Search: “models”

Schedule-Informed Temporal Fusion Forecasting of Hourly Airport Security-Checkpoint Throughput

arXiv:2608.02950v1 Announce Type: new Abstract: Checkpoint staffing requires accurate forecasts of when screening demand will occur, yet flight schedules record departure times rather than passenger a

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Local Ai

SGFormer: Structure-Guided Transformer for Robust Local Feature Matching

DGX agent

arXiv:2608.03423v1 Announce Type: new Abstract: Local feature matching is a fundamental component of photogrammetry, enabling accurate image correspondence critical for tasks such as 3D reconstruction

local-aiarxiv-cs-cv

5 Aug 2026

Sparse Weight Decomposition for Efficient Circuit Extraction

arXiv:2608.03913v1 Announce Type: cross Abstract: Dense pretrained transformers do not naturally expose interpretable units for circuit extraction. Existing approaches obtain such units by learning au

tutorialsarxiv-cs-cl

Speech LLMs in Low-Resource Scenarios: Data Volume Requirements and the Impact of Pretraining on High-Resource Languages

arXiv:2508.05149v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated potential in handling spoken inputs for high-resource languages, reaching state-of-the-art perf

researcharxiv-cs-ai

StreamDAM: Presence-Aware Memory for Real-Time Streaming Video Object Segmentation

arXiv:2608.03912v1 Announce Type: new Abstract: Quality-tier video object segmentation (VOS) trackers such as DAM4SAM top accuracy leaderboards, but they are measured offline, one frame at a time with

TaskPress: Query-Agnostic KV Cache Compression via Task-Guided Pruning

arXiv:2608.03276v1 Announce Type: new Abstract: Long-context inference with large language models is constrained by the linear growth of the key-value cache to sequence length. While pruning offers mi

tutorialsarxiv-cs-ai

Test Time Adaptation Methods for Point Cloud Registration in Laparoscopic Surgery

arXiv:2608.02883v1 Announce Type: new Abstract: 3D point cloud registration in laparoscopic surgery estimates the transformation between an intraoperative organ reconstructed from video and its preope

Test-Time Scaling for Safe Text-Guided Image Generation via Intermediate Clean Estimates

arXiv:2608.03284v1 Announce Type: cross Abstract: Ensuring safety and policy compliance in text-to-image diffusion models remains a critical challenge, as benign or adversarial prompts can often elici

Applications

The Eloquence team submission for task 1 of MLC-SLM challenge

arXiv:2507.19308v2 Announce Type: replace-cross Abstract: In this paper, we present our studies and experiments carried out for the task 1 of the Challenge and Workshop on Multilingual Conversational

applicationsarxiv-cs-cl

The Evolutionary Origin of Values: implications for AI alignment, sentience and existential risk

arXiv:2608.03361v1 Announce Type: cross Abstract: AI systems based on Large Language Models (LLMs) have prompted fears that they may harbor hidden goals, seek to dominate or eliminate humanity, or eve

The Signal Horizon: Local Blindness and the Contraction of Pauli-Weight Spectra in Noisy Quantum Encodings

arXiv:2602.14735v2 Announce Type: replace-cross Abstract: The performance of quantum classifiers is typically analyzed through global state distinguishability or the trainability of variational models

The Transformer Revolution, Part 1: Dynamic Processing through Output- Weight Interconnections

arXiv:2608.03921v1 Announce Type: new Abstract: This paper offers a new interpretation of the Transformer during inference. Against the 'stochastic parrot' view that large language models merely repro

researcharxiv-cs-ai

ToolLIFT: Lifting Tool-Specific Trajectories into Function-Level Graphs for Generalizable Tool Planning

arXiv:2608.03468v1 Announce Type: new Abstract: Historical tool-use trajectories provide valuable experience for large language model (LLM) agents to plan and coordinate tool usage. Existing approache

agentsarxiv-cs-ai

Topological Simplification in Predictive Coding Networks

arXiv:2608.02816v1 Announce Type: new Abstract: We study the topology of learned representations in predictive coding networks (PCNs), a neuro-inspired bidirectional architecture, using a quantitative

Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidance

arXiv:2608.03403v1 Announce Type: new Abstract: The performance bottleneck of agents is increasingly shifting from model capability to the robustness of their execution processes. Tools play a central

agentsarxiv-cs-ai

Unified Visuomotor Targets: Supervising VLAs Beyond Physical Actions

arXiv:2608.03563v1 Announce Type: new Abstract: VLA models are trained to predict robot actions from visual and language observations. This is a natural choice, but it creates a mismatch: VLMs encode

safetyarxiv-cs-ro

Applications

UNVaMP: Neural Knowledge Tracing with Variational Regularization of Latent Knowledge Dynamics

arXiv:2608.03811v1 Announce Type: new Abstract: We introduce the Unified Neural Variational Measurement of Proficiency (UNVaMP) architecture, a knowledge tracing method that integrates observed studen

applicationsarxiv-cs-lg

Variational Approximated Restricted Maximum Likelihood Estimation for Spatial Data

arXiv:2604.07635v2 Announce Type: replace-cross Abstract: This research considers a scalable inference for spatial data modeled through Gaussian intrinsic conditional autoregressive (ICAR) structures.

What Language Does and What the Evidence Supports: A Functional Role Taxonomy and Evidence Audit of Language Grounding in Embodied Agents

arXiv:2608.03099v1 Announce Type: new Abstract: Foundation models place language throughout embodied agents, but its presence does not show what it contributes or how well that contribution is grounde

When and Where to Look: Adaptive Visual Evidence Scheduling for Efficient Long Video Understanding

arXiv:2608.03918v1 Announce Type: cross Abstract: Efficient long-video understanding requires vision--language models (VLMs) to reason over a small number of frames selected as sparse visual evidence.

local-aiarxiv-cs-ai

When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning

arXiv:2608.02940v1 Announce Type: new Abstract: A reproducible compression statistic can still select the wrong candidate. A dense pruning score with 0.906 split-half reliability predicted a 16.1% gai

local-aiarxiv-cs-ai

When Oracle Conditioning Misleads Deployment: Conditioning-Availability Bias in Echocardiographic Segmentation

arXiv:2608.03342v1 Announce Type: cross Abstract: Conditional segmentation models may be trained and evaluated with auxiliary signals cleaner than those available at deployment. We study this protocol

When Teachers Mislead: Spurious-Signal-Aware On-Policy Distillation

arXiv:2608.03632v1 Announce Type: new Abstract: On-Policy distillation (OPD) transfers teacher capabilities by supervising student-sampled trajectories with dense token-level teacher signals. Recent s

3D-CovDiffusion: 3D-Aware Diffusion Policy for Coverage Path Planning

arXiv:2510.03011v2 Announce Type: replace Abstract: Diffusion models have shown strong potential for robot skill learning, yet their role in coverage path planning remains underexplored. In industrial

safetyarxiv-cs-ro

3DZip: Spatial-Aware Feature Diversity-Guided Token Compression for 3D Question Answering

arXiv:2608.01185v1 Announce Type: new Abstract: Recent 3D vision-language models (3D VLMs) construct geometry aware tokens by projecting 2D visual features into world coordinates, enabling spatial rea

Abstention as an Action Can Kill Both the Reward Gradient and the KL Anchor: Collapse Law and Repair for Error-Penalized Reinforcement Learning

arXiv:2608.00301v1 Announce Type: cross Abstract: Error-penalized scoring rules (+1 for a correct answer, -lambda for a wrong one, 0 for abstaining) are increasingly prescribed against hallucination:

agentsarxiv-cs-cl

Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning

arXiv:2608.02250v1 Announce Type: new Abstract: Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally up

local-aiarxiv-cs-lg

Attend to Your Own Thoughts: Breaking the Barrier for Post-Training Quantization of Reasoning LLMs through the Lens of 1.58-Bit Quantization

arXiv:2608.01078v1 Announce Type: new Abstract: We propose ScaleQ-1.58, a scalable ternary post-training quantization (PTQ) framework for reasoning LLMs. Its core insight stems from an empirical findi

Benign Overfitting in Linear Classifiers with a Bias Term

arXiv:2511.12840v2 Announce Type: replace-cross Abstract: Overparameterized models often generalize well even when they interpolate noisy training data. This is known as benign overfitting. For linear

Breaking the Statistical Similarity Trap in Extreme Convection Detection

arXiv:2509.09195v2 Announce Type: replace-cross Abstract: Current evaluation metrics for deep learning weather models create a 'Statistical Similarity Trap', rewarding blurry predictions while missing

Can AI Agents Simulate A/B Test Outcomes? A Validation Framework for Agentic Experimentation

arXiv:2608.02345v1 Announce Type: new Abstract: A/B testing remains the standard for rolling out new features in the technology industry. Each experiment, however, consumes real traffic, engineering e

agentsarxiv-cs-cl

CascadeLUT: Information-Ordered Streaming Inference for Bandwidth-Constrained FPGAs

arXiv:2608.00720v1 Announce Type: cross Abstract: Mapping neural networks to FPGAs enables low-latency, energy-efficient inference, particularly for lookup table (LUT)-based models that eliminate mult

local-aiarxiv-cs-lg

Coverage-Driven Adaptive Keyframe Selection for Video Understanding

arXiv:2608.00714v1 Announce Type: new Abstract: Recent advances in large vision-language models (LVLMs) have enabled long-video understanding and analysis. However, processing the large number of fram

tutorialsarxiv-cs-cv

CoWAM: Coordination Contracts for Selective Policy Intervention with WAMs

arXiv:2608.02578v1 Announce Type: cross Abstract: World Action Models (WAMs) augment robot policies with action-conditioned predicted futures, but a plausible future alone does not justify changing th

Credit the Right Box: Marginal Contribution Assignment for Structured Visual Perception

arXiv:2608.01055v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) are increasingly expected to solve structured perception tasks that require visual recognition, language-to-obj

Cultural Awareness is Represented but Not Decoded: Tracing Mythological Knowledge across 18 Open-Source LLMs

arXiv:2608.02486v1 Announce Type: new Abstract: Open-source LLMs reliably name Zeus, Jupiter, and Thor, but recover their counterparts in less-represented traditions like Finnish, Slavic, Egyptian, or

D^2-4DGS: Dual-Depth Guided Sparse-Camera 4D Gaussian Splatting

arXiv:2608.01588v1 Announce Type: new Abstract: Dynamic 4D Gaussian Splatting has emerged as an efficient representation for dynamic novel view synthesis through explicit scene modeling and real-time

Decisions over Sequences: Computability and Choice

arXiv:2203.00070v3 Announce Type: replace-cross Abstract: We develop a framework to study situations where decision makers face alternatives sequentially. Within this framework, we focus on endogenous

Deep Research Pretraining via Predictive Navigation

arXiv:2608.00432v1 Announce Type: new Abstract: Deep research agents are often trained on expensive, environment-grounded tool-use trajectories that require repeated retrieval, document inspection, an

safetyarxiv-cs-cl

DeepDefense: Robust Learning via Layer-Wise Gradient-Feature Alignment

arXiv:2511.13749v2 Announce Type: replace Abstract: Deep neural networks are known to be vulnerable to adversarial perturbations, which are small, carefully crafted inputs that lead to incorrect predi

Demystifying When and Why VLAs Fail in Contact-Rich Tasks and How to Fix Them

arXiv:2608.01402v1 Announce Type: new Abstract: We address the problem of understanding when and why Vision-Language-Action models struggle with contact-rich manipulation tasks that require precise ph

safetyarxiv-cs-ro

Detecting Nonproperness of Likelihood Equations

arXiv:2608.01976v1 Announce Type: cross Abstract: Given an algebraic statistical model, a challenging problem is classifying the data according to the number of positive critical points of the likelih

DiffuseAgent-MI: Distributionally-Grounded,Tool-Integrated Self-Evolving Agents for Faithful Visual Reasoning

arXiv:2608.00540v1 Announce Type: new Abstract: Tool-integrated vision-language agents have made remarkable progress on compositional and multi-step visual reasoning. Yet their outputs frequently exhi

Diffusion-Based Body Schema Learning Enabling Abnormal-State Adaptation in Musculoskeletal Robots

arXiv:2608.01029v1 Announce Type: new Abstract: Musculoskeletal robots require an internal body schema that remains consistent under a wide range of physical state changes, including abnormalities suc

tutorialsarxiv-cs-ro

Disentangled Contrastive Learning for Zero-Shot Multilingual Dense Retrieval

arXiv:2608.02189v1 Announce Type: cross Abstract: Multilingual dense retrieval aims to handle queries and documents across different languages based on a unified retriever model. The challenge lies in

safetyarxiv-cs-cl

Distributional Matching for Vector Quantization: A Unified Theoretical and Empirical Framework

arXiv:2607.15933v2 Announce Type: replace Abstract: The effectiveness of modern visual representation learning and autoregressive models critically depends on vector quantization (VQ), which discretiz

Applications

Does the Competitive Component of Adversarial Self-Play Improve Legal Reasoning? A Controlled Negative Result

arXiv:2608.01559v1 Announce Type: cross Abstract: Adversarial self-play is an appealing recipe for legal reasoning: have a student model draft an argument, have an adversary attack it, and reward the

applicationsarxiv-cs-cl

DreamTraj: Generating 6-DoF Object Trajectories by Reading Unrendered Video Diffusion Latents

arXiv:2608.00486v1 Announce Type: new Abstract: Accurate prediction of object trajectories during manipulation is essential for closing the perception-action loop. Progress is limited on two fronts: a