Search: “models”

Back-Tracking from Clarity: Self-Learning to See Text from Afar

arXiv:2609.32477v1 Announce Type: new Abstract: We propose a self-supervised framework designed to enhance the capability of scene text detectors in identifying and recognizing text in scenarios where

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Be Careful Who You Trust: Coordination Dynamics under Corrupted Communication in LLM Multi-Agent Games

DGX agent

arXiv:2609.31704v1 Announce Type: cross Abstract: Large language models are increasingly used as interacting agents, but it remains unclear how robust their coordination is when public communication i

agentsarxiv-cs-lg

29 Sept 2026

Beyond Energy: When Sustainability Dimensions Reshape LLM Serving Decisions

arXiv:2609.35569v1 Announce Type: cross Abstract: Large language model (LLM) serving has environmental impacts across energy consumption, carbon emission, water consumption, and biodiversity loss. Yet

Beyond Token Scale: Chunk-Level Sparse Autoencoders for Reliable Semantic Feature Discovery

arXiv:2609.35521v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) expose features that help us understand and steer language models, but faithful reconstruction does not guarantee informati

Local Ai

Binaural Audio-Visual Instance Segmentation

arXiv:2609.32180v1 Announce Type: new Abstract: Audio-visual segmentation (AVS) aims to segment sounding objects at the pixel level by integrating auditory and visual cues. However, existing methods a

local-aiarxiv-cs-cv

BudgetVerify: Budget-Tiered Verification for Financial QA

arXiv:2609.33052v1 Announce Type: cross Abstract: Financial question answering often requires precise numerical extraction, unit handling, and arithmetic over tables and text, but applying expensive v

Can Circuit Alignment Predict OOD Generalization?

arXiv:2609.31996v1 Announce Type: new Abstract: Can out-of-distribution (OOD) generalization be predicted from a trained model's weights alone, without any target-domain data? Existing representationa

Tutorials

CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?

arXiv:2512.11352v2 Announce Type: replace Abstract: Trust prediction provides valuable support for decision-making, risk mitigation, and system security enhancement. Recently, Graph Neural Networks (G

tutorialsarxiv-cs-lg

Certified Multi-Source Integrity for Structured Agent Actions

arXiv:2609.34245v1 Announce Type: cross Abstract: LLM agents increasingly take privileged, often irreversible structured actions, such as paying an invoice. They assemble each action from action-criti

CityToolVQA: Tool-Augmented Visual Question Answering for 3D Spatial Cognition in Urban Low-Altitude Environments

arXiv:2609.32427v1 Announce Type: new Abstract: CityToolVQA addresses the weak performance of Vision-Language Models (VLMs) on quantitative tasks in urban low-altitude visual question answering. We di

Constrained Edit Fields for Training-Free Flow Editing

arXiv:2609.33735v1 Announce Type: new Abstract: Text-guided image editing aims to perform a desired edit while preserving source content unrelated to it. Pretrained rectified-flow models enable traini

safetyarxiv-cs-cv

Applications

Coverage-aware Semantic Representation Learning for Underrepresented Visual Concepts

arXiv:2602.16917v2 Announce Type: replace Abstract: Modern vision models increasingly rely on rich semantic representations that extend beyond class labels to include descriptive concepts, attributes,

applicationsarxiv-cs-cv

Applications

Cross-Domain Few-Shot Writer Adaptation for Real-World Handwritten Mathematical Expression Recognition

arXiv:2609.32513v1 Announce Type: new Abstract: Handwritten mathematical expression recognition (HMER) refers to the task of recognizing and converting handwritten mathematics into a parsable markup l

applicationsarxiv-cs-cv

DCEmbed: Scalable Optimization over Neural Surrogates

arXiv:2609.33879v1 Announce Type: cross Abstract: Neural surrogates can accelerate large-scale optimization by replacing expensive or intractable model components with efficient learned approximations

DEALS: Decentralized Expertise-Aware Load Serving for Multi-Agent LLM Systems

arXiv:2609.33768v1 Announce Type: new Abstract: Multi-agent systems (MAS) have recently emerged as an effective approach for coordinating large language model (LLM)-based agents to solve complex tasks

agentsarxiv-cs-lg

Deep Epistemic Value Functions for Optimistic Exploration

arXiv:2609.35525v1 Announce Type: new Abstract: Principled exploration in reinforcement learning requires an agent to quantify its epistemic uncertainty and act to resolve it. Uncertainty over the val

agentsarxiv-cs-lg

Design of Experiment for Discovering Directed Mixed Graph

arXiv:2509.01887v2 Announce Type: replace-cross Abstract: We study the design of interventions for causal discovery in simple structural causal models whose causal graphs are directed mixed graphs (DM

Disciplined Biconvex Programming

arXiv:2511.01813v3 Announce Type: replace-cross Abstract: We introduce disciplined biconvex programming (DBCP), a modeling framework for specifying and solving biconvex optimization problems. Biconvex

Tutorials

Distilling Visual Reasoning into Text Space

arXiv:2609.34408v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have shown strong promise for multimodal reasoning, yet often struggle with tasks requiring concepts beyond what is

tutorialsarxiv-cs-cv

Do Temporal Link Predictors Need Learned Memory? A Smoothed-Count Baseline with a Handful of Parameters

arXiv:2609.35364v1 Announce Type: new Abstract: Many temporal link predictors summarize past interactions through learned node representations. We examine whether simple counts of recurring interactio

Easier Said Than Done: Unpacking Intent-Behavior Gap in Jailbreaking LLM-based Robots

arXiv:2412.16633v4 Announce Type: replace Abstract: LLM-based robots use Large Language Models (LLMs) as planners to translate natural language instructions into policies such as grasp(), move_to(), a

safetyarxiv-cs-ro

Elucidating the Design Space of Regression-based Diffusion Reinforcement Learning

arXiv:2609.33444v1 Announce Type: cross Abstract: A nascent family of methods that forgoes the policy gradient and reweights a supervised regression instead has garnered momentum in reinforcement lear

safetyarxiv-cs-cv

Enhancing generalization in endwall film cooling prediction: Incorporating the superposition principle into transformer-based neural operators

arXiv:2609.31633v1 Announce Type: new Abstract: In this study, a physics-enhanced neural operator framework is proposed to enhance the generalization prediction ability of the cooling layout of a turb

F4R: Failure-Driven Recognition, Reconstruction, Refinement, and Redeployment for Continual Robot Self-Improvement

arXiv:2609.35575v1 Announce Type: new Abstract: The real-world performance of current vision-language-action models is fundamentally constrained by the limited coverage of expert demonstrations and th

safetyarxiv-cs-ro

FARE: Deep Reinforcement Learning For Fair Exposure Constrained Uncertainty Aware Financial Content Personalization

arXiv:2609.31890v1 Announce Type: new Abstract: Content personalization systems in financial services must ensure fair exposure across diverse offerings-a requirement driven by contractual obligations

Fast Learning Rate Transfer in Shallow Linear Networks at Growing Training Horizons

arXiv:2609.35029v1 Announce Type: new Abstract: Hyperparameter transfer across model width can substantially reduce the cost of tuning large neural networks, but its behavior when the training horizon

Feedback Makes Perfect: A Closed-Loop Framework for NL-to-STL Translation

arXiv:2609.33287v1 Announce Type: cross Abstract: Signal Temporal Logic (STL) enables rigorous verification and control of cyber-physical systems, but writing correct specifications requires expertise

researcharxiv-cs-ro

Flow-Matching-Based Protein Structure Tokenizer Made Efficient and Easy

arXiv:2609.33129v1 Announce Type: new Abstract: As the bridge between protein modality and discrete modeling, protein structure tokenization still largely relies on heavily engineered training objecti

Focus on Likely Classes for Test-Time Prediction

arXiv:2505.03819v4 Announce Type: replace Abstract: We ask: Can focusing on likely classes of a single, in-domain sample improve accuracy? Prior work argued 'no', we answer 'yes' on average. Standard

ForensicZoom: Adaptive Visual Inspection with Multimodal LLMs for Industrial-Grade Face Forgery Detection

arXiv:2609.31661v1 Announce Type: new Abstract: Reliable face forgery detection is critical to the security of online identity verification systems, where missed attacks compromise security and excess

From Distributions to Stochastic Processes: Neural Approximation of Measure-Valued Maps

arXiv:2609.33649v1 Announce Type: new Abstract: Learning mappings between probability distributions arises naturally when inputs and outputs are represented by populations of samples rather than indiv

From Pixel Generation to Topological Inference: Structural Dual Super-Resolution for Trustworthy Cross-Physical-Domain Trabecular Morphology Learning

arXiv:2609.34716v1 Announce Type: new Abstract: Clinical CT and UHRCT cannot resolve individual trabeculae, whereas synchrotron radiation microCT (SR{mu}CT) provides 3.2{mu}m high-resolution reference

From Pixel to Poses: Object-centric Tool Manipulation Learning from Human Demonstrations

arXiv:2609.35375v1 Announce Type: new Abstract: Scaling up robotic manipulation is primarily bottlenecked by the scarcity of real-world robot data. While recent approaches leverage human video demonst

safetyarxiv-cs-ro

Frozen Judges, Moving Agents: Version-Dependent LLM-Judge Error and the Limits of Judge-Assisted Agent Evaluation

arXiv:2609.34198v1 Announce Type: new Abstract: Language-model judges compare agent upgrades with their predecessors, but a fixed judge can make version-dependent mistakes. We analyze 35 public coding

agentsarxiv-cs-lg

Tutorials

Generative Uncertainty as a Self-supervised Signal for Semantic Similarity Learning

arXiv:2609.35341v1 Announce Type: new Abstract: Evaluating semantic similarity between videos is a fundamental challenge in computer vision, essential for tasks ranging from out-of-distribution (OOD)

tutorialsarxiv-cs-cv

Local Ai

Graph-Based Learning for Multi-Horizon Martian Atmospheric Forecasting

arXiv:2609.35042v1 Announce Type: cross Abstract: Martian weather forecasting is important for future exploration, but atmospheric behaviour on Mars combines spatial, temporal, vertical, and dust-driv

local-aiarxiv-cs-lg

High-Level Text Preprocessing for Semantic Similarity Analysis of Discursive Texts: A Framework and Empirical Demonstration

arXiv:2609.33983v1 Announce Type: cross Abstract: Semantic Textual Similarity (STS) methods assume that a document's lexical content faithfully represents what it asserts. This assumption fails for di

Informative Viewpoint Selection for Episodic-Memory Embodied Question Answering using Omnidirectional Images

arXiv:2609.33288v1 Announce Type: new Abstract: Embodied Question Answering (EQA) requires agents to answer natural language questions about surrounding environments from visual observations. In this

agentsarxiv-cs-cv

Is invariance all you need for algorithmic fairness? Removing demographic information can create new bias

arXiv:2609.32004v1 Announce Type: new Abstract: Encoded demographic information in internal model representations is a commonly assumed risk factor for algorithmic bias, with demographic representatio

JIVE: Jacobian-Informed Volume Expansion for Diverse Generative Sampling

arXiv:2609.33906v1 Announce Type: cross Abstract: Generative models often suffer from mode collapse and limited sample diversity. While prior works attempt to mitigate this by jointly generating a bat

Kolmogorov-Arnold networks in nuclear binding energy prediction

arXiv:2407.20737v3 Announce Type: cross Abstract: This study explores the application of Kolmogorov-Arnold networks (KANs) in predicting nuclear binding energies, leveraging their ability to decompose

Lagrangian--Hamiltonian Flows for Video Prediction and Image Generation: A Symplectic Perspective

arXiv:2609.35710v1 Announce Type: new Abstract: We introduce LHFM, a geometric framework for learning image dynamics. Drawing on structures central to classical mechanics, symplectic geometry, and geo

Applications

Language-Augmented Video Action Anticipation: Design Fundamentals, Benchmarks, and Open Challenges

arXiv:2609.31665v1 Announce Type: new Abstract: Action anticipation predicts future human actions from partial video under incomplete context and temporal uncertainty. Recent systems introduce large l

applicationsarxiv-cs-cv

Local Ai

Learn Here, Move Less Elsewhere: Input-Conditioned Plasticity from Retained-Domain Activation Atlases

arXiv:2609.34478v1 Announce Type: new Abstract: Task-specific fine-tuning can rewrite a language model's answers beyond the training task, complicating updates that must preserve existing behavior. We

local-aiarxiv-cs-lg

Learning Perturbation Robust Policies for LLM Agents with Stable Optimization

arXiv:2609.34064v1 Announce Type: new Abstract: Reinforcement learning (RL) has become an effective post-training paradigm for long-horizon large language model (LLM) agents. However, we find that the

Learning What to Evaluate: Correlation-Aware Decoupling for Multiobjective Bayesian Optimization

arXiv:2609.32632v1 Announce Type: cross Abstract: Multiobjective Bayesian optimization (MOBO) with Gaussian process (GP) surrogates is a sample efficient approach to solving multiobjective optimizatio

Low-Confidence Remasking Traps Flexibility: Realizing Arbitrary-Order Potential for Diverse Rollouts in Diffusion LLMs

arXiv:2609.34509v1 Announce Type: new Abstract: Masked diffusion language models support arbitrary-order generation, suggesting a natural way to produce diverse outputs. However, recent work argues th

LTV-CTDNet: Compositional Turning Decomposition for Short-Term Turning-Movement Forecasting

arXiv:2609.34014v1 Announce Type: new Abstract: Short-term turning-movement forecasts can support signal control and corridor operations, but unconstrained neural networks may produce physically impos