3,694 items tracked: 32 reported by two or more outlets, the rest by a single source. Most-corroborated first.
Three engineers put GPT, Claude, and Grok in charge of a real car.
Biohub, the research organization backed by Mark Zuckerberg and Priscilla Chan, is coordinating a $1.8 billion initiative to train AI models that predict cell behavior.
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks.
arXiv:2610.07428v2 Announce Type: replace Abstract: Adaptive gait biofeedback may support repeated practice in chronic ankle instability, but its evaluation must address model performance and human response.
arXiv:2610.08258v2 Announce Type: replace Abstract: Auditing the claimed outcomes of large language model (LLM) training is challenging when model weights and training data are private, while cryptographically proving th…
arXiv:2610.06985v2 Announce Type: replace-cross Abstract: Atomistic foundation models screen hypothetical crystals by thresholding predicted energies, an output that states neither how likely a candidate is to be stable…
arXiv:2610.07324v2 Announce Type: replace-cross Abstract: Time series foundation models (TSFMs) are trained on large collections of time series datasets that span various morphologies and domains.
arXiv:2610.08678v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates inference for a large language model (LLM), referred to as the \emph{target model}, by first using a smaller model, referred to a…
arXiv:2610.07037v1 Announce Type: new Abstract: AlphaFold 3-style cofolding models predict biomolecular complexes with high structural accuracy, yet a large fraction of their outputs are physically invalid: chains overla…
arXiv:2610.07093v1 Announce Type: new Abstract: The rapid proliferation of heterogeneous data sources within the Internet of Things (IoT) across domains such as smart cities, energy management, and environmental monitori…
arXiv:2610.07270v1 Announce Type: new Abstract: Internal features in LLMs are often interpreted as mechanisms when they track a concept and their manipulation changes a related behavior.
arXiv:2610.07350v1 Announce Type: new Abstract: Long chain-of-thought reasoning increases sequential decoding cost while creating a growing history of potentially reusable continuations.
arXiv:2610.07469v2 Announce Type: replace Abstract: Explicitly eliciting reasoning substantially improves LLM performance.
arXiv:2610.07514v1 Announce Type: new Abstract: A vision-language model may need to combine an image with a prompt to recognize a safety risk that neither reveals alone.
arXiv:2610.07544v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to judge how well an advertisement matches a query, but the fairness of these judgments has received limited attention.
arXiv:2610.07606v1 Announce Type: new Abstract: This paper establishes a theoretical framework for vertical adaptive layer skipping, proving three foundational results: (i) an Expected FLOPs formula (theorem 2) giving a…
arXiv:2610.07620v1 Announce Type: new Abstract: Scientific law discovery requires selecting measurements and converting evidence into a governing equation.
arXiv:2610.07661v1 Announce Type: new Abstract: Massive activations, a phenomenon in which a small number of hidden channels exhibit exceptionally large magnitudes, are pervasive in large language models (LLMs).
arXiv:2610.07766v3 Announce Type: replace Abstract: We present Open Telco (OTel), an open telecom AI resource that releases derived telecom datasets for retrieval, reranking, instruction tuning, and safety/abstention, to…
arXiv:2610.07791v1 Announce Type: new Abstract: Illusory pattern perception is a well-documented human cognitive tendency to infer meaningful relationships in data that is actually random.
arXiv:2610.07798v1 Announce Type: new Abstract: People increasingly ask large language models what to do with their money, yet seldom describe their finances in full.
arXiv:2610.07803v1 Announce Type: new Abstract: Small reasoning models (SRMs) have shown strong performance on complex reasoning tasks by generating extended chain-of-thought trajectories, but they often fail to recover…
arXiv:2610.08314v1 Announce Type: new Abstract: Linear regression and kernel smoothing offer tractable explanations of in-context learning: in both, the features determine the weight assigned to each context label.
arXiv:2610.08330v1 Announce Type: new Abstract: Proprietary Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet aligning their outputs with diverse user preferences re…
arXiv:2610.08627v1 Announce Type: new Abstract: Long-horizon world-model planning typically relies on autoregressive rollouts, where predicted states are repeatedly fed back into the model.
arXiv:2610.03829v1 Announce Type: cross Abstract: Real-world outpatient oncology notes contain specialised terminology, tumour staging expressions, treatment names, toxicity descriptions, and institution-specific de-iden…
arXiv:2610.05094v1 Announce Type: cross Abstract: Researchers increasingly use Large Language Models as judges (LLM-as-a-judge) to evaluate model outputs.
arXiv:2610.06877v1 Announce Type: cross Abstract: Accurate prediction does not establish that a world model has recovered a physical law: distinct dynamics can generate identical records under the same observation protoc…
arXiv:2610.06881v2 Announce Type: replace-cross Abstract: In this paper, a comparative review of different hybrid models for short-term forecasting of building thermal demand is carried out.
arXiv:2610.06996v1 Announce Type: cross Abstract: Block diffusion language models keep a large key-value (KV) cache throughout generation and attend to it at every denoising step, limiting both memory capacity and genera…
arXiv:2610.07032v1 Announce Type: cross Abstract: Large-scale pretrained transformer models have achieved state-of-the-art performance across diverse machine translation tasks, including multilingual settings.
arXiv:2610.07081v2 Announce Type: replace-cross Abstract: An electromagnetic (EM) digital twin gives mobile robots wireless situational awareness but depends on material conductivities that change with the environment.
arXiv:2610.07111v1 Announce Type: cross Abstract: UAVs that act as relays or base stations need accurate air-to-ground path-loss predictions for rate adaptation and placement, but propagation conditions change as a UAV m…
arXiv:2610.07177v1 Announce Type: cross Abstract: An open contrastive decision model is near chance as a judge on the hard public benchmarks: Contrastive-LM/CLM-v0.1-8B scores between 0.351 (best- of-four, chance 0.250)…
arXiv:2610.07224v1 Announce Type: cross Abstract: Clinical notes capture most of what is documented about a patient's care, but they cannot be used for research until protected health information (PHI) is removed.
arXiv:2610.07276v1 Announce Type: cross Abstract: Deployment-time safety of language models is commonly implemented through runtime guardrails such as input moderation, routing, retrieval verification, and output filteri…
arXiv:2610.07340v1 Announce Type: cross Abstract: Protein-molecule virtual screening is increasingly cast as a problem of representation learning in a shared embedding space.
arXiv:2610.07356v1 Announce Type: cross Abstract: Systems engineers use several diagrams to describe the structure and behavior of systems.
arXiv:2610.07388v2 Announce Type: replace-cross Abstract: Joint Models simultaneously model longitudinal and survival outcomes, leveraging patterns in patients' longitudinal trajectory to improve the prediction of surviv…
arXiv:2610.07532v1 Announce Type: cross Abstract: LLMs have advanced rapidly, raising growing concerns about their safety.
arXiv:2610.07553v1 Announce Type: cross Abstract: LoRA fine-tuning adapts small language models (SLMs) to heterogeneous instruction data within a low-rank update subspace, making it vulnerable to three structural problem…
arXiv:2610.07599v1 Announce Type: cross Abstract: In this paper, we study robust decision-making in the latent space of world models (WMs).
arXiv:2610.07607v1 Announce Type: cross Abstract: Model-guided directed evolution seeks to identify high-fitness protein variants under limited oracle budgets.
arXiv:2610.07654v1 Announce Type: cross Abstract: On-policy distillation (OPD) has attracted growing attention as an effective way to transfer capabilities from teacher models to student models.
arXiv:2610.07668v1 Announce Type: cross Abstract: Modern cache replacement designs saturate because they operate within fixed representational structures, hand-crafted and heuristic based feature-engineered predictors, o…
arXiv:2610.07684v1 Announce Type: cross Abstract: Personalized image generation aims to synthesize text-driven images conditioned on reference images, while mainly casting the generation as image customization for foregr…
arXiv:2610.07706v1 Announce Type: cross Abstract: Autoregressive large language models (LLMs) have rapidly advanced in capability, but their increasing scale comes with substantial computational and memory costs at infer…
arXiv:2610.07778v1 Announce Type: cross Abstract: Attributed graph clustering aims to discover node groups by jointly exploiting node attributes and graph topology, yet its unsupervised nature makes model selection and a…
arXiv:2610.07848v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has become a standard approach for adapting large language models to downstream tasks.
arXiv:2610.07887v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) integrate understanding and generation, yet their generative behavior is rarely governed by what they understand about the task.