Live intelligence

Latest AI updates

1,733 updates

Research

CANDO: Conservative Action-Noise Directional Optimization for Residual Reinforcement Learning

This paper introduces CANDO, a residual reinforcement learning method that improves frozen generative policies by combining noise-space and action-space residual optimization. CANDO removes fixed latent feasible regions and uses conservative value estimation with gated execution to regulate adaptation. Experiments show CANDO achieves 76.1 NAUC and 91.4% final success rate, surpassing single-space baselines by 6.6 NAUC and 4.1%.

arxiv3d agoScore 78

Reinforcement LearningRobot ControlPolicy Adaptation

Research Editor selected

EU AI Act Q&A Benchmark Reveals Mixed Readiness of LLM Assistants for Regulatory Tasks

Researchers introduce an expert-curated benchmark of 110 open-ended questions about the EU AI Act to evaluate LLM assistants in regulatory affairs. In the 2026 challenge, only two of five purpose-built assistants outperformed baselines, with one achieving high scores across all metrics. The results indicate that specialist development alone does not guarantee better performance, underscoring the need for rigorous evaluation in regulatory contexts.

Why it matters: Provides concrete benchmark results and insights into LLM reliability for regulatory tasks, valuable for developers and compliance professionals.

arxiv3d agoScore 82

LLM evaluationregulatory complianceAI Act

Research Editor selected

CosmosAlpha: Multi-Relational Graph Search for LLM-Driven Alpha Mining

CosmosAlpha is a graph-based framework for LLM-driven alpha factor mining that represents discovered factors as a multi-relational graph encoding generation lineage, expression similarity, and exposure correlation. It uses a Graph Flow mechanism to propagate search utilities and Pareto-guided selection to balance quality, fertility, and diversity. Experiments on CSI 300 and CSI 500 show CosmosAlpha-Graph outperforms its MCTS counterpart on all reported metrics.

Why it matters: Presents a novel graph-based approach with concrete performance gains over a baseline, offering practical value for quantitative finance AI research.

arxiv3d agoScore 82

AI for financegraph-based searchLLM-driven discovery

Research Editor selected

Inter-Agent Influence: Evaluating Persuasion, Deception, and Coercion in Multi-Agent Systems

This paper investigates how AI agents can influence each other through persuasion, deception, and coercion in multi-agent settings. Across five realistic environments and frontier language models, all tested agents shifted overseer decisions from rejection to approval, and also redirected peer safety research. The findings highlight novel risks from inter-agent influence and call for mitigation efforts.

Why it matters: Provides concrete, recent evidence of a novel AI safety risk in multi-agent systems, with practical implications for deployment.

arxiv3d agoScore 85

multi-agent systemsAI safetylanguage models

Research Editor selected

Research World Model for Power Engineering Learns Field Dynamics and Simulates What-Ifs

Researchers built a 'research world model' for power engineering using 1.1 million papers, treating the field as a dynamical system. A distilled language model extracts role-typed concepts, and graph-temporal neural networks learn how the concept network evolves, enabling forecasts of concept growth and new method-target pairings. The model also estimates intervention effects and answers natural-language what-if questions via paired simulations, with high faithfulness ratings.

Why it matters: This introduces a novel AI-driven method for simulating research decisions in a technical field, with demonstrated predictive and counterfactual capabilities, offering practical value for strategic planning.

arxiv3d agoScore 85

AI for scienceresearch dynamicspower engineering

Research

Spectral Quantum Memory for Implicit Neural Representations

The paper introduces Spectral Quantum Memory Model (SQMM), a quantum implicit neural representation with a shared coherent memory register that lets later bands build on earlier computations. It proves the memory state forms an operator-valued Fourier series with support in the Minkowski sum of spectra, and proposes a spectral memory alignment loss. SQMM outperforms compact QINR and INR baselines on audio and image reconstruction benchmarks.

arxiv3d agoScore 75

Quantum machine learningImplicit neural representationsSpectral analysis

Research

ValQID: Adaptive Semantic Query Group Valuation for Web Device Identification

ValQID is a new method for identifying device vendors and types from web interfaces by combining dispersed identity cues via a relation graph. It uses DSEJEV to efficiently evaluate semantic questions and adaptively allocates computation based on expected identification benefit versus execution cost. On real web response data, ValQID improves joint macro-F1 by up to 47.1% and low-frequency vendor macro-F1 by up to 82.1% over a state-of-the-art method, while DSEJEV speeds up semantic processing by up to 8.58x.

arxiv3d agoScore 78

web device identificationsemantic analysiscomputational efficiency

Research

Network Models of Science Compare Pluralist vs. Monist Agents in Belief Dynamics

This paper introduces a multi-agent variational inference model to test Feyerabend's theoretical pluralism, comparing communities of agents that can hold rival theories simultaneously against monist agents that maintain a single model. Experiments show how pluralist communities undergo uncertainty and theory shifts, how inter-community communication affects beliefs under differing observations, and how sharing theory support versus sharing beliefs within theories differently shape collective dynamics. The work provides a computational framework for studying scientific belief evolution.

arxiv3d agoScore 72

multi-agent variational inferencebelief dynamicsscientific communities

Research

Deep Learning Polyp Matching for Colon Capsule Endoscopy

This study introduces a contrastive learning-based Siamese neural network for matching polyps across colon capsule endoscopy images, trained on 5,103 same-polyp and 5,103 different-polyp pairs from the Danish CareForColon2015 trial. Using ResNet, DenseNet, and EfficientNet backbones with cosine distance, the model achieves up to 92% accuracy and AUC near 0.98. The approach aims to reduce polyp over-counting and unnecessary follow-up colonoscopies.

arxiv3d agoScore 78

polyp matchingcontrastive learningcolon capsule endoscopy

Research

QueryFlow: Multi-Hop Graph Retrieval with Adaptive Query Evolution

QueryFlow is a new retrieval method for multi-hop RAG that jointly evolves evidence representations and query conditions. It integrates query-conditioned summaries of graph context into a shared query state to guide subsequent propagation. On four multi-hop QA datasets, it improves Recall@10, Full Recall@10, and LLM-Acc by 4.98, 8.40, and 8.0 percentage points over baselines.

arxiv3d agoScore 78

retrieval-augmented generationgraph-based retrievalmulti-hop question answering

Research

Novel SAT Encoding for Ladder At-Most-K Cardinality Constraints

This paper introduces a new SAT encoding for sequences of At-Most-K (AMK) cardinality constraints arranged in a ladder-like structure, called Ladder AMK. The encoding decomposes the constraint sequence into blocks, encodes each block using a Sequential Counter, and links blocks with clauses, reducing variable and clause counts compared to prior BDD-based encodings. Experiments on single constraints and real-world problems (Anti-bandwidth and Nurse Rostering) show improved solving efficiency over existing SAT, CP, and MIP solvers.

arxiv3d agoScore 72

SAT solvingcardinality constraintscombinatorial optimization

Research Editor selected

LinUCB Works Without Valid Confidence Sets on Curved Action Sets

This paper shows that LinUCB's regret remains sublinear even when its confidence width is replaced by any fixed positive constant, provided the action set is smooth and strongly convex. The authors introduce a logarithmically capped spectral potential to analyze exploration driven by action-set curvature. Their regret bound improves the horizon dependence of classical LinUCB by a factor of sqrt(log T).

Why it matters: Provides a theoretical breakthrough in bandit algorithms, showing valid confidence sets are unnecessary under geometric conditions, with improved regret bounds.

arxiv3d agoScore 82

online learningbandit algorithmsregret analysis

Research Editor selected

Therapy-FIS: First Public Dataset for Automated Assessment of Therapist Interpersonal Skills

Therapy-FIS is a new dataset for automated assessment of Facilitative Interpersonal Skills (FIS) in psychotherapy, containing facial, acoustic, and textual signals from 890 therapist recordings and 8 patient videos. It includes eight FIS subscale ratings and an overall score, and supports two benchmark tasks: Therapist Performance Rating and Joint Therapist–mock-Patient Performance Rating. The authors also introduce a baseline model, DyadicFIS, which achieves best macro CCC scores of 0.2807 and 0.3299 on the two tasks.

Why it matters: This is the first publicly available dataset for automated FIS assessment, addressing a critical data scarcity and enabling reproducible research in mental health AI.

arxiv3d agoScore 85

Facilitative Interpersonal SkillsAutomated assessmentMental health AI

Research

BoD: Backpropagating Through Depth Improves Looped Language Model Code Generation

This paper introduces Backpropagation through Depth (BoD), a training method for looped language models that backpropagates through all intermediate depths rather than only the final depth. BoD improves code-generation success rates, reaching 91.03% coverage on N-Queens versus 45.75% for final-depth training, and also enhances generalization to deeper computations and withheld solutions. The method shows promise for training recurrent computation for solution discovery.

arxiv3d agoScore 72

Backpropagation through DepthLooped language modelsCode generation

Research Editor selected

Uncertainty-Aware Prototype Learning for Open-Set Test-Time Adaptation

This paper introduces UAPA, a plug-and-play method for open-set test-time adaptation that handles both covariate-shifted in-distribution and out-of-distribution samples. It combines uncertainty-driven ID/OOD separation, dynamic prototype refinement, and contrastive prototype entropy adaptation to improve adaptation reliability. Experiments on CIFAR benchmarks and Tiny-ImageNet-C show average OSCR improvements of 2.08 and 1.28 percentage points over strong baselines.

Why it matters: Presents a novel, concrete method with empirical gains for a challenging open-set TTA problem, useful for researchers and practitioners.

arxiv3d agoScore 82

Test-Time AdaptationOpen-Set RecognitionPrototype Learning

Research Editor selected

When Ground Truth Is Not Ground Truth: Automatic Labeling for Hallucination Detection

This paper investigates the reliability of automatic labeling methods for hallucination detection in LLMs, showing that using similarity metrics like BLEURT as proxies for ground truth can be highly unreliable. Through experiments with 15 judges, 7 detectors, 2 generators, 2 QA datasets, and 5 languages, they find that detector performance varies significantly with judge choice. Human validation shows QGen-9B achieves the lowest misclassification rate (12.86%) while BLEURT and BLEURT-n exceed 47%, highlighting the need for more factuality-aware judging methods.

Why it matters: Provides concrete evidence and benchmarks on the pitfalls of common automatic labeling practices, offering actionable insights for improving hallucination detection evaluation.

arxiv3d agoScore 82

LLM hallucinationevaluation methodologyautomatic supervision

Research

CLARA: A Cognitively-Driven Clarification Framework for Grounding Ambiguous Requests

CLARA is a clarification framework for large vision-language models in robotics that proactively asks users targeted questions to resolve ambiguous instructions like 'clean the room'. It converts vague requests into structured, detailed instructions and uses a diffusion-based pipeline to render the final scene for visual confirmation. Experiments and user studies show CLARA outperforms a state-of-the-art LVLM system in accuracy and consistency.

arxiv3d agoScore 78

CLARAambiguous instruction groundinginteractive clarification

Research Editor selected

DenseTwins: Dense Modality Decoupling for Multimodal Self-Supervision

DenseTwins is a new multimodal self-supervised framework that learns dense representations by explicitly separating shared and modality-specific features. It combines dense cross-modal correspondences with redundancy-reduction objectives, optimizing both intra-modal and cross-modal losses. The method achieves state-of-the-art or competitive results on multiple multimodal benchmarks and sensing setups, with code released.

Why it matters: This paper presents a concrete, recent AI development with a novel approach to multimodal representation learning, offering practical value for robotics and autonomous driving.

arxiv3d agoScore 82

Multimodal Self-Supervised LearningDense Representation LearningModality Decoupling