AI news archive.
10,659 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchWhen Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors
arXiv:2610.13765v1 Announce Type: cross Abstract: Discrete graph diffusion models corrupt graph structure through independent edge updates with a Markovian noise model. Combined with the exchangeability requirement on th…
- arXiv cs.AIresearchDualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs
arXiv:2610.13767v1 Announce Type: cross Abstract: Low-bit inference on accelerators with native 4-bit support requires quantizing both weights and activations to realize the full throughput gains these formats provide. W…
- arXiv cs.AIresearchReplaySSM: Cache SSM Inputs, Not State
arXiv:2610.13778v1 Announce Type: cross Abstract: State-space models (SSMs) maintain a fixed-size recurrent state, but reading and writing it at every decoding step can bottleneck hybrid models. This eager state manageme…
- arXiv cs.AIresearchYour Discrete Graph Diffusion Model is Secretly Memorizing
arXiv:2610.13786v1 Announce Type: cross Abstract: Discrete graph diffusion models (GDMs) corrupt the graph structure by adding noise independently to nodes and edges under a Markovian noise model. In the unconditional se…
- arXiv cs.AIresearchFORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems
arXiv:2610.13806v1 Announce Type: cross Abstract: Production recommender systems have feature-lifecycle asymmetry: adding new features must produce gains on some metric, while feature removal must not regress any metric.…
- arXiv cs.AIresearchSame Query, Different Answer: Run-to-Run Variance in Semantic Query Processing
arXiv:2610.13823v1 Announce Type: cross Abstract: Semantic query engines ask a large language model (LLM) to judge individual rows, and benchmarks compare these engines on the quality of a few executions at temperature 0…
- arXiv cs.AIresearchSpatialASM: Internalizing Active Mentalization for Spatial Reasoning
arXiv:2610.13832v1 Announce Type: cross Abstract: When solving complex spatial reasoning tasks, ordinary humans naturally revisit the scenes from task-relevant viewpoints and mentally reconstruct the spatial layouts from…
- arXiv cs.AIresearchTask Context Shapes Component Roles in Language Models
arXiv:2610.13895v1 Announce Type: cross Abstract: Large language models reuse the same attention heads across tasks, but identifying these shared heads does not explain what they do differently in each task. A natural so…
- arXiv cs.AIresearchRevisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping
arXiv:2610.13909v1 Announce Type: cross Abstract: While deep learning models for tabular data have recently received significant research attention, the choice of objective functions for regression tasks remains an under…
- arXiv cs.AIresearchHide and Seek: HIDE Concepts to SEEK explanations for black box models
arXiv:2610.13935v3 Announce Type: replace-cross Abstract: Interpreting machine learning model predictions is a central challenge, particularly for black-box vision models. Concept-based attribution methods interpret mode…
- arXiv cs.AIresearchFrom the Fluency Fallacy to the Micro-to-Macro Validity Gap: Opportunities and Pitfalls of LLMs in Social Simulation
arXiv:2507.19364v4 Announce Type: replace Abstract: The integration of Large Language Models (LLMs) into social simulation has generated enthusiasm but also raises methodological and epistemological challenges. This crit…
- arXiv cs.AIresearchDeltaLLM: Delta-Key Sparse Attention for Edge LLMs
arXiv:2507.19608v2 Announce Type: replace Abstract: On-device LLM inference is increasingly important for edge platforms but remains challenging due to high computational and memory demands and tight resource constraints…
- arXiv cs.AIresearchGlobal Consistency Repair for Natural-Language Claim Sets
arXiv:2601.13600v2 Announce Type: replace Abstract: NLP systems increasingly aggregate claims from retrieved passages, documents, and generated reasoning steps. Yet current factuality methods mostly verify claims individ…
- arXiv cs.AIresearchNeuronSpark: Selective Spiking State Space Models with Adaptive Computation for Language Modeling
arXiv:2603.16148v2 Announce Type: replace Abstract: Spiking neural networks promise brain-inspired neural dynamics, yet native large-scale spiking language-model pretraining remains largely unexplored relative to convers…
- arXiv cs.AIresearchDEAF: A Benchmark for Diagnostic Evaluation of Acoustic Faithfulness in Audio Language Models
arXiv:2603.18048v3 Announce Type: replace Abstract: Recent Audio Multimodal Large Language Models (Audio MLLMs) demonstrate impressive performance on speech benchmarks, yet it remains unclear whether these models genuine…
- arXiv cs.AIresearchFactoryBench: Evaluating Industrial Machine Understanding
arXiv:2605.07675v2 Announce Type: replace Abstract: We introduce FactoryBench, a benchmark for evaluating time-series models and LLMs on machine understanding over industrial robotic telemetry. Q&A pairs are organized al…
- arXiv cs.AIresearchUxSID: Semantic-Aware User Interests Modeling for Ultra-Long Sequence
arXiv:2605.09040v4 Announce Type: replace Abstract: Modeling ultra-long user sequences involves a difficult trade-off between efficiency and effectiveness. While current paradigms rely on either item-specific search or i…
- arXiv cs.AIresearchFrom SGD to Muon: Adaptive Optimization via Schatten-p Norms
arXiv:2605.19781v3 Announce Type: replace Abstract: Modern optimizers, like Muon, impose matrix-wise geometry constraints on their updates. These matrix-wise constraints can be unified under Linear Minimization Oracle (L…
- arXiv cs.AIresearchMPMMine: Better Benchmarks for Constraint Acquisition
arXiv:2605.26279v2 Announce Type: replace Abstract: Constraint Acquisition (CA) and related research on the validation and enhancement of Mathematical Programming (MP) models using domain knowledge artifacts are currentl…
- arXiv cs.AIresearchLatent Reward Steering: An Adaptive Inference-Time Framework that Implicitly Promotes Cognitive Behaviors in Reasoning LLMs
arXiv:2606.00726v4 Announce Type: replace Abstract: Strong reasoning depends not only on model knowledge but also on how effectively cognitive behaviors are deployed during generation. Existing methods often rely on expl…
- arXiv cs.AIresearchMIRROR: Learning from the Other View for Multi-Modal Reasoning
arXiv:2607.21552v2 Announce Type: replace Abstract: Vision-language models (VLMs) can behave very differently depending on whether the same underlying problem is presented textually or visually. This inconsistency sugges…
- arXiv cs.AIresearchEnhancing Tabular Learners with Context-Aware Semantic Embeddings
arXiv:2608.03565v2 Announce Type: replace Abstract: While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discrete symbols, igno…
- arXiv cs.AIresearchResidencyRL: Reinforcement Learning in Simulated Clinical Environments
arXiv:2608.07418v2 Announce Type: replace Abstract: In medical education, physicians convert academic knowledge into clinical expertise through residency: years of training across thousands of encounters, with diverse so…
- arXiv cs.AIresearchDifferent Feedback, Different Updates: Selective Self-Learning from User Interactions for Large Language Models
arXiv:2608.09109v2 Announce Type: replace Abstract: User feedback offers natural supervision for persistent LLM improvement, but a single message may support multiple behavioral changes with different scopes of generaliz…
- arXiv cs.AIresearchFrom Mastery Profile to Simulated Response: Stochastic Student Knowledge Graphs (SSKG) for Faithful LLM Student Simulation
arXiv:2608.21668v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to simulate students at different mastery levels to generate synthetic training data and stress-test tutoring systems…
- arXiv cs.AIresearchPreference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Values
arXiv:2608.28620v2 Announce Type: replace Abstract: Preference elicitation is essential for aligning AI systems with human values. Prior approaches (e.g., for organ allocation) often ask stakeholders to compare the decis…
- arXiv cs.AIresearchH2Table: Hierarchical Hypergraph-Enhanced Large Language Models for Complex Table Reasoning
arXiv:2609.01216v2 Announce Type: replace Abstract: Tables are ubiquitous across diverse domains, yet reasoning over them remains a significant challenge for modern large language models (LLMs). Current approaches typica…
- arXiv cs.AIresearchAPI Benchmark Scores Do Not Reliably Transfer to Chatbot Interfaces
arXiv:2609.08861v3 Announce Type: replace Abstract: Benchmark scores are a central currency in model releases: they inform purchasing decisions, shape public trust, and influence policy. Yet, a key assumption underlying…
- arXiv cs.AIresearchProvably Accurate Shapley Value Estimation via Leverage Score Sampling
arXiv:2410.01917v3 Announce Type: replace-cross Abstract: Originally introduced in game theory, Shapley values have emerged as a central tool in explainable machine learning, where they are used to attribute model predic…
- arXiv cs.AIresearchConditional Vendi Score: Prompt-Aware Diversity Evaluation for Generative AI Models and LLMs
arXiv:2411.02817v3 Announce Type: replace-cross Abstract: Generative models guided by text prompts are widely evaluated for fidelity and prompt alignment, yet their ability to produce diverse outputs remains underexplore…
- arXiv cs.AIresearchUnderstanding Transformer-based Vision Models through Inversion
arXiv:2412.06534v5 Announce Type: replace-cross Abstract: Understanding the internal mechanisms of deep neural networks remains a central challenge in machine learning. In computer vision, one promising yet only prelimin…
- arXiv cs.AIresearchCRM: Retrieval Model with Controllable Condition
arXiv:2412.13844v2 Announce Type: replace-cross Abstract: Retrieval models for recommendation need to account for outcomes such as watch time while searching large item corpora efficiently. We present the Controllable Re…
- arXiv cs.AIresearchWoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care
arXiv:2504.06185v3 Announce Type: replace-cross Abstract: Chronic wounds affect a large population, particularly the elderly and diabetic patients, who often exhibit limited mobility and co-existing health conditions. Au…
- arXiv cs.AIresearchHeterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning
arXiv:2505.11304v4 Announce Type: replace-cross Abstract: Federated learning (FL) commonly involves clients with diverse communication and computational capabilities. Such heterogeneity can significantly distort the opti…
- arXiv cs.AIresearchAuditing Prompt Injection Defenses Before They Reach Users: What Strong Adversaries Reveal
arXiv:2505.15738v3 Announce Type: replace-cross Abstract: Large language models are increasingly deployed in user-facing products that handle sensitive personal data while interacting with untrusted external content. Thi…
- arXiv cs.AIresearchReasoningShield: Safety Detection over Reasoning Traces of Large Reasoning Models
arXiv:2505.17244v3 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) leverage explicit reasoning traces, known as Chain-of-Thought (CoT) reasoning, to decompose complex problems into intermediate steps…
- arXiv cs.AIresearchBreaking the Cloak! Unveiling Chinese Cloaked Toxicity with Homophone Graph and Toxic Lexicon
arXiv:2505.22184v3 Announce Type: replace-cross Abstract: Social media platforms have experienced a significant rise in toxic content, including abusive language and discriminatory remarks, presenting growing challenges…
- arXiv cs.AIresearchPhysics-inspired Generative AI models via real hardware-based noisy quantum diffusion
arXiv:2505.22193v2 Announce Type: replace-cross Abstract: Quantum Diffusion Models (QDMs) are an emerging paradigm in Generative AI that aims to use quantum properties to improve the performances of their classical count…
- arXiv cs.AIresearchUniConFlow: A Unified Constrained Flow-Matching Framework for Certified Motion Planning
arXiv:2506.02955v3 Announce Type: replace-cross Abstract: Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation across various tasks…
- arXiv cs.AIresearchLLMail-Inject: A Dataset from a Realistic Adaptive Prompt Injection Challenge
arXiv:2506.09956v2 Announce Type: replace-cross Abstract: Indirect prompt injection attacks exploit a fundamental weakness of LLMs: executing unauthorized tasks from untrusted sources. Despite the practical significance…
- arXiv cs.AIresearchRegression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values
arXiv:2506.11849v3 Announce Type: replace-cross Abstract: With origins in game theory, probabilistic values like Shapley values, Banzhaf values, and semi-values have emerged as a central tool in explainable AI. They are…
- arXiv cs.AIresearchTowards a Measure Theory of Semantic Information
arXiv:2508.00525v2 Announce Type: replace-cross Abstract: A classic account of the quantification of semantic information is that of Bar-Hiller and Carnap. Their account proposes an inverse relation between the informati…
- arXiv cs.AIresearchAccess Paths for Efficient Ordering with Large Language Models
arXiv:2509.00303v5 Announce Type: replace-cross Abstract: In this work, we present the \texttt{LLM ORDER BY} semantic operator as a logical abstraction and conduct a systematic study of its physical implementations. Firs…
- arXiv cs.AIresearchDiscovering Phase Space Structure in Learned Hamiltonian Systems
arXiv:2509.23003v3 Announce Type: replace-cross Abstract: Mechanics-inspired neural dynamical models improve interpretability and physical consistency, but they usually assume a known configuration space, fixed physical…
- arXiv cs.AIresearchABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios
arXiv:2510.10998v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly under scrutiny for perpetuating identity-based discrimination in high-stakes domains such as hiring, particularly ag…
- arXiv cs.AIresearchA Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
arXiv:2510.22809v2 Announce Type: replace-cross Abstract: Traditional machine learning relies on explicit models and domain assumptions, often limiting flexibility and interpretability. We introduce a "model-free" framew…
- arXiv cs.AIresearchCOBALT: Contextual Bandit Learning with Offline Trajectories for Multi-Turn Code Generation
arXiv:2602.03806v2 Announce Type: replace-cross Abstract: Training large language models (LLMs) for multi-turn code generation typically relies on online reinforcement learning (RL), in which models learn from repeatedly…
- arXiv cs.AIresearchBeyond State-Wise Mirror Descent: Offline Policy Optimization with Parametric Policies
arXiv:2602.23811v5 Announce Type: replace-cross Abstract: We investigate the theoretical aspects of offline reinforcement learning (RL) under general function approximation. While prior works (e.g., Xie et al., 2021) hav…
- arXiv cs.AIresearchOn the Rate of Convergence of GD in Non-linear Neural Networks: An Adversarial Robustness Perspective
arXiv:2603.02095v2 Announce Type: replace-cross Abstract: We study the convergence dynamics of Gradient Descent (GD) in a minimal binary classification setting, consisting of a two-neuron ReLU network and two training in…
- arXiv cs.AIresearchCORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning
arXiv:2603.05911v2 Announce Type: replace-cross Abstract: Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis. Although Multimodal Large Lan…