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Analytic Distribution of Classifier-Free Guidance for Schedule Design

arXiv:2607.19725v1 Announce Type: new Abstract: Classifier-free guidance (CFG) is the default mechanism for conditional generation in diffusion models, but the distribution sampled by its deterministi

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Anatomy-Aware 3D Mesh Refinement of Pericardium Segmentations on Computed Tomography

DGX agent

arXiv:2607.19210v1 Announce Type: new Abstract: Accurate delineation of the pericardium in a cardiac CT scan is essential for quantifying epicardial adipose tissue, yet it remains one of the most chal

hardwarearxiv-cs-cv

23 Jul 2026

Anatomy of a Sound Neural Reasoner: One-Shot Amortization, First-Pass Poisoning, and Search Inertness in Clue-Rich Completion

arXiv:2607.19635v1 Announce Type: cross Abstract: Neural solvers are built to deduce, branch, and revise intermediate states. The Lattice Deduction Transformer (LDT) appears to do exactly that. In clu

AniGS: Bridging Rendering and Diffusion Prior for 3D Scene Animation

arXiv:2607.18539v1 Announce Type: new Abstract: Novel view rendering of large and complex reconstructed scenes is becoming increasingly photorealistic. However, most reconstructions remain static and

applicationsarxiv-cs-cv

Tutorials

Antigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design

arXiv:2607.20057v1 Announce Type: cross Abstract: Antibodies are essential proteins that play a central role in immune recognition by binding specific antigen molecules. Although recent protein langua

tutorialsarxiv-cs-lg

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Any-Time Regret-Guaranteed Algorithm for Control of Linear Quadratic Systems

arXiv:2406.07746v4 Announce Type: replace-cross Abstract: We propose a computationally efficient algorithm that achieves anytime regret of order O(sqrt{t}), with explicit dependence on the system dime

safetyarxiv-cs-lg

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

Approximate Nearest Neighbor Search for Modern AI: A Projection-Augmented Graph Approach

arXiv:2603.06660v2 Announce Type: replace-cross Abstract: Approximate Nearest Neighbor Search (ANNS) is fundamental to modern AI applications. Most existing solutions optimize query efficiency but fai

Are Attributions of Consciousness to AI Chatbots Epistemically Innocent?

arXiv:2607.20001v1 Announce Type: cross Abstract: Artificial intelligence (AI) chatbots (e.g., ChatGPT) can communicate in strikingly humanlike ways. This has prompted many chatbot users to attribute

ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking

arXiv:2601.06487v3 Announce Type: replace-cross Abstract: Reinforcement learning has substantially improved the performance of LLM agents on tasks with verifiable outcomes, but it still struggles on o

Associative Emotional Learning in Convolutional Neural Networks

arXiv:2607.19327v2 Announce Type: replace Abstract: Associative emotional learning enables organisms to adaptively link pleasant or unpleasant outcomes to the presence of predictive stimuli. Whereas c

Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence

arXiv:2607.19854v1 Announce Type: new Abstract: We study horizon-free regret minimization for finite-horizon time-homogeneous tabular Markov decision processes with S states, A actions, horizon H, and

Hardware

ATSplat: Compact Feed-forward 3D Gaussian Splatting with Adaptive Token Expansion

arXiv:2607.20417v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) achieves high-quality novel-view synthesis by optimizing freely placed primitives in 3D and adaptively densifying them in u

hardwarearxiv-cs-cv

Attention Without Grounding: Causal Evaluation of Visual Explanations in Medical VLMs

arXiv:2607.18577v1 Announce Type: new Abstract: Attention and saliency heatmaps are widely used to explain medical Vision-Language Model (VLM) outputs on chest X-rays, yet whether they truly highlight

Attributes Should Come from Images, Not Class Names: Distribution-Conditioned Attribute Selection for Vision-Language Models

arXiv:2607.18695v1 Announce Type: new Abstract: A popular route to interpretable zero-shot classification asks a large language model (LLM) to describe each class name and prompts CLIP with the result

Audio-Zero: Label-Free Self-Evolution for Fine-Grained Audio Reasoning

arXiv:2607.20166v1 Announce Type: cross Abstract: Large Audio Language models (LALMs) have made rapid progress on acoustic understanding, yet they still struggle with fine-grained audio reasoning (e.g

Tutorials

Auditing Retrieval-Augmented LLM Hypotheses for Longitudinal Cell Painting Morphology

arXiv:2607.19415v1 Announce Type: cross Abstract: High-content morphological profiling (Cell Painting) yields sensitive, high-dimensional signatures of cellular state, but translating longitudinal mor

tutorialsarxiv-cs-ai

AuditVotes: Elevating Provable Defense for GNNs with Efficient Augmentation and Conditional Smoothing

arXiv:2503.22998v3 Announce Type: replace-cross Abstract: Despite advancements in Graph Neural Networks (GNNs), adaptive attacks continue to challenge their robustness. Certified robustness via random

AugAbEx: Bridging Abstractive and Extractive Legal Summarization

arXiv:2511.12290v2 Announce Type: replace Abstract: Automatic summarization of legal judgments liberates law professionals from heavy cognitive burden due to the complexity of the language, context-se

applicationsarxiv-cs-cl

Auto-adaptive Resonance Equalization using Dilated Residual Networks

arXiv:1807.08636v2 Announce Type: replace-cross Abstract: In music and audio production, attenuation of spectral resonances is an important step towards a technically correct result. In this paper we

applicationsarxiv-cs-lg

Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models

arXiv:2607.19847v1 Announce Type: cross Abstract: Predicting missing cell values in tabular data is a fundamental problem in data cleaning. While state-of-the-art reasoning models show great promise i

Autonomous Collaborative Learning Among an Ensemble of Tsetlin Machines with Consensus-Based Inference

arXiv:2607.20124v1 Announce Type: new Abstract: Tsetlin Machine (TM) is a rule-based machine-learning algorithm comprising collectives of two-action Tsetlin Automata (TAs) that cooperatively form conj

agentsarxiv-cs-lg

AutoVSR: Automatic Visual-to-Symbolic Reasoning for Symbolic Expression Generation from Circuit Schematic

arXiv:2607.11338v2 Announce Type: replace Abstract: Symbolic expressions can effectively characterize and predict circuit behavior, but deriving them directly from circuit schematics is challenging. T

Avoiding Obfuscation with Prover-Estimator Debate

arXiv:2506.13609v2 Announce Type: replace Abstract: Training powerful AI systems to exhibit desired behaviors hinges on the ability to provide accurate human supervision on increasingly complex tasks.

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Back to Back with a Copy: A Computational Analysis of AI-Generated Visual Contemporary Art Pastiches

arXiv:2607.20127v1 Announce Type: new Abstract: The aim of this paper is twofold. First, it investigates whether newer generative models are getting better at pastiching contemporary artworks. Second,

safetyarxiv-cs-cl

BaseRT: Advancing Best-in-Class LLM Inference with Apple M5 Neural Accelerators

arXiv:2607.19438v1 Announce Type: cross Abstract: Apple's M5 generation introduces a redesigned GPU architecture in which every core carries a dedicated Neural Accelerator: on-die matrix units exposed

Bayesian Wind Tunnels for Model Selection

model-releasesarxiv-cs-lg

arXiv:2607.19379v1 Announce Type: new Abstract: Prior work has shown that transformers can perform exact Bayesian filtering within a fixed hypothesis class. Can they also perform Bayesian model select

Benchmarking Confidential GPU Inference on NVIDIA H100 under Intel TDX

arXiv:2607.19353v1 Announce Type: new Abstract: Confidential computing is becoming a practical deployment requirement for AI inference workloads that process sensitive inputs or protect proprietary mo

Benchmarking Deep Learning Approaches for AEC Engineering Drawing Layout Detection and Information Extraction

arXiv:2607.18997v1 Announce Type: new Abstract: Information Extraction (IE) from Architecture, Engineering, and Construction (AEC) drawings remains hindered by manual inefficiency, while Layout Detect

Tutorials

Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes

arXiv:2607.19843v1 Announce Type: cross Abstract: Large language models (LLMs) have made automated program repair (APR) increasingly practical for real-world bugs, but repairing directly from bug repo

tutorialsarxiv-cs-ai

Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking

model-releasesarxiv-cs-cl

arXiv:2607.19747v1 Announce Type: new Abstract: As large language models and AI agents become the primary consumers of search results, document set quality determines the upper bound of downstream gen

Beyond Tracking or Shortcut: Composition-Bounded Predictive States in Poker Autoregressive Models

arXiv:2607.19369v1 Announce Type: new Abstract: Hidden-state probes often recover latent labels in imperfect-information sequence models, but this alone does not establish that a model maintains a pos

applicationsarxiv-cs-ai

BLUE: Semantics-Preserving Video Compression for Efficient Vision-Language Surveillance Analytics

arXiv:2607.19515v1 Announce Type: cross Abstract: Continuous surveillance video creates a growing storage, transmission, and inference burden for enterprise video analytics systems. While modern codec

applicationsarxiv-cs-cv

Boltzmann-Expected Molecular Design with Decoupled Annealing Flows

arXiv:2607.19519v1 Announce Type: cross Abstract: Most 3D properties relevant to molecular design, including free energies and shape descriptors, are extit{expectations} over the Boltzmann distributio

Boundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement

arXiv:2606.20283v2 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) excel at aggregating neighbor information for classification, yet their performance is hindered by graph structur

Bounding Boxes to Improve Small Language Model Performance on Vision-Based Grading Tasks

arXiv:2607.18767v1 Announce Type: new Abstract: The deployment of Small Language Models (SLMs) in educational settings offers significant advantages in terms of privacy, cost, and scalability. However

Breaking the T^{3/4} Barrier for Regret Minimization With Bi-Dimensional CDFs

arXiv:2607.20258v1 Announce Type: new Abstract: We study regret minimization for learning CDF-related objectives of the form [ g(x)dotP_{XsimD}(Xle x), ] over [0,1]^2, where g is a known Lipschitz fun

Local Ai

Brewing Stronger Features: Dual-Teacher Distillation for Multispectral Earth Observation

arXiv:2602.19863v3 Announce Type: replace Abstract: Foundation models are transforming Earth Observation (EO), yet the diversity of EO sensors and modalities makes a single universal model unrealistic

local-aiarxiv-cs-cv

BRIM: Workload-Balanced Dual-Sided Bit-Serial Sparse Inference Accelerator

arXiv:2607.19431v1 Announce Type: cross Abstract: Bit-serial accelerators exploit bit-level sparsity to reduce DNN inference cost, but existing designs exploit sparsity on only one operand, bounding t

Building Fast, Evaluating Slow: Pipeline Choices Dominate Autointerpretability Score Variance

arXiv:2607.19386v1 Announce Type: cross Abstract: Cross-paper comparison of sparse autoencoder (SAE) interpretability often relies on autointerpretability scores. In this evaluation pipeline, a langua

researcharxiv-cs-cl

Building Trust in Autonomous Commerce: A Verifiable Global Event Timeline and AI-Ready Fraud Intelligence Layer

arXiv:2607.19436v1 Announce Type: cross Abstract: Agentic commerce protocols such as AP2 and ACP define mechanisms for secure agent-initiated transactions but do not provide interoperable, tamper-evid

CANDOR: Chance-Calibrated Discordance in Frozen Foundation Encoders

arXiv:2607.18451v1 Announce Type: cross Abstract: Frozen encoders are chosen by how well a lightweight head reads a finding from their features, not whether the geometry separates it. Nearest-neighbor

Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models

arXiv:2607.19618v1 Announce Type: cross Abstract: Genomic language models achieve strong performance across regulatory-genomics tasks, yet what these models internally represent remains opaque, and th

CEO-Bench: Can Agents Play the Long Game?

arXiv:2606.18543v2 Announce Type: replace Abstract: Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service. Yet real

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CGCE: Classifier-Guided Concept Erasure in Generative Models

arXiv:2511.05865v3 Announce Type: replace-cross Abstract: Recent advancements in large-scale generative models have enabled the creation of high-quality images and videos, but have also raised signifi

safetyarxiv-cs-ai

CGMap: A Geospatially Aware Deep Learning Framework for Crop Gap Mapping Using UAV

arXiv:2607.18779v1 Announce Type: new Abstract: In India, crop germination is primarily monitored by visual inspection and manual counting, which are prone to errors, despite their crucial role in det

ChainWatch: A Kill Chain-Aligned Sequential Detection Framework for Multi-Step Attacks in MCP-Based AI Agent Systems

arXiv:2607.19432v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) is an open-source standard that allows AI agents to connect to external tools, databases, and services. While this co

Challenges of Explainability in Continual Learning for Time Series Forecasting

arXiv:2607.19382v1 Announce Type: cross Abstract: Deep learning models have shown strong potential for time series forecasting, yet their deployment in real-world environmental monitoring remains chal

applicationsarxiv-cs-ai