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Behind the Refusal: Determining Guardrail Activation via Behavioral Monitoring

arXiv:2607.02121v1 Announce Type: cross Abstract: As Large Language Models (LLMs) and agentic systems become integrated into real-world applications, ensuring their safety and security is critical. Gu

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Benchmarking Federated Learning and Knowledge Distillation for Point Cloud Classification

DGX agent

arXiv:2607.01272v1 Announce Type: cross Abstract: Deploying 3D point cloud analysis in privacy-sensitive, resource-constrained settings faces two barriers: data cannot be centralized, and models must

model-releasesarxiv-cs-ai

3 Jul 2026

Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials

arXiv:2607.02499v1 Announce Type: cross Abstract: Machine learning interatomic potentials (MLIPs) have become a hallmark of AI for scientific simulation. While efforts on new architectures and dataset

Beyond Detection: Redesigning Assessment and Governande of Generative AI at the Universidad Politecnica de Madrid (UPM)

arXiv:2607.01255v1 Announce Type: cross Abstract: Universities have responded to generative artificial intelligence (GenAI) in noticeably different ways, both internationally and within Spain. So far,

Beyond Gradient-Based Attacks: Adversarial Robustness and Explainability Stability in Cybersecurity Classifiers

arXiv:2607.01679v1 Announce Type: cross Abstract: Adversarial attacks on cybersecurity classifiers pose a dual threat: degrading predictions and destabilising the SHAP-based explanations that security

Beyond Next-Token Prediction: An RLVR Proof of Concept for Tool-Use Agents on Atlassian Workflows

arXiv:2607.01465v1 Announce Type: new Abstract: Large language models are trained to predict the next token, not to act inside a specific API. In niche enterprise SaaS workflows -- where success means

Beyond Pixel Diffs: Benchmarking Image Change Captioning for Web UI Visual Regression Testing

model-releasesarxiv-cs-cl

arXiv:2607.01728v1 Announce Type: cross Abstract: Visual regression testing (VRT) is a standard quality assurance step in modern software release pipelines. On every change, it re-renders user interfa

Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Framework

model-releasesarxiv-cs-cl

arXiv:2607.01581v1 Announce Type: new Abstract: The capacity of Large Language Models (LLMs) to reason about pedagogical intent within instructional communication remains underexplored, particularly i

Beyond Supervised Clarification: Input Rewriting with LLMs for Dialogue Discourse Parsing

arXiv:2607.01964v1 Announce Type: new Abstract: Rewriting inputs to improve frozen downstream models has become a common strategy in modern NLP pipelines. Prior work on incremental dialogue discourse

agentsarxiv-cs-cl

Beyond the Performance Illusion: Structure-Aware Stratified Partitioning and Curriculum Distributionally Robust Optimization for Spatially Correlated Domains

arXiv:2607.02055v1 Announce Type: cross Abstract: Performance evaluation in AI systems commonly assumes that random dataset splits produce independent and identically distributed (i.i.d.) subsets. We

Applications

Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search

arXiv:2607.01387v1 Announce Type: cross Abstract: Recommender systems are vital in helping users navigate vast amounts of information, offering personalized suggestions and effective explanations for

applicationsarxiv-cs-lg

BIFROST: Bridging Invariant Feature Representation for Observation-space Sim2Real Transfer

arXiv:2607.01410v1 Announce Type: cross Abstract: Sim2real transfer for robot policy learning suffers due to mismatch between simulation and reality. Existing methods typically address each gap in iso

safetyarxiv-cs-lg

Black-Box Inference of LLM Architectural Properties with Restrictive API Access

arXiv:2607.01313v1 Announce Type: cross Abstract: In practice, most commercial LLM providers do not publicly release details of underlying LLM architectures. However, prior work has shown that given l

Born Discrete, Made Smooth: Variational Formulation of Shallow Neural Networks

arXiv:2607.02003v1 Announce Type: cross Abstract: Although neural networks are remarkably effective, their underlying optimization principles remain theoretically elusive, often characterized by non-c

Boundary-Aware Quantization: Finite-Scale Decision Geometry of Neural Classifiers

arXiv:2607.01478v1 Announce Type: cross Abstract: We measured quantization-induced decision-boundary changes using local logit-margin radii, first-order boundary displacement, normal variation, slice-

BOUNDARY_SYNC: Measuring Communication-Induced Representational Coupling in Multi-Agent LLM Systems

model-releasesarxiv-cs-cl

arXiv:2607.01600v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed as communicating agents, does inter-agent communication cause outputs to converge? We introduce BOUNDARY_

Breaking Safety at the Token Boundary: How BPE Tokenization Creates Exploitable Gaps in LLM Alignment

arXiv:2607.01239v1 Announce Type: cross Abstract: Character-level perturbations bypass safety alignment in modern LLMs despite leaving prompts human-readable. We identify and test a central structural

BRIDGE: Predicting Human Task Completion Time From Model Performance

arXiv:2602.07267v2 Announce Type: replace Abstract: Evaluating the real-world capabilities of AI systems requires grounding benchmark performance in human-interpretable measures of task difficulty. Ex

Local Ai

Bridge-WA: Predicting Where and How the World Changes for Robotic Action

arXiv:2607.02195v1 Announce Type: new Abstract: General-purpose vision-language-action models benefit from large vision-language priors, but effective manipulation also requires anticipating action-re

local-aiarxiv-cs-ro

Bringing Agentic Search to Earth Observation Data Discovery

arXiv:2607.02387v1 Announce Type: cross Abstract: NASA and its data centers hold thousands of geoscience datasets and tools like Worldview, Giovanni, the Science Discovery Engine, and Harmony. Finding

BuilderBench: The Building Blocks of Intelligent Agents

arXiv:2510.06288v4 Announce Type: replace Abstract: Today's AI models learn primarily through mimicry and refining, so it is not surprising that they struggle to solve problems beyond the limits set b

CALM: Interpretable Cross-Modal Alignment for Biomarker Discovery from Unpaired Data

arXiv:2607.01656v1 Announce Type: new Abstract: The interaction between brain structure and genetic influences is key to understanding neuropsychiatric disorders. However, most large-scale datasets ar

safetyarxiv-cs-lg

CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detection

arXiv:2607.01870v1 Announce Type: new Abstract: Camouflaged Object Detection (COD) aims to locate and segment objects that blend into their surroundings, presenting challenges due to weak edge cues an

Can Language Models Actually Retrieve In-Context? Drowning in Documents at Million Token Scale

arXiv:2607.01538v1 Announce Type: new Abstract: Language models (LMs) raise an intriguing alternative to vector-based retrieval: conditioning on an in-context corpus and directly generating a relevant

researcharxiv-cs-cl

CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation

arXiv:2603.22435v2 Announce Type: replace-cross Abstract: 'Code-as-Policy' considers how executable code can complement data-intensive Vision-Language-Action (VLA) methods, yet their effectiveness as

Causal Explanations for Image Classifiers

arXiv:2411.08875v4 Announce Type: replace Abstract: Existing algorithms for explaining the output of image classifiers use different definitions of explanations and a variety of techniques to find the

CausalSteward: An Agentic Divide-Conquer-Combine Copilot for Causal Discovery

arXiv:2607.01936v1 Announce Type: cross Abstract: Learning causal models from high-dimensional data is a significant challenge, particularly in real-world settings where violations of core assumptions

Certified World Models as Sensing Clocks: Drift-Aware Deadlines for Active Perception

arXiv:2607.01537v1 Announce Type: new Abstract: Certified world models estimate how long their predictions remain valid. We turn this validity horizon into an operational sensing clock: a rule for whe

agentsarxiv-cs-lg

Tutorials

Challenges and Recommendations for LLMs-as-a-Judge in Multilingual Settings and Low-Resource Languages

arXiv:2607.02235v1 Announce Type: cross Abstract: LLM-as-a-Judge has become the dominant evaluation paradigm for many natural language generation tasks, due to shortcomings of conventional metrics and

tutorialsarxiv-cs-ai

Local Ai

CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning

arXiv:2607.02262v1 Announce Type: new Abstract: Reasoning Language Models (RLMs) have significantly improved performance on complex tasks by extending the reasoning chain. However, these chains are pr

local-aiarxiv-cs-cl

Choreographing the Way of Water: A Computational Framework for Aquatic Robotic Art

arXiv:2607.02174v1 Announce Type: new Abstract: Robotic choreography in open water is governed by nonlinear fluid dynamics, which impose significant challenges due to environmental disturbances and no

agentsarxiv-cs-ro

CLAP: Closed-Loop Training, Evaluation, and Release Control for Domain Agent Post-training

arXiv:2607.01846v1 Announce Type: new Abstract: Domain agents often face noisy business data, uncertain post-training gains, offline/application mismatch, and adapter-release risk. This paper presents

Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning

arXiv:2607.01474v1 Announce Type: new Abstract: Class imbalance poses a critical challenge in federated learning (FL), where underrepresented classes suffer from poor predictive performance yet cannot

researcharxiv-cs-lg

Applications

CNN Models for Microphone Array Covariance Matrix Upsampling and Acoustic Imaging

arXiv:2607.01295v1 Announce Type: cross Abstract: Acoustic imaging visualization is a core methodology in acoustics, enabling spatial analysis of sound sources and acoustic scenes. However, limited se

applicationsarxiv-cs-lg

Coding-agents can replicate scientific machine learning papers

arXiv:2607.02134v1 Announce Type: new Abstract: Scientific machine learning papers typically make computational claims, e.g., that the relative mean square error is less than 5% or that the 95% predic

CoFL-S: Spatially Queryable Sector Flow Fields for Local Language-Conditioned Navigation

arXiv:2607.02222v1 Announce Type: cross Abstract: Vision-Language Navigation has increasingly emphasized high-level instruction reasoning, memory, global map construction, and instruction decompositio

Collaborative Disagreement Resolution for Scalable Oversight

arXiv:2607.01251v1 Announce Type: cross Abstract: Debate, where AI agents argue opposing positions, has emerged as a key approach to scalable oversight. However, debate faces a fundamental tension: mo

Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning

arXiv:2607.02484v1 Announce Type: cross Abstract: Visual token pruning is a crucial strategy for accelerating VLMs by compressing redundant image patches, yet existing methods often fail to preserve c

COMFYCLAW: Self-Evolving Skill Harnesses for Image Generation Workflows

arXiv:2607.01709v1 Announce Type: new Abstract: Agents are increasingly used to construct workflows and assist humans in completing recurring tasks more efficiently. As these workflows become repeated

CommonRoad-Game: A Human-in-the-Loop Simulation Framework for Autonomous Driving

arXiv:2607.01382v1 Announce Type: new Abstract: Motion planning algorithms should be evaluated in human-in-the-loop environments to ensure they produce safe and efficient behaviors during interactions

agentsarxiv-cs-ro

Comparing Architectures for Supervised Political Scaling

arXiv:2607.01464v1 Announce Type: new Abstract: Text scaling, the task of positioning political actors on an ideological scale, is a fundamental task in political analysis. To ease the need for manual

researcharxiv-cs-cl

Composite Reward Design in PPO-Driven Adaptive Filtering

arXiv:2506.06323v2 Announce Type: replace-cross Abstract: Model-free and reinforcement learning-based adaptive filtering methods are gaining traction for denoising in dynamic, non-stationary environme

Conditional Co-Ablation: Recovering Self-Repair Backups in Transformer Circuits

arXiv:2607.01940v1 Announce Type: cross Abstract: Mechanistic interpretability often relies on component-level interventions to discover how a model produces a behavior. This guides attribution, capab

Conditional Inference Trees and Forests for Feature Selection

arXiv:2607.01417v1 Announce Type: new Abstract: Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split threshol

safetyarxiv-cs-lg

ContextNest: Verifiable Context Governance for Autonomous AI Agent

arXiv:2607.02116v1 Announce Type: new Abstract: Autonomous AI agents increasingly depend on external knowledge stores, yet most retrieval pipelines provide relevance without durable guarantees of prov

ContextSniper: AntTrail's Token-Efficient Code Memory for Repository-Level Program Repair

arXiv:2607.01916v1 Announce Type: new Abstract: Large language model agents can repair real repository issues, but they often spend large context budgets on whole-file reads, broad searches, and long

Contrastive Deep Learning Reveals Age Biomarkers in Histopathological Skin Biopsies

arXiv:2411.16956v2 Announce Type: replace-cross Abstract: As global life expectancy increases, so does the burden of chronic diseases, yet individuals exhibit considerable variability in the rate at w

Controllable Sim Agents with Behavior Latents

arXiv:2607.02496v1 Announce Type: cross Abstract: Realistic traffic simulation requires agents that imitate logged behavior and can also be steered along interpretable axes. Such controllability enabl