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Beyond Local Linearity: Scale-Resolved Geometry of Learned Image Encoders

arXiv:2609.39115v1 Announce Type: new Abstract: Understanding how learned representations respond to finite input changes is important for characterizing their sensitivity, invariances, and robustness

researcharxiv-cs-cv

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Human DGX agent

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Research

Beyond Mode Collapse: Generating Diverse Synthetic Expert Conversations via Generative Flow Networks

DGX agent

arXiv:2609.38359v1 Announce Type: new Abstract: High quality synthetic data is central to post training LLMs for adaptive AI applications that represent the diverse expert strategies and decisions in

researcharxiv-cs-ai

1 Oct 2026

Beyond Model Ranking: Regime Diagnosis for Distributional-Statistical Misspecification in Industrial Time-Series Forecasting

model-releasesarxiv-cs-lg

arXiv:2609.40117v1 Announce Type: new Abstract: Time-series forecasting models achieve strong benchmark performance but exhibit severe systematic bias in industrial deployments. This train--deploy gap

Beyond Oracle Communication: Benchmarking Interactive Intent Alignment Under Miscommunication and Evolving User Intent

arXiv:2609.38604v1 Announce Type: cross Abstract: Modern LLM agents increasingly tackle complex tasks through interactive, long-horizon exchanges with users, while existing benchmarks generally assume

Beyond Policy Alignment: Closing the Planning-Learning Loop for Robot Control with Learned World Models

model-releasesarxiv-cs-ro

arXiv:2609.39751v1 Announce Type: new Abstract: Planning with learned world models combines online trajectory optimization with learned value and policy functions for high-dimensional control. Because

Beyond Prediction: Steering VLM Agents with Retrospective World Modeling

arXiv:2609.39101v1 Announce Type: new Abstract: Equipping VLM agents with world modeling capabilities has shown strong potential for complex reasoning and long-horizon planning, while reducing the dep

safetyarxiv-cs-ai

Beyond Rephrasing: Book-Level Organization Improves Synthetic Textbook Data for Mid-Training

arXiv:2607.28109v2 Announce Type: replace Abstract: Synthetic textbook data has improved language model pre-training, but prior work largely treats the benefit as a property of generated content or lo

Applications

Beyond Simulation: Retain-and-Repair Neural Operators for Real-World Adaptation

arXiv:2609.39387v1 Announce Type: new Abstract: Neural operators increasingly benefit from pretraining on numerical simulations, yet adapting them for real-world prediction remains challenging. We int

applicationsarxiv-cs-lg

Beyond Spatial-Domain Supervision: A Relation Constrained Space for Multi-Modal Image Fusion

arXiv:2609.38968v1 Announce Type: new Abstract: Multi-modal image fusion (MMIF) aims to form a single image by integrating shared information, preserving complementary cues, and coordinating cross-mod

safetyarxiv-cs-cv

Beyond Text: LLM-Based Dimensional Emotion Evaluation in Multimodal Dialogue

arXiv:2609.39072v1 Announce Type: cross Abstract: Emotion recognition in conversation has been widely studied, but applying Large Language Models (LLMs) to continuous dimensional emotion evaluation in

Beyond the Commitment Boundary: Probing Epiphenomenal Chain-of-Thought in Large Reasoning Models

arXiv:2606.13603v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning is the dominant paradigm for inference-time scaling in language models, yet the causal influence of individua

Beyond the Current Scene: Event-Referential Grasping with Active View Selection

arXiv:2609.39375v1 Announce Type: cross Abstract: A robot that observes people interacting with objects should be able to carry out later requests that refer back to those interactions. Such requests

researcharxiv-cs-cv

Beyond the Remembered World: Predictive 4D Belief for Persistent Navigation in Evolving Worlds

arXiv:2609.39166v1 Announce Type: new Abstract: Persistent spatial memory enables embodied agents to navigate familiar environments across repeated visits. However, targets may move while unobserved,

Beyond the Shadows of Plato's Cave: Evaluating False Memory in Autonomous Agents via Counterfactual Reasoning

arXiv:2609.39473v1 Announce Type: new Abstract: Autonomous agents increasingly rely on memory to generalize beyond their training environments. However, agents are bounded by what they have seen and b

agentsarxiv-cs-ai

Beyond Uncertainty Sets: Leveraging Optimal Transport to Extend Conformal Predictive Distributions to Multivariate Settings

arXiv:2511.15146v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) constructs uncertainty sets for model outputs with finite-sample coverage guarantees. Yet ranking scores is straight

researcharxiv-cs-lg

Beyond Uniform Compression: Budgeted Transmission Allocation for Extreme Federated Learning

model-releasesarxiv-cs-lg

arXiv:2609.39646v1 Announce Type: new Abstract: Federated learning faces severe communication bottlenecks when clients upload high-dimensional model updates. Existing methods often compress these upda

Local Ai

BIM Informed Visual SLAM for Construction Environments

arXiv:2509.13972v4 Announce Type: replace Abstract: Monitoring building construction sites requires comparing the as-planned design with the as-built state, which can be estimated in real time using S

local-aiarxiv-cs-ro

BIND: Binding 3D Robot Actions to 2D Image Features

arXiv:2609.38443v1 Announce Type: cross Abstract: We introduce BIND, a new action representation for visuomotor robot policies that binds 3D robot actions to their corresponding 2D image features, yie

safetyarxiv-cs-cv

Tutorials

Blackboard Intelligence Can Surpass Autoregressive on Globally Constrained Problems

arXiv:2609.38806v1 Announce Type: cross Abstract: Next-token prediction has driven remarkable progress in large language models, yet a growing body of evidence suggests that they can struggle on probl

tutorialsarxiv-cs-cl

Blackout vs. Freeze: Analyzing Physical Failure Modes of VLAs under Camera Faults

arXiv:2609.39145v1 Announce Type: cross Abstract: Unreliable visual inputs can harm task performance and cause potential physical safety risks for vision-language-action (VLA) models. We analyze how p

safetyarxiv-cs-ai

BMASH: Ball-Motion-Aware Soccer Header Spotting

arXiv:2609.39300v1 Announce Type: new Abstract: Recent advances in computer vision have made broadcast sports videos increasingly useful for event analysis, performance assessment, and player-safety a

safetyarxiv-cs-cv

Hardware

Bongard: Training Machine Intuition

arXiv:2609.39111v1 Announce Type: new Abstract: Human intelligence relies heavily on learned intuition: recognising patterns and judging situations without explicitly unfolding every intermediate step

hardwarearxiv-cs-cl

Bounded Channel-Adaptive Spectral Learning for Forward-Consistent Inverse Flapping-Wing Aerodynamics

arXiv:2609.38730v1 Announce Type: new Abstract: Flapping-wing vehicles regulate aerodynamic forces and moments through coordinated variations in stroke, deviation, and pitch motion. Because the result

researcharxiv-cs-ro

Breaking Babel: A Self-Evolving Multi-Agent System for Long-Form Subtitle Translation

arXiv:2609.38660v1 Announce Type: cross Abstract: Long-form subtitle translation requires reasoning over discourse and cultural context spanning episodes or entire series, while maintaining consistent

Local Ai

BTC3D: Blended Tile Conditioning for Detail-Enhancing Image-to-3D Generation

arXiv:2609.39709v1 Announce Type: new Abstract: Recent diffusion-based pipelines have achieved promising progress in image-to-3D synthesis. However, generating high-fidelity details remains challengin

local-aiarxiv-cs-cv

Budget Boundary Effects in Test-Time Mathematical Reasoning

arXiv:2609.38699v1 Announce Type: new Abstract: A cumulative token cap can fall inside a mathematical derivation, forcing a test-time controller to choose between stopping at the cap (strict) and allo

C-STRIDE: An Observation-Driven AI Digital Twin for Predicting Basin-Wide Flood Fields from Sparse Stream-Gauge Histories

arXiv:2609.39005v1 Announce Type: new Abstract: Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During a

CADeT: Causal-Aware Deformation Transmission for Indirect Robotic Manipulation of Soft Tissue

arXiv:2609.38483v1 Announce Type: new Abstract: Indirect manipulation of deep-seated deformable anatomy inaccessible to the robot is challenging in robot-assisted minimally invasive surgery (RAMIS) be

researcharxiv-cs-ro

CamAgent: An LLM-Agent Framework for Multi-Species Camera-Trap Workflows

arXiv:2609.39112v1 Announce Type: new Abstract: Camera traps accumulated vast, multidimensional data for wildlife monitoring, yet translating raw media archives into meaningful ecological insights rem

agentsarxiv-cs-cv

CAMOS: Coupled Oscillatory State-Space Model for Multimodal Clinical Time-Series

arXiv:2609.39484v1 Announce Type: cross Abstract: Longitudinal clinical cohorts are multimodal, irregularly sampled and pervasively incomplete: in ADNI, positron emission tomography and cerebrospinal

Can Agents Trust Their Skills? Uncovering Unsafe Chains of Trust in Skill-Based LLM Agents

arXiv:2609.39065v1 Announce Type: cross Abstract: LLM agents increasingly rely on installable skills, which are packages of instructions, code, and resources that equip them with task-specific capabil

agentsarxiv-cs-ai

Can an AI Agent Rediscover a Blaschke-Curve Invariant?

arXiv:2609.38369v1 Announce Type: new Abstract: We study generalized Blaschke curves as a controlled environment for AI-assisted mathematical rediscovery. For one fixed degree-four Blaschke product, a

Can Computation from Earlier Problems Help LLMs Solve New Ones?

arXiv:2609.39394v1 Announce Type: new Abstract: Large language models often solve independent problems in the same conversation. Can computation from earlier problems help them solve new ones? To answ

Can Domain Generalization be Guaranteed in Small-Sample Learning?

arXiv:2609.39512v1 Announce Type: new Abstract: The small-sample learning problem remains a fundamental challenge in machine learning because limited training data lead to unstable model estimation an

researcharxiv-cs-lg

Can Terminal Agents Trust Their Own Verification? Diagnosing and Improving Self-Verification

arXiv:2609.38812v1 Announce Type: new Abstract: Terminal agents rely on self-verification to assess and correct their solutions as they solve tasks through interaction with command-line environments.

agentsarxiv-cs-cl

Can We Anticipate Violence? Multimodal Learning from Pre-Incident Behavioral Cues

arXiv:2609.40014v1 Announce Type: new Abstract: Detecting violence after it begins is important from recognizing behavioral cues that appear immediately beforehand. This work studies short-horizon pre

researcharxiv-cs-cv

Tutorials

Candidate Retention for Abductive Learning

arXiv:2609.39561v1 Announce Type: cross Abstract: Abductive learning combines neural perception with symbolic reasoning, using explanations generated by abduction to supervise the perception model. Mu

tutorialsarxiv-cs-ai

Caption-Mediated Perceived-Safety Estimation for Pedestrian Routing

arXiv:2609.38479v1 Announce Type: cross Abstract: This paper presents an explainable approach to pedestrian routing, in which perceived safety is estimated from street-level imagery through an explici

CARAT: Do Materials LLMs Reason or Recite?

arXiv:2609.38340v1 Announce Type: new Abstract: When a materials LLM answers a question about crystal structure, does it reason from the structure or copy an answer already printed in its input? Accur

CAS II: Symmetric Partitions as Kolmogorov Models

arXiv:2609.40290v1 Announce Type: cross Abstract: In algorithmic statistics a string x is explained by a finite set containing it, and Kolmogorov's structure function records the smallest such model a

Cascadia: A Control-Plane-Free Alternative to Hyperconverged AI Infrastructure

arXiv:2609.38697v1 Announce Type: cross Abstract: We present Cascadia, a system for serving large language models on fleets of commodity Intel AIPCs using their CPU, integrated-GPU, and NPU resources.

CAST: Causal Advantage-Structured Training with Spatially Grounded Compositional Rewards for Diffusion Models

arXiv:2609.39441v1 Announce Type: cross Abstract: Online reinforcement learning has been extended to flow matching for diffusion model (DM) image generation. However, this paradigm faces three limitat

CATCH: A Controllable Analysis Testbed for Reward Hacking in Coding RL

model-releasesarxiv-cs-cl

arXiv:2609.39533v1 Announce Type: new Abstract: During reinforcement learning with verifiable rewards (RLVR), large language models (LLMs) can exploit loopholes in their environments to obtain high re

Applications

CDMD: A Cross-Dataset Mixed-Type Diffusion Model for Tabular Data

arXiv:2609.39124v1 Announce Type: new Abstract: Generative models for tabular data are typically trained separately for each dataset, limiting knowledge transfer and requiring the storage of many spec

applicationsarxiv-cs-lg

CEER2: Directional and Tunable End-Effector and Root Compliance for Humanoid Loco-Manipulation

arXiv:2609.38709v1 Announce Type: new Abstract: Humanoids are increasingly capable of tracking complex whole-body motions, but physical interaction introduces a different challenge. When a robot makes

safetyarxiv-cs-ro

CellMSA: Context Modeling for Single-Cell Representation Learning

arXiv:2609.38908v1 Announce Type: cross Abstract: Single-cell transcriptomics enables profiling of cellular states at unprecedented resolution, but its high dimensionality, sparsity, and technical bat

safetyarxiv-cs-ai

Centralized Multi-UAV Exploration and 3D Reconstruction Using Single-UAV Planners

arXiv:2609.40208v1 Announce Type: new Abstract: Extending single Unmanned Aerial Vehicles (UAVs) exploration methods to multi-UAV teams can improve coverage speed and robustness, but introduces challe

researcharxiv-cs-ro

Certification-Based Differentially Private Learning

model-releasesarxiv-cs-lg

arXiv:2609.39629v1 Announce Type: new Abstract: Differential privacy (DP) in machine learning is typically achieved by adding noise to model parameters (private learning) or to model outputs (private