AI news archive.
10,659 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchBrain-Token Learning: Microstate-Based Tokenization and Multi-Scale Interaction for Long-Horizon EEG Sequence Modeling
arXiv:2609.24324v1 Announce Type: new Abstract: Electroencephalography (EEG) provides a non-invasive window into dynamic brain activity, yet modeling long-horizon EEG sequences remains challenging due to their high tempo…
- arXiv cs.AIresearchLADDER: Graph-Guided Diffusion Language Models for Efficient Multi-Hop Reasoning
arXiv:2609.24346v1 Announce Type: new Abstract: Graph Retrieval-Augmented Generation (GraphRAG) has remarkably enhanced large language models on complex reasoning by leveraging structured entity topologies. However, exis…
- arXiv cs.AIresearchPredicting Postprandial Glycemic Response from Meal Images, Clinical Variables, and Gut Microbiome Information
arXiv:2609.24453v1 Announce Type: new Abstract: Predicting postprandial glycemic response (PPGR) is fundamental to personalized nutrition and type 2 diabetes management, yet existing approaches typically rely on manually…
- arXiv cs.AIresearchFathom-Vaidya: Advancing Medical Reasoning with Rubric-Based Rewards
arXiv:2609.24480v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) in healthcare requires robust performance across two complementary dimensions - diagnostic reasoning: the convergent, evidence-driven…
- arXiv cs.AIresearchNot All Task Vectors Need Equal Rank: Energy-Proportional Allocation for Model Merging
arXiv:2609.24517v1 Announce Type: new Abstract: Model merging aims to combine multiple fine-tuned models derived from a common pretrained model into a single multi-task model without additional joint training. Recent spe…
- arXiv cs.AIresearchThe Endless Exam: Mathematical Constructions from Today's Models toward Superintelligence
arXiv:2609.24555v2 Announce Type: replace Abstract: We introduce the Endless Exam, a benchmark spanning fourteen parameterised families of mathematical construction problems, with verifiable scores that distinguish progr…
- arXiv cs.AIresearchCustom Named Entity Recognition and Topic Classification for Global Health Publications
arXiv:2609.24625v1 Announce Type: new Abstract: How should natural language processing models be selected and adapted for global health literature in environments where annotated data and computational resources are limi…
- arXiv cs.AIresearchTimeLitmus: A Diagnostic Benchmark for Cross-Modal Understanding and Explanation Faithfulness in Event-Conditioned Time-Series Prediction
arXiv:2609.24677v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to make predictions from numerical time-series histories and textual events. Yet accuracy alone cannot reveal whether cor…
- arXiv cs.AIresearchConstruting Reverse Thinking: Developing Large Language Models' Reverse Thingking Ability
arXiv:2609.24760v1 Announce Type: new Abstract: When facing complex problems, humans tend to try various ideas for different issues. Human thinking patterns exhibit remarkable flexibility in adapting to diverse scenarios…
- arXiv cs.AIresearchConvex AI Compositionality and the Governance of AI System Populations
arXiv:2609.24784v1 Announce Type: new Abstract: AI governance increasingly requires providers and public authorities to reason about multiple AI instantiations, alternative versions, and deployment configurations of mult…
- arXiv cs.AIresearchExtracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detection
arXiv:2609.24855v1 Announce Type: new Abstract: Argumentative component detection (ACD) is a core subtask of Argument(ation) Mining (AM) and one of its most challenging aspects, as it requires jointly delimiting argument…
- arXiv cs.AIresearchPinocchio: Fast Uncertainty Estimates for Black-Box Language Models
arXiv:2609.24881v2 Announce Type: replace Abstract: In high-stakes decision-making applications of large language models (LLMs), practitioners require not only accurate LLMs but also uncertainty estimates for their predi…
- arXiv cs.AIresearchA Global Comparison of Schemas, Transparency, and Interoperability in Public-Sector AI Registers and Inventories
arXiv:2609.24883v1 Announce Type: new Abstract: Artificial intelligence (AI) registers and inventories aim to make governmental AI visible, but their institutional scope, schemas, and reporting practices construct differ…
- arXiv cs.AIresearchBackTrend: Evaluating Scientific Weak-Signal Prediction via Backward Reconstruction
arXiv:2609.24921v1 Announce Type: new Abstract: Scientific weak signals are early, low-visibility research directions that later become central to mature scientific topics, yet existing resources such as trend tracking,…
- arXiv cs.AIresearchContext Poisoning as Extreme-Value Attention Interference in Long-Context Language Models
arXiv:2609.22101v1 Announce Type: cross Abstract: Large language models can process increasingly long prompts, yet their ability to locate and use decisive evidence may degrade as irrelevant or confusable context is adde…
- arXiv cs.AIresearchMultilingual Safety Signals Are Multi-Layered: Filtering Safety-Degrading Data for Safer LLMs
arXiv:2609.22144v1 Announce Type: cross Abstract: Preserving safety alignment during large language models fine-tuning is critical, however, recent studies have demonstrated that even benign fine-tuning data may contain…
- arXiv cs.AIresearchMultiple latent orderings better predict language model preferences
arXiv:2609.22170v1 Announce Type: cross Abstract: Language models are frequently employed in settings where they are asked to make value judgments and choices. These observed choices often exhibit intransitivity: A model…
- arXiv cs.AIresearchContrastive World Models
arXiv:2609.22175v1 Announce Type: cross Abstract: World models trained via pixel reconstruction can struggle in visually complex environments, where irrelevant information dominates the objective and distract the model f…
- arXiv cs.AIresearchDPTM-DT: Dual-Pretrained Transformer Multitask Representation Learning for Drug-Target Prediction
arXiv:2609.22184v1 Announce Type: cross Abstract: Drug-target relation prediction supports candidate screening, drug repositioning, and mechanism analysis. Existing models often use incomplete drug or protein representat…
- arXiv cs.AIresearchSegTSim: A Big Data Driven Segmented Temporal Simulation Framework for Heterogeneous Multivariate Systems
arXiv:2609.22192v1 Announce Type: cross Abstract: Heterogeneous multivariate time-series systems exhibit segment-specific nonlinear dynamics that challenge monolithic forecasting architectures. We propose SegTSim, a big-…
- arXiv cs.AIresearchThe Situated Identity Test: Distinguishing Persistent Cognitive Identity from Persona Imitation
arXiv:2609.22195v1 Announce Type: cross Abstract: Large language models can convincingly adopt personas, recall past dialogues, and weave rich autobiographies. Yet this conversational eloquence conceals a fundamental att…
- arXiv cs.AIresearchDissecting Hierarchical Reasoning Models: A Mechanistic Study
arXiv:2609.22197v1 Announce Type: cross Abstract: We study Hierarchical Reasoning Model (HRM), a representative hierarchical Transformer-based latent reasoning model with many variants, on Sudoku, Maze, and ARC-AGI-2. We…
- arXiv cs.AIresearchThe Role of AI in Online Reviews
arXiv:2609.22198v1 Announce Type: cross Abstract: The rapid adoption of large language models (LLMs) creates new opportunities for strategic content generation on online platforms, including potentially harmful forms of…
- arXiv cs.AIresearchImproving Parameter Utilization by Sharing Neural Experts Across Layers in Transformers
arXiv:2609.22199v1 Announce Type: cross Abstract: Transformer-based large language models often suffer from inter-layer parameter redundancy, where functional transformations are redundantly learned across network depths…
- arXiv cs.AIresearchEvaluating Personal Information Output from Conversational Interactions in Generative AI Systems
arXiv:2609.22204v1 Announce Type: cross Abstract: This exploratory pilot study evaluates the scope and perceived accuracy of personal information output from ongoing conversational interactions in generative AI systems u…
- arXiv cs.AIresearchAn Implant-to-Wearable IR-UWB Transmitter-Receiver Architecture and Layered Protocol for High-Density Brain-Computer Interfaces
arXiv:2609.22207v1 Announce Type: cross Abstract: High-rate neural telemetry requires an implant-towearable link whose circuit partition, waveform mapping, packet processing, and failure semantics are mutually consistent…
- arXiv cs.AIresearchReplicating the Geometry of Emotion Representations in a Base Open-Weights Model
arXiv:2609.22208v1 Announce Type: cross Abstract: Sofroniew et al. (2026) report that emotion concepts in Claude Sonnet 4.5 are represented as vectors whose geometry mirrors human affect psychology. We replicate the repr…
- arXiv cs.AIresearchKnowing, and Saying It Only When Asked: LLM Endognostics and the Schizognosis of Minerva-7B
arXiv:2609.22219v1 Announce Type: cross Abstract: Evaluating an aligned language model by reading its answers assumes the answers carry the distinction the evaluator cares about. We introduce LLM endognostics, a white-bo…
- arXiv cs.AIresearchTeam DArgk at the 2026 ELOQUENT lab for evaluating generative language model quality: Residuals of Humanity: AI Detection Evasion via GRPO Fine-Tuning
arXiv:2609.22221v1 Announce Type: cross Abstract: Large language models (LLMs) can generate fluent and coherent text that is increasingly difficult to distinguish from human writing, motivating the development of automat…
- arXiv cs.AIresearchNot All Ranks Are Equal: Budget-Aware LoRA Merging Across Tasks
arXiv:2609.22237v1 Announce Type: cross Abstract: Merging low-rank adapters (LoRAs) promises to eliminate the overhead of swapping task-specific weights at inference time. However, existing merging methods assume every l…
- arXiv cs.AIresearchKnowledge Graph-Augmented Ambient AI for Clinical Note Generation
arXiv:2609.22239v1 Announce Type: cross Abstract: Ambient AI is increasingly adopted in healthcare to automatically generate clinical notes from patient-clinician conversations, with the potential to substantially reduce…
- arXiv cs.AIresearchReplay-Gated Neural Execution: Decoupling Persistent Behavioral Specifications from Neural Realizations in Frozen Language Models
arXiv:2609.22243v1 Announce Type: cross Abstract: Input-conditioned neural interventions raise a runtime question: what persists when one behavioral specification admits multiple actions whose validity depends on executi…
- arXiv cs.AIresearchDo Chess Explanations Reflect Model Decisions? Behavioral and Token-Level Tests of LLM Reasoning Faithfulness
arXiv:2609.22245v1 Announce Type: cross Abstract: Large language models can produce fluent explanations for chess moves, but plausible language does not necessarily reflect the reasoning behind a decision. We study this…
- arXiv cs.AIresearchThe Corroboration Illusion: When More News Makes LLM Forecasts Less True
arXiv:2609.22246v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to forecast real-world events by retrieving and reasoning over news. We show that this dependence on an open, crawlable…
- arXiv cs.AIresearchCheckpoints Are Not Enough: Trust Calibration in CoSLR, a Human-AI System for Systematic Literature Reviews
arXiv:2609.22248v1 Announce Type: cross Abstract: Systematic Literature Reviews (SLRs) are essential for evidence-based research but remain time-consuming, requiring researchers to manage large volumes of publications ac…
- arXiv cs.AIresearchCALM: A Calibrated LLM Choice Network Framework for Activity-Based Traveler Simulation
arXiv:2609.22252v1 Announce Type: cross Abstract: We present CALM, a reproducible hybrid framework that integrates an optional large language model (LLM) activity planner with calibrated stochastic choice, shared network…
- arXiv cs.AIresearchCAMFT: Conflict-Aware Mergeable Fine-Tuning for Large Language Models
arXiv:2609.22253v1 Announce Type: cross Abstract: Model merging has emerged as a promising paradigm for integrating multiple task-specific capabilities into a single large language model. However, existing methods predom…
- arXiv cs.AIresearchTeacher Should Think Ahead: Adaptive Continuations for Reliable On-Policy Distillation
arXiv:2609.22254v1 Announce Type: cross Abstract: On-policy distillation (OPD) is a promising approach for transferring knowledge between language models, where a student receives dense token-level supervision along its…
- arXiv cs.AIresearchDIPLOMAT: Dialogue-Span-Aware Direct Preference Optimization for Polite Persuasive Workplace Negotiation Dialogues
arXiv:2609.22256v1 Announce Type: cross Abstract: Effective workplace negotiation requires balancing multiple objectives, including achieving task goals, preserving professional relationships, and resolving conflicts con…
- arXiv cs.AIresearchStrategy Accumulation and Guided Execution for Automated LLM Fine-Tuning
arXiv:2609.22257v1 Announce Type: cross Abstract: Producing task-specific large language models requires discovering effective training strategies through experimentation. Automated fine-tuning systems have made this exp…
- arXiv cs.AIresearchUsed, Mentioned, or Condemned? A Controlled Contrast-Set Diagnostic for the Use-Mention Distinction in Code-Mixed Hinglish Misogyny Detection
arXiv:2609.22261v1 Announce Type: cross Abstract: Lexicon-driven misogyny detectors cannot, by construction, distinguish a slur used against a woman from the same slur mentioned in counter-speech ("don't call her that")…
- arXiv cs.AIresearchLarge language models in medical time series analysis
arXiv:2609.22262v1 Announce Type: cross Abstract: Medical time series (MedTS), including electrocardiograms (ECG), electroencephalograms (EEG), photoplethysmography (PPG), and vital-sign recordings, are central to clinic…
- arXiv cs.AIresearchHi-Singers: A Comprehensive High-Quality Dataset for Expressive Audio-Driven Singing Head Synthesis
arXiv:2609.22264v1 Announce Type: cross Abstract: State-of-the-art models for audio-driven digital human generation have achieved photo-realistic results in talking-head synthesis. However, extending these models to sing…
- arXiv cs.AIresearchEnabling Vision and Cross-Modal Learning for Multimodal Stroke Recurrence Prediction: An Interpretable Two-Step Framework
arXiv:2609.22271v1 Announce Type: cross Abstract: Multimodal stroke recurrence prediction requires effective integration of heterogeneous clinical and imaging data, yet modality imbalance often causes models to over-rely…
- arXiv cs.AIresearchPerformance vs Consistency: Evaluating a Foundation Model in Lung-RADS Screening
arXiv:2609.22281v1 Announce Type: cross Abstract: Foundation models have recently demonstrated strong capabilities across a wide range of medical imaging tasks. However, their performance in structured clinical interpret…
- arXiv cs.AIresearchBrain-to-Image Generation: Reconstructing Visual Stimuli from EEG using Generative Adversarial Networks
arXiv:2609.22282v1 Announce Type: cross Abstract: Reconstructing visual stimuli from electroencephalography (EEG) is difficult because scalp measurements have high temporal but limited spatial resolution, and paired EEG-…
- arXiv cs.AIresearchRethinking Streaming Video Diffusion Model: Context, Execution, and Training
arXiv:2609.22283v1 Announce Type: cross Abstract: Understanding the design space of streaming video diffusion is essential to exploring its potential for generation quality and computational efficiency. We develop a unif…
- arXiv cs.AIresearchComplementary rPPG-Derived and Lip-Region Frequency Cues for Talking-Face Deepfake Detection
arXiv:2609.22284v1 Announce Type: cross Abstract: Talking-face (TF) deepfakes are detected unevenly by rPPG-based methods across generators. We study two lightweight visual-only cues, rPPG-derived waveforms extracted by…
- arXiv cs.AIresearchORDER: A Fictitious-World Benchmark for Domain-Adaptive Embodied AI
arXiv:2609.22285v1 Announce Type: cross Abstract: Adapting language models to new domains via continual pre-training raises a basic evaluation problem: if the training corpus overlaps with what the model already knows, p…
- arXiv cs.AIresearchValidating, Not Sampling: Region-Level Robustness of Vision-Language and Vision-Language-Action Models
arXiv:2609.22293v1 Announce Type: cross Abstract: Vision-language models (VLMs) and vision-language-action models (VLAs) are increasingly deployed in real-world applications. There, a small perturbation to the recorded c…