their extension to 3D volumetric

CrochetBench: Can Vision-Language Models Move from Describing to Doing in Crochet Domain?

model-releasesarxiv-cs-ai

arXiv:2511.09483v3 Announce Type: replace Abstract: While multimodal large language models can describe visual content, their ability to generate executable procedures remains underexplored. CrochetBe

Domain-Incremental Remote Sensing Change Detection via Difference-Guided Adaptation and Frequency-Decoupled Distillation

arXiv:2607.12934v1 Announce Type: new Abstract: Remote sensing change detection (RSCD) models are prone to catastrophic forgetting when incrementally adapted to new domains. Existing domain-incrementa

GAINS: Gaussian-based Inverse Rendering from Sparse Multi-View Captures

arXiv:2512.09925v2 Announce Type: replace Abstract: Recent advances in Gaussian Splatting-based inverse rendering extend Gaussian primitives with shading parameters and physically grounded light trans

Generalization and Memorization in Rectified Flow

arXiv:2603.13421v2 Announce Type: replace-cross Abstract: Generative models based on the Flow Matching objective, particularly Rectified Flow, have emerged as a dominant paradigm for efficient, high-f

LakeQuest: A Three-Domain Benchmark for Grounded Question Answering across Data Lakes

model-releasesarxiv-cs-ai

arXiv:2607.12310v1 Announce Type: cross Abstract: While modern question answering (QA) systems excel on clean, schema-aligned corpora, real-world knowledge is rarely so neatly packaged. Answering ques

LARAD: Layout-Aware Road Anomaly Detection via Spatial-Logic Reasoning

arXiv:2607.12858v1 Announce Type: new Abstract: Accurate open-world obstacle detection is critical for autonomous driving. Current anomaly segmentation methods suffer from a fundamental blind spot: th

agentsarxiv-cs-cv