AI news archive.
10,660 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchCross-Lingual TTS for Indian Languages: A Benchmark and Inference-Time Duration Estimation Strategies
arXiv:2610.14826v1 Announce Type: cross Abstract: Recent alignment-free Text-to-Speech (TTS) models achieve high synthesis quality by removing explicit alignment modules and simplifying training. However, they often stru…
- arXiv cs.AIresearchProbabilistic Multi-Source Data Selection via Learnable Categorical Priors: Variance Reduction and Guaranteed Derandomization
arXiv:2610.14942v1 Announce Type: cross Abstract: Bilevel data selection is a fundamental task with numerous applications in machine learning and statistics. A common strategy for tackling this problem is to relax the or…
- arXiv cs.AIresearchCounterfactual Rollouts for Offline Contrastive Goal-Conditioned Reinforcement Learning
arXiv:2610.14953v1 Announce Type: cross Abstract: Contrastive reinforcement learning (CRL) learns goal-conditioned values by associating state--action pairs with the states subsequently reached, assuming that no unobserv…
- arXiv cs.AIresearchFitting Every Hint Is Not Enough: Premature Floods in Recurrent Algorithmic Reasoners
arXiv:2610.14982v1 Announce Type: cross Abstract: Recurrent algorithmic reasoners are trained with per-step hints on small graphs and run for many more steps on large ones. On breadth-first search in SALSA-CLRS, we find…
- arXiv cs.AIresearchToo Late to Prune: Generative AI, the Screen, and Team Creative Decisions
arXiv:2610.14984v1 Announce Type: cross Abstract: Generative AI makes options cheap and shifts the bottleneck of creative work from generating options to screening them. We argue that AI's value depends on whether its op…
- arXiv cs.AIresearchMixing Regularization for Average-Reward $Q$-Learning and $Q$-Planning
arXiv:2610.14988v1 Announce Type: cross Abstract: The average-reward setting for reinforcement learning is challenging because its Bellman operator is known to contract in span only when the underlying Markov chain is mi…
- arXiv cs.AIresearchRecurrent In-Context Learning for Tabular Foundation Models
arXiv:2610.14998v1 Announce Type: cross Abstract: Tabular foundation models adapt to new datasets through in-context learning (ICL), and, like most transformers, they tie computational depth to the number of independentl…
- arXiv cs.AIresearchUltraBERT: Beyond the Limit of BERT Pre-Training
arXiv:2610.15028v1 Announce Type: cross Abstract: Recent bidirectional encoders have been modernized with innovations from causal large language models (LLMs). However, the pretraining recipe itself remains inherited fro…
- arXiv cs.AIresearchIs a 9B Decision Foundation Model Needed for Symptom-to-Disease Classification? Fine-Tuning Clef-Flash Against TF-IDF Baselines
arXiv:2610.15029v2 Announce Type: replace-cross Abstract: Disease classification from patient-reported symptom descriptions is usually approached with lightweight discriminative models or with autoregressive large langua…
- arXiv cs.AIresearchInCLAD: A Continual Learning Benchmark for Industrial Visual Anomaly Detection
arXiv:2610.15032v1 Announce Type: cross Abstract: Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in which inspection systems…
- arXiv cs.AIresearchSelect or Rewrite: Oracle-Guided Fine-Tuning for Generative ASR Post-Editing from N-Best Hypotheses
arXiv:2610.15056v1 Announce Type: cross Abstract: Large language models can post-edit ASR by using linguistic context to compare N-best hypotheses and rewrite the transcript when needed, often improving WER without retra…
- arXiv cs.AIresearchDeep Learning-based Semantic Segmentation of Dental Radiographs: Comparative Evaluation of PSPNet, DeepLabv3, UNet, and UNet++
arXiv:2610.15061v3 Announce Type: replace-cross Abstract: Accurate and efficient analysis of dental radiographs has become increasingly important for disease diagnosis and clinical decision making. In particular, dental…
- arXiv cs.AIresearchGAMW: Generation-Aware Mel-Domain Watermarking for TTS Traceability
arXiv:2610.15082v1 Announce Type: cross Abstract: As text-to-speech (TTS) systems continue to advance, provenance tracking and security verification of synthesized speech have become increasingly important. Audio waterma…
- arXiv cs.AIresearchHave You Been Convinced? A Neuro-symbolic Argumentation Reasoning Approach to Detecting Persuasiveness in ChangeMyView Discussions
arXiv:2610.15091v1 Announce Type: cross Abstract: Argument structures represent how individual arguments interact within a discourse. However, leveraging these structures to infer meaningful knowledge remains challenging…
- arXiv cs.AIresearchVLCQ: Post-training quantization for deep neural networks using variable length coding
arXiv:2610.15092v1 Announce Type: cross Abstract: Quantization plays a crucial role in efficiently deploying deep learning models on resource-constrained devices. Post-training quantization does not require access to the…
- arXiv cs.AIresearchscHFBiMamba: a bidirectional Mamba autoencoder for cardiac disease-state classification from single-nucleus RNA-seq data
arXiv:2610.15117v1 Announce Type: cross Abstract: To evaluate whether a compact bidirectional state-space autoencoder improves joint classification of species, cell type, and disease state from single-nucleus RNA-sequenc…
- arXiv cs.AIresearchShellElastic: Scalable Elastic Pretraining with Simultaneous Maximal Updates and Hyperparameter Transfer
arXiv:2610.15124v1 Announce Type: cross Abstract: Foundation models need to support diverse inference budgets, yet training a separate model for each budget duplicates pretraining effort. Elastic models train several nes…
- arXiv cs.AIresearchTo Defer or Not to Defer? Online Learning with LLMs and Congested Experts
arXiv:2610.15128v1 Announce Type: cross Abstract: Modern AI systems increasingly combine large language models (LLMs) with human experts, using the LLM to handle routine queries while deferring uncertain cases for expert…
- arXiv cs.AIresearchUDHRBench: Benchmarking Human Rights in Large Language Models
arXiv:2610.15179v1 Announce Type: cross Abstract: Large language models are increasingly deployed in contexts requiring adherence to ethical and legal norms, yet their alignment with internationally recognized human righ…
- arXiv cs.AIresearchExploring Jailbreak Vulnerability Across Depth-Based Dynamic Inference in Large Language Models
arXiv:2610.15207v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been shown to be computationally expensive and vulnerable to jailbreak attacks, in which adversarial prompts bypass safety align…
- arXiv cs.AIresearchFramework for an AI-Ready ARPES Community Database
arXiv:2610.15236v1 Announce Type: cross Abstract: Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI). However, its current means of data organization impedes this endeavor…
- arXiv cs.AIresearchLeHami: Symplectic Latent Dynamics for World Models
arXiv:2610.15267v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) learn world models directly in latent space, but unconstrained autoregressive predictors may contract the latent distribu…
- arXiv cs.AIresearchGeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision
arXiv:2610.15280v2 Announce Type: replace-cross Abstract: Depth estimation and Gaussian reconstruction share a need for multi-scale feature processing, yet use different prediction interfaces and training objectives. Whe…
- arXiv cs.AIresearchNorth or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge
arXiv:2610.15282v1 Announce Type: cross Abstract: Large Language Models (LLMs) have made substantial progress in multilingual question answering, yet their ability to understand low-resource sociolinguistic contexts rema…
- arXiv cs.AIresearchProtein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images
arXiv:2610.15286v1 Announce Type: cross Abstract: The accuracy of detecting protein crystals for fluorescence microscopy images is very critical for high throughput and automated systems. Although the trace fluorescent l…
- arXiv cs.AIresearchCo-DSL: Human-LLM Co-design of Reusable Spatial Concepts for Layout Generation
arXiv:2610.15290v1 Announce Type: cross Abstract: We present Co-DSL, a framework in which users and LLMs co-design reusable spatial concepts for layout generation. In the first stage, our system allows users to create a…
- arXiv cs.AIresearchHGA-Quant: Heterogeneity-Aware Quantization for AutoRegressive Visual Generation Models
arXiv:2610.15297v1 Announce Type: cross Abstract: Recently, AutoRegressive Visual Generation (ARVG) models have emerged as a competitive paradigm for high-quality image synthesis. However, their substantial computational…
- arXiv cs.AIresearchWisdomNet: trustable machine learning toward error-free classification
arXiv:2610.15299v1 Announce Type: cross Abstract: Misclassification is a critical problem in many machine learning applications. Since even the classifier models with high accuracy (e.g., > 95%) still introduce some misc…
- arXiv cs.AIresearchAccuracy and Uncertainty in Machine Deception Detection (Research White Paper)
arXiv:2610.15314v1 Announce Type: cross Abstract: A deception detector is only as useful as the trust its alarms deserve. Like a polygraph, detectors that read a language model's internal activations can catch deceptive…
- arXiv cs.AIresearchAnchor-and-Harvest Curriculum: A Fully Unmasked Non-LLM Curriculum for Craftax
arXiv:2610.15328v1 Announce Type: cross Abstract: We introduce Anchor-and-Harvest Curriculum (AHC), a domain-informed, rule-based training system for Craftax-Full. AHC combines achievement-oriented tasks, harvested initi…
- arXiv cs.AIresearchZOIF: Zeroth-Order Influence Function Estimation for LLM Data Selection Using Only Forward Passes
arXiv:2610.15336v1 Announce Type: cross Abstract: Influence functions enable principled data selection for large language models (e.g., identifying influential training samples for model editing, debugging, or efficient…
- arXiv cs.AIresearchStimFlow: Synthesizing Brain Activity from Images via Stimulus-conditioned Flow Matching
arXiv:2610.15341v1 Announce Type: cross Abstract: The inherent variability of fMRI responses across trials encourages a generative view of the stimulus-to-response mapping. Prior generative encoders often operate in comp…
- arXiv cs.AIresearchAcross Viewpoints and Time: Trajectory-Generalizable Physical Attacks on World Models and Video Understanding Models
arXiv:2610.15343v1 Announce Type: cross Abstract: World models and video understanding models are increasingly deployed in dynamic visual environments, yet their robustness to physical adversarial threats remains largely…
- arXiv cs.AIresearchCodeWalk: Generating Coding Benchmarks by Walking a Problem Graph
arXiv:2610.15345v1 Announce Type: cross Abstract: High-quality coding problems are expensive to collect. Benchmarks such as SWE-bench rely on substantial human curation, while synthetic alternatives typically rely on cos…
- arXiv cs.AIresearchGugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations
arXiv:2610.15350v1 Announce Type: cross Abstract: We present Gugak-VocalSet, the first publicly available audio dataset for Korean traditional singing with dense annotations of sigimsae, a structured pitch contour system…
- arXiv cs.AIresearchSiam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals
arXiv:2610.15351v1 Announce Type: cross Abstract: Amyotrophic lateral sclerosis (ALS) diagnosis remains challenging due to heterogeneous symptoms and reliance on clinical assessment, underscoring the need for objective,…
- arXiv cs.AIresearchA Sovereign, Indic-First, Reasoning Model
arXiv:2610.15371v1 Announce Type: cross Abstract: Gnani-Evon-v3.3-30B-A3B is a long-context hybrid Mamba-Transformer mixture-of-experts model for reasoning, instruction following, and tool use in English and eight Indian…
- arXiv cs.AIresearchIn-Place Indic Tokenizer Expansion and Continual Pretraining of Nemotron-3-Nano-30B-A3B
arXiv:2610.15373v1 Announce Type: cross Abstract: We adapted Nemotron-3-Nano-30B-A3B for eight Indian languages without changing its backbone architecture. We expanded the vocabulary in place from 131,072 to 163,072 entr…
- arXiv cs.AIresearchE8 Lattice Key Quantization for Low-Bit KV-Cache Compression
arXiv:2610.15384v1 Announce Type: cross Abstract: Compressing the key-value (KV) cache is essential for serving long-context large language models (LLMs) under memory constraints. While current scalar quantization method…
- arXiv cs.AIresearchPessimistic Latent Task-aware Optimization for Robust Offline Meta-Reinforcement Learning
arXiv:2610.15428v2 Announce Type: replace-cross Abstract: Context-based offline meta-reinforcement learning (COMRL) has advanced almost entirely through better task representations, which separate task identity from arti…
- arXiv cs.AIresearchWorld Modeling through Spectral Alignment
arXiv:2610.15429v2 Announce Type: replace-cross Abstract: How do we tell a world model what information to preserve? We study this question through the lens of spectral representation learning. We introduce SpecWM, a JEP…
- arXiv cs.AIresearchBridgeCodec: Mamba Enhanced Neural Audio Codec with Schr\"odinger Bridge at Low Bitrate
arXiv:2610.15453v1 Announce Type: cross Abstract: Neural audio codecs achieve remarkable performance but operate as closed ecosystems with rigidly co-trained encoderdecoder pairs, severely hindering interoperability. To…
- arXiv cs.AIresearchSecurity in LLM-Assisted Coding: A Systematic Literature Review
arXiv:2610.15482v1 Announce Type: cross Abstract: Large Language Models accelerate software development but introduce severe security vulnerabilities. This systematic review investigates security risks in automated codin…
- arXiv cs.AIresearchThe Imperishable Record, Forgiveness, and the Future of Criminal Justice
arXiv:2610.15499v1 Announce Type: cross Abstract: Digital technologies can preserve records of wrongdoing indefinitely, and AI can give those records continuing influence over a person's future. We describe this conditio…
- arXiv cs.AIresearchCacheLink: Functional KV Transport for Zero-Prefill Cross-Model Context Handoff
arXiv:2610.15509v1 Announce Type: cross Abstract: KV caches make long-context serving efficient by reusing past computation, but model switching breaks this reuse and forces the receiver to prefill the context again. We…
- arXiv cs.AIresearchREPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming
arXiv:2610.15513v1 Announce Type: cross Abstract: Safety-aligned large language models (LLMs) remain vulnerable to adversarial prompts that bypass their safety training. Existing automated red-teaming methods either requ…
- arXiv cs.AIresearchSARATHI: Decoupled Data-Free Probing and Adaptive Reconstruction for Structured LLM Pruning
arXiv:2610.15514v1 Announce Type: cross Abstract: Structured pruning reduces the inference cost of large language models (LLMs) by permanently removing neurons and weight rows, yielding dense, hardware-agnostic speedup.…
- arXiv cs.AIresearchOceanFlow: Coherent Flow Matching for Probabilistic Ocean Modeling
arXiv:2610.15518v1 Announce Type: cross Abstract: Probabilistic ocean modeling is essential for risk-aware marine operations, climate analysis, and scientific decision-making, where downstream decisions depend on the con…
- arXiv cs.AIresearchELECT: Settling Identity Before Rendering for Scalable Story Visualization
arXiv:2610.15555v1 Announce Type: cross Abstract: Training-free story visualization without reference images must preserve recurring entities across frames while scaling to long narratives. Existing methods typically mai…
- arXiv cs.AIresearchRethinking Human Intent-to-CAD: Parametric CAD Model Generation via Cooperative Multi-Task Alignment and Spatial-Aware Reinforcement Learning
arXiv:2610.15681v1 Announce Type: cross Abstract: Parametric Computer-Aided-Design (CAD) modeling from human intent remains challenging, particularly during the conceptual design stage, where design goals are expressed t…