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arXiv:2601.03093v2 Announce Type: replace Abstract: Recent work on activation and latent steering has demonstrated that modifying internal representations can effectively guide large language models (

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ATN3D: Density-Aware LiDAR-Radar Early 3D Object Detection Under Extreme Sparsity

DGX agent

arXiv:2606.09634v1 Announce Type: cross Abstract: 3D object detection is the backbone of perception for automated vehicles (AV) and broader intelligent transportation systems applications. Long-range

model-releasesarxiv-cs-ai

9 Jun 2026

Auditable Graph-Guided Root Cause Analysis for Kubernetes Incidents

arXiv:2606.08590v1 Announce Type: cross Abstract: Kubernetes incidents are diagnosed reliably only when a root-cause system's reported gains come from incident evidence rather than scenario-specific s

Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge

arXiv:2606.08611v1 Announce Type: cross Abstract: We study data-driven real-time economic optimization of a multi-product chemical reactor when no reliable first-principles model is available beyond a

Bayesian Selective Latent Inference for Wastewater-First Influenza Monitoring

arXiv:2606.09433v1 Announce Type: new Abstract: Wastewater influenza surveillance can reveal community circulation before clinical reporting, but wastewater alone is not a fully identifiable proxy for

BEACON: Behavioral Entropy Aggregation for Cross-Model Hallucination Detection in Large Language Models

arXiv:2606.07528v1 Announce Type: cross Abstract: Hallucination in large language models (LLMs), defined as the generation of factually incorrect or unsupported content, remains a critical barrier to

researcharxiv-cs-ai

Beyond Consistency: Preserving Temporal Structure in Zero-Shot Video Editing

arXiv:2606.08780v1 Announce Type: new Abstract: Existing zero-shot video editing methods rely on pre-trained diffusion models, successfully achieving spatial control and basic temporal consistency but

Beyond Homophily: Towards Generalized Graph Reconstruction Attack and Defense

arXiv:2606.08067v1 Announce Type: new Abstract: Graph neural networks (GNNs) are widely deployed on relational data, yet they can leak sensitive or proprietary information about the training graph adj

Beyond Linear Activation Steering: Invertible Latent Transformations for Controlling LLM Behavior

arXiv:2606.08454v1 Announce Type: new Abstract: Activation steering provides a lightweight inference-time mechanism for controlling large language models (LLMs) by modifying their internal activation

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving

arXiv:2606.08684v1 Announce Type: new Abstract: We present BLUE, a minimal method for better language use in vision-language-action (VLA) models for autonomous driving (AD). Through extensive analysis

Hardware

BRAIN: Bayesian Reasoning via Active Inference for Agentic and Embodied Intelligence in Mobile Networks

arXiv:2602.14033v1 Announce Type: cross Abstract: Future sixth-generation (6G) mobile networks will demand artificial intelligence (AI) agents that are not only autonomous and efficient, but also capa

hardwarearxiv-cs-ai

Breaking the Tokenizer Barrier: On-Policy Distillation across Model Families

arXiv:2606.09456v1 Announce Type: new Abstract: On-Policy Distillation (OPD) has become a core technique in the post-training of Large Language Models (LLMs) for transferring knowledge from domain exp

BSTabDiff: Block-Subunit Diffusion Priors for High-Dimensional Tabular Data Generation

arXiv:2606.09257v1 Announce Type: cross Abstract: High-Dimensional Low-Sample Size (HDLSS) tabular domains (e.g., omics) are characterized by n ll m, where n = number of samples, and m = number of fea

Tutorials

C^3ache: Accelerating World Action Models with Cross Inference Chunk Cache

arXiv:2606.08962v1 Announce Type: cross Abstract: World Action Models (WAMs) generalize better than standard Vision-Language-Action (VLA) policies to novel motions and environments, because a video-mo

tutorialsarxiv-cs-cv

C3VD-DEFCOL: A Deformable Colonoscopy Dataset with Time-Resolved 3D Ground Truth and Realistic Appearance

arXiv:2606.07891v1 Announce Type: new Abstract: 3D reconstruction could improve colonoscopy by estimating mucosal coverage and alerting clinicians to missed regions during screening. However, algorith

CamoSAM2: SAM2-oriented Prompt Auto-Refinement for Video Camouflaged Object Detection

arXiv:2504.00375v2 Announce Type: replace Abstract: The Segment Anything Model 2 (SAM2), a prompt-guided video foundation model, has remarkably performed in video object segmentation, drawing signific

Can Data Work be Reparative?

arXiv:2606.09408v1 Announce Type: cross Abstract: We present an ethnographic study of an alternative approach to data work, developed by a civic-tech initiative that builds datasets for training and b

Can we stabilize an inverted pendulum with feedback from a time-of-flight camera?

model-releasesarxiv-cs-ro

arXiv:2606.09237v1 Announce Type: new Abstract: Time-of-flight cameras are popular in robotics for providing direct depth information while being compact, inexpensive, and robust to lighting condition

Capability-Aligned Hierarchical Learning for Tool-Augmented LLMs

arXiv:2606.09371v1 Announce Type: new Abstract: Tool learning enables LLMs to invoke external tools to accomplish tasks. Prior studies have demonstrated the effectiveness of a hierarchical structure:

Capacity, Not Format: Rethinking Structured Reasoning Failures

arXiv:2606.09410v1 Announce Type: new Abstract: Prior work treats structured output as a reasoning tax, but this framing is incomplete: the cost of formatting depends strongly on a model's spare capac

researcharxiv-cs-ai

CapRL++: Unified Reinforcement Learning with Verifiable Rewards for Dense Image and Video Captioning

arXiv:2606.09393v1 Announce Type: new Abstract: Image and video captioning are fundamental tasks that bridge the visual and linguistic domains, playing a critical role in pre-training Large Vision-Lan

researcharxiv-cs-cv

CATPO: Critique-Augmented Tree Policy Optimization

arXiv:2606.08346v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving the reasoning capabilities of large language models

Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures

arXiv:2606.08275v1 Announce Type: cross Abstract: When an LLM agent fails -- issues a refund it should not have, calls the wrong tool, leaks data -- existing tooling answers what happened (observabili

Causal Transfer in Medical Image Analysis

arXiv:2603.24388v2 Announce Type: replace Abstract: Medical imaging models frequently fail when deployed across hospitals, scanners, populations, or imaging protocols due to domain shift, limiting the

safetyarxiv-cs-cv

Chain of Flow: ECG-Conditioned 4D Cardiac Cine Generation from Patient-Specific Anatomical Anchor

arXiv:2602.22919v2 Announce Type: replace Abstract: Cardiac cine magnetic resonance imaging (MRI) is central to functional cardiac assessment, yet a full current cine sequence may not always be direct

CHROMA: Detecting AI-Generated Images through Inter-Channel Color-Space Correlations

arXiv:2606.08864v1 Announce Type: new Abstract: The rapid adoption of diffusion and large-scale generative models has made it increasingly challenging to distinguish synthetic imagery from real photog

CLPO: Curriculum Learning meets Policy Optimization for LLM Reasoning

arXiv:2509.25004v2 Announce Type: replace Abstract: Online reinforcement learning with verifiable rewards (RLVR) has become an effective paradigm for improving the reasoning abilities of large languag

Local Ai

Collaborative Edge-to-Server Inference for Vision-Language Models

arXiv:2512.16349v2 Announce Type: replace-cross Abstract: We propose a collaborative edge-to-server inference framework for vision-language models (VLMs) that reduces communication cost while maintain

local-aiarxiv-cs-ai

Agents

ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systems

arXiv:2606.08702v1 Announce Type: new Abstract: Recent advances have improved the adaptive capabilities of LLM-based multi-agent systems (MAS) through memory-, skill-, and learning-based approaches, y

agentsarxiv-cs-ai

Constrained Paraphrase Consistency for LLM Hallucination Detection

arXiv:2606.08158v1 Announce Type: cross Abstract: Large language models (LLMs) can generate factually inconsistent claims, motivating accurate and scalable hallucination detectors. Prior work largely

Constrained user-item allocation for e-commerce marketing campaigns

arXiv:2606.09623v1 Announce Type: new Abstract: When running marketing campaigns, retailers must decide which products to promote and which users to target. These decisions are inherently coupled: eff

Context-Fractured Decomposition Attacks on Tool-Using LLM Agents: Exploiting Artifact Provenance Gaps

arXiv:2606.09084v1 Announce Type: cross Abstract: Tool-using LLM agents interact with the world through actions that persist state in artifacts (e.g., workspace files or logs). Consequently, jailbreak

Agents

Coop-WD: Cooperative Perception with Weighting and Denoising for Robust V2V Communication

arXiv:2505.03528v2 Announce Type: replace Abstract: Cooperative perception, leveraging shared information from multiple vehicles via vehicle-to-vehicle (V2V) communication, plays a vital role in auton

agentsarxiv-cs-cv

Correct Looks Better: Pairwise Comparisons Reveal Accuracy Rankings

arXiv:2606.09409v1 Announce Type: new Abstract: Pairwise comparisons combined with aggregation methods like Elo have become central to evaluating generative models, yet concerns remain that they rewar

Correcting Mean Bias in Text Embeddings: A Refined Renormalization with Training-Free Improvements on MMTEB

arXiv:2511.11041v2 Announce Type: replace-cross Abstract: We find that current sentence-embedding models produce outputs with a consistent bias: every embedding e decomposes as ilde e + mu, where the

Correlation Is Not Enough: Embedding Human Metadata for Individual Causal Discovery

arXiv:2606.09672v1 Announce Type: new Abstract: Ask a pretrained biomedical language model whether 'cortisol 28 ug/dL' and 'stock-market volatility' are related, and it returns a cosine similarity of

Counterfactual Transport Flows for Offline Conservative Trajectory Refinement

arXiv:2606.09115v1 Announce Type: new Abstract: Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as

CRANE: Knowledge Editing for Reasoning MLLMs

arXiv:2606.09033v1 Announce Type: new Abstract: The emergence of reasoning multimodal large language models (MLLMs), which generate explicit chain-of-thought (CoT) reasoning before producing answers,

Curvature-Guided LoRA: Matching Full Fine-Tuning in Function Space

arXiv:2603.29824v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning methods such as LoRA enable efficient adaptation of large pretrained models, but often lag behind full fine-tuning i

DAL-PCQA: Enabling Distortion-Level and Language-Driven Reasoning for Point Cloud Quality Assessment

arXiv:2606.07938v1 Announce Type: new Abstract: Point Cloud Quality Assessment (PCQA) methods typically predict scalar Mean Opinion Scores (MOS), which quantify overall perceptual degradation but do n

Decision-Aware Memory Cards: Counterfactual-Inspired Context Selection and Compression for Tool-Using LLM Agents

arXiv:2606.08151v1 Announce Type: new Abstract: Tool-using LLM agents often fail not because relevant text is absent, but because decisive evidence is not selected, compressed, or surfaced at action t

Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models

arXiv:2606.09142v1 Announce Type: cross Abstract: Egocentric vision offers a first-person view of human perception and decision making, yet its potential for traffic-safety prediction remains underexp

Decomposable Neuro Symbolic Regression

arXiv:2511.04124v3 Announce Type: replace Abstract: Symbolic regression (SR) models complex systems by discovering mathematical expressions that capture underlying relationships in observed data. Howe

Decoy-Calibrated Failure Audits for Language Models

arXiv:2606.09046v1 Announce Type: new Abstract: Useful audits reveal not only how often a model fails, but also where its failures concentrate. An auditor may test many candidate explanations: long in

researcharxiv-cs-lg

Deep Tree Tensor Networks

arXiv:2502.09928v2 Announce Type: replace-cross Abstract: Originating in quantum physics, tensor networks (TNs) have been widely adopted as exponential machines and parametric decomposers for recognit

Agents

DeltaBox: Scaling Stateful AI Agents with Millisecond-Level Sandbox Checkpoint/Rollback

arXiv:2605.22781v2 Announce Type: replace-cross Abstract: LLM-powered AI agents require high-frequency state exploration (e.g., test-time tree search and reinforcement learning), relying on rapid chec

agentsarxiv-cs-ai

Developing Distance-Aware Physics-Constrained Probabilistic Frameworks for Industrial Prognostics

arXiv:2512.08499v3 Announce Type: replace-cross Abstract: Development of reliable and physically interpretable probabilistic frameworks for industrial prognostics remain nascent, and existing literatu

DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination

arXiv:2606.08068v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often fail to reliably outperform a single strong model equipped with best-of-N sampling. We argue that a