Knowledge catalogue
arXiv:2602.01267v2 Announce Type: replace Abstract: Kronecker adapters have emerged as a promising approach for fine-tuning large-scale models, enabling high-rank updates through tunable component str
arXiv:2605.30876v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive models, offering competitive performance whil
arXiv:2603.29972v2 Announce Type: replace-cross Abstract: Scientists often want to explain why an outcome is different in two groups. For instance, differences in patient mortality rates across two ho
arXiv:2605.30934v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit systematic differences in moral reasoning across languages, yet the source of this variation remains unclear. We
I need to check the actual content of this Reddit discussion to provide an accurate summary. Graph Neural Networks can learn environmental effects on galaxy properties, incorporating spatial relations
arXiv:2605.31432v1 Announce Type: cross Abstract: Simultaneous speech-to-text translation (SimulST) generates translations while speech is still unfolding, requiring a streaming policy that decides wh
arXiv:2605.31041v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated promising capability in autonomous driving, highlighting the potential of unified multimodal arc
arXiv:2605.30415v1 Announce Type: cross Abstract: We investigate how domain adaptation reshapes explanatory behavior in language models using historical cosmology as a controlled setting. In Phase 1,
arXiv:2601.21686v2 Announce Type: replace Abstract: Key-value (KV) caching enables fast autoregressive decoding but at long contexts becomes a dominant bottleneck in High Bandwidth Memory (HBM) capaci
arXiv:2605.31119v1 Announce Type: cross Abstract: In robotics, dangers and adversity modes are often embodiment-specific and relative to each agent. A frontier of autonomous mobile robotics is to enab
On June 1, 2026, multiple official packages from the @redhat-cloud-services scope on NPM were compromised with a credential-stealing worm, affecting 96 versions across 32 packages totaling 116,991 wee
arXiv:2605.31361v1 Announce Type: cross Abstract: In cooperative multi-agent reinforcement learning (MARL), agents must coordinate with partners whose internal policies and intentions are not directly
arXiv:2605.31455v1 Announce Type: cross Abstract: Large language models are increasingly deployed in multi-turn interactive settings where users or environments can iteratively provide lightweight fee
arXiv:2605.31065v1 Announce Type: cross Abstract: Non-terrestrial networks (NTNs) are expected to play a pivotal role in sixth-generation (6G) systems by enabling ubiquitous connectivity and massive c
arXiv:2605.31271v1 Announce Type: new Abstract: Driving Vision-Language-Action Models (Driving VLAs) aim to use language to improve end-to-end planning, but the language-action gap limits this promise
arXiv:2605.30863v1 Announce Type: new Abstract: Dynamic scene reconstruction and novel view synthesis are fundamental to next-generation visual intelligence applications such as virtual reality, robot
arXiv:2602.13812v3 Announce Type: replace-cross Abstract: Document-to-table (Doc2Table) extraction derives structured tables from unstructured documents under a target schema, enabling reliable and ve
arXiv:2605.30431v1 Announce Type: new Abstract: Recent progress in video diffusion models has enabled remarkable generative fidelity, yet leveraging these priors for restoration remains limited by the
arXiv:2512.13996v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts architectures are essential for scaling model capacity efficiently, yet the standard Top-k routing imposes a rigid sparsit
arXiv:2509.24319v4 Announce Type: replace-cross Abstract: Large language models can express values in two main ways: (1) intrinsic expression, reflecting the model's inherent values learned during tra
arXiv:2505.05168v3 Announce Type: replace-cross Abstract: Under mild conditions, this paper derives a least-squares local linear Frechet curve predictor for response and regressor evaluated in a separ
arXiv:2605.31377v1 Announce Type: cross Abstract: Agentic Retrieval-Augmented Generation improves retrieval by integrating planning, tool use, and iterative reasoning, but existing agentic RAG methods
arXiv:2605.30550v1 Announce Type: new Abstract: Adaptive systems often need to make task-specific decisions about people from limited evidence: a tutor may need to anticipate how a learner will approa
arXiv:2603.28579v2 Announce Type: replace Abstract: This paper presents EBuddy, a voice-guided workflow orchestrator for natural human-machine collaboration in industrial environments. EBuddy targets
arXiv:2605.31228v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards is an effective route for post-training to strengthen the reasoning capability of large language models
arXiv:2602.01173v3 Announce Type: replace Abstract: Understanding the multi-dimensional attributes and intensity nuances of image-evoked emotions is pivotal for advancing machine empathy and empowerin
Eero P. Simoncelli, CDS founding member and professor, was elected to the National Academy of Sciences, recognizing “distinguished and continuing achievements in original research.” He studies how bra
arXiv:2605.31562v1 Announce Type: new Abstract: RNA sequencing produces rich and diverse datasets of gene expression, offering compelling insights into cellular state and function that have many appli
arXiv:2602.09276v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning and its variants have substantially improved the performance of language models on complex reasoning tasks, y
arXiv:2605.30776v1 Announce Type: new Abstract: Offline-to-Online Reinforcement Learning (O2O-RL) leverages an offline, pre-trained policy to minimize costly online interactions. Although data-efficie
arXiv:2605.30753v1 Announce Type: new Abstract: Diffusion-based large language models (dLLMs) support parallel text generation via iterative denoising, yet inference remains latency-heavy because many
arXiv:2605.31557v1 Announce Type: new Abstract: Continuous episodic memory is a core capability for autonomous agents operating in dynamic, real-world environments, yet current streaming video benchma
arXiv:2605.30637v1 Announce Type: new Abstract: Clinical decision-making (CDM) is central to real-world clinical workflows, where clinicians infer diagnoses, select treatments, or anticipate future he
arXiv:2605.30992v1 Announce Type: new Abstract: Sparse Mixture of Experts (SMoE) architectures improve the training efficiency of Large Language Models (LLMs) by routing input tokens to a selected sub
arXiv:2605.30484v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown promise for robotic manipulation, yet most existing policies operate reactively by directly regressing ac
Vercel has enhanced its Elastic Build Machines with protections against out-of-memory errors during builds, improving build reliability and preventing failures caused by insufficient memory allocation
arXiv:2510.17700v2 Announce Type: replace Abstract: Vision foundation models achieve remarkable performance but are only available in a limited set of pre-determined sizes, forcing sub-optimal deploym
arXiv:2605.30690v1 Announce Type: new Abstract: Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. Howeve
Elon was right that what OpenAI did in its for profit turn was shameful. But what he is doing with the SpaceX, at the likely expense of many people’s retirement funds, is just as shameful. SpaceX bein
arXiv:2605.30924v1 Announce Type: new Abstract: MLLM-powered embodied agents deployed in real-world environments encounter physical hazards. However, existing approaches lack explicit mechanisms for i
arXiv:2503.05846v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved impressive progress across a wide range of tasks, yet their heavy reliance on English-centric train
arXiv:2605.31170v1 Announce Type: cross Abstract: Monitoring autonomous language model agents currently relies mostly on surface behavior. But what happens when agent populations invent new languages
arXiv:2603.19262v2 Announce Type: replace-cross Abstract: Large language models increasingly rely on inference-time procedures such as chain-of-thought reasoning, self-refinement, retrieval augmentati
arXiv:2602.05649v2 Announce Type: replace Abstract: The long-standing dominance of gradient-boosted decision trees for tabular data has recently been challenged by in-context learning tabular foundati
Endlich konnte ich FSD in Deutschland testen. Wir sind fast eine Stunde durch Dörfer, über Landstraßen und Autobahnen gefahren. Alles lief perfekt. @PSchnieder, wann retten wir Leben in Deutschland?🇩🇪
arXiv:2605.31487v1 Announce Type: new Abstract: Deploying computer vision models in Warehouse Facilities traditionally requires extensive resources for camera mounting, image collection, annotation, t
arXiv:2605.30928v1 Announce Type: new Abstract: Human-like agents are a long-standing goal of artificial intelligence. Despite strong performance, most reinforcement learning (RL) agents remain reward
arXiv:2605.30363v1 Announce Type: cross Abstract: Regime shifts in financial markets reorganise the joint dynamics of asset prices and macro variables, breaking any single-regime calibration. They are
arXiv:2605.31250v1 Announce Type: cross Abstract: We propose a unified framework for addressing three key challenges of distribution shift: (1) estimating a model's performance on an unlabeled target
arXiv:2605.31266v1 Announce Type: cross Abstract: The layout-to-image (L2I) task enables fine-grained control over image generation via object categories and spatial layouts. However, existing L2I met
arXiv:2605.30705v1 Announce Type: new Abstract: Geometry-aware generative models and novel view synthesis approaches have shown strong potential in visual fidelity and consistency. In parallel, equiva
arXiv:2605.31251v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential as embodied agents, yet embodied geo-localization remains underexplored due to th
arXiv:2601.21778v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) can achieve competitive performance by converting already existing well-trained Artificial Neural Networks (ANN
arXiv:2506.01928v4 Announce Type: replace Abstract: Diffusion-based language models offer a compelling alternative to autoregressive (AR) models by enabling parallel and controllable generation. Withi
arXiv:2605.30654v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conversational partners for companionship, emotional disclosure, and interpersonal advice, but t
arXiv:2605.31506v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is the current industry standard for grounding AI in real-world facts. Traditional retrieval methods rely on keyw
arXiv:2605.30521v1 Announce Type: new Abstract: Large language models must frequently process untrusted inputs, such as judging an answer from another model or running tasks like spam and harm classif
Every month I publish the now-a-month-old previous newsletter so people can see what they're missing out on. Here's what you would have got a month ago covering what happened in LLMs in April: https:/
arXiv:2603.17306v3 Announce Type: replace Abstract: A foundational assumption in linguistics holds that sound-meaning relations are largely arbitrary. Here we show that this assumption fails at the le
arXiv:2605.31140v1 Announce Type: cross Abstract: Large Language Models (LLMs) remain highly vulnerable to diverse attacks, particularly in black-box settings where the internals of target models are