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The Price of Consistency: Exploiting Visual Anchors for Multimodal Jailbreaking in Video Generation

arXiv:2609.07216v1 Announce Type: new Abstract: The rapid evolution of video generation has shifted the paradigm from pure text-driven to multi-conditional controllable generation, with reference imag

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Research

The Price of Sparsity: Sufficient Conditions for Sparse Recovery using Sparse and Sparsified Measurements

DGX agent

arXiv:2509.01809v2 Announce Type: replace-cross Abstract: We consider the problem of support recovery for sparse binary signals from noisy linear measurements. For sparse Gaussian measurement matrices

researcharxiv-cs-lg

9 Sept 2026

The Role of Uncertainty in Assessing the Fairness of Machine Learning Models

arXiv:2609.07959v1 Announce Type: cross Abstract: Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure. Verifying whether their o

Tutorials

The Rules-and-Facts Model for Simultaneous Generalization and Memorization in Neural Networks

arXiv:2603.25579v2 Announce Type: replace-cross Abstract: A key capability of modern neural networks is their capacity to simultaneously learn underlying rules and memorize specific facts or exception

tutorialsarxiv-cs-lg

The same people that are building the most advanced AI models and agents are those that are calling them alien, and state that they do not k…

The same people that are building the most advanced AI models and agents are those that are calling them alien, and state that they do not know how to reach alignment with AI. It might be possible tha

safetyitamar-friedman--x

there is a difference between 'i am ok with models being trained on my data and released publicly later' and 'i am ok with internal models t…

model-releasesgary-marcus--x

there is a difference between 'i am ok with models being trained on my data and released publicly later' and 'i am ok with internal models trained on my very recent efforts, being used to race the sol

There is a nice visualization of how task bundles change with AI & some good interactive simulations of GDP growth under different assumptio…

model-releasesethan-mollick--x

There is a nice visualization of how task bundles change with AI & some good interactive simulations of GDP growth under different assumptions in this. But missing are the right policy responses if we

Think Wider: Mitigating Latent Rank Collapse in Implicit Chain-of-Thought Reasoning

arXiv:2609.07406v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning improves the reasoning ability of large language models by introducing intermediate computation, but explicit rationale

Thinking with Cameras: Active Visual Reasoning via Dynamic Viewpoint Control for Surveillance Video Understanding

arXiv:2609.06475v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have recently achieved remarkable progress in general-purpose video understanding. However, their application to su

safetyarxiv-cs-cv

ThinkPrior: Zero-Rollout Difficulty Priors for Cold-Start Prompt Selection in RLVR

arXiv:2609.09075v1 Announce Type: new Abstract: In reinforcement learning with verifiable rewards (RLVR) trained with group relative policy optimization (GRPO), the KL-free reward-advantage term studi

This is a really neat benchmark. It's super interesting that Astra starts strong and holds the lead for up to 19 hours, but Fable 5.1 catche…

This is a really neat benchmark. It's super interesting that Astra starts strong and holds the lead for up to 19 hours, but Fable 5.1 catches up in the final hours. Not entirely clear why. Different m

model-releasesdair-ai--x

Tight Lower Bounds for State Tomography with Limited Entanglement

arXiv:2609.05718v1 Announce Type: cross Abstract: We study state tomography when each measurement acts on at most k fresh copies and no quantum memory is retained between blocks. We prove a lower boun

Time-Aware Assistive Navigation

arXiv:2609.05596v1 Announce Type: new Abstract: Can interactive vision-and-language agents learn not just what to say but also extbf{extit{when}} to say it? Current language models rarely plan over wh

TimeBlind: A Spatio-Temporal Compositionality Benchmark for Video LLMs

arXiv:2602.00288v4 Announce Type: replace Abstract: Fine-grained spatio-temporal understanding is essential for video reasoning and embodied AI. Yet, while Multimodal Large Language Models (MLLMs) mas

TimeWarp: Evaluating Web Agents by Revisiting the Past

arXiv:2603.04949v2 Announce Type: replace-cross Abstract: As web agents close the gap with humans on benchmarks, one question arises: Do today's agents perform just as well on tomorrow's web? We intro

To Adapt or Not to Adapt? Selective Adaptation for Vision-Language Models

arXiv:2609.08367v1 Announce Type: new Abstract: Test-time adaptation (TTA) has emerged as a prominent strategy for adapting vision-language models to distribution shifts during inference. We conduct a

TontaubeV1: Streaming Text-to-Speech with Hierarchical Codec Modeling and Bounded Context

arXiv:2609.08703v1 Announce Type: cross Abstract: Text-to-speech systems often face a trade-off between natural prosody and efficient inference: higher perceptual quality typically comes at increased

Topological Fraud Detection in Latent Transaction Spaces

arXiv:2609.08445v1 Announce Type: new Abstract: Working entirely on topologically anonymized embeddings, we perform fraud detection using iterative rounds of unsupervised filtering followed by supervi

Topologically Consistent Agricultural Parcel Vectorization with Semantic-Guided Diffusion and Topology-Aware Polygonization

arXiv:2609.07520v1 Announce Type: new Abstract: Agricultural parcel polygons play a fundamental role in geospatial applications such as precision agriculture, land administration, and crop monitoring.

Topology-induced Operators Reveal Complementary Graph Representations without Training

arXiv:2609.08152v1 Announce Type: new Abstract: Graph representation learning has largely focused on designing increasingly sophisticated models to transform graph topology into vector representations

Applications

Toward Robust Single-Photon Perception for Robots: A Condition-Aware Active Learning Approach

arXiv:2505.04376v2 Announce Type: replace-cross Abstract: LiDAR-based perception plays a fundamental role in modern robotic systems for environment understanding and navigation. Single-photon LiDAR (S

applicationsarxiv-cs-cv

Toward Sustainable Distributed LLM Inference: A Systems Synthesis and Research Agenda for an Energy-, Carbon-, and Cache-Aware llm-d Control Plane

arXiv:2609.05565v1 Announce Type: cross Abstract: Large language model (LLM) sustainability is increasingly a serving-systems problem, not only a training problem. In production, energy and carbon imp

Towards Bridging the Gap Between Offline and Iterative Alignment via Preference Distillation

arXiv:2609.06893v1 Announce Type: cross Abstract: Direct preference optimization DPO is a promising offline approach for aligning large language models (LLMs) due to its simplicity, computational effi

Towards Embodied Air-Ground Cooperative Object Search: Benchmark, Dataset and Agentic Method

arXiv:2609.08402v1 Announce Type: new Abstract: Air-Ground Object Search (AGOS) in urban environments is a challenging embodied task, which requires an Unmanned Aerial Vehicle (UAV) and an Unmanned Gr

Towards Unified Multimodal Graph Foundation Model: A Bridge-Router-Adapter Based Approach

arXiv:2609.06668v1 Announce Type: new Abstract: Multimodal graphs couple node attributes in different modalities, such as text and images, with relational structure, enabling topological structure and

Towards Vision-Language Geo-Foundation Model: A Survey

arXiv:2406.09385v3 Announce Type: replace Abstract: Vision-Language Foundation Models (VLFMs) have made remarkable progress on various multimodal tasks, such as image captioning, image-text retrieval,

Tracing Mathematical Proficiency Through Problem-Solving Processes

arXiv:2512.00311v3 Announce Type: replace Abstract: Knowledge Tracing (KT) aims to model student's knowledge state and predict future performance to enable personalized learning in Intelligent Tutorin

Agents

Tracking-by-detection in Multi-object Tracking: Survey and Experiments

arXiv:2609.08265v1 Announce Type: new Abstract: Multi-object tracking (MOT) is an essential computer vision task that simultaneously tracks multiple objects in video sequences, with various applicatio

agentsarxiv-cs-cv

Tracking the Moving Frontier: Long-Short Term Advantage Estimator

arXiv:2609.06671v1 Announce Type: new Abstract: Group-based RLVR methods estimate advantages by repeatedly sampling multiple trajectories for each prompt, making long-horizon agent training expensive

TRAIL: Trajectory-Aware Visual Place Recognition against Unordered Databases

arXiv:2609.07373v1 Announce Type: new Abstract: Modern Visual Place Recognition (VPR) methods excel on standard benchmarks yet remain brittle in feature-poor environments. By treating each query image

Train Smarter, Not Harder: Switching Signal-Guided Training in Active Learning

arXiv:2609.06806v1 Announce Type: new Abstract: Training strategy, namely whether to retrain from scratch or fine-tune from the previous checkpoint, is an overlooked decision variable in active learni

Training-Free Task Vectors for LLM Behavioral Control

arXiv:2609.09054v1 Announce Type: new Abstract: Task vectors enable post-training model editing by identifying semantically meaningful directions in weight space, typically computed as the difference

Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling

arXiv:2609.08981v1 Announce Type: new Abstract: A growing body of work establishes that large language models are not mere statistical memorizers, but are capable of in-context learning: performing in

Translation of Black-Box Clinical Prediction Models into Standalone Transparent Nomograms: Temporal External Validation in Heart Transplantation

arXiv:2609.07610v1 Announce Type: new Abstract: We convert black-box clinical prediction models for tabular data into standalone nomograms that can be audited term by term. PRiSM (Partial Responses in

TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation

arXiv:2609.06665v1 Announce Type: new Abstract: Diffusion-based models enable monocular geometry estimation, yet their pixel-space precision is limited by a shared, under-studied error source: VAE rec

Tri-PvP: Exposing Modality Bias in Omni-Modal Large Language Models through Perceptual-Propositional Evidence Conflicts

arXiv:2609.06011v1 Announce Type: cross Abstract: Omni-modal large language models (OLLMs) jointly process vision, audio, and text, yet their modality bias under cross-modal conflict remains underexpl

Applications

TriCCOT: Tri-part Convolutional Conformal Transformer for Onboard Space Object Detection

arXiv:2609.08659v1 Announce Type: new Abstract: Onboard object detection in Earth observation is constrained by limited computational resources and the absence of fully corrected imagery. While convol

applicationsarxiv-cs-cv

TRIUNE-Net: Harmonizing Scale, Shape, and Efficiency in Pancreatic Tumor Segmentation

arXiv:2609.08303v1 Announce Type: new Abstract: Pancreatic tumor segmentation in 3D CT volumes is challenged by extreme scale variability across both the pancreas and tumor, and highly irregular tumor

Agents

TrojanWorld: Backdooring World-Model Agents via Imagination Steering

arXiv:2609.07051v1 Announce Type: new Abstract: World models increasingly serve as the predictive core of model-based reinforcement learning agents, enabling them to simulate future dynamics and reaso

agentsarxiv-cs-lg

Trump’s ignorance about AI is appalling and extremely dangerous. No, Mr. President. We won’t have a “little gear” to protect humanity from t…

Trump’s ignorance about AI is appalling and extremely dangerous. No, Mr. President. We won’t have a “little gear” to protect humanity from the threat of an uncontrolled AI. We must pause development N

safetygary-marcus--x

Trust-But-Verify: Poisoning-Resilient Locally Private Graph Learning Protocols

arXiv:2609.07063v1 Announce Type: new Abstract: Built upon local differential privacy (LDP), locally private graph learning protocols have emerged as an important paradigm for decentralized graph lear

TV-Regulated OPD: Direction Matters in On-Policy Distillation

arXiv:2609.08341v1 Announce Type: new Abstract: On-Policy Distillation (OPD) facilitates the transfer of knowledge from domain expert to student in the post-training phase of Large Language Models (LL

TV-SGS: Gaussian Splatting with Geometric Information Propagation via Tensor Voting under sparse views

arXiv:2609.07734v1 Announce Type: new Abstract: Gaussian Splatting has been effective in inferring scene representations that excel in novel view synthesis. Multiple splats cooperate seamlessly to syn

Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods

arXiv:2506.05626v3 Announce Type: replace Abstract: Real-world knowledge can take various forms, including structured, semi-structured, and unstructured data. Among these, Knowledge Graphs (KGs) are s

Two-Scale Localized PCA-Net: Coarse-Global and Local-Residual Representations for Artifact-Reduced PDE Operator Learning

arXiv:2609.08034v1 Announce Type: new Abstract: Localized dimensionality reduction improves the scalability of operator learning for high-dimensional partial differential equations (PDEs), but indepen

Understanding Cross-Modal Contributions in Continual Vision-Language Models: A Theoretical Perspective

arXiv:2606.14883v2 Announce Type: replace Abstract: Continual vision-language models are commonly addressed through sequential fine-tuning; however, although this paradigm enables adaptation to new en

Understanding Uncertainty Sampling via Equivalent Loss

arXiv:2307.02719v5 Announce Type: replace Abstract: Uncertainty sampling is a classical active-learning strategy, yet the statistical objective induced by its query rule is often implicit. We introduc

Unexplored flaws in multiple-choice VQA make benchmarking unreliable

arXiv:2511.22341v2 Announce Type: replace Abstract: Previous works identify sensitivity to option order as a key issue in multiple-choice VQA (MC-VQA) evaluation and propose protocols to mitigate this