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BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks

arXiv:2606.02947v1 Announce Type: cross Abstract: Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks. Recent work has shown that

researcharxiv-cs-cv

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Research

CAD-to-CT Registration of Cylindrical Objects via Ellipse-Based Axis Estimation

DGX agent

arXiv:2606.02935v1 Announce Type: new Abstract: Accurate registration of CAD models to CT scans is essential for establishing ground truth geometry in volumetric imaging. Obtaining reliable object mas

researcharxiv-cs-cv

3 Jun 2026

Applications

Calibrating Urban Traffic Simulation from Sparse Road Observations via Genetic Optimization

arXiv:2606.03823v1 Announce Type: new Abstract: Urban traffic simulation is a critical tool for infrastructure planning, including the placement of electric vehicle charging stations. However, realist

applicationsarxiv-cs-ai

Calibration Data Trade-offs Across Capability Dimensions: Why Multi-Source Mixing Matters for High-Sparsity LLM Pruning

arXiv:2606.03328v1 Announce Type: cross Abstract: Post-training pruning compresses large language models to high sparsity using a small unlabelled calibration set, and recent work has concluded that t

Can Factual Opinions Be Edited (Manipulated) in Large Language Models?

arXiv:2606.03096v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly integrated into various domains, making knowledge editing techniques crucial yet potentially hazardous. Cu

Can LLM Rerankers Predict Their Own Ranking Performance?

arXiv:2606.03535v1 Announce Type: cross Abstract: Retrieval effectiveness varies substantially across queries, making it important to estimate ranking quality before relevance judgments are available.

researcharxiv-cs-cl

Can Local Learning Match Self-Supervised Backpropagation?

arXiv:2601.21683v2 Announce Type: replace Abstract: While end-to-end self-supervised learning with backpropagation (global BP-SSL) has become central for training modern AI systems, theories of local

researcharxiv-cs-lg

Can Structural Cues Save LLMs? Evaluating Language Models in Massive Document Streams

arXiv:2603.19250v2 Announce Type: replace Abstract: Evaluating language models in streaming environments is critical, yet underexplored. Existing benchmarks either focus on single complex events or pr

CANMOT: Class-Aware Noise Modeling for Multi-Object Tracking in Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2606.03590v1 Announce Type: new Abstract: Kalman filter (KF)-based multi-object tracking (MOT) remains a strong baseline for autonomous driving due to its strong performance, computational effic

Capability Advertisement as a Market for Lemons: A Trust Layer for Heterogeneous Agent Networks

arXiv:2606.03034v1 Announce Type: cross Abstract: Large language model (LLM) agents have begun to delegate work to one another. Protocols such as the Model Context Protocol (MCP) and the Agent2Agent p

CAPER: Clause-Aligned Process Supervision for Text-to-SQL

arXiv:2606.03327v1 Announce Type: cross Abstract: Text-to-SQL systems are typically evaluated by query-level execution correctness, but this terminal signal provides little guidance about which interm

CARVE: Certified Affordable Repair of Vetoed Maneuvers via Envelopes for Interactive Driving

arXiv:2606.02641v1 Announce Type: cross Abstract: Interactive driving exposes a failure mode that is easy to miss in rule-aware autonomous-driving stacks: a hard-rule margin can be negative for an ego

Tutorials

Causal Evidence of Stack Representations in Modeling Counter Languages Using Transformers

arXiv:2606.03398v1 Announce Type: cross Abstract: Formal languages have proven to be effective conduits to understand the inner mechanisms of transformers. Past work has shown that transformers traine

tutorialsarxiv-cs-ai

Causal Neural Probabilistic Circuits

arXiv:2603.01372v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of end-to-end neural networks by introducing a layer of concepts and predicting

Causal Preference Elicitation

arXiv:2602.01483v2 Announce Type: replace-cross Abstract: We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relati

CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery

arXiv:2606.03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical dis

Characterizing Detectability in 3DGS Poisoning: A Stage-wise Benchmark

arXiv:2606.03499v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has rapidly emerged as a leading representation for real-time novel view synthesis, but recent work shows it is vulnerable

Chatbots Output Meaningful (but Problematic) Language

arXiv:2606.02973v1 Announce Type: new Abstract: Are utterances by AI chatbots meaningful? Concretely, if a user asks, say, Anthropic's agent Claude, 'What is the capital of Spain?' and Claude answers,

ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning

arXiv:2606.02802v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively model struct

Local Ai

CL-DMDF:Dynamic Multimodal Data Fusion Model Based on Contrastive Learning

arXiv:2606.02659v1 Announce Type: cross Abstract: Multimodal data fusion involves integrating and analyzing information from multiple modalities to uncover latent correlations and complementary patter

local-aiarxiv-cs-ai

ClinicalMC: A Benchmark for Multi-Course Clinical Decision-Making with Large Language Models

arXiv:2606.03157v1 Announce Type: new Abstract: Large language models (LLMs) have been widely adopted in healthcare, yet they still encounter significant challenges in complex clinical decision-making

Closed-Loop Molecular Design with Calibrated Deference

arXiv:2606.02618v1 Announce Type: cross Abstract: We present Cognitive Loop via In-Situ Optimization (CLIO), an agent that couples a continuously-updated belief-state graph with a recursive plan-then-

Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models

arXiv:2606.03846v1 Announce Type: cross Abstract: Large language models (LLMs) demonstrate remarkable performance across diverse tasks, but they often generate responses that appear plausible while be

Co-evolving Agent Architectures and Interpretable Reasoning for Automated Optimization

arXiv:2604.17708v2 Announce Type: replace Abstract: Automating operations research (OR) with large language models (LLMs) remains limited by hand-crafted reasoning--execution workflows. Complex OR tas

COD10K-C: Benchmarking Robustness of Camouflaged Object Detection Under Natural Image Corruptions

arXiv:2606.02603v1 Announce Type: new Abstract: Camouflaged object detection has improved substantially, but most standard benchmarks evaluate models only on clean images. This is not realistic becaus

Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs

arXiv:2606.03705v1 Announce Type: new Abstract: Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations. Exist

CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions

arXiv:2602.20213v2 Announce Type: replace-cross Abstract: The evaluation of Large Language Models (LLMs) for code generation relies heavily on the quality and robustness of test cases. However, existi

CoEval: Ranking Language Models for Custom Tasks Without Labeled Data or Trustworthy Benchmarks

arXiv:2606.03650v1 Announce Type: cross Abstract: Choosing or ranking language models for a specific application is hardest when no task-specific labeled data exists, and standard public benchmarks ca

Coherence Maximization Improves Pluralistic Alignment

arXiv:2606.03110v1 Announce Type: new Abstract: Aligning AI systems with diverse human values requires value specifications grounded in concrete examples, but generating such examples without extensiv

safetyarxiv-cs-cl

Coherent Swap Regret and Channel-Proof Learning

model-releasesarxiv-cs-lg

arXiv:2606.02655v1 Announce Type: cross Abstract: External regret certifies stability only against replacing one's behavior by a fixed alternative. In a quantum game, this misses a natural physical mo

Collab-REC: An LLM-based Agentic Framework for Balancing Recommendations in Tourism

arXiv:2508.15030v5 Announce Type: replace Abstract: We propose COLLAB-REC, a multi-agent framework designed to counteract popularity bias and improve diversity in tourism recommendations. In our setup

Combining Statistical Features and Deep Encodings for Rehearsal-Based Class-Incremental Time Series Classification

model-releasesarxiv-cs-lg

arXiv:2606.03292v1 Announce Type: cross Abstract: Many systems used in real-world environments require adding new categories and incorporating new information without forgetting what was previously le

CoMPAS3D: A Dataset and Benchmark for Interactive Motion

arXiv:2507.19684v2 Announce Type: replace-cross Abstract: Socially interactive humanoid robots must engage with humans through their bodies, adapting in real time to a partner's movement, intent, and

Compress then Merge: From Multiple LoRAs into One Low-Rank Adapter

model-releasesarxiv-cs-lg

arXiv:2606.03723v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) enables parameter-efficient specialization of foundation models, but the proliferation of task-specific adapters fragments ca

Applications

Conditional Hypothesis Generation for LLM-Based Text Analysis with Researcher-Specified Covariates

arXiv:2606.03029v1 Announce Type: cross Abstract: A core goal of computational social science is to discover interpretable differences in how language varies across outcomes of interest, such as polit

applicationsarxiv-cs-ai

Conditional Latent Diffusion Model with Fourier-based Motion Modelling for Virtual Population Synthesis

arXiv:2606.03827v1 Announce Type: cross Abstract: In-silico trials of medical devices require the generation of virtual populations of anatomies. In cardiovascular applications, virtual anatomy is typ

Conformal Language Modeling via Posterior Sampling

arXiv:2606.03731v1 Announce Type: new Abstract: Large Language Models remain plagued by hallucinations. Recent work has sought to tame their prevalence using statistical techniques based on conformal

researcharxiv-cs-lg

Consistency Training Can Entrench Misalignment

arXiv:2606.03810v1 Announce Type: cross Abstract: Consistency training encourages a model to produce similar outputs across related inputs or sampling procedures. Such methods are simple, scalable, an

Consistent Yet Wrong: Evidence Insensitivity in Spatial Vision-Language Models

arXiv:2606.02742v1 Announce Type: new Abstract: Spatial reasoning is fundamental to robotics, autonomy, and embodied AI, yet modern vision-language models (VLMs) remain unreliable on metric distance q

Constitutional On-Policy Safe Distillation

arXiv:2606.03089v1 Announce Type: cross Abstract: On-policy self-distillation (OPSD) has emerged as an efficient post-training paradigm by using a teacher conditioned on privileged information to prov

ConTrack: Constrained Hand Motion Tracking with Adaptive Trade-off Control

arXiv:2606.03177v1 Announce Type: new Abstract: Human demonstrations provide strong priors for robot manipulation, yet it is non-trivial to transfer them to execute on real robots due to the kinematic

safetyarxiv-cs-ro

ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL

arXiv:2606.03017v1 Announce Type: cross Abstract: Reward transfer in Inverse Reinforcement Learning (IRL) is unreliable when policies must generalize to unseen combinations of environment dynamics and

Contrastive Neural Algorithmic Reasoning for Graph Coloring

arXiv:2606.03923v1 Announce Type: new Abstract: Graph coloring seeks to assigns colors to a graph's nodes so that adjacent nodes receive different colors, using as few colors as possible. Here, we stu

safetyarxiv-cs-lg

CoralBay: A Self-Supervised CT Foundation Model

arXiv:2606.03888v1 Announce Type: new Abstract: Self-supervised learning has enabled large-scale pre-training on 2D natural images, producing general-purpose visual representations that transfer effec

Applications

Core-based Hierarchies for Efficient GraphRAG

arXiv:2603.05207v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge. However, existing vector-based method

applicationsarxiv-cs-cl

CORE: Conflict-Oriented Reasoning for General Multimodal Manipulation Detection

arXiv:2606.03066v1 Announce Type: new Abstract: The rapid rise of generative AI has made multimodal fake news increasingly realistic and pervasive, posing severe threats to public trust and social sta

Correcting Neural Operator Spectral Bias via Diffusion Posterior Sampling with Sparse Observations

arXiv:2606.03936v1 Announce Type: new Abstract: Neural operator surrogates (NO) approximate PDE solutions orders of magnitude faster than numerical solvers, but suffer from spectral bias: high-frequen

safetyarxiv-cs-lg

Cosmos 3: Omnimodal World Models for Physical AI

arXiv:2606.02800v1 Announce Type: cross Abstract: We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences