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When to Retrieve, When to Stay: Uncertainty-Aware Temporal Evidence Allocation for Streaming Video-LLMs

arXiv:2609.37345v1 Announce Type: new Abstract: Streaming video understanding requires Video Large Language Models (Video-LLMs) to reason over continuous visual streams under causal constraints. As th

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Agents

When Tools Silently Lie: Evaluating and Mitigating Blind Compliance in Tool-Augmented Data Agents

DGX agent

arXiv:2609.37153v1 Announce Type: new Abstract: Tool-augmented data agents rely on tool outputs for analytical decisions. Yet successful execution can return plausible but incorrect evidence, requirin

agentsarxiv-cs-ai

30 Sept 2026

When Trees Are Not Enough: Learning Mixed-Topology Feature Graphs with Adaptive Graph Sparse Autoencoders

arXiv:2609.36294v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) expose interpretable features in large language model activations, yet existing structured SAEs impose single-parent trees

tutorialsarxiv-cs-cl

When Updating Stops Being Learning: Rethinking LLM Self-Evolution via learnable information gain

arXiv:2609.36535v1 Announce Type: new Abstract: Self-evolution lets large language models (LLMs) improve iteratively using their own generated data, but often suffers from self-evolution degeneration:

researcharxiv-cs-cl

Agents

When Upstream Messages Override Correct Answers: A Controlled Study of Multi-Agent LLM Collaboration

arXiv:2609.36855v1 Announce Type: new Abstract: Multi-agent LLM systems rely on message passing among specialized agents to accomplish complex tasks. However, an upstream agent may provide useful info

agentsarxiv-cs-ai

Where Does Randomness Matter in Neural Cellular Automata?

arXiv:2609.36797v1 Announce Type: new Abstract: Stochastic cell updates are often used throughout the life of a neural cellular automaton (NCA), from backpropagation through time to final rollout. Thi

tutorialsarxiv-cs-lg

Where Does Staleness Accumulate? Pool Aware Effective Staleness Control for Asynchronous RL in LLM Post-Training

model-releasesarxiv-cs-ai

arXiv:2609.36830v1 Announce Type: new Abstract: Fully asynchronous reinforcement learning (RL) improves resource utilization in large language model post-training by overlapping rollout generation wit

Where Predictive Supervision Goes Shapes What VLA Policies Learn

arXiv:2609.36645v1 Announce Type: cross Abstract: Future prediction is increasingly used to improve vision-language-action (VLA) policies, based on the premise that anticipating scene evolution encour

safetyarxiv-cs-ai

Where Privacy Belongs: Placement Diagnosis and Certified Selection for Private Counterfactual Explanations on Graphs

arXiv:2609.37667v1 Announce Type: new Abstract: Counterfactual explanations for graph neural networks (GNNs) find the minimal intervention that flips a node's prediction--but computing one requires re

Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition

arXiv:2609.36686v1 Announce Type: new Abstract: Identifying the root cause of an anomaly among hundreds of sensors is critical for preventing safety incidents and costly downtime in complex monitored

Local Ai

Where Should Physics Enter a Molecular Crystal Generator?

arXiv:2609.36398v1 Announce Type: cross Abstract: Generative models make molecular crystal structure prediction fast, but their samples still exhibit geometric and packing violations. Physics can be i

local-aiarxiv-cs-ai

Where the Model Changes Its Mind: Hindsight-Divergence Localization for Efficient Reinforcement Learning with Verifiable Rewards

arXiv:2609.36864v1 Announce Type: new Abstract: Group-relative methods for reinforcement learning with verifiable rewards (RLVR) learn from differences in rollout outcomes. Independently sampling comp

Which Attention Heads are like the Human Head? Not the Ones that Compute

arXiv:2609.37991v1 Announce Type: new Abstract: Brain-AI alignment is often interpreted as a sign that model and brain perform similar computations. Whether the aligned units are causally involved in

safetyarxiv-cs-ai

Which papyrus HTR is good enough? Character-error-rate tolerance of four papyrological tasks on Greek texts

arXiv:2609.37755v1 Announce Type: new Abstract: Purpose: Most Greek papyri remain unpublished and undigitised; a handwritten text recognition (HTR) pipeline that transcribes them automatically would l

Who Warmed the Archives? LLMs Overestimate Historical Warmth

arXiv:2609.37499v1 Announce Type: new Abstract: Historical archives are an under-used source for extending the instrumental climate record backward in time, and LLMs offer a way to extract the indices

Why Adaptive Optimizers Underestimate Rare Tokens

arXiv:2609.37535v1 Announce Type: new Abstract: In the softmax output layer, a rare token receives a small positive logit gradient on most steps and a much larger negative gradient on the few steps wh

Why Backdooring Neural Networks is so Easy?

arXiv:2609.36117v1 Announce Type: new Abstract: Securing modern AI systems against backdoor attacks remains an open challenge and requires fundamentally principled estimates of the adversary's budget

researcharxiv-cs-lg

Why Cross-Skeleton Retargeting Is Non-Identifiable: Structural Limits of Generative Motion Models

arXiv:2609.37297v1 Announce Type: new Abstract: Cross-skeleton motion generation trains generative models to carry action structure and motion intention from one body to another. Yet a target motion t

tutorialsarxiv-cs-cv

Local Ai

Why MLLMs Struggle to Count: Overcoming Individuation and Aggregation Bottlenecks with ConvStack

arXiv:2609.37096v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) consistently struggle with fine-grained visual counting, yet the underlying causes remain poorly understood. In

local-aiarxiv-cs-cv

Width Expansion as a Method for Class Incremental Learning

arXiv:2609.37702v1 Announce Type: cross Abstract: Class Incremental Learning (Class-IL) requires models to learn new classes over time while preserving previously acquired knowledge without access to

tutorialsarxiv-cs-ai

WINGS: Reference-Free Gaussian Splatting Inpainting with 3D-Native Generative Priors

arXiv:2609.37816v1 Announce Type: new Abstract: Inpainting 3D Gaussian Splatting scenes, a key challenge in 3D editing, requires generating plausible content within a masked region of 3D space. Prior

WISE-ATTA: When to Ask for Labels in Budgeted Active Test-Time Adaptation

arXiv:2609.37687v1 Announce Type: new Abstract: Active test-time adaptation (ATTA) improves robustness under distribution shift by updating a deployed model during inference while selectively querying

researcharxiv-cs-ai

WitnessGym: Benchmarking Coding Agents on the Construction of Bug Witnesses

model-releasesarxiv-cs-ai

arXiv:2609.36635v1 Announce Type: cross Abstract: Bug validation asks a coding agent to produce an executable witness for a reported bug. The witness combines a concrete input with a testing harness a

World2Motion: Turning Video World Models into 3D Human Motion Generators

arXiv:2609.37004v1 Announce Type: new Abstract: We present World2Motion, a framework that generates scene-aware 3D human motion and corresponding video from a single image and a text prompt. While exi

World4Scorer: Outcome-Grounded World Modeling for Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2609.36438v1 Announce Type: new Abstract: Autonomous driving requires choosing a safe and efficient plan as surrounding traffic evolves. Generate-and-select planners propose multiple trajectorie

WorldLine: Action-Driven Visual Simulation for Robotic Manipulation

arXiv:2609.38059v1 Announce Type: cross Abstract: Real-world robot learning is constrained by the cost of collecting experience and evaluating candidate behaviors. Video generation models offer a scal

safetyarxiv-cs-cv

Wrench-ACT: Enhancing Robot Policies for Contact Rich Behavior Using Direct Wrench Control

arXiv:2609.37552v1 Announce Type: new Abstract: While contact-rich manipulation requires deliberate regulation of interaction forces, recent approaches to robot manipulation learning predominantly rep

safetyarxiv-cs-ro

WUSH-KV: KV Cache Quantization with Data-Adaptive Transforms

arXiv:2609.38121v1 Announce Type: new Abstract: KV cache memory and bandwidth costs grow with context length and batch size, which limits efficient long-context inference. To address this bottleneck,

researcharxiv-cs-lg

Xiaomi-OCR-0 Technical Report

arXiv:2609.36136v1 Announce Type: new Abstract: Compact OCR-specific vision-language models achieve strong document parsing performance, but often rely on costly supervision and focus primarily on vis

XU-RS: Explaining Credal Width in Random-Set Language Models

model-releasesarxiv-cs-ai

arXiv:2609.37594v1 Announce Type: new Abstract: Uncertainty estimates tell us how unsure a model is, but not why. Without knowing which parts of an input influences a model's uncertainty, we cannot te

You Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight LLM Inference

arXiv:2609.37902v1 Announce Type: new Abstract: Existing LLM routers choose among models using static per-model costs. We show that open-weight inference markets introduce a second, largely ignored de

safetyarxiv-cs-ai

You Cannot Recover What Was Never Measured: Quantifying the Information Ceiling of Ultra-Low-Field MRI Super-Resolution

arXiv:2609.36837v1 Announce Type: new Abstract: Generative super-resolution models can turn portable 64 mT MRI into images that look like 3T scans, and the field evaluates them with PSNR, SSIM, and pi

You Only Reprogram Once: Rethinking Prolonged Training for Visual Reprogramming

arXiv:2609.36661v1 Announce Type: new Abstract: Visual reprogramming is a parameter-efficient method for adapting pretrained models, yet its training can remain computationally expensive: even with a

Your Benchmark Is Not Saturated: Reviving Multiple-Choice Evaluation with Answer Pooling

arXiv:2609.37494v1 Announce Type: new Abstract: Multiple-choice benchmarks are cheap to grade and are running out of room, and the standard remedy, writing harder items, is slow and repeated for every

Zero-shot Dependency Parsing with Unsupervised Cross-Lingual Bootstrapping

arXiv:2609.37883v1 Announce Type: new Abstract: Pre-trained language models (PLMs) with encoder-based architectures have shown impressive capabilities in zero-shot cross-lingual transfer for various l

Zero Sum SVD: Balancing Loss Sensitivity for Low Rank LLM Compression

arXiv:2602.02848v2 Announce Type: replace Abstract: Advances in large language models have driven strong performance across many tasks, but their memory and compute costs still hinder deployment. SVD-

researcharxiv-cs-lg

Applications

ZeroDiff: Zero-Shot Time Series Reconstruction via Informed-Prior Diffusion

arXiv:2609.37078v1 Announce Type: new Abstract: Time series modeling increasingly demands high-quality supervision, yet target observations remain scarce - exogenous inputs are broadly available, but

applicationsarxiv-cs-lg

3-D Emissions Mapping and Social Cost Estimation for US Domestic Aviation at West Coast Hubs

arXiv:2609.31686v1 Announce Type: new Abstract: Existing aviation emissions inventories lack accurate trajectory data for high-resolution social cost and health impact assessment. This paper develops

3D Point Tracking with State Space Models

arXiv:2609.34035v1 Announce Type: new Abstract: Tracking any point of a dynamic scene in metric 3D - in absolute meters, not up to an unknown scale - underpins 3D and 4D reconstruction, robot navigati

3dgs-sc: a controlled static screen-content benchmark for 3d gaussian splatting

arXiv:2609.31756v1 Announce Type: new Abstract: Does high image fidelity imply readable screen content in 3D Gaussian Splatting (3DGS)? We introduce 3DGS-SC, a controlled static screen-content dataset

A Cheap Verifier is Good Enough: LLM Post-training is Robust to Erroneous Rewards

arXiv:2609.33467v1 Announce Type: new Abstract: When post-training large language models on tasks with semi-verifiable rewards, there are many factors (training steps, base model size, training order,

A Comparative Analysis of Attention versus State-Space Models for In-Context Learning

arXiv:2609.32341v1 Announce Type: new Abstract: Transformers and state-space models (SSMs) are two prominent sequential learning architectures, yet their comparison remains largely empirical and exist

A Comparative Transfer-Learning Study of CNN Backbones for Partial Face Recognition on the SoF Dataset

arXiv:2609.31677v1 Announce Type: new Abstract: Face recognition is widely deployed in surveillance, access control, and forensic workflows, yet accuracy degrades sharply once the face is occluded by

A Computer Vision Approach to Visual Fraud Detection in Phishing Websites Using YOLOv8

arXiv:2609.34015v1 Announce Type: cross Abstract: Phishing remains one of the most common vectors for financial and identity fraud, and most detection systems still rely on inspecting a page's URL, HT

A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models

arXiv:2607.14522v2 Announce Type: replace Abstract: We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control

A Disk-Shaped Magnetoelastic Torque Sensor for Robotic Joints Using Permanent Magnetization

arXiv:2609.33542v1 Announce Type: new Abstract: Direct torque sensing is a growing need in the robotics community to enable precise control and interactions where torque estimation from motor current

researcharxiv-cs-ro

Applications

A family of graph GOSPA metrics for graphs with different sizes

arXiv:2506.17316v2 Announce Type: replace-cross Abstract: This paper proposes a family of graph metrics for measuring distances between graphs of different sizes. The proposed metric family defines a

applicationsarxiv-cs-lg

A Free Knob: Decoupling Calibration and Predictive Skill in Threshold-Based Evaluation

arXiv:2609.33457v1 Announce Type: new Abstract: Many dense-prediction benchmarks evaluate rare events by pooling prediction and target over spatial blocks, thresholding each, and scoring the contingen