Search: “framework”

arXiv:2610.20053v1 Announce Type: new Abstract: Regression models used in real-world applications are often challenged by noise, outliers, data heterogeneity, and asymmetric error characteristics, whi

applicationsarxiv-cs-lg

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GDBA-Net: A Gated Dual-Branch Adaptation of SAM2 for Robust Cross-Domain Nuclei Segmentation

DGX agent

arXiv:2610.20073v1 Announce Type: new Abstract: Accurate nuclei segmentation underpins quantitative computational pathology, yet remains challenging due to morphological variability, weak boundaries,

model-releasesarxiv-cs-cv

15 Oct 2026

Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings

model-releasesarxiv-cs-lg

arXiv:2610.19211v1 Announce Type: cross Abstract: While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general suffic

Tutorials

GeoThinker-F: Endogenous Predictive World Representations for Fast Spatial Intelligence

arXiv:2610.20592v1 Announce Type: new Abstract: Existing geometry-aware Multimodal Large Language Models (MLLMs) increasingly rely on external 3D encoders to provide structural priors, but executing t

tutorialsarxiv-cs-cv

Hardware

Glance Forcing: Making Bidirectional Diffusion Autoregressive at a Glance

arXiv:2610.20220v1 Announce Type: new Abstract: Autoregressive generation underlies real-time, causal video synthesis and is the backbone of video world models. Today's dominant approach distills a bi

hardwarearxiv-cs-cv

Glasseek: A Transparent Recipe for Training and Trustworthy Evaluation of DeepSearch Agents

arXiv:2610.16998v1 Announce Type: new Abstract: Deep search enables language agents to solve complex information-seeking tasks through iterative web exploration. However, developing robust open-source

Governing Copyright Responsibly in the Era of Generative AI

arXiv:2610.17002v1 Announce Type: cross Abstract: Large language models are trained on collections that may include scholarly works, while authors generally lack work-level information about whether a

Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT

arXiv:2610.19043v1 Announce Type: cross Abstract: Because its binary optimization landscape contains exponentially many local optima, MAX-CUT is highly sensitive to initialization. We study Particle P

Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Records

arXiv:2610.20502v1 Announce Type: new Abstract: Longitudinal electronic health records (EHRs) can reveal disease-progression structure, while useful forecasting requires leakage-controlled discovery,

researcharxiv-cs-lg

Local Ai

Graph-Reweighted Behavior Cloning with Human Interventions for Precise Robotic Manipulation

arXiv:2610.20504v1 Announce Type: new Abstract: Long-horizon precise manipulation produces mixed-quality data for post-training vision-language-action (VLA) models: even successful demonstrations and

local-aiarxiv-cs-ro

Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations

arXiv:2610.15350v1 Announce Type: cross Abstract: We present Gugak-VocalSet, the first publicly available audio dataset for Korean traditional singing with dense annotations of sigimsae, a structured

GUIDE: Computationally Augmented Consensus for Trustworthy Clinical Practice Guidelines

arXiv:2610.16108v1 Announce Type: new Abstract: Maintaining up-to-date clinical practice guidelines (CPGs) is essential for evidence-based medicine, but rapid evidence growth and complex expert consen

safetyarxiv-cs-ai

Harmony Harness: Multi-Objective Self-Evolution of Agent Harnesses

arXiv:2610.15150v1 Announce Type: new Abstract: Recent work on self-evolving LLM agents treats the agent harness as an optimization target that can be iteratively improved through execution feedback.

HarnessNet: Feedback-driven Hierarchical Joint Harness Evolution for Multi-agent Systems

arXiv:2610.15221v1 Announce Type: new Abstract: AI agents can evolve by using environment feedback to revise their harnesses, the software managing model inputs, tool use, memory, and execution, while

agentsarxiv-cs-ai

Applications

Have You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions

arXiv:2610.15091v1 Announce Type: cross Abstract: Argument structures represent how individual arguments interact within a discourse. However, leveraging these structures to infer meaningful knowledge

applicationsarxiv-cs-ai

Heterogeneous Agentic Coevolution

arXiv:2610.16078v1 Announce Type: new Abstract: Recent advances in self-improving LLM agents have demonstrated the potential of AI systems that drive their own intelligence. However, many domain-speci

safetyarxiv-cs-ai

HiPPT: Physiology-Grounded Wearable PPG Representation Learning via Multi-Scope Text Alignment

arXiv:2610.14489v1 Announce Type: new Abstract: Reusable wearable PPG representations should capture local pulse morphology and broader physiological regularities. Existing pretraining objectives, how

Holding Agents Accountable with Verifiable Agent Insurance

arXiv:2610.18230v1 Announce Type: cross Abstract: While AI agents are useful for many tasks, they regularly misbehave in destructive ways, such as deleting important data. While prior work has focused

safetyarxiv-cs-ai

Holi-Spatial 360: Generative Reconstruction from Sparse Panoramas for Spatial Rendering and Intelligence

model-releasesarxiv-cs-cv

arXiv:2610.19502v1 Announce Type: new Abstract: Spatial intelligence requires scalable 3D environments that are both photorealistic and semantically grounded, yet existing indoor datasets are dominate

How Do LLMs Understand Legal Philosophy? Diagnosing Legal-Philosophical Reasoning in LLMs

arXiv:2610.19427v1 Announce Type: new Abstract: Legal reasoning is a fundamental capability for large language models (LLMs) to complete legal AI tasks such as legal judgment prediction. In legal prac

HyperLoop: Hyperspectral-Assisted Loop Closure Detection in 3D-SLAM for Planetary Exploration

arXiv:2610.19742v1 Announce Type: new Abstract: Autonomous navigation of planetary rovers is challenged by visually repetitive, featureless terrain. SLAM systems based on 3D Gaussian Splatting (3DGS)

agentsarxiv-cs-cv

Local Ai

HyrCap: Hybrid Rank-Calibration of Action Proposals for Temporal Event Understanding

arXiv:2610.19526v1 Announce Type: new Abstract: Temporal action detection in long untrimmed videos suffers from inaccurate localization and low-quality proposals, especially with sparse annotations an

local-aiarxiv-cs-cv

HyWe: Unifying Search and Operation for Real-World Hybrid Web Tasks

arXiv:2610.16319v1 Announce Type: new Abstract: Real-world hybrid web tasks need both information gathering and page interaction, yet search-based and operation-based agents are developed and evaluate

IBM ties intelligent operations to agent safety and business value

Intelligent operations begin with a business question: What should artificial intelligence help an enterprise accomplish? As agents take on more work, organizations need an operating model that connec

safetysiliconangle

Infant Fingerprints: Quality Assessment & Recognition

model-releasesarxiv-cs-cv

arXiv:2610.20432v1 Announce Type: new Abstract: Infant fingerprint recognition is hindered by a combination of physiological characteristics, such as small ridge width and spacing, and acquisition cha

Integrated Novelty Detection Systems: From Sensor Networks to Multi-Novelty Computer Vision

arXiv:2610.19347v1 Announce Type: cross Abstract: Novelty detection is crucial in various technological and scientific domains. Its importance spans from ensuring the reliability of sensor networks to

researcharxiv-cs-cv

Interlock: From Local Correctness to Task Completion in Long-Horizon Agents

arXiv:2610.19686v1 Announce Type: new Abstract: Large language model agents increasingly undertake long-horizon tasks in complex environments characterized by fragmented evidence, partially completed

agentsarxiv-cs-cl

Hardware

Into the LidarVerse: A Real-Time Unified LiDAR World Model Across Tasks and Sensors

arXiv:2610.19201v1 Announce Type: new Abstract: We present LidarVerse, a real-time unified LiDAR world model for sensor simulation across tasks and sensors. Existing LiDAR simulators largely follow on

hardwarearxiv-cs-cv

IR-PRM+ with Previous-Utility Prediction: Constant Regret and Best-Iterate Convergence

arXiv:2610.19016v1 Announce Type: new Abstract: Regret matching (and its variants) and counterfactual regret minimization are central algorithms in practice for normal-form and extensive-form game sol

researcharxiv-cs-lg

Is the Future Predictable? Modeling Future Predictability for Reliable Temporal Reasoning

arXiv:2610.14073v1 Announce Type: cross Abstract: Large language models (LLMs) remain unreliable when reasoning about the future. Existing models often overlook the difference among future states, com

Joint Discrete and SE(3) Diffusion for Protein Interface Design

arXiv:2610.14699v1 Announce Type: new Abstract: The design of high-affinity, target-specific protein binders is central to the development of modern therapeutics. However, existing protein-generation

Knowledge transfer between simple discrete worlds

arXiv:2610.15081v1 Announce Type: new Abstract: Adapting to a new world and reasoning correctly about which outcomes are possible under rules one has not met before is a basic capacity underlying gene

KoAGE: Do Large Language Models Know, Learn, and Coin Korean Neologisms?

arXiv:2610.19399v1 Announce Type: new Abstract: Korean neologisms arise through diverse mechanisms, a demanding test of language models beyond lexical memorization. To enable systematic evaluation, we

LAMP: 3D Source-Aware Lighting Estimation at Multiple Points

arXiv:2610.19266v1 Announce Type: new Abstract: Existing illumination estimation methods primarily recover lighting distributions for rendering, but typically leave the contributions of individual phy

researcharxiv-cs-cv

Tutorials

Learning How to Remember: Memory Modality Selection for GUI Agents

arXiv:2610.17928v1 Announce Type: new Abstract: Recent advances in vision-language models (VLMs) have substantially improved the capabilities of GUI agents, while memory augmentation has emerged as a

tutorialsarxiv-cs-ai

Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI

arXiv:2610.15158v2 Announce Type: replace Abstract: We study neuro-symbolic natural language inference (NLI) with independently translated propositional representations of the premise and hypothesis.

researcharxiv-cs-cl

Learning to Play: Reinforcement Learning and Games

arXiv:2610.17391v1 Announce Type: new Abstract: Amazing breakthroughs in reinforcement learning have taken place. Computers teach themselves to play Chess and Go and beat world champions. There is tal

Learning Visual Speech Representations via Cross-Modal Distillation and Joint Face-Lip Modeling

model-releasesarxiv-cs-cv

arXiv:2610.19559v1 Announce Type: new Abstract: Lipreading remains challenging due to the inherent ambiguity of visual speech cues and their sensitivity to head pose and video resolution. In this work

Learning What to Keep: Transfer-Aware Personal Skill Learning from User Feedback

arXiv:2610.19380v1 Announce Type: new Abstract: User feedback enables LLMs to refine reusable personalized skills, but often mixes persistent preferences with request-specific corrections. Indiscrimin

LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints

arXiv:2610.16782v1 Announce Type: new Abstract: Industrial manipulation in flexible manufacturing, e.g., high-mix, low-volume (HMLV) electronics assembly, requires robot policies to adapt to changing

LiFi: LiDAR Generation from Multi-View Images via Geometric and Semantic Collaborative Guidance

arXiv:2610.19786v1 Announce Type: new Abstract: Generating LiDAR point clouds from multi-view camera images is a valuable task with applications in controllable data synthesis, cross-modal simulation

researcharxiv-cs-cv

Linearly Convergent Algorithms for Nonsmooth Problems with Unknown Smooth Regions

model-releasesarxiv-cs-lg

arXiv:2507.19465v2 Announce Type: replace-cross Abstract: We develop efficient algorithms for optimizing piecewise smooth (PWS) functions when their smooth regions are unknown to the algorithm. For co

Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine

arXiv:2610.14705v1 Announce Type: cross Abstract: Large language models have advanced medical reasoning, but clinically grounded reasoning in traditional Chinese medicine (TCM) remains challenging bec

Look Before You Restore: Learning to Predict Before Acting for Agentic Image Restoration

arXiv:2610.20384v1 Announce Type: new Abstract: Agents for mixed-degradation image restoration typically learn what a tool does to an image only after running it: failed calls waste computation and ha

agentsarxiv-cs-cv

LoopRPT: Reinforcement Pre-Training for Looped Language Models

arXiv:2603.19714v2 Announce Type: replace Abstract: Looped language models (LoopLMs) perform iterative latent computation to refine internal representations, offering an alternative to explicit chain-

researcharxiv-cs-cl

MAEM: Mitigating Recency Bias via a Replay-Free, Capacity-Controlled Dynamic Mixture of Experts for Class-Incremental Graph Learning

arXiv:2610.20238v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) excel on graph-structured data but suffer catastrophic forgetting when classes arrive sequentially. In class-incremental gr

safetyarxiv-cs-lg

MAPLE: A Multi-Axis Parametric Knowledge Extraction Lens for LLM Evaluation

arXiv:2610.19827v1 Announce Type: cross Abstract: LLMs absorb vast knowledge from training data, yet their inherently probabilistic nature offers no guarantee that this knowledge will surface in their

researcharxiv-cs-cl

MARS: Harmonizing Multimodal Convergence via Adaptive Rank Search

model-releasesarxiv-cs-lg

arXiv:2603.00720v2 Announce Type: replace Abstract: Fine-tuning Multimodal Large Language Models (MLLMs) with parameter-efficient methods like Low-Rank Adaptation (LoRA) is crucial for task adaptation