3,694 items tracked: 32 reported by two or more outlets, the rest by a single source. Most-corroborated first.
Summary : Science has a long list of documented failure modes, such as p-hacking, the File Drawer Problem, shallow peer review, and ignored retractions.
The Association for Human Mathematics is calling for an OpenAI boycott after the company released more than 700 AI-generated math manuscripts at once.
OpenAI's flood of proofs deviated from the guidelines set by a group of mathematical researchers consulted by the frontier lab.
While learning dexterous manipulation from a single human video offers a promising alternative to costly robot demonstrations, many recent methods predominantly imitate demonstrated motions.
USA Today Co., along with the several local newspapers it owns, is suing OpenAI over claims that the company copied "hundreds of thousands" of articles to train its AI models, as reported earlier by Reuters.
Ethereum researcher Justin Drake is urging the crypto industry to prepare a "bunker mode" for a scenario where AI-powered math could break wallet signature schemes within months.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
On-policy distillation (OPD) has become increasingly popular for transferring teacher capabilities to student models.
Reconstructing complete, scene-aligned 3D objects from casual images requires integrating sparse, uncertain observations and inferring surfaces hidden by occlusions.
arXiv:2610.08923v1 Announce Type: new Abstract: Enterprise generative AI applications require robust safety mechanisms that can accommodate diverse risk postures, evolving policies, and varying latency constraints.
arXiv:2610.08967v1 Announce Type: new Abstract: Simulating individual behavior requires large language models (LLMs) to preserve persona traits while adapting to dynamic social contexts.
arXiv:2610.09007v1 Announce Type: new Abstract: Detection engineers must translate threat reports, forensic observations, and hunt hypotheses into precise, testable rules.
arXiv:2610.09016v1 Announce Type: new Abstract: Vision-language-action (VLA) models map visual observations and language instructions to continuous robot actions.
arXiv:2610.09053v1 Announce Type: new Abstract: The search for a common view of justice and fairness has challenged human collective activity, as our diverging judgments are unavoidably shaped by the self-interests of so…
arXiv:2610.09139v1 Announce Type: new Abstract: Language models are more and more often asked for structured output: JSON that follows a schema, or a tool call with typed arguments.
arXiv:2610.09146v1 Announce Type: new Abstract: Large language models (LLMs) and vision-language models (VLMs) are usually frozen after deployment, so they do not learn from the cases they solve.
arXiv:2610.09164v1 Announce Type: new Abstract: Reliable decision-making requires more than accurate prediction: a model must preserve its beliefs, apply the relevant utilities, and recognize when the information needed…
arXiv:2610.09416v1 Announce Type: new Abstract: Flow Language Models (FLMs) have emerged as a continuous-state alternative to discrete diffusion language models, yet the role of their continuous representations in reason…
arXiv:2610.09493v1 Announce Type: new Abstract: A retrieval-augmented model can match a document without relying on it.
arXiv:2610.09600v1 Announce Type: new Abstract: Safety interpretability advances the study of Large Language Model (LLM) alignment from behavioral constraints driven by data or algorithms towards a deeper understanding o…
arXiv:2610.09644v1 Announce Type: new Abstract: Complementing the many efforts in providing semantic representations of concepts, notions, and entities in materials science, we report and illustrate a process by which we…
arXiv:2610.09876v1 Announce Type: new Abstract: Diffusion models generate realistic images but often fail on visual reasoning tasks, such as filling in a Sudoku or drawing the path through a maze.
arXiv:2610.09937v1 Announce Type: new Abstract: Artificial intelligence supports building operations in several forms, each with its own barrier.
arXiv:2610.09964v1 Announce Type: new Abstract: Large language models (LLMs) can be tuned to influence human attitudes, yet the respective contributions of successive post-training stages remain un-clear.
arXiv:2610.10405v2 Announce Type: replace Abstract: Monitoring the chain-of-thought of reasoning artificial intelligence (AI) models remains a key approach to detecting deception and other forms of misbehavior in such mo…
arXiv:2610.10506v1 Announce Type: new Abstract: Many of the questions now put to large language models have no correct answer to score against: what a policy is worth, which option a user should choose, how to weigh comp…
arXiv:2610.08810v1 Announce Type: cross Abstract: Fine-tuned geospatial foundation models (GeoFMs) pretrained on large satellite archives have been shown to improve crop classification accuracy and geographic transferabi…
arXiv:2610.08834v1 Announce Type: cross Abstract: We present JoyAI-Voice~2.0, an end-to-end anthropomorphic speech generation model built upon a fully continuous, dual-encoder architecture.
arXiv:2610.08840v1 Announce Type: cross Abstract: Large language models (LLMs) often abandon a correct answer, or endorse a user's position, once the user pushes back.
arXiv:2610.08892v1 Announce Type: cross Abstract: Multiplicative gamma noise is one of the dominant noise factors in Synthetic Aperture Radar (SAR) and medical ultrasound images.
arXiv:2610.08944v1 Announce Type: cross Abstract: Scanner variation changes how pathology foundation models represent the same tissue.
arXiv:2610.09033v1 Announce Type: cross Abstract: Standard safety evaluations of large language models assess harmful requests written in canonical plain text, while models in real-world deployment routinely receive inpu…
arXiv:2610.09055v1 Announce Type: cross Abstract: Human videos provide rich motion targets for humanoid learning, yet visually plausible references can still produce persistent failures under physics-based execution.
arXiv:2610.09064v1 Announce Type: cross Abstract: When we interact with large language models (LLMs), are we having a conversation?
arXiv:2610.09087v1 Announce Type: cross Abstract: Large language models are informing decisions with ever-higher stakes.
arXiv:2610.09092v2 Announce Type: replace-cross Abstract: State Space Models (SSMs) offer an efficient alternative to Transformers for sequence modeling, yet conditioning pre-trained SSMs for iterative generation typical…
arXiv:2610.09152v1 Announce Type: cross Abstract: Multilingual LLM benchmarks omit Slovak, a morphologically rich West Slavic language of five million speakers, or cover it only by machine translation.
arXiv:2610.09194v1 Announce Type: cross Abstract: Longitudinal models forecast how a patient's imaging state evolves, but accuracy does not show whether the patient's observed trajectory drives the prediction.
arXiv:2610.09205v1 Announce Type: cross Abstract: Modern image editing models can satisfy a text instruction while breaking the physics of the edited scene.
arXiv:2610.09209v1 Announce Type: cross Abstract: Substance Use Disorder (SUD) counseling requires patient responses that reflect underlying cognitive states such as beliefs, coping strategies, and readiness for change.
arXiv:2610.09247v1 Announce Type: cross Abstract: The difficulty of learning goal-reaching policies is often attributed to a "curse of horizon" that manifests as bias accumulation in temporal-difference backups and noisy…
arXiv:2610.09250v1 Announce Type: cross Abstract: Best-of-N (BoN) is a common inference-time alignment method that selects the highest-scoring response among N samples from a reference model.
arXiv:2610.09346v1 Announce Type: cross Abstract: Quantization-aware training (QAT) can recover much of the accuracy lost when large language models are compressed below four bits.
arXiv:2610.09355v1 Announce Type: cross Abstract: Learning EEG representations that generalize across cognitive tasks, subjects, and recording conditions remains a key challenge in electroencephalography (EEG) decoding.
arXiv:2610.09473v1 Announce Type: cross Abstract: Time-series anomaly detection (TSAD) identifies deviations from patterns learned from historical data.
arXiv:2610.09496v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong generalization in robotic manipulation by leveraging rich representations from pretrained vision-language models.
arXiv:2610.09498v1 Announce Type: cross Abstract: Clinical vision models are often deployed as frozen black boxes with no access to internals, retraining, or ground truth at inference time.
arXiv:2610.09535v1 Announce Type: cross Abstract: Real-world applications require 6D pose estimation to be accurate, fast, and scalable to unseen objects.
arXiv:2610.09759v1 Announce Type: cross Abstract: Flow matching enables language generation in few steps, but whether additional integration steps improve reasoning remains unclear.