AI news archive.
10,660 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchContext Value Informed In-Context Reinforcement Learning
arXiv:2509.23209v2 Announce Type: replace-cross Abstract: In-context reinforcement learning (ICRL) has emerged as an effective paradigm for test time adaptation to unseen tasks without parameter updates. However, existin…
- arXiv cs.AIresearchEconCausal: A Context-Aware Economic Reasoning Benchmark for Large Language Models
arXiv:2510.07231v5 Announce Type: replace-cross Abstract: Socio-economic causal effects depend heavily on their institutional and environmental contexts. The same intervention can produce different, even opposite, effect…
- arXiv cs.AIresearchLLMs Can Get "Brain Rot": A Pilot Study on Twitter/X
arXiv:2510.13928v3 Announce Type: replace-cross Abstract: We propose and test the LLM Brain Rot Hypothesis: continual exposure to junk web text induces lasting cognitive decline in large language models (LLMs). To unveil…
- arXiv cs.AIresearchChain of Time: In-Context Physical Simulation with Image Generation Models
arXiv:2511.00110v2 Announce Type: replace-cross Abstract: We propose a novel cognitively-inspired prompting method to stabilize and interpret the physical simulation produced by autoregressive image generation models, ca…
- arXiv cs.AIresearchWhen is a System Discoverable from Data? Discovery Requires Chaos
arXiv:2511.08860v3 Announce Type: replace-cross Abstract: The deep learning revolution has enabled major AI advances in physical sciences, particularly in discovering dynamical systems from observational data. Yet the re…
- arXiv cs.AIresearchVEDA: Generation of 3D Molecules via Variance-Exploding Diffusion with Annealing
arXiv:2511.09568v3 Announce Type: replace-cross Abstract: Diffusion models show promise for 3D molecular generation, but face a fundamental trade-off between sampling efficiency and conformational accuracy. While flow-ba…
- arXiv cs.AIresearchWhat If They Took the Shot? A Hierarchical Bayesian Framework for Counterfactual Expected Goals
arXiv:2511.23072v2 Announce Type: replace-cross Abstract: How would the probability of scoring change if a different player took the same shot? Although expected goals models describe the difficulty of scoring opportunit…
- arXiv cs.AIresearchThe Instability of Safety: How Random Seeds and Temperature Expose Inconsistent LLM Refusal Behavior
arXiv:2512.12066v4 Announce Type: replace-cross Abstract: Current safety evaluations of large language models rely on single-shot testing, implicitly assuming that model responses are deterministic and representative of…
- arXiv cs.AIresearchSchr\"odinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation
arXiv:2512.21201v5 Announce Type: replace-cross Abstract: Zero-shot object navigation (ZSON) requires robots to find target objects in unseen environments without task-specific fine-tuning or pre-built maps, a key capabi…
- arXiv cs.AIresearchImaging-anchored multiomics in cardiovascular disease: integrating cardiac imaging, bulk, single-cell, and spatial transcriptomics
arXiv:2601.07871v3 Announce Type: replace-cross Abstract: Cardiovascular disease arises from interactions between inherited risk, molecular programmes, and tissue-scale remodelling that are observed clinically through im…
- arXiv cs.AIresearchDeep Time-series Forecasting Needs Kernelized Moment Balancing
arXiv:2602.00717v2 Announce Type: replace-cross Abstract: Deep time-series forecasting can be formulated as a distribution balancing problem aimed at aligning the distribution of the forecasts and ground truths. Accordin…
- arXiv cs.AIresearchParameter-Efficient Temporal Domain Generalization
arXiv:2602.11965v2 Announce Type: replace-cross Abstract: Temporal domain generalization (TDG) learns from a sequence of observed domains to predict models for unseen future domains. Existing parameter-dynamics methods b…
- arXiv cs.AIresearchLLMON: An LLM-native Markup Language to Leverage Structure and Semantics at the LLM Interface
arXiv:2603.22519v4 Announce Type: replace-cross Abstract: Textual Large Language Models (LLMs) provide a simple and familiar interface: a string of text is used for both input and output. However, the information conveye…
- arXiv cs.AIresearchPLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguation
arXiv:2603.23678v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) offer transformative solutions across many domains, but healthcare integration is hindered by strict data privacy constraints. Clinic…
- arXiv cs.AIresearchA Wireless World Model for AI-Native 6G Networks
arXiv:2603.25216v2 Announce Type: replace-cross Abstract: Integrating AI into the physical layer is a cornerstone of 6G networks. However, current data-driven approaches struggle to generalize across dynamic environments…
- arXiv cs.AIresearchCan We Change "Stroke Size" to Simplify Regression Learning?
arXiv:2603.26783v5 Announce Type: replace-cross Abstract: Deep-learning-based image generation is increasingly useful but expensive. Across the different timesteps in the denoising chain, high-noise timesteps present dis…
- arXiv cs.AIresearchMapping data literacy trajectories in K-12 education
arXiv:2603.28317v2 Announce Type: replace-cross Abstract: Data literacy skills are fundamental in computer science education. However, understanding how data-driven systems work represents a paradigm shift from tradition…
- arXiv cs.AIresearchExplorable Proofs: Making Written Proofs Explorable by Grounding Them in Formal Representations
arXiv:2604.02598v3 Announce Type: replace-cross Abstract: LLM-generated explanations can make technical content more accessible, but there is a ceiling on what they can support interactively. Because LLM outputs are stat…
- arXiv cs.AIresearchAdaptFuse: Training-Free Sequential Preference Inference via Externalized Bayesian Computation
arXiv:2604.03925v2 Announce Type: replace-cross Abstract: Large language models (LLMs) struggle to accumulate evidence across multiple rounds of user interactions, failing to update their beliefs in a manner consistent w…
- arXiv cs.AIresearchSnapFlow: One-Step Action Generation for Flow-Matching VLAs via Progressive Self-Distillation
arXiv:2604.05656v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models with flow-matching action heads, such as {\pi}0 and {\pi}0.5, have become a standard recipe for generalist robot manipulation,…
- arXiv cs.AIresearchDocument-as-Image Representations Fall Short for Scientific Retrieval
arXiv:2604.18508v2 Announce Type: replace-cross Abstract: Many recent document embedding models are trained on document-as-image representations, embedding rendered pages as images rather than the underlying source. Mean…
- arXiv cs.AIresearchRemix the Timbre: Diffusion-Based Style Transfer Across Polyphonic Stems
arXiv:2605.09259v2 Announce Type: replace-cross Abstract: Timbre transfer aims to modify the timbral identity of a musical recording while preserving its original melody and rhythm. While single-instrument timbre transfe…
- arXiv cs.AIresearchLEAP: Unlocking dLLM Parallelism via Lookahead Early-Convergence Token Detection
arXiv:2605.10980v2 Announce Type: replace-cross Abstract: Diffusion Language Models (dLLMs) have garnered significant attention for their potential in highly parallel processing. The parallel capabilities of existing dLL…
- arXiv cs.AIresearchFew-Shot Truly Benign DPO Attack for Jailbreaking LLMs
arXiv:2605.10998v2 Announce Type: replace-cross Abstract: Fine-tuning APIs make frontier LLMs easy to customize, but they can also weaken safety alignment during fine-tuning. While prior work shows that benign supervised…
- arXiv cs.AIresearchLearning, Fast and Slow: Towards LLMs That Adapt Continually
arXiv:2605.12484v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are trained for downstream tasks by updating their parameters (e.g., via RL). However, updating model parameters forces them to absor…
- arXiv cs.AIresearchGQLA: Group-Query Latent Attention for Hardware-Adaptive Decoding
arXiv:2605.15250v4 Announce Type: replace-cross Abstract: Decoding efficiency depends on KV-cache traffic, computation, and parallelism. Multi-head Latent Attention (MLA) offers absorbed and head-expanded forms, but its…
- arXiv cs.AIresearchWhen and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
arXiv:2605.15959v2 Announce Type: replace-cross Abstract: Physics-informed neural networks (PINNs) are powerful surrogates for differential equations but are notoriously difficult to train due to spectral bias, stiffness…
- arXiv cs.AIresearchSymmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers
arXiv:2605.18106v5 Announce Type: replace-cross Abstract: A striking geometric disparity has long persisted in the practice of deep learning. While modern neural network architectures naturally exhibit rich symmetry and…
- arXiv cs.AIresearchHARP: Efficient Data Selection for Finetuning Large Language Models
arXiv:2606.07690v2 Announce Type: replace-cross Abstract: Finetuning data selection requires balancing two competing goals: selecting examples that improve the downstream objective, and doing so without repeatedly finetu…
- arXiv cs.AIresearchActivation Steering Induces Emergent Misalignment: Steering Vector Construction and Comprehensive Evaluation
arXiv:2606.08682v2 Announce Type: replace-cross Abstract: Activation steering (AS) has emerged as a popular inference-time technique for modulating the behavior of large language models (LLMs). AS modulates model behavio…
- arXiv cs.AIresearchPoLAR: Factorizing Extent and Mode in Latent Actions for Robot Policy Learning
arXiv:2606.21139v2 Announce Type: replace-cross Abstract: Robot actions vary in both extent and mode: how much change they produce and what kind of change they produce. A latent action space that reflects this structure…
- arXiv cs.AIresearchG$^3$VLA: Geometric Inductive Bias for Vision-Language-Action Models
arXiv:2606.24472v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models have rapidly advanced generalist robot manipulation by harnessing the semantic knowledge of pretrained vision-language backbon…
- arXiv cs.AIresearchSelf-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discovery
arXiv:2606.29403v3 Announce Type: replace-cross Abstract: Conformal prediction guarantees marginal coverage, but a pooled calibration quantile can hide systematic undercoverage across heterogeneous regions of the feature…
- arXiv cs.AIresearchWhat Does a Discrete Diffusion Model Learn? One Reverse Process in Three Coordinates
arXiv:2607.05381v2 Announce Type: replace-cross Abstract: What does a discrete diffusion model actually learn? The literature trains denoisers, bridge plug-ins, and concrete scores, each deriving its own ELBO objective u…
- arXiv cs.AIresearchRhyMix: A Lightweight Adaptive Multi-Rhythm Network for Long-Term Time Series Forecasting
arXiv:2607.08234v2 Announce Type: replace-cross Abstract: Real-world time series exhibit complex dynamics characterized by multiple simultaneous temporal patterns: short-term fluctuations, periodic seasonal cycles, long-…
- arXiv cs.AIresearchCorrelation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing
arXiv:2607.09015v2 Announce Type: replace-cross Abstract: We study contextual bandit problems with correlated arms and access to surrogate reward signals produced by a machine learning model, motivated by applications su…
- arXiv cs.AIresearchDoes Depth Survive Composition, and Can a Router Use It? Adaptive Compute in Latent World Models
arXiv:2607.10203v4 Announce Type: replace-cross Abstract: Adaptive-compute world models run fewer predictor blocks on some rollout steps than on others. This rests on two assumptions: that a deeper predictor gives a more…
- arXiv cs.AIresearchFormalizing Flag Algebras in Lean
arXiv:2607.23500v2 Announce Type: replace-cross Abstract: Razborov's flag algebra method is one of the most successful tools for proving asymptotic density inequalities in extremal graph theory. It reduces an inequality…
- arXiv cs.AIresearchEvery Time I Hire a Linguist, Inference Costs Go Down: On Linguistic Rules as Effective Prompt Compressors
arXiv:2607.25335v3 Announce Type: replace-cross Abstract: Prompt compression shortens LLM input to reduce inference cost, yet existing methods score token importance through LM forward passes. It remains questionable whe…
- arXiv cs.AIresearchCommit Locally, Exit Globally: Coordinating Adaptive Sampling and Early Exit in Diffusion Language Models
arXiv:2607.28166v3 Announce Type: replace-cross Abstract: Diffusion language models predict tokens in parallel but still require many iterative forward passes. Adaptive sampling accelerates local token commitment, while…
- arXiv cs.AIresearchIACM-RL: Intent-Aware Context Management and Reinforcement Learning for Complex Tool Invocation under Dynamic Intent Fluctuations
arXiv:2608.02110v2 Announce Type: replace-cross Abstract: Executing long-horizon tool invocations in real-world environments is severely challenged by dynamic user intent noise. Existing methods attempt robustness via im…
- arXiv cs.AIresearchFACT: Failure-Aware Causal Training for World-Action Models
arXiv:2608.10232v2 Announce Type: replace-cross Abstract: Recent world-action models (WAMs) show that co-training policies with future prediction can provide physical priors for action generation. Building on the future-…
- arXiv cs.AIresearchRubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-Based RL
arXiv:2608.11669v4 Announce Type: replace-cross Abstract: Rubric-based reinforcement learning, in which an LLM judge scores responses against prompt-specific criteria, has become a standard way to post-train language mod…
- arXiv cs.AIresearchUncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision
arXiv:2608.12027v2 Announce Type: replace-cross Abstract: Pairwise constrained clustering typically relies on hard must-link/cannot-link labels, whereas realistic pairwise supervision may be real-valued and entangle intr…
- arXiv cs.AIresearchEquilibrium Forcing: Adaptive Video Generation Without Noise Conditioning
arXiv:2608.14706v2 Announce Type: replace-cross Abstract: Standard autoregressive video generation algorithms based on Diffusion and Flow Matching rely on rigid training objectives and static sampling schedules, limiting…
- arXiv cs.AIresearchMechanistic Circuit Identification for Controllable Data Generation
arXiv:2608.24065v2 Announce Type: replace-cross Abstract: While recent advances in data synthesis aim to curate high-quality datasets, most generation pipelines still rely on heuristic prompt-based control. This black-bo…
- arXiv cs.AIresearchDCGC: Draft-Conditioned Global Correction for Complex Reasoning with Masked Diffusion Models
arXiv:2608.25428v2 Announce Type: replace-cross Abstract: Correcting flawed reasoning traces remains a significant challenge for Large Language Models (LLMs), whose autoregressive generation can propagate early mistakes…
- arXiv cs.AIresearchAdaVDR: Adaptive Tool Use and Reflection for Video Deep Research
arXiv:2608.25559v2 Announce Type: replace-cross Abstract: Video deep research aims to answer questions by jointly understanding video content and retrieving external knowledge from the open Web. However, these tasks ofte…
- arXiv cs.AIresearchTEAR: Table Extraction with Attribute Recommendation from Texts via Large Language Models
arXiv:2609.15205v2 Announce Type: replace-cross Abstract: Table extraction from texts is an important task for information systems, and recent approaches that prompt large language models (LLMs) with instructions have dr…
- Apple Machine Learning ResearchresearchSelection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents
Multimodal agents commonly generate freeform reasoning before each action. For small models, limited model capacity can result in lengthy reasoning that provides little useful guidance for action generation while incurri…