All entries

arXiv:2607.19054v2 Announce Type: replace Abstract: In this work, we incorporate first principle physics into the construction of data-driven methods by considering a model that accounts for the diffe

tutorialsarxiv-cs-lg

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Probabilistic Residual Learning for Online Recommendations

DGX agent

arXiv:2607.20863v1 Announce Type: cross Abstract: Modern recommender systems are typically based on deep learning (DL) models, where a dense encoder learns representations of users and items. As a res

researcharxiv-cs-ai

24 Jul 2026

ProCap: Prominence-guided Object Rectification for Faithful and Comprehensive Video Captioning

model-releasesarxiv-cs-cv

arXiv:2607.21022v1 Announce Type: new Abstract: Improving video captioning quality typically demands retraining large vision-language models, an expensive and often impractical requirement. Existing t

Profiling Lightweight Large Language Models

arXiv:2607.20806v1 Announce Type: new Abstract: Lightweight large language models (LLMs) are increasingly being deployed locally on personal computers and are expected to play a growing role in resour

Progressive Cramming: Reliable Token Compression and What It Reveals

arXiv:2607.21231v1 Announce Type: new Abstract: Token cramming compresses sequences into learned embeddings with near-perfect reconstruction, but fixed token budgets and 99% accuracy thresholds leave

researcharxiv-cs-cl

PromptPack: Scaling LLM Annotation Agents for Online Recommendation

arXiv:2607.20528v1 Announce Type: new Abstract: Online recommendation platforms increasingly use Large Language Models (LLMs) to extract structured features from ad creatives. While deploying a single

QATMA: Quantization-Aware Training with Multimodal Alignment for Open-Vocabulary Object Detection

arXiv:2603.05964v3 Announce Type: replace Abstract: Quantizing open-vocabulary object detection (OVOD) models reduces their memory and computational costs, but extremely low-bit quantization severely

safetyarxiv-cs-cv

Tutorials

Quality-Aware Multimodal Fusion Reveals Implicit Identity in Valence-Arousal Features

arXiv:2607.21347v1 Announce Type: new Abstract: Conventional face recognition relies on static appearance cues and degrades in unconstrained settings with expression variation, occlusion, and poor lig

tutorialsarxiv-cs-cv

QuantiBias: Benchmarking Quantization-Induced Bias in LLMs

model-releasesarxiv-cs-cl

arXiv:2607.21063v1 Announce Type: new Abstract: Almost every large language model that reaches a broad audience is quantized: trained in full precision, then compressed for efficiency. This step is as

RadioTrace: Transmitter-Aware Diffusion for Radio Map Estimation without Deployment-Time Fine-Tuning

arXiv:2607.20909v1 Announce Type: cross Abstract: Radio map (RM) estimation aims to reconstruct the spatial distribution of wireless signal characteristics, such as received signal strength (RSS), fro

local-aiarxiv-cs-lg

RE-AD: Real-Time Requirement Adherence for Data Labeling

arXiv:2607.20455v1 Announce Type: cross Abstract: Human-annotated data remains fundamental to training frontier Large Language Models (LLMs). However, crowd-sourced annotations often suffer from quali

RealVDeblur: One-Step Diffusion for Generalizable Real-World Video Deblurring

arXiv:2607.20628v1 Announce Type: cross Abstract: Real-world video deblurring remains challenging due to diverse motion patterns, complex degradations, and the scarcity of realistic training data, yet

applicationsarxiv-cs-ai

Agents

RECO: Region-Aware Compensation for Extrinsic Perturbations in Roadside 3D Detection

arXiv:2607.20947v1 Announce Type: new Abstract: In intelligent transportation systems, roadside 3D object detection provides wide-area perception crucial for traffic understanding, cooperative early w

agentsarxiv-cs-cv

Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation

arXiv:2607.21485v1 Announce Type: new Abstract: We study sinusoidal recurrence as an iterative mechanism for harmonic spectral enrichment in implicit neural representations (INRs). Our analysis reveal

researcharxiv-cs-cv

Reexamining zero-shot summarization: Empirical investigation of trustworthiness of LLM-summarizers

arXiv:2607.21010v1 Announce Type: new Abstract: Zero-shot summarization using Large Language Models (LLMs) has significantly advanced the abstractive summarization task by producing coherent and fluen

REFACT: Adaptive Fact Restatement for Compact and Faithful Chain-of-Thought Reasoning

arXiv:2607.20833v1 Announce Type: new Abstract: Large language models increasingly rely on long-form reasoning for complex tasks, yet their reasoning traces may drift away from the supplied context wh

local-aiarxiv-cs-cl

Refusal-Gated Decoding: Preserving Refusal Behavior Under High-Temperature Sampling

arXiv:2607.20791v1 Announce Type: new Abstract: High-temperature sampling is one of the primary mechanisms for increasing diversity in LLMs. Recent advances in truncation-based sampling techniques hav

REGARD: Regional Affective Differences in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2607.20722v1 Announce Type: new Abstract: Large language models trained and aligned within different linguistic and regional ecosystems may frame the same political, cultural, and geopolitical e

Regularized Optimization on Grassmann Manifold: Theory, Algorithm and Applications

arXiv:2607.21039v1 Announce Type: new Abstract: Spectral methods are among the most widely used techniques for community detection, clustering, and graph learning. Their performance, however, critical

applicationsarxiv-cs-lg

Regulating autonomous and agentic AI

arXiv:2607.21345v1 Announce Type: new Abstract: Regulating activities where regulatees use autonomous and agentic AI is challenging. Regulatory assumptions about regulatee knowledge and control no lon

Relative Value Learning

arXiv:2607.21120v1 Announce Type: cross Abstract: In reinforcement learning, critics typically estimate absolute state values V(s), estimating how good a particular situation is in isolation. However,

Reliability-Aware LLM Alignment from Inconsistent Human Feedback

arXiv:2607.20515v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is critical for aligning Large Language Models (LLMs) with human preferences. However, its efficacy is

ReliableTableQA:How Much Supervision Does Reliability Annotation Need?

arXiv:2607.20537v1 Announce Type: cross Abstract: We introduce ReliableTableQA, a framework for training an LLM to annotate the statistical reliability of tabular QA results, not whether the query is

applicationsarxiv-cs-ai

Representation Robustness Under Executable Reasoning Constraints in Large Language Models for Mathematical Problem Solving

arXiv:2607.20520v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated on mathematical problem solving, yet prior work often treats representationally equivalent formu

local-aiarxiv-cs-ai

Representative Sets in Propositional Abduction

arXiv:2607.21183v1 Announce Type: cross Abstract: The propositional abduction problem is a well-known form of non-monotonic reasoning where we are asked to find an explanation of a given manifestation

researcharxiv-cs-ai

Representing Entity Importance in AI Knowledge Systems: A Dual-Signal Framework of Audience Evaluation and Structural Authority

arXiv:2607.20925v1 Announce Type: new Abstract: AI knowledge systems require representations of entity importance for retrieval, recommendation, evidence selection, and knowledge-intensive reasoning.

Response drift across frontier large language models

arXiv:2607.20454v1 Announce Type: cross Abstract: All frontier large language models (LLMs) exhibit response drift -- producing outputs that deviate from expert-validated references -- yet the magnitu

researcharxiv-cs-ai

Rethinking Open-World Video Anomaly Detection: Diagnosing Definition Blindness

arXiv:2607.20780v1 Announce Type: new Abstract: Open-world video anomaly detection (OWVAD) is expected to detect events that match a user-specified definition of abnormality. This requirement is stron

local-aiarxiv-cs-cv

Riemannian Deep Learning: Modules, Networks, and Geometries

arXiv:2607.19305v2 Announce Type: replace-cross Abstract: Deep neural networks on manifold-valued representations have attracted growing interest, but many basic components remain tied to specific man

researcharxiv-cs-ai

RL-MACRO: A Cybernetic Closed-Loop Intelligence Framework for Multimodal Adaptive Robotic Craniotomy

arXiv:2607.21113v1 Announce Type: new Abstract: Autonomous robotic craniotomy requires continuous regulation of tool-tissue interactions to mitigate mechanical overload and thermal damage while mainta

safetyarxiv-cs-ro

Robostral Navigate

arXiv:2607.20785v1 Announce Type: cross Abstract: Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficientl

Robust Asynchronous Q-Learning under Reward and State Corruption via Batching

arXiv:2607.20822v1 Announce Type: new Abstract: Motivated by reinforcement learning in harsh environments, we consider the problem of learning an optimal policy subject to adversarially corrupted feed

safetyarxiv-cs-lg

Robust Critics: Defending LLMs Against Multi-Turn Attacks

arXiv:2607.20472v1 Announce Type: new Abstract: When a user asks a language model something harmful, is it a genuine attack or a misunderstood but well-meaning question? This ambiguity is one of the c

Routing Subspaces: Auditing Evaluation-to-Deployment Mismatch in Fine-Tuned Language Models

arXiv:2607.20436v1 Announce Type: cross Abstract: Safety evaluations often assume that behavior observed during testing reflects behavior in ordinary use, but fine-tuning can break this assumption. A

Routing Without Training: Controllable-Ratio LLM Offloading via Reliability Gating

arXiv:2607.20481v1 Announce Type: new Abstract: Local-cloud collaboration is a practical way to deploy large language models under resource constraints, but existing methods often rely on trained rout

local-aiarxiv-cs-ai

RUMBA: Russian User Memory Benchmark

arXiv:2607.21447v1 Announce Type: cross Abstract: The ability to handle long-term memory in LLMs is becoming increasingly critical, yet existing benchmarks remain English-centric and rely on aggregate

Rushes: A Human Preference Dataset for Pluralistic Alignment

model-releasesarxiv-cs-cl

arXiv:2607.20767v1 Announce Type: new Abstract: We introduce Rushes, a dataset and benchmark for studying revealed human engagement preferences in interactive narrative environments. Rushes is collect

Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer

arXiv:2607.20665v1 Announce Type: new Abstract: Multi-drone payload transportation has emerged as a promising research paradigm with potential applications in construction, logistics, and disaster res

safetyarxiv-cs-ro

Safeguards for Speech2Speech LLM-Assistants: A Case Study in Automotive Applications

arXiv:2607.21180v1 Announce Type: new Abstract: Recent advances have introduced speech-to-speech (S2S) conversational assistants capable of producing natural-sounding interactions, including non-verba

applicationsarxiv-cs-ai

SafeStep: AI-powered Travel Assistance for Elderly People with Frailty or Dementia

arXiv:2607.21156v1 Announce Type: new Abstract: More than a million people in the UK suffer from frailty or dementia, which severely compromise their ability to travel in urban environments. This pape

Safety-oriented sidewalk and road segmentation for smartphone-based assistive navigation

arXiv:2607.21137v1 Announce Type: new Abstract: Independent sidewalk mobility is essential for blind and visually impaired pedestrians (BVIPs), yet smartphone-based assistive navigation requires perce

safetyarxiv-cs-cv

SAGE: A Socially-Aware Generative Engine for Heterogeneous Multi-Agent Navigation

arXiv:2607.16619v2 Announce Type: replace Abstract: Safe and socially compliant navigation in open human-robot environments requires robots to reason about heterogeneous participants with different dy

safetyarxiv-cs-ro

SalesLoop: Reinforcement Learning from Performance Feedback for Sales Lead Ranking

arXiv:2607.20655v1 Announce Type: cross Abstract: Lead ranking in Customer Relationship Management (CRM) systems faces a persistent challenge: models achieving high offline accuracy often underperform

Same Dangerous Objective, Opposite Advice: Direct Exposure versus Multi-Agent Mediation

arXiv:2607.21518v1 Announce Type: new Abstract: Even a current high-capability LLM can appear safer when shown a dangerous objective directly than when other agents transform and relay its direction.

Agents

Sample-Efficient Learning from Agent Experience

arXiv:2607.21051v1 Announce Type: new Abstract: Real-world agent learning is often constrained by costly environment interactions, such as running time-consuming experiments or obtaining human feedbac

agentsarxiv-cs-cl

Hardware

SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation

arXiv:2607.21553v1 Announce Type: new Abstract: We introduce SANA-Video 2.0, a hybrid video diffusion transformer instantiated at 5B and 14B scales under a unified architecture. Designed to generate h

hardwarearxiv-cs-cv

Agents

Scalable Low-Cost Laboratory Automation: A Digital Twin-Integrated Robotic Platform for Autonomous Liquid Handling (RAINBOTTM)

arXiv:2607.20662v1 Announce Type: new Abstract: Laboratory automation accelerates discovery, yet its adoption is constrained by the high cost, proprietary design, and limited remote supervisability of

agentsarxiv-cs-ro

Scale Up Strategically: Learning Compositional Generalization via Bias-Aware Evaluation and Data Collection for Robotic Manipulation

arXiv:2607.21582v1 Announce Type: cross Abstract: Compositional generalization is essential for robot to follow diverse instructions. However, pretrained policies are known to take shortcuts, deferrin

safetyarxiv-cs-cv