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The KV Cache Is the New Memory Wall

arXiv:2609.30854v1 Announce Type: cross Abstract: Autoregressive LLM inference at long context is bounded by memory bandwidth, not arithmetic throughput, and the binding resource shifts from model wei

The Linear Representation Hypothesis for Vision-Language-Action Models

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arXiv:2609.30996v1 Announce Type: cross Abstract: The linear representation hypothesis (LRH) has become a standard lens for measuring and intervening on semantic information through the internal repre

model-releasesarxiv-cs-ai

28 Sept 2026

The N-5 Scaling Law: Topological Dimensionality Reduction in the Optimal Design of Fully-actuated Multirotors

arXiv:2512.23619v3 Announce Type: replace Abstract: We investigate the topological structure of the optimal actuation landscape for fully-actuated N-rotor aerial vehicles. By formulating the design pr

researcharxiv-cs-ro

The Plot Twist: Jailbreaking Unified Multimodal Models with a Three-Act NarrativeAttack

arXiv:2509.26473v2 Announce Type: replace Abstract: Unified Multimodal Understanding and Generation Models (UMMs) increasingly combine visual understanding and image generation within a single interac

The Price of Thought: Does Test-Time Reasoning Pay in LLM Trading?

arXiv:2609.30705v1 Announce Type: new Abstract: While inference-time reasoning in large language models (LLMs) promises better decision making, its higher computational cost may not yield better econo

The Residual Stream's Effective Depth

arXiv:2609.31098v1 Announce Type: new Abstract: We introduce effective depth (Deff), a scalar diagnostic that treats the layer-wise residual stream of a transformer as a discrete-time process, measure

researcharxiv-cs-lg

The Right Information Extraction Pipeline Depends on the Document: Accuracy-Energy Trade-offs for Small, Local Models

arXiv:2609.31341v1 Announce Type: new Abstract: Whether an information extraction pipeline should process page images or parsed text depends on the document, and the answer flips across the layout spe

The Shape of Events: Edge-Based Inductive Biases via Cross-Domain Distillation

arXiv:2609.30478v1 Announce Type: new Abstract: Convolutional neural networks trained on ImageNet are known to exhibit a strong preference for local high-frequency texture, an inductive bias that tran

local-aiarxiv-cs-cv

The Shrinking Lifespan of LLMs in Science

arXiv:2604.07530v3 Announce Type: replace-cross Abstract: Scaling laws describe how language model capabilities grow with compute and data, but say nothing about how long a model matters once released

The Uncontrolled Variable: Vision-Language Refusal Is Conditioned on the Image-Attachment Interface, and Not Robust to Irrelevant Image Properties

arXiv:2609.26174v2 Announce Type: replace-cross Abstract: We show that aligned vision-language models also condition refusal on a property of a request's form: whether an image is attached, holding ev

Agents

Thinking Less to Simulate Better: Intuitive Prompting Improves LLM Agents Simulating Individual Social Media Reactions, Including Unfamiliar Content

arXiv:2609.30563v1 Announce Type: new Abstract: Platform policies are increasingly tested on artificial users, making agent fidelity important. Yet convincing fake profiles could also manipulate perce

agentsarxiv-cs-ai

Threat-Aware Energy-Efficient Deployment for Dynamic UAV Networks: A Multi-Agent RL Approach

arXiv:2609.30690v1 Announce Type: cross Abstract: Ensuring operational safety in threat-prone environments remains a critical challenge for multi-UAV networks serving as aerial base stations. This pap

Tight Stochastic Condition-Number Dependence in Nonconvex-Strongly-Concave Minimax Optimization

arXiv:2609.30877v1 Announce Type: cross Abstract: We study whether the linear condition-number dependence in the stochastic complexity of SAPD+ is necessary for nonconvex-strongly-concave minimax opti

Timo: extbf{T}aming Multextbf{i}modal Diffusion Transformer for Human extbf{Mo}tion Generation

model-releasesarxiv-cs-cv

arXiv:2609.30761v1 Announce Type: new Abstract: Most existing human motion generation (HMG) methods use cross-attention modules to inject text semantics, but ignore the importance of bidirectional mod

TinyCVIO: A Constellation-Aided Visual-Inertial Odometry System for Nanodrones

arXiv:2609.30358v1 Announce Type: new Abstract: Nanodrones require accurate, real-time state estimation under severe sensing and computational constraints. We present TinyCVIO, a visual-inertial odome

researcharxiv-cs-ro

TISD: On-Policy Self-Distillation with Trajectory Intervention

arXiv:2609.30878v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) provides dense teacher targets, but evaluates them only along student-sampled rollouts. When the privileged teacher f

To Solve Bilevel Optimization with Nonconvex Lower Levels, We Need Second-Order Stationarity

arXiv:2609.30501v1 Announce Type: new Abstract: Although bilevel optimization (BLO) has emerged as a powerful framework for addressing many complex and nested machine learning problems in recent years

local-aiarxiv-cs-lg

Agents

ToolSearcher: Optimizing Tool Selection at Scale via Reinforcement Learning

arXiv:2609.30906v1 Announce Type: new Abstract: Large language models (LLMs) excel at natural language processing but struggle to interact with external environments. Tool learning provides a promisin

agentsarxiv-cs-cl

Agents

Topology-Driven Anti-Entanglement Control for Soft Robots

arXiv:2605.05236v2 Announce Type: replace-cross Abstract: In the field of precision manufacturing in complex constrained environments, the role of soft robots is increasingly prominent, and the realiz

agentsarxiv-cs-ai

Toward AI-Augmented Cooperative Engineering Workflows: Requirements and Architecture the European Rover Challenge

arXiv:2609.31136v1 Announce Type: new Abstract: The growing availability of Artificial Intelligence (AI) tools creates new opportunities to support engineering design processes, yet their current use

researcharxiv-cs-ai

Towards Automated Lexicography: Generating and Evaluating Definitions for Learner's Dictionaries

arXiv:2601.01842v2 Announce Type: replace Abstract: Dictionary definitions are an essential resource for learning word senses, but manually creating them is costly. We thus study dictionary definition

researcharxiv-cs-cl

Towards Mitigating Fabricated Consensus: The Active Provenance Gate for Multi-Agent Debate Synthesis

arXiv:2609.31422v1 Announce Type: cross Abstract: Large language model-based multi-agent debate (MAD) systems are being increasingly used as complex decision pipelines in distributed processes. Howeve

Towards Understanding LLM-Based Log Anomaly Detection: An Empirical Study of Performance, Efficiency, and Robustness

arXiv:2609.31371v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated promising performance in log anomaly detection, yet how their adaptation strategies, architectures, and d

Towards Understanding Momentum Acceleration in River-Valley Loss Landscape

arXiv:2609.30957v1 Announce Type: new Abstract: The empirical success of pretraining large language models has inspired a deeper investigation into the underlying loss landscapes and the optimization

researcharxiv-cs-lg

Towards Universal Representation-Based Process Control

arXiv:2609.30790v1 Announce Type: new Abstract: Many temporal process learning and monitoring pipelines operate in local windows, making window-level decisions unavoidable in practice. In such setting

local-aiarxiv-cs-lg

Applications

Towards VLA-Dreamer: Refining VLA Behavior Using World Models

arXiv:2609.31313v1 Announce Type: cross Abstract: Vision-Language-Action models (VLAs), while showing strong potential for robot control, require massive amounts of high-quality imitation learning dat

applicationsarxiv-cs-ai

Towards Whole-Study Screening for Congenital Heart Disease in Fetal Ultrasound Using Multiple Instance Learning

arXiv:2609.31376v1 Announce Type: cross Abstract: Congenital heart disease (CHD) is the most common birth defect, yet a large fraction of cases remain undetected on prenatal ultrasound, in part becaus

researcharxiv-cs-cv

TR-SSQP: A Trust-Region Method for Constrained Stochastic Optimization under Heavy-Tailed Noise

arXiv:2609.30732v1 Announce Type: cross Abstract: We consider stochastic nonlinear optimization problems with deterministic equality constraints. While unconstrained stochastic optimization is well un

TRACE: Temporal Audit and Condition-aware Evaluation of Streaming Video Understanding

model-releasesarxiv-cs-cl

arXiv:2609.30670v1 Announce Type: new Abstract: Streaming video understanding requires models to interpret evidence as it arrives, yet current evaluations often report task scores without specifying w

Hardware

TRACKGRAPH: Online Open-Vocabulary 3D Scene Graphs via Image-Space Tracking

arXiv:2609.31005v1 Announce Type: new Abstract: Open-vocabulary 3D maps enable robots to reason about previously unknown environments using natural language. However, existing systems typically segmen

hardwarearxiv-cs-cv

TrafficImag: A Benchmark for Counterfactual Roadside Traffic Video Generation

arXiv:2609.30722v1 Announce Type: cross Abstract: Existing roadside traffic datasets support perception, forecasting, and visual question answering, but they do not evaluate counterfactual video gener

Training-Free Bottleneck Width Planning for Convolutional Autoencoders

model-releasesarxiv-cs-cv

arXiv:2609.30755v1 Announce Type: new Abstract: Multiscale Spectral Rate-Distortion (MS-SRD) estimates the bottleneck channels required at user-supplied spatial cuts from training images and a normali

Training-Free Pronunciation Transcription via Text-Constrained Acoustic Rescoring

arXiv:2609.30924v1 Announce Type: new Abstract: Accurate and efficient pronunciation transcription is essential for preparing text-to-speech training data at scale. Existing approaches have different

researcharxiv-cs-cl

Training Graph Foundation Models on The Web Graph

arXiv:2609.30894v1 Announce Type: new Abstract: We introduce Acacia, a graph foundation model, trained on the web graph. Acacia (i) supports arbitrary feature dimensionalities and semantics without ad

researcharxiv-cs-ai

Transformer-based Monte Carlo Localization in Construction Meshes

arXiv:2609.31357v1 Announce Type: new Abstract: To be able to perform inspection or digitization tasks, mobile robots on construction sites must be able to localize themselves reliably with respect to

local-aiarxiv-cs-ro

Trust Guided Decision Transformer

arXiv:2609.31586v1 Announce Type: new Abstract: Decision Transformer performance degrades on long rollouts because the conditioning context drifts out of the training distribution. We show that this d

researcharxiv-cs-lg

Two Conformal Constructions for Adaptive Within-Document AI-Text Screening

arXiv:2609.31547v1 Announce Type: cross Abstract: We study false-alert control when screening for text generated by artificial intelligence (AI). The screening procedure selects document prefixes and

safetyarxiv-cs-cl

UltraG-Bench: A Multi-task Benchmark for assessing Large Vision-Language Models on Pixel-level Evidence Grounding in Ultrasound

arXiv:2609.30928v1 Announce Type: cross Abstract: Ultrasound is one of the most widely used medical imaging modalities, and recent large vision-language models(VLMs) have shown increasing capabilities

Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach

arXiv:2609.31570v1 Announce Type: cross Abstract: Deep learning has substantially accelerated the calibration of complex stochastic-volatility models, but neural point calibration alone does not captu

Uncertainty-Aware Federated Learning for Infant Movement Analysis

arXiv:2609.31463v1 Announce Type: cross Abstract: Infant movement analysis provides valuable biomarkers for the early identification of neurodevelopmental disorders. Recent advances in deep learning h

researcharxiv-cs-ai

Understanding Perturbed Parameter Ensemble Sensitivities Using A Contrastive Learning Approach

arXiv:2609.30420v1 Announce Type: cross Abstract: Perturbed parameter ensembles (PPEs) reveal how physics parameters affect climate simulations, but interpreting parameter sensitivities across multiva

Understanding the Role of Prompt Template in Knowledge Distillation for Safety Alignment

model-releasesarxiv-cs-cl

arXiv:2609.30802v1 Announce Type: new Abstract: Prior research has demonstrated that the choice of prompt template during Supervised Fine-Tuning (SFT) significantly impacts the robustness of safety al

UniAR: A Unified Framework for Autism Recognition Enhanced by Multi-View Prompt Learning

arXiv:2609.31298v1 Announce Type: cross Abstract: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder for which early and accurate diagnosis is critical to improving long-term deve

UniPrefill: Universal Long-Context Prefill Acceleration via Block-wise Dynamic Sparsification

arXiv:2605.06221v2 Announce Type: replace Abstract: As large language models (LLMs) continue to advance rapidly, they are becoming increasingly capable while simultaneously demanding ever-longer conte

researcharxiv-cs-cl

Hardware

Universal Drift Correction for Multidimensional Scanning Microscopy

arXiv:2609.30866v1 Announce Type: cross Abstract: In scanning microscopy, drift causes the specimen to be sampled at positions displaced from the nominal probe positions. This displacement alters the

hardwarearxiv-cs-cv

Unknown-Traffic Detection, Calibration and Shortcut Reliance in Distilled Encrypted-Traffic Classifiers over One Year

arXiv:2609.31141v1 Announce Type: cross Abstract: Knowledge distillation is the standard way to compress encrypted-traffic classifiers for the edge, and almost all such work judges students by accurac

Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques

arXiv:2507.08375v2 Announce Type: replace Abstract: Video restoration and enhancement are critical not only for improving visual quality, but also as essential pre-processing steps to boost the perfor

researcharxiv-cs-cv

Up and Down the Abstraction Ladder: Code-Based Skills for Language Agents

arXiv:2609.31076v1 Announce Type: new Abstract: Language agents struggle to act and learn in environments that require long sequences of low-level actions. Code-based abstractions can make these agent

local-aiarxiv-cs-ai