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Research Editor selected

Germline Execution Blocks Data Exfiltration in Signed MCP Tools

This paper demonstrates that authenticated MCP tools can hide malicious code that leaks data, bypassing runtime defenses like Meta's MCPGuard-Dynamic. The authors introduce germline execution, which runs each tool invocation in an ephemeral copy forked from a data-free process, limiting disclosure to only authorized data. In 153 attack runs, germline execution prevented all unauthorized disclosures, while baseline defenses failed in 5.9-74.5% of runs, with invocation latency of 10-49 ms.

Why it matters: Provides a novel, proven defense against a critical security flaw in AI agent toolchains, with concrete experimental results and formal guarantees.

arxiv2d agoScore 88
AI agent securityModel Context Protocoldata isolation
Research

Time-Division Multiplexing Layer for Continual Agents

The paper proposes a temporal organization layer for continual agents using phase-tagged micro-epochs and nested clocks to manage urgent interactions and long-term commitments. It provides a projected duty-cycle update with deterministic and stochastic guarantees, and specifies coupled busy/idle execution contracts. Synthetic experiments test descent, noise, misalignment, gain asymmetry, resource coupling, and dropped replay logs, with a frozen-backbone Qwen3-8B prototype for orchestration checks.

arxiv2d agoScore 68
continual learningagent orchestrationtemporal organization
Research

Test-Time Optimization Turns Frozen Reconstructors into Imputers for Missing Time-Series Channels

This paper introduces a test-time optimization method that converts a frozen time-series reconstructor into an imputer for complete channel failures, without mask augmentation during training. Missing entries are optimized to minimize reconstruction error on observed coordinates, with an optional temporal-smoothness prior. Across five datasets and four architectures, the method reduces MAE in 19 of 20 combinations versus one-pass reconstruction, though it trails mask-aware imputers on three datasets and requires more inference time.

arxiv2d agoScore 78
time-series imputationtest-time adaptationmissing data handling
Research Editor selected

Interactive World Simulation with Cosmos for Physical AI

NVIDIA introduces a framework adapting Cosmos world foundation models for real-time interactive simulation in Physical AI. It streams visual observations in response to actions and scene controls, supporting both action-conditioned prediction and external simulator state. Demonstrations include camera navigation, robot manipulation, controllable video transfer, and multi-view AV simulation. Code and checkpoints are publicly available.

Why it matters: Provides a concrete, recent framework for interactive world simulation, directly useful for AI training and evaluation in embodied and autonomous systems.

arxiv2d agoScore 85
world simulationPhysical AINVIDIA Cosmos
Research Editor selected

Reasoner-Checked Crosswalks for the Information Exchange Standard: From Pairs to a Certified Hub

This paper formalizes ontology alignment validation for a national digital twin ecosystem, applying coherence and conservativity checks to crosswalks from the Information Exchange Standard (IES) into thirteen ontologies. Pairwise certification catches many violations but fails to compose, as merging three building crosswalks into a hub causes unsatisfiable classes due to a homonym. The authors show that a hub, not a pair, is the correct unit for certification, and they identify an inconsistency between two published standards that must be resolved.

Why it matters: Provides a concrete, logic-based method for validating ontology alignments and demonstrates a critical compositionality failure, offering practical guidance for digital twin interoperability.

arxiv2d agoScore 85
ontology alignmentdigital twinknowledge representation
Research Editor selected

Frontier AI Access Boosts Cyber Defense but Creates Global Asymmetry

A study of Project Glasswing, which gave select US tech firms early access to a frontier AI vulnerability-finding model, shows treated firms more than doubled their vulnerability disclosures and fixed flaws quickly. External researchers shifted reporting away from treated firms, and exploited vulnerabilities fell by two-thirds. However, restricted access created a security asymmetry between firms in different countries.

Why it matters: Provides concrete evidence on how frontier AI affects cyber defense, with implications for policy and security strategy.

arxiv2d agoScore 85
AI in cybersecurityvulnerability discoverytechnology access policy
Research

Noise-regularized training of quantum circuit Born machines under a finite shot budget

This paper analyzes how hardware noise affects generalization in quantum circuit Born machines (QCBMs) trained with maximum mean discrepancy. It proves that the generalization gap is bounded by the complexity of the noisy model's reachable distributions, and that scrambled local Pauli noise acts as a regularizer with an optimal intermediate strength. The authors propose GENIE, a framework that selects a noise level during training using held-out data under a fixed execution budget, with provably bounded regret. Simulations on synthetic and census data show GENIE matches the best of four noise-control strategies, while fixed noise levels often underperform.

arxiv2d agoScore 78
Quantum circuit Born machinesNoise regularizationGeneralization bounds
Research Editor selected

Trick-or-Treat: Adversarial Multimodal Medical Benchmark Exposes Hallucinations in Frontier Models

Researchers introduced Trick-or-Treat, an adversarial multimodal medical benchmark pairing real chest radiographs with synthetic clinical photographs to test cross-modal reasoning. In mismatched pairs, frontier models hallucinated clinically significant findings in 45-57.5% of cases, up from 25-37.5% when photographs were shown alone. The study demonstrates a concerning error pattern and validates synthetic images for adversarial evaluation.

Why it matters: This paper provides concrete evidence of hallucination in medical AI and introduces a novel evaluation method with synthetic images, offering immediate value for AI safety and medical reasoning research.

arxiv2d agoScore 85
AI safetymedical AImultimodal reasoning
Research Editor selected

Continual Prior Compensation for Human Motion Prediction

This paper introduces Continual Prior Compensation (CPC), a temporal continual learning framework for human motion prediction that trains models progressively across short- and long-term prediction subtasks to avoid interference. It adds a learnable Prior Compensation Factor (PCF) to mitigate prior knowledge forgetting, with a theoretical bound for joint optimization)Skip. An enhanced version, CPC++, uses fine-grained factors per subtask. Experiments on three benchmarks with multiple backbones show improved flexibility and effectiveness.

Why it matters: Provides a novel continual learning method with theoretical grounding and broad backbone compatibility, offering practical gains for HMP.

arxiv2d agoScore 82
Human Motion PredictionContinual LearningTemporal Modeling
Research Editor selected

GraphRail: Structured Claim Verification for Multi-Hop QA Reasoning

GraphRail is a new verification framework that represents requirements, evidence, and reasoning as structured claims and compares them without using a generative judge or gold answers. Across three multi-hop QA benchmarks and three reasoning frameworks, it improves error recall over generative verifiers while remaining stable under stochastic extraction. Its diagnoses can also guide online reasoning, providing a traceable way to detect and correct intermediate errors.

Why it matters: This is a concrete research contribution with benchmarked results and a novel verification approach, offering practical value for improving reasoning reliability in AI systems.

arxiv2d agoScore 82
AI verificationmulti-hop question answeringreasoning frameworks
Products

AgentTrustCI: Build-Time Policy, Grounding, and Trajectory Tests for Java LLM Agents

AgentTrustCI is a Java/JUnit 5 framework that adds application-layer behavioral checks for LLM agents, covering tool selection, answer grounding, and workflow trajectories via recorded traces. It uses deterministic replay for repeatable build-time tests and an optional judge interface for semantic evaluation. In a synthetic corpus of 55 cases across seven enterprise domains, the full pipeline correctly classified all 30 injected faults and 25 passing controls, including 10 non-trivial controls, with no missed critical cases. The paper emphasizes that results reflect the authored regression suite, not production accuracy, and describes a production-trace validation protocol.

arxiv2d agoScore 78
LLM agent testingbuild-time validationbehavioral contracts
Research Editor selected

LifeVal: A Benchmark for Personalized Agentic Consumer Tasks

LifeVal is a new benchmark that evaluates AI models on personalized agentic consumer tasks, covering 148 categories with an estimated $1.7 trillion in annual willingness-to-pay. It is built from a survey of over 3,800 consumers and cross-referenced with time-use studies, with tasks created by experts and quality-controlled. The benchmark aims to measure performance on high-value real-life tasks like managing finances and job searching.

Why it matters: This introduces a novel, data-driven benchmark for a growing area of AI, offering concrete methodology and economic grounding that is valuable for researchers and developers.

arxiv2d agoScore 82
AI benchmarkspersonalized assistantsagentic AI
Research

PLUS: A Detector-Agnostic Subspace Signal for Near-OOD Detection

The paper introduces PLUS, a post-hoc signal that complements existing out-of-distribution (OOD) detectors to improve near-OOD detection. PLUS builds per-class low-dimensional subspaces from sparse autoencoder decoder directions and scores inputs via whitened squared Mahalanobis distance. Fused additively with any calibrated base score at a fixed global weight, it improves near-OOD AUROC across all 126 detector-backbone combinations tested, while largely maintaining far-OOD performance.

arxiv2d agoScore 78
out-of-distribution detectiondeep learninganomaly detection
Research

ALTO: A New Multilingual Dataset for Tech-Facilitated Gender-Based Violence Detection

Researchers introduced ALTO, a crowdsourced and annotated dataset of 3,022 instances of tech-facilitated gender-based violence (TFGBV) in Levantine Arabic, African French, and Swahili, developed with civil society organizations. Benchmarking shows existing harmful content detection models have poor TFGBV recall, especially for covert harms and in Swahili, but fine-tuning improves performance. The dataset, taxonomy, and models are released to support better detection in underrepresented languages.

arxiv2d agoScore 78
gender-based violenceNLPdataset creation
Research

Beyond the principle-practice gap: Responsible AI as situated epistemic and ethical practice

This paper challenges the common framing of the gap between AI ethics principles and practice as a mere implementation or compliance deficit. Based on interviews with actors in AI-enabled anti-trafficking systems, it argues that ethical concerns are shaped by institutional and professional contexts, and that epistemic choices are intertwined with ethical judgments. The authors propose a model of four moments—perception, judgment, situated action, and socio-technical materialization—through which concerns become actionable or are deferred. They conclude that responsible AI governance requires epistemic infrastructures to make affected communities' knowledge receivable in design.

arxiv2d agoScore 72
AI ethicsresponsible AIinstitutional practice
Research Editor selected

Rel-Bloom: Multilingual Benchmark Reveals LLM Reasoning Gaps over Supplied Relational Facts

This paper introduces Rel-Bloom, a controlled multilingual diagnostic of 450 multiple-choice questions built from 30 Wikidata relations, testing evidence-conditioned relational reasoning across five Bloom-inspired levels and three veracity conditions. Across 43 models, six languages, and 226,800 predictions, it finds sharp accuracy drops from Apply to Analyze in open-weight models, persistent bottlenecks on Evaluate items even for strong models, and cross-language consistency in difficulty rankings. The results show that greater model capability does not guarantee reliable reasoning over supplied relational evidence.

Why it matters: Provides concrete, recent benchmark results and insights into LLM reasoning limitations over non-parametric memory, valuable for model evaluation and development.

arxiv2d agoScore 85
Retrieval-augmented generationMultilingual LLM evaluationRelational knowledge reasoning
Research Editor selected

Full-Stack Survey of Dexterous Robotic Hands in the Foundation-Model Era

This survey reviews dexterous robotic hand research from January 2024 to early November 2026, organizing the field into five layers: hardware, data, cross-embodiment transfer, learning, and evaluation. It highlights that human data value depends on kinematic, visual, and contact alignment, and that foundation policies still face embodiment-specific constraints. The paper identifies eleven open problems and proposes reporting standards for more measurable progress.

Why it matters: Provides a structured, current synthesis of a rapidly evolving area, offering actionable insights for researchers and practitioners.

arxiv2d agoScore 85
robotic manipulationfoundation modelsembodiment
Research Editor selected

ConceptARC at Scale: New Benchmark and Analysis of Conceptual Understanding in Reasoning Models

The paper introduces ConceptARC at Scale, a generator that creates thousands of ARC-style tasks targeting specific spatial and semantic concepts, and presents ConceptARC-100, a dataset of 1,600 manually reviewed tasks across 16 concepts—ten times the size of the original benchmark. Evaluations of recent large reasoning models and models trained from scratch show which concepts are solved reliably and where they struggle. Internal representation analysis reveals that task representations cluster by transformation operations (e.g., recoloring, removal, translation), suggesting models form stable abstractions that generalize across diverse tasks.

Why it matters: Provides a new large-scale benchmark and insights into how reasoning models abstract concepts, valuable for AI researchers and practitioners.

arxiv2d agoScore 85
Abstraction and Reasoning CorpusLarge Reasoning ModelsConceptual Understanding