AI news archive.
10,659 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchNeuro-Symbolic Meta-Policies for Temporal Knowledge-Graph Memory under Partial Observability
arXiv:2607.18368v5 Announce Type: replace Abstract: Partially observable reinforcement learning requires deciding what to retain, retrieve, and forget over time. We introduce a neuro-symbolic meta-policy that learns whic…
- arXiv cs.AIresearchTILT: Model-Intrinsic Reward Alignment For Compositional Diffusion
arXiv:2607.21606v2 Announce Type: replace Abstract: Consider conditional generation $p(x \mid C=\{c_1, c_2, \dots c_k\})$ where $C$ is a prompt composed of multiple concepts $c_i$. Diffusion models often struggle with co…
- arXiv cs.AIresearchDeconstructing Off-Policy Ratios: Entropy-Normalized Trust Regions for Asynchronous Reinforcement Learning
arXiv:2607.22186v5 Announce Type: replace Abstract: Asynchronous reinforcement learning (RL) accelerates large language model (LLM) post-training by overlapping rollout generation with policy optimization, but the result…
- arXiv cs.AIresearchTowards Trustworthy Physical Intelligence: From Theory to Practice Across Life Cycle
arXiv:2607.22877v4 Announce Type: replace Abstract: Physical intelligence refers to intelligence systems that understand, reason about, and act in accordance with the physical world and its underlying laws, dynamics, and…
- arXiv cs.AIresearchDimensionality and Measurement Precision in HLE's Multiple-Choice Subset
arXiv:2607.27420v2 Announce Type: replace Abstract: Humanity's Last Exam (HLE) is widely used to evaluate frontier language models. HLE organizes its questions into eight subject-domain categories, whose subscores are of…
- arXiv cs.AIresearchRA-CAD: Learning Post-Execution Critique for State-Aware Text-to-CAD Generation
arXiv:2608.05714v4 Announce Type: replace Abstract: Text-to-CAD generation translates natural-language design intent into editable and executable parametric computer-aided design (CAD) codes, reducing the expertise and e…
- arXiv cs.AIresearchNxN E-valuation: Hypothesis Certification via a Conformal CRT Null
arXiv:2608.06621v5 Announce Type: replace Abstract: Language models now propose hypotheses faster than anyone can check them, so the step that certifies them must need nothing from the proposer but the hypothesis. We sta…
- arXiv cs.AIresearchThe Value of a Prompt: An LLM-Relative Kolmogorov-Complexity Approach
arXiv:2608.16438v2 Announce Type: replace Abstract: In a world where valuable artifacts are increasingly created, completed, or processed by LLMs, the central economic question is not only what the LLM can produce, but w…
- arXiv cs.AIresearchHidden in the Request: Explaining Unethical LLM Compliance through Token Relevance
arXiv:2608.23264v3 Announce Type: replace Abstract: Although Large Language Models (LLMs) are aligned to optimize for both helpfulness and harmlessness, these dual objectives may conflict, inevitably leading to alignment…
- arXiv cs.AIresearchTowards Reliable, Generalizable, and Specific In-Context Knowledge Editing via Multi-Objective Reinforcement Learning
arXiv:2608.25100v3 Announce Type: replace Abstract: Large Language Models (LLMs) are powerful but limited by static parametric knowledge that becomes outdated once pretraining ends. Knowledge editing addresses this probl…
- arXiv cs.AIresearchSEPO: Evidence-Grounded Prompt Optimization via Structural Editing
arXiv:2608.28067v2 Announce Type: replace Abstract: Existing API-only prompt optimisers are often described as interpretable, but in practice, this usually means only post-hoc inspectability: each iteration still rewrite…
- arXiv cs.AIresearchDramaChain Bench: An End-to-End Benchmark for Short-Drama Generation
arXiv:2609.00646v2 Announce Type: replace Abstract: Commercial short-drama production follows a multi-stage chain: script, storyboard, keyframe imagery, shot-level video, and the finished short drama. Most existing bench…
- arXiv cs.AIresearchCan LLMs Discover Scientific Laws in Real and Parallel Worlds?
arXiv:2609.01552v2 Announce Type: replace Abstract: Scientific law discovery has long been central to scientific progress, proceeding through iterative cycles of generating hypotheses, testing them against empirical evid…
- arXiv cs.AIresearchAnswer-Distribution Trajectories: A Stochastic-Dynamics View of LLM Reasoning
arXiv:2609.09030v2 Announce Type: replace Abstract: Chain-of-thought reasoning provides a structured computation between a model's input and final answer. Yet it is often evaluated through endpoint accuracy, which ignore…
- arXiv cs.AIresearchA Function-Space Approach to the Statistical Mechanics of Learning Dynamics
arXiv:2609.09589v2 Announce Type: replace Abstract: In the kernel regime, neural-network learning inherits its preferences from a frozen spectrum. During feature learning, this spectrum evolves, yet networks retain syste…
- arXiv cs.AIresearchReading the Whole Heart: Latent-Attention Masked Autoencoders for Multimodal Cardiac Representation Learning
arXiv:2609.12035v3 Announce Type: replace Abstract: Cardiovascular diagnosis and treatment rest on integrating complementary modalities, such as electrocardiogram, echocardiography, and chest X-rays, each capturing disti…
- arXiv cs.AIresearchSchizophrenia Detection from EEG Signals: A Transformer Framework with Spectrogram Representation
arXiv:2609.14015v2 Announce Type: replace Abstract: Schizophrenia is a serious psychiatric disorder that affects millions of people worldwide, and its diagnosis remains primarily dependent on clinical assessment. Electro…
- arXiv cs.AIresearchUnderstanding Certified Training with Interval Bound Propagation
arXiv:2306.10426v3 Announce Type: replace-cross Abstract: As robustness verification methods are becoming more precise, training certifiably robust neural networks is becoming ever more relevant. To this end, certified t…
- arXiv cs.AIresearchPPFedIT: Towards Privacy-Preserving Federated Instruction Tuning with Few-shot Local Examples
arXiv:2403.06131v3 Announce Type: replace-cross Abstract: Instruction tuning aligns large language models (LLMs) with human intentions but requires diverse, high-quality data that are difficult to collect in privacy-sens…
- arXiv cs.AIresearchCHAOSMINING: Benchmarking Post-Hoc Attribution with Sparse Informative Features in High Dimensions
arXiv:2406.12150v2 Announce Type: replace-cross Abstract: Post-hoc attribution is widely used to identify important model inputs, but evaluating whether these attributions identify truly informative features is difficult…
- arXiv cs.AIresearchEfficient Hierarchical Transformers for Representing Log Data
arXiv:2408.16803v2 Announce Type: replace-cross Abstract: Transformers have gained widespread acclaim for their versatility in handling diverse data structures, yet their application to log data remains underexplored. Lo…
- arXiv cs.AIresearchHardCore Generation: Generating Hard UNSAT Problems for Data Augmentation
arXiv:2409.18778v2 Announce Type: replace-cross Abstract: Efficiently determining the satisfiability of a boolean equation -- known as the SAT problem for brevity -- is crucial in various industrial problems. Recently, t…
- arXiv cs.AIresearchScrew Geometry Meets Bandits: Incremental Acquisition of Demonstrations to Generate Manipulation Plans
arXiv:2410.18275v2 Announce Type: replace-cross Abstract: In this paper, we study the problem of methodically obtaining a sufficient set of kinesthetic demonstrations, one at a time, such that a robot can be confident of…
- arXiv cs.AIresearchMoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation
arXiv:2503.09950v2 Announce Type: replace-cross Abstract: In this paper, we address the problem of human trajectory forecasting, which aims to predict the inherently multi-modal future movements of humans based on their…
- arXiv cs.AIresearch1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
arXiv:2503.14858v5 Announce Type: replace-cross Abstract: Scaling up self-supervised learning has driven breakthroughs in language and vision, yet comparable progress has remained elusive in reinforcement learning (RL).…
- arXiv cs.AIresearchOptimizing Breast Cancer Detection in Mammograms: A Comprehensive Study of Transfer Learning, Resolution Reduction, and Multi-View Classification
arXiv:2503.19945v4 Announce Type: replace-cross Abstract: Mammography, an X-ray-based imaging technique, remains central to the early detection of breast cancer. Recent advances in artificial intelligence have enabled in…
- arXiv cs.AIresearchRobotic Long-Horizon Manipulation with Progressive In-Context Code Generation and Episodic Feedback
arXiv:2503.21969v4 Announce Type: replace-cross Abstract: Robotic long-horizon manipulation requires robots to compose perception, reasoning, and action over extended task sequences, yet existing language-conditioned fra…
- arXiv cs.AIresearchRobotic Long-Horizon Manipulation with Bayesian Non-parametric Skill Priors
arXiv:2503.21975v2 Announce Type: replace-cross Abstract: Long-horizon manipulation under sparse rewards remains challenging for reinforcement learning due to delayed feedback and inefficient exploration. Existing skill-…
- arXiv cs.AIresearchErased but Not Forgotten: How Backdoors Compromise Concept Erasure
arXiv:2504.21072v4 Announce Type: replace-cross Abstract: The expansion of text-to-image diffusion models has raised concerns about harmful outputs, from fabricated depictions of public figures to sexually explicit image…
- arXiv cs.AIresearchGraph-Based Floor Separation Using Node Embeddings and Clustering of WiFi Trajectories
arXiv:2505.08088v5 Announce Type: replace-cross Abstract: Indoor positioning systems (IPSs) are increasingly vital for location-based services in complex multi-storey environments. This study proposes a novel graph-based…
- arXiv cs.AIresearchUsing Echo-State Networks to Reproduce Rare Events in Chaotic Systems
arXiv:2505.16208v3 Announce Type: replace-cross Abstract: Echo-State Networks (ESNs) are known to reproduce trajectories and main properties of chaotic attractors. Here, we investigate the question of whether ESNs can re…
- arXiv cs.AIresearchAnomaly Detection and Generation with Diffusion Models: A Survey
arXiv:2506.09368v2 Announce Type: replace-cross Abstract: Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing, by identifying une…
- arXiv cs.AIresearchEduAlign: Aligning Educational LLMs for Helpfulness, Personalization, and Creativity
arXiv:2507.20335v2 Announce Type: replace-cross Abstract: Educational language models should pursue three complementary objectives: helpfulness through responsible guidance, personalization to learner needs, and creativi…
- arXiv cs.AIresearchOCSVM-Guided Representation Learning for Unsupervised Anomaly Detection
arXiv:2507.21164v3 Announce Type: replace-cross Abstract: Unsupervised anomaly detection (UAD) aims to detect anomalies without labeled data, a necessity in many machine learning applications where anomalous samples are…
- arXiv cs.AIresearchEnhancing Knowledge Tracing through Leakage-Free and Recency-Aware Embeddings
arXiv:2508.17092v3 Announce Type: replace-cross Abstract: Knowledge Tracing (KT) aims to predict a student's future performance based on their sequence of interactions with learning content. Many KT models rely on knowle…
- arXiv cs.AIresearchPhysics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
arXiv:2509.22458v3 Announce Type: replace-cross Abstract: Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers, especially whe…
- arXiv cs.AIresearchPATCH: Learnable Tile-level Hybrid Sparsity for LLMs
arXiv:2509.23410v5 Announce Type: replace-cross Abstract: Large language models (LLMs) deliver impressive performance but incur prohibitive memory and compute costs at deployment. Model pruning is an effective way to red…
- arXiv cs.AIresearchReinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers
arXiv:2510.00915v5 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) replaces costly human labeling with automated verifiers. To reduce verifier hacking, many RLVR systems binar…
- arXiv cs.AIresearchNeural Bayesian Filtering
arXiv:2510.03614v2 Announce Type: replace-cross Abstract: Sequential estimation under partial observability requires tracking beliefs that may be high-dimensional, multimodal, and non-Gaussian. Classical Bayesian filters…
- arXiv cs.AIresearchInverse Mixed-Integer Programming: Learning Constraints then Objective Functions
arXiv:2510.04455v3 Announce Type: replace-cross Abstract: Data-driven inverse optimization for mixed-integer linear programs (MILPs), which seeks to learn an objective function and constraints consistent with observed de…
- arXiv cs.AIresearchDemystifying Hybrid Thinking: Can LLMs Truly Switch Between Think and No-Think?
arXiv:2510.12680v2 Announce Type: replace-cross Abstract: Hybrid thinking enables LLMs to switch between reasoning and direct answering, offering a balance between efficiency and reasoning capability. Yet our experiments…
- arXiv cs.AIresearchMCA: Modality Composition Awareness for Robust Composed Multimodal Retrieval
arXiv:2510.15543v2 Announce Type: replace-cross Abstract: Multimodal retrieval, which seeks to retrieve relevant content across modalities such as text or image, supports applications from AI search to contents productio…
- arXiv cs.AIresearchA Model Can Help Itself: Reward-Free Self-Training for LLM Reasoning
arXiv:2510.18814v5 Announce Type: replace-cross Abstract: Can a language model improve reasoning by learning from its own imperfect responses, without rewards or teacher-provided solutions? We present Self-evolving Post-…
- arXiv cs.AIresearchLearning Granger Causality under Latent Confounding via Intervention-Induced Heterogeneity
arXiv:2510.19138v2 Announce Type: replace-cross Abstract: Granger causality characterizes directed predictive dependencies in multivariate time series, but recovering such dependencies becomes challenging in the presence…
- arXiv cs.AIresearchMedForj: An open, large-scale foundational generative prior for high-resolution 3D brain MRI
arXiv:2510.26834v2 Announce Type: replace-cross Abstract: This work introduces MedForj, a suite of 3D foundational generative priors based on diffusion models. The MedForj models were trained on $72{,}659$ 1~mm isotropic…
- arXiv cs.AIresearchOolong: Evaluating Long Context Reasoning and Aggregation Capabilities
arXiv:2511.02817v2 Announce Type: replace-cross Abstract: As model context lengths continue to grow, concerns about whether models effectively use the full context length have persisted. While several carefully designed…
- arXiv cs.AIresearchDeNoise: Learning Robust Graph Representations for Unsupervised Graph-Level Anomaly Detection
arXiv:2511.04086v2 Announce Type: replace-cross Abstract: With the rapid growth of graph-structured data in critical domains, unsupervised graph-level anomaly detection (UGAD) has become a pivotal task. UGAD seeks to ide…
- arXiv cs.AIresearchProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids
arXiv:2511.05420v4 Announce Type: replace-cross Abstract: Data-driven fault diagnosis models for smart grids are usually trained once on a fixed dataset, whereas in operation new fault types appear and monitoring is exte…
- arXiv cs.AIresearchGlobal Optimization on Graph-Structured Data via Gaussian Processes with Spectral Representations
arXiv:2511.07734v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) is a powerful framework for optimizing expensive black-box objectives, yet extending it to graph-structured domains remains challenging…
- arXiv cs.AIresearchBi-objective chance-constrained evolutionary optimization for large-scale open-pit mine scheduling under geological uncertainty
arXiv:2511.08275v2 Announce Type: replace-cross Abstract: The open-pit mine scheduling problem (OPMSP) is a complex optimization problem in long-term mine planning that involves numerous operational and geological constr…