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Beyond Isolated Entities: Relation-Aware Multi-Entity Modeling for Unsupervised Video Anomaly Detection

arXiv:2609.31753v1 Announce Type: cross Abstract: Video anomaly detection for patrol robots and surveillance systems must recognize abnormal interactions among familiar entities. Existing pixel-recons

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Model Releases

Beyond Selection: Token Parameterization for Extreme Visual Token Compression

DGX agent

arXiv:2609.35232v1 Announce Type: new Abstract: Visual-token compression is effective for improving the efficiency of vision-language models, but under extreme compression budgets, token pruning can b

model-releasesarxiv-cs-cv

29 Sept 2026

Beyond Token Savings: A Systematic Study of Context Compression in LLM Agents

arXiv:2609.32961v1 Announce Type: new Abstract: As LLM agents tackle longer tasks, they increasingly compress growing histories of reasoning, actions, and tool outputs. Compression can reduce token us

Bidirectional Neural Networks for Global Nucleon-Nucleus Optical Model Calculations

arXiv:2512.22500v4 Announce Type: cross Abstract: Modern nuclear data evaluation increasingly requires not only accurate scattering calculations, but also efficient methods for uncertainty quantificat

Binding Multiple Modalities via Multimodal Wasserstein Barycenter

arXiv:2609.33800v1 Announce Type: new Abstract: Multimodal learning beyond two modalities commonly leverages a specific modality (e.g., text) to bind other modalities. However, how to establish a more

Bison: Cross-Dataset Learning for Unseen-Compound Perturbation Prediction

arXiv:2609.32467v1 Announce Type: new Abstract: Predicting transcriptional responses to unseen compounds is limited by fragmented chemical coverage and heterogeneous experimental platforms and gene pa

BITS: Rethinking Fair and Comprehensive Evaluation for Irregular Time Series Forecasting

arXiv:2609.33303v1 Announce Type: new Abstract: Despite recent progress in irregular time series forecasting, the field still lacks a unified benchmark for fair and comprehensive evaluation. Existing

BMND: Direct Poisson Denoising by N-Dimensional Block Matching and Collaborative Filtering

arXiv:2609.34622v1 Announce Type: new Abstract: Poisson denoising of scientific data requires methods that account for signal-dependent noise while accommodating different data dimensionalities and pr

BOCCHI: A More Realistic and Challenging Benchmark for Local Motion Blur Detection with MSDCT-UNet

arXiv:2607.10427v2 Announce Type: replace Abstract: Local motion blur detection requires pixel-level localization of blurred regions. Existing benchmarks let models rely on gradient shortcuts that fai

BOReFT: Manifold Steering of Language Models for Black-box Optimization

arXiv:2609.33722v1 Announce Type: new Abstract: Language models are increasingly used as proposal models for black-box search, from program optimization to molecular design. Existing approaches typica

BR-MPPI: Barrier-Rate Guided MPPI for Enforcing Multiple Inequality Constraints with Learned Signed Distance Fields

arXiv:2506.07325v2 Announce Type: replace Abstract: Model Predictive Path Integral (MPPI) control provides a sampling-based framework for optimal control, while Control Barrier Functions (CBFs) provid

Broken Symmetry in BF16 Attention: Why FlashAttention Gradients Blow Up Late in Training

arXiv:2609.34272v1 Announce Type: new Abstract: BF16 is now standard in large-scale pretraining, including in fused attention kernels such as FlashAttention, and these kernels are widely trusted. When

CAESAR: Clustering via Autonomous Embedding-Space Agglomerative Reorganization

arXiv:2609.32570v1 Announce Type: new Abstract: Clustering algorithms that operate on nearest-neighbor graphs, such as FINCH (First Integer Neighbor Clustering Hierarchy), depend heavily on the qualit

Can Motion-Language Models Ground Structure? STRIDE for Evaluating the Evaluators

arXiv:2609.32462v1 Announce Type: new Abstract: Motion-language models are typically scored by motion-language evaluators, but how well these evaluators ground language structure remains unclear. Here

Can Tabular Foundation Models Amortize Statistical Inference?

arXiv:2609.33114v1 Announce Type: cross Abstract: For decades, statistical inference has largely been developed one problem at a time. Given a scientific target, such as a treatment effect or a regres

CAR-VLA: Complexity-Aware and Risk-Adaptive Reasoning for Autonomous Driving

arXiv:2609.34387v1 Announce Type: new Abstract: Existing adaptive reasoning methods for driving Vision-Language-Action (VLA) models primarily focus on whether to reason, overlooking how reasoning shou

CarveMix-RC: Addressing Rare-Class Imbalance Through Lesion-Aware Synthetic Augmentation for Brain Metastasis Segmentation

arXiv:2609.35195v1 Announce Type: new Abstract: Accurate segmentation of post-treatment brain metastases is essential for treatment planning, longitudinal disease monitoring, and quantitative assessme

Causal Routing for Unlearning

arXiv:2609.34475v1 Announce Type: new Abstract: LLMs cannot forget the way we delete a file. Strangely, we are asked to remove something that was never put anywhere in particular. What the model took

CausalDriveBench: Evaluating Causal Reasoning in Vision-Language-Action Models for Autonomous Driving

arXiv:2609.32157v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models for autonomous driving produce natural-language reasoning alongside predicted trajectories, but whether this reasoni

CFLoRA: Federated Fine-tuning of LLMs with Complementary Factors for Error-free Aggregation

arXiv:2609.33147v1 Announce Type: new Abstract: Federated low-rank adaptation (LoRA) enables collaborative fine-tuning of large language models without centralizing private client data. Its factorized

Change the Product, Keep the Parameters: Associative Algebra Layers for Transformers

arXiv:2609.32814v1 Announce Type: new Abstract: Fast matrix multiplication algorithms keep the product fixed and search for a cheaper way to evaluate it. We instead ask whether a Transformer's learned

CLIMB: A Clinical Multimorbidity Benchmark for Diagnosing Co-occurring Conditions through Multiturn Conversations

arXiv:2609.35462v1 Announce Type: new Abstract: Patients often have several co-occurring clinical conditions, and the findings needed to identify and disambiguate them emerge over the course of a cons

Climbing the Hill: Prompt Injection Red-Teaming Against Frontier Models with Curriculum Reinforcement Learning

arXiv:2609.33628v1 Announce Type: new Abstract: Prompt injection is a leading security risk for LLMs and LLM-based applications such as agents. State-of-the-art red-teaming methods for prompt injectio

CodeActionBench: Evaluating Agentic Code-as-Policy for Embodied Manipulation

arXiv:2609.33807v1 Announce Type: new Abstract: How well can general-purpose multimodal models turn visual understanding and reasoning into embodied manipulation via executable code? We introduce Code

CoDrive: Cross-Vehicle World-Consistent Video Generation with Precise Trajectory Control for Cooperative Driving

arXiv:2609.34749v1 Announce Type: new Abstract: Real-world driving is inherently multi-agent, yet most existing driving world models generate observations from a single ego vehicle. Independently exte

CoHuB: A Simulation Benchmark for Multi-Humanoid Collaboration

arXiv:2609.34782v1 Announce Type: cross Abstract: Many physical tasks in human environments require collaboration, from assisting a partner to jointly manipulating an object. Yet, existing humanoid be

Collaborative Principle Evolution via Evidence Transfer for Scientific Discovery

arXiv:2609.35315v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents promise to automate scientific discovery, yet exploring the vast hypothesis space remains costly. Existing princ

ColNanoVDR: Document-Free Query Distillation for Multi-Vector Visual Document Retrieval via Optimal Transport

arXiv:2609.34899v1 Announce Type: cross Abstract: Multi-vector retrievers built on vision-language models lead visual document retrieval (VDR), but they run a multi-billion-parameter query encoder on

Communication-Aware Heterogeneous Graph Learning for Decentralized Multi-Human Multi-Robot Task Allocation

arXiv:2609.32935v1 Announce Type: new Abstract: Multi-human multi-robot (MH-MR) teams combine robotic autonomy with human expertise, but effective task allocation requires coordinating scarce, dynamic

Commutator Memory: Sparse, Path-Local Reading and Steering in Language Models

arXiv:2609.34348v1 Announce Type: new Abstract: Gradient updates on different data generally do not commute: training a language model on two data sources in opposite orders gives different weights, e

Composable Decoding on the Probability Simplex: Theory and Implementation

arXiv:2609.34992v1 Announce Type: new Abstract: Decoding for large language models is typically treated as a collection of isolated sampling strategies, with limited theoretical understanding of the b

Compute Time Scaling with Recursive Models for Combinatorial Optimization

arXiv:2609.34585v1 Announce Type: new Abstract: We propose Tiny Recursive Models for Combinatorial Optimization (ours{}), a general neural method for combinatorial optimization that scales both depth

Concept Score Relearning: A Unified Cross-Architecture Attack on Concept Erasure

arXiv:2609.33445v1 Announce Type: new Abstract: Concept erasure aims to suppress undesirable knowledge in text-to-image generative models. However, existing robustness evaluations typically rely on re

ConflictVLA-Bench: Benchmarking Behavioral Responses of Vision-Language-Action Models to Premise Conflicts

arXiv:2609.31792v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models perform strongly on manipulation tasks, their responses to invalid task premises remain underexplored. Exist

Contamination, Prior, or Evidence? Decomposing and Training Evidence Use in Whole-Slide Vision-Language Models

arXiv:2609.32185v1 Announce Type: new Abstract: Pathology vision-language models (VLMs) are conventionally evaluated by accuracy, but accuracy alone does not measure evidence use: it may conflate data

Context-dependent time-series prediction via HyperReservoirs

arXiv:2609.34847v1 Announce Type: new Abstract: Time series prediction is a common application of reservoir computing. When the training and testing time series data contains multiple dynamical regime

Continual Data Unlearning in Diffusion Models via Transition-based Regularization

arXiv:2609.32328v1 Announce Type: new Abstract: Data unlearning in diffusion models aims to remove the influence of specific training examples without suppressing the broader concepts they represent.

Continual Learning via Self-Probe Gradients

arXiv:2609.32771v1 Announce Type: new Abstract: Adapting pretrained models to new data can cause catastrophic forgetting of previously learned behavior. When only a few past samples remain, they give

Controlling Speaking Rate in Autoregressive TTS via Activation Steering

arXiv:2609.33810v1 Announce Type: cross Abstract: Autoregressive text-to-speech (TTS) systems synthesize natural speech but, once trained, offer little control over speaking rate. We show that speakin

ControlTrace: Recovering Control Fields for Hidden-Content Recognition

arXiv:2609.34807v1 Announce Type: new Abstract: Spatially conditioned diffusion models can embed words and contours in natural-looking images, but vision-language models (VLMs) may fail to recognize t

ConvCue: Complementary Visual Inductive Biases for Vision-Language Models

arXiv:2609.34196v1 Announce Type: new Abstract: Modern vision-language models (VLMs) achieve strong performance across a broad range of multimodal tasks, yet still struggle with visual questions that

Convergence of Practical Muon

arXiv:2609.33152v1 Announce Type: new Abstract: Muon is emerging as a promising alternative to AdamW for large-scale neural network training, yet theoretical understanding of its practical implementat

Copper-Policy: Focus on the Representation for Robust Robot Manipulation

arXiv:2609.32779v1 Announce Type: cross Abstract: World Action Models (WAMs) acquire behavioral priors by modeling future scene evolution, but predicting detailed futures in pixel or latent space incu

Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning

arXiv:2502.02332v3 Announce Type: replace-cross Abstract: We study task selection to enhance sample efficiency in model-agnostic meta-reinforcement learning (MAML-RL). Traditional meta-RL typically as

Correct then Forecast: Observer State-Space Models for Time Series Forecasting

arXiv:2609.33566v1 Announce Type: new Abstract: Time series forecasting requires extrapolating the dynamics of an observed process beyond the last available measurement. Yet recurrent forecasting mode

Correction-space Cross-variate Interaction for Test-time Adaptation in Time Series Forecasting

arXiv:2609.34638v1 Announce Type: new Abstract: Test-time adaptation (TTA) is a promising paradigm for handling distribution shift in time-series forecasting (TSF), where models adapt at inference tim

CoSE-E: A Benchmark for Code-switched Speech Evaluation in Enterprise Settings

arXiv:2609.35645v1 Announce Type: cross Abstract: Code-switching (CS), a seamless alternation between languages within a single utterance, remains a critical challenge in automatic speech recognition

Cross-Dataset Generalization of Bangladeshi Rice Leaf Disease Classifiers: Benchmark, Diagnosis, and Mitigation

arXiv:2609.31709v1 Announce Type: new Abstract: Cross-dataset transfer in rice leaf disease classification remains a significant challenge, with models trained on one image collection performing subst