AI news archive.
10,659 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchTowards participatory speech dataset curation: A queer case study and conceptual framework
arXiv:2609.25496v1 Announce Type: new Abstract: In this paper, we motivate the need for a participatory speech dataset creation framework through a case study of the LGBTQIA+, or queer, community - a community with docum…
- arXiv cs.AIresearchSMTB: Fast Structure-Mapping with Tight Bounds
arXiv:2609.25508v1 Announce Type: new Abstract: Structure-mapping forms analogies by aligning systems of relationally connected elements based on shared structure instead of surface features. We introduce a new structure…
- arXiv cs.AIresearchWeakly Supervised Quantum Error Mitigation
arXiv:2609.25555v1 Announce Type: new Abstract: Supervised approaches to quantum error mitigation learn a map from noisy circuit outputs to ideal ones, and therefore require the ideal outputs. Producing those ideal outpu…
- arXiv cs.AIresearchA Behavioral Trait Leaks into Preferences: Diagnosing Trait Interference in LLM User Simulators
arXiv:2609.25572v1 Announce Type: new Abstract: LLM-based user simulators aim to bridge the offline-online gap in recommender evaluation by emulating users through injected traits, where preference attributes determine w…
- arXiv cs.AIresearchDirect Optimization of Generators for Search in Automated Theorem Proving
arXiv:2609.25575v1 Announce Type: new Abstract: Fine-tuned Large Language Models (LLMs) significantly advance Automated Theorem Proving (ATP), but are often deployed as guiding policies within tree search rather than for…
- arXiv cs.AIresearchGaze responses to false-positive computer-aided detection prompts during colonoscopy: a paired-video and real-time eye-tracking study
arXiv:2609.25581v1 Announce Type: new Abstract: False-positive computer-aided detection (CADe) prompts may divert endoscopists' attention during colonoscopy, yet the attentional impact of individual prompts remains uncle…
- arXiv cs.AIresearchTransformer Heads Looking for Order
arXiv:2609.25588v1 Announce Type: new Abstract: In this note, we show that the problem of checking, whether a sequence of bits is ordered, is not doable by 1-head 1-layer transformers but is doable by a 2-head 1-layer tr…
- arXiv cs.AIresearchArticleMiner: Ontology-Guided Knowledge Graph Construction from Scientific Publications
arXiv:2609.25607v1 Announce Type: new Abstract: Scientific papers keep much of their quantitative content in tables and supplementary files, where a number means something only through its header, caption, unit, analytic…
- arXiv cs.AIresearchReasoning-Preserving Fine-Tuning of Post-RL LLMs with Null-Basis LoRA
arXiv:2609.25618v1 Announce Type: new Abstract: Reinforcement learning (RL)-based post-training has become an effective approach for eliciting reasoning capabilities in large language models (LLMs). However, adapting pos…
- arXiv cs.AIresearchLadders of Thought: A Self-Evolving Curriculum of Progressively Simplified Reasoning Traces
arXiv:2609.25643v1 Announce Type: new Abstract: Large language models (LLMs) excel at reasoning when scaled to hundreds of billions of parameters, but small- and mid-scale models remain brittle reasoners even with knowle…
- arXiv cs.AIresearchTCMaster: Confidence-Aware Querying and Workload-Guided Physical Design for Multi-Source Traditional Chinese Medicine Knowledge Graphs
arXiv:2609.25712v1 Announce Type: new Abstract: Multi-source knowledge graphs (KGs) need query mechanisms that expose reliability and exploit domain structure. This paper presents TCMaster, a property-graph query substra…
- arXiv cs.AIresearchLingLan: An Advancing Traditional Chinese Medicine Diagnosis LLM with Multimodal Data
arXiv:2609.25715v1 Announce Type: new Abstract: Though artificial intelligence (AI) increasingly transforms modern medicine, its integration into Traditional Chinese Medicine (TCM) has been relatively slow, primarily due…
- arXiv cs.AIresearchOmniFysics-Nano-V2 Technical Report: Understanding the Physical World Across Modalities
arXiv:2609.25738v1 Announce Type: new Abstract: Omni-modal models have expanded multimodal interaction across vision, audio, speech, and language. However, their training is predominantly organized around semantic descri…
- arXiv cs.AIresearchThe Limits of Simulated Societies: How Post-Training and Survey Fine-Tuning Erase Cross-Cultural Variance
arXiv:2609.25760v1 Announce Type: new Abstract: Using large language models (LLMs) to simulate diverse human populations has the potential to transform many aspects of computational social science, yet many evaluations s…
- arXiv cs.AIresearchOptimizing the Score, Losing Sight of the Task: Reward Hacking Across Weights, Selection, and Prompts
arXiv:2609.25848v1 Announce Type: new Abstract: A higher evaluation score does not always mean a better language model system. When optimization exploits an evaluator's mistakes, measured progress can conceal unchanged o…
- arXiv cs.AIresearchWhen Clinical Judgment Becomes a Predictive Shortcut: Two Questions Clinical AI Must Answer Before Deployment
arXiv:2609.25852v2 Announce Type: replace Abstract: Clinical AI can predict outcomes using information generated by existing clinical judgment. Such performance alone cannot establish usefulness before recognition or acr…
- arXiv cs.AIresearchSemantiScene-Edit: Expert-Synthesized Supervision and Dual-Path Source Conditioning for Instruction-Based Video Editing
arXiv:2609.26015v2 Announce Type: replace Abstract: Instruction-guided video editing requires executing a requested change while preserving the source video's appearance and temporal structure. We present SemantiScene-Ed…
- arXiv cs.AIresearchCQ4OE: A benchmark for assessing LLM-assisted ontology generation from competency questions
arXiv:2609.26029v1 Announce Type: new Abstract: Ontology generation from Competency Questions (CQs) is a central yet labor-intensive phase of Ontology Engineering. While large language models (LLMs) offer promising autom…
- arXiv cs.AIresearchModern GLOM: Canonical locks that encode part-whole hierarchies
arXiv:2609.26046v3 Announce Type: replace Abstract: One of the challenges in representational learning is how to encode part-whole hierarchies in a neural net. Prior works rely on flattening tree-like structures into str…
- arXiv cs.AIresearchChainUQ: Reasoning Consistency-Aware Uncertainty Quantification for Large Language Models
arXiv:2609.26060v1 Announce Type: new Abstract: While large language models (LLMs) exhibit impressive reasoning capabilities, response-level confidence may remain unreliable when intermediate claims conflict with the fin…
- arXiv cs.AIresearchRankCert: When Can Simulated Learners Safely Select an AI Tutor? Robust Decision Certification Under Structural Uncertainty
arXiv:2609.26069v1 Announce Type: new Abstract: Simulation-based tutor selection can be unstable when predictively adequate learner models imply different policy rankings. RankCert certifies one of eight equal-budget tut…
- arXiv cs.AIresearchThe Architect, the Adversary, and the Judge: Closed-Loop Generation of Standards-Aligned Assessment Items at Scale
arXiv:2609.26087v1 Announce Type: new Abstract: We present CLAIM, a production pipeline for K-12 assessment-item generation coupling a two-stage generate-then-attack protocol (the model drafts as a "curriculum architect"…
- arXiv cs.AIresearchFusionMMT: A Unified Multimodal and Multitask Learning Framework for Nuclear Fusion
arXiv:2609.26095v1 Announce Type: new Abstract: With the growing global demand for energy, nuclear fusion has emerged as a promising direction for future clean energy. Tokamaks represent one of the leading approaches to…
- arXiv cs.AIresearchNeoadjuvant chemotherapy response prediction using pretreatment diffusion and contrast-enhanced magnetic resonance imaging with clinical variables
arXiv:2609.26105v1 Announce Type: new Abstract: Prediction of pathological complete response before neoadjuvant chemotherapy may facilitate more tailored therapeutic planning for breast cancer patients. This work propose…
- arXiv cs.AIresearchEarly Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy Using Temporal Deep Learning on DWI
arXiv:2609.26106v1 Announce Type: new Abstract: Early identification of non-responders to neoadjuvant chemotherapy (NACT) is crucial for timely treatment adaptation in breast cancer. However, many existing predictive mod…
- arXiv cs.AIresearchWhen Big Data Becomes a Curse: Spatial Heterogeneity and the Limits of Learning from Passive Acoustic Monitoring Data
arXiv:2609.26125v1 Announce Type: new Abstract: Passive Acoustic Monitoring produces large archives whose recordings are clustered by deployment, season, station identifier, and acquisition configuration. We analyze 908,…
- arXiv cs.AIresearchThe Cost of Conservation: Coordination-Memory Laws for Exact-Support Generation
arXiv:2609.26126v1 Announce Type: new Abstract: Many AI systems make decisions locally, even when every realized output must obey an additive conservation law, such as selecting exactly a fixed number of items. This cons…
- arXiv cs.AIresearchWhen Verifiers Vote Backwards under Verdict Substitution: Signed Pivotal Value in Correlated Self-Consistency
arXiv:2609.26144v1 Announce Type: new Abstract: Replacing one ballot can change a majority decision only on queries decided by a single vote; this structural fact requires no independence assumption. We study the sign of…
- arXiv cs.AIresearchUnanimity Without Persuasion: A Single Round of Debate Erases the Disagreement That Verification Needs
arXiv:2609.26145v1 Announce Type: new Abstract: A debate panel can become unanimous without becoming more correct. This is dangerous for downstream safeguards: a substituted verification ballot can change only narrow-mar…
- arXiv cs.AIresearchThe Free-Recipe Limit: Every Recipe Effect Measures Which Premise of an Idealised Learner Broke
arXiv:2609.26160v1 Announce Type: new Abstract: Fix a corpus and send recipe search to infinity: try every order of the skills, every arrangement from blocked to interleaved, every composition, and keep the best. Two qua…
- arXiv cs.AIresearchEADC: Evaluation of Advanced and Deep-level Compliance in Large Language Models
arXiv:2609.26175v1 Announce Type: new Abstract: Large Language Models (LLMs) have been used in various industries. However, ensuring their compliance with complex laws and regulatory frameworks remains a great challenge.…
- arXiv cs.AIresearchTREND-10K: A Comprehensive Dataset for Next-Generation Video Quality Assessment Based on Preference-Driven Media
arXiv:2609.26187v1 Announce Type: new Abstract: The increasing prominence of short-video platforms, coupled with the advanced commercialization of AI-generated content (AIGC) videos, has led to a shift in the types of vi…
- arXiv cs.AIresearchIdentifying Intelligent Processes via Online Sequential Testing
arXiv:2609.26193v1 Announce Type: new Abstract: Active sequential hypothesis testing studies how to identify an unknown hypothesis with a given set of sensing actions. We study this in the setting of identifying large la…
- arXiv cs.AIresearchRCShift: Certifying When Partial Linkage Suffices for Finite-Sample Decisions
arXiv:2609.26207v1 Announce Type: new Abstract: Systems with costly gold outcomes and cheaper auxiliary observations must decide how much record linkage to retain. Complete pairing retains every joint counter, while sepa…
- arXiv cs.AIresearchImproved Multiplayer Bandit Algorithm for Bernoulli Rewards
arXiv:2609.26213v1 Announce Type: new Abstract: We study the multiplayer multi-armed bandit problem with information asymmetry under Bernoulli rewards, for three information structures: asymmetry in actions, in rewards,…
- arXiv cs.AIresearchA Hybrid AI Framework for Academic Advising: Integrating Ensemble-Based Grade Prediction and a Rule-Based Expert System
arXiv:2609.26243v1 Announce Type: new Abstract: The rapidly increasing student population has posed serious challenges to the traditional academic advising process. This study designs and implements a multi-purpose intel…
- arXiv cs.AIresearchA Multi-Timestep LSTM Ensemble regressor for Enhanced Short-Term Runoff Prediction
arXiv:2609.26244v1 Announce Type: new Abstract: Accurately forecasting river runoff is key to managing water resources, controlling floods, and planning agriculture. This study examines the Ajichay River in northwest Ira…
- arXiv cs.AIresearchDecoupling Is Not Identification: Supervised Evidential Learning in Next-Token Prediction
arXiv:2609.26268v1 Announce Type: new Abstract: A next-token probability says what a model predicts, not how much training support lies behind it. A Dirichlet head can represent this distinction by separating mean $m$ fr…
- arXiv cs.AIresearchFISSION: Label Augmentation for Bot Detection
arXiv:2609.26279v1 Announce Type: new Abstract: Bot accounts and coordinated influence operations are often discovered via heuristic methods, leaving a dearth of reliable ground-truth labels for training detection system…
- arXiv cs.AIresearchReliability Theory for AI Control
arXiv:2609.26419v1 Announce Type: new Abstract: Reliability theory gives a mature language for layered systems, but its formal tools are not yet standard in frontier AI control. We apply them to Google DeepMind's defense…
- arXiv cs.AIresearchReproducible AI Requires Reproducible Randomness
arXiv:2609.26461v1 Announce Type: new Abstract: Pseudorandom number generators (PRNGs) constitute indispensable computational tools across multiple scientific domains, including Monte Carlo simulations, stochastic comput…
- arXiv cs.AIresearchJEV-as-a-Judge: Accept When Confident, Escalate When Unsure
arXiv:2609.26550v5 Announce Type: replace Abstract: LLM-as-a-judge scales evaluation, but reasoning judges are slow and costly. We study JEV-as-a-Judge: evaluation with JEV, a decision-only judge that returns label proba…
- arXiv cs.AIresearchNeutral-Atom-based Quantum Optimization for Resource Allocation in NOMA Networks
arXiv:2609.26556v1 Announce Type: new Abstract: In wireless communication networks, many resource optimization problems are nondeterministic polynomial-time hard (NP-hard) due to their combinatorial nature and high compu…
- arXiv cs.AIresearchQuantum-Aided Active Device Detection in Energy-Harvesting Symbiotic Radio Networks
arXiv:2609.26565v1 Announce Type: new Abstract: Massive connectivity in next-generation networks demands energy- and spectrum-efficient solutions for large-scale Internet of Things (IoT) deployments. Symbiotic radio (SR)…
- arXiv cs.AIresearchType-Safe Is Not Error-Free: Typed Decision Models Follow the Option Name, Not the Definition Bound to It
arXiv:2609.26758v3 Announce Type: replace Abstract: Typed decision models return structured results, but output-type correctness alone does not ensure that decisions follow explicit option definitions. Each option pairs…
- arXiv cs.AIresearchFinancial sentiment analysis using FinBERT with application in predicting stock movement
arXiv:2306.02136v3 Announce Type: cross Abstract: In this study, we integrate sentiment analysis within a financial framework by leveraging FinBERT, a fine-tuned BERT model specialized for financial text, to construct an…
- arXiv cs.AIresearchNot All 4-bit Quantizers Are Equal: Deployment-Time Mitigation of PII Leakage in Fine-Tuned Small Language Models
arXiv:2609.25014v1 Announce Type: cross Abstract: Organizations fine-tune small language models on private data and then compress them to 4 bits for resource-efficient deployment. We show that the compression method also…
- arXiv cs.AIresearch"As a Language Model...": Chat Template Switches LLM Self-Referential Voice and Activation Steering Reproduces It
arXiv:2609.25021v1 Announce Type: cross Abstract: Large Language Models (LLMs) tend to add disclaimers like "I'm just an AI" when asked about something related to themselves. The self-reports from such responses are used…
- arXiv cs.AIresearchTeaching a Moving Student: Rethinking the Curriculum of On-Policy Distillation
arXiv:2609.25048v2 Announce Type: replace-cross Abstract: In on-policy distillation (OPD), the student determines which states receive teacher supervision. As its policy evolves, earlier response prefixes become less lik…
- arXiv cs.AIresearchMitigating LLM Over-Refusal via Dynamic Semantic Routing Calibration
arXiv:2609.25049v2 Announce Type: replace-cross Abstract: Large language models (LLMs) aligned for safety often suffer from over-refusal, incorrectly rejecting benign yet safety-related instructions. Prior studies primar…