Archi: Agentic Operations at the CMS Experiment
arXiv:2606.04755v1 Announce Type: cross Abstract: We present Archi, an open-source, end-to-end framework for scientific collaborations that combines the systematic ingestion and organization of hetero
Knowledge catalogue
arXiv:2606.04755v1 Announce Type: cross Abstract: We present Archi, an open-source, end-to-end framework for scientific collaborations that combines the systematic ingestion and organization of hetero
Based on anecdotal conversations with peers, there is enthusiasm for AI among academics in the humanities (while still being worried - rightly - about the negative consequences as well), but they gene
arXiv:2606.04494v1 Announce Type: new Abstract: Biomedical agents promise to automate complex biological workflows, yet current systems face two fundamental bottlenecks: bioinformatics tools are highl
arXiv:2601.09719v3 Announce Type: replace-cross Abstract: Pre-Layer Normalization (Pre-LN) is the de facto choice for large language models (LLMs) and is crucial for stable pretraining and effective t
arXiv:2510.01529v3 Announce Type: replace Abstract: Ball et al. recently established that prompt filtering for AI alignment faces a fundamental barrier: under standard cryptographic assumptions, no fi
arXiv:2606.04273v1 Announce Type: new Abstract: For centuries, human mathematicians have written proofs to substantiate their mathematical arguments; yet, the ability to automatically verify the valid
arXiv:2407.03884v4 Announce Type: replace-cross Abstract: Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and
arXiv:2606.04130v1 Announce Type: new Abstract: We introduce CLAW, a fully end-to-end self-supervised framework for learning a world model jointly with continuous latent action representations directl
arXiv:2606.04661v1 Announce Type: new Abstract: Prompts tuned for accuracy often grow long, raising inference cost on every model call. The best accuracy-cost trade-off depends on the task and the bud
arXiv:2606.04378v1 Announce Type: new Abstract: Leveraging multiple specialized LLMs can combine complementary strengths, but existing approaches trade adaptability for stability: routing commits prem
arXiv:2606.04115v1 Announce Type: cross Abstract: Quantizing large language models (LLMs) to low-precision floating-point representations is central to efficient deployment, yet applying a single bit-
arXiv:2606.04591v1 Announce Type: new Abstract: With the widespread adoption of multi-modal communication platforms, long-form dialogues interleaving text and images have become increasingly common. U
arXiv:2606.05116v1 Announce Type: new Abstract: We introduce the Graph Set Transformer (GST), a neural network architecture for learning on sets of graphs, designed for tasks in which per-element pred
arXiv:2606.04184v1 Announce Type: new Abstract: True general intelligence requires not only a model of the physical world but also a social world model: the capacity to infer how individual mental sta
here’s why i think the situation may be dire: https://x.com/GaryMarcus/status/2062369417372258652?s=20 ⚠️Why didn’t the hyperscalers wait until after the IPO in switching to pay-by-usage-charging? My
arXiv:2602.04101v2 Announce Type: replace Abstract: We present Interfaze, a native hybrid model that fuses task-specific deep neural networks (CNNs and DNNs) directly into a transformer decoder throug
arXiv:2510.09953v3 Announce Type: replace Abstract: Manual medical image segmentation by clinicians, though accurate, is time-consuming and variable across experts, whereas AI-based models automate th
arXiv:2606.04672v1 Announce Type: cross Abstract: Continuous-time dynamic graphs (CTDGs) provide a richer framework to capture fine-grained temporal patterns in evolving relational data. Long-range in
arXiv:2606.04815v1 Announce Type: cross Abstract: Lifelong learning is essential for Large Language Model (LLM) agents operating in dynamic, interactive environments. However, existing lifelong learni
arXiv:2606.04450v1 Announce Type: new Abstract: Worker safety attitudes are key determinants of whether protective practices are applied or bypassed on construction sites. Yet measuring them at scale
arXiv:2606.04063v1 Announce Type: cross Abstract: Deploying large language models (LLMs) is challenging due to their significant memory and computational requirements. While some methods address this
arXiv:2606.05008v1 Announce Type: cross Abstract: As multi-modal models advance towards long-form video understanding, memory emerges as a critical capability. Despite substantial efforts in developin
arXiv:2606.04513v1 Announce Type: new Abstract: Lane-level maps are critical infrastructure for autonomous driving and lane-level navigation, yet constructing and maintaining standardized lane network
arXiv:2511.00801v4 Announce Type: replace Abstract: Text-guided medical image editing must satisfy the requested pathology while preserving anatomy, modality-specific appearance, and clinical plausibi
arXiv:2511.07107v3 Announce Type: replace Abstract: Ensuring the safety of Large Language Models (LLMs) is critical for real-world deployment. However, current safety measures often fail to address im
The search results primarily discuss the Nemotron 3 Nano Omni model rather than Nemotron 3 Ultra. However, I found a recent NVIDIA blog post reference that indicates Nemotron 3 Ultra is available thro
ollama run gemma4:12b Gemma 4 12B is updated on Ollama, and available across all platforms! Try it on: Claude Code ollama launch claude --model gemma4:12b Hermes Agent ollama launch hermes --model gem
arXiv:2606.04325v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a popular parameter-efficient fine-tuning (PEFT) method that restricts weight updates to low-rank adapters, introducing a
arXiv:2603.23841v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact thei
arXiv:2606.04978v1 Announce Type: new Abstract: LLMs can appear cautious in risk decision-making tasks, yet cautious-looking outputs do not necessarily indicate alignment with human decision-making me
This post likely discusses concerns about AI systems that merely imitate or reproduce existing walkthroughs and instructional content rather than demonstrating genuine understanding or original proble
arXiv:2606.04739v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong potential for automated software vulnerability detection, particularly in retrieval-augmented generatio
arXiv:2606.04272v1 Announce Type: new Abstract: The standard LLM training pipeline applies reinforcement learning (RL) only after pre-training and supervised fine-tuning (SFT). We question this status
arXiv:2502.01576v2 Announce Type: replace Abstract: Multi-modal Large Language Models (MLLMs) excel in vision-language tasks but remain vulnerable to visual adversarial perturbations that can induce h
arXiv:2606.04120v1 Announce Type: cross Abstract: Conversational agents that serve as lifelong companions must maintain persistent memory across all interactions. However, simply expanding context win
See also: https://x.com/emollick/status/1799046218917822682?s=20 Alignment, of a sort: this paper conducts what they call a “moral Turing Test,” asking people to compare GPT-4o to humans on ethical qu
arXiv:2606.05037v1 Announce Type: cross Abstract: When an AI agent calls an API and hits a validation error, it needs more than what went wrong -- it needs what to do next. A self-reflective API retur
arXiv:2606.04964v1 Announce Type: new Abstract: Diffusion language models (DLMs) generate text through iterative denoising, and blockwise decoding improves their practicality by committing tokens in l
arXiv:2606.04465v1 Announce Type: cross Abstract: System prompt optimization improves agent behavior without modifying the underlying model, yielding human-readable, model-agnostic instructions. Exist
arXiv:2603.02697v2 Announce Type: replace-cross Abstract: This paper presents ShareVerse, a video generation framework enabling multi-agent shared world modeling, addressing the gap in existing works
arXiv:2606.04202v1 Announce Type: new Abstract: As LLMs become more widely deployed, they are increasingly expected to work alongside other AI agents rather than operating in isolation. Effective coor
arXiv:2606.04691v1 Announce Type: new Abstract: Zero-shot information extraction (IE) with large language models (LLMs) has attracted increasing attention due to its flexibility in adapting to new sch
arXiv:2606.04236v1 Announce Type: cross Abstract: Discrete diffusion language models can generate text efficiently by updating multiple masked positions in parallel, but this parallelism introduces a
arXiv:2606.04678v1 Announce Type: new Abstract: End-to-end ASR systems typically use fixed-depth acoustic encoders at inference, making it difficult to trade additional test-time computation for impro
arXiv:2606.04296v1 Announce Type: new Abstract: As autonomous AI agents move from conversational systems to long-horizon software execution, runtime safety layers that decide when to interrupt an agen
arXiv:2606.04152v1 Announce Type: new Abstract: Large language models are reshaping research practice while quietly eroding researchers epistemic accountability. This commentary introduces PEEL - Prot
transitions like this are why we think it's helpful to have a provider-agnostic harness we used to talk more about swapping models when the latest and greatest came out -- but the latest and greatest
arXiv:2601.11214v5 Announce Type: replace Abstract: We present T^star, a simple TraceRL-based training curriculum for progressive block-size scaling in masked diffusion language models (MDMs). Startin
arXiv:2606.04907v1 Announce Type: new Abstract: Visual navigation requires generating smooth and collision-free trajectories under complex geometric and physical constraints. Existing reactive policie
We're presenting ParseBench at CVPR 2026! ParseBench is the most comprehensive document understanding benchmark for VLMs. ✅ It contains 2k pages of real-world enterprise documents ✅ It has comprehensi
arXiv:2606.04389v1 Announce Type: new Abstract: Large Language Models (LLMs) show promise in psychological counseling, yet existing benchmarks rely heavily on highly cooperative simulated clients. We
arXiv:2606.03609v1 Announce Type: cross Abstract: Embodied agents that navigate cities rely on world models that predict how their surroundings will change as they move. But for navigation, what matte
arXiv:2606.03685v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) improves end-to-end classical planning in large language models (LLMs), but do these models also learn to represent and r
arXiv:2606.02670v1 Announce Type: cross Abstract: Many recent multivariate time series anomaly detection (MT-SAD) models incorporate cross-channel modeling, under the implicit assumption that the stru
arXiv:2606.03116v1 Announce Type: cross Abstract: The rapid advancement of instruction-guided audio generation has highlighted the critical need for robust alignment evaluation. Current automated eval
arXiv:2606.03070v1 Announce Type: cross Abstract: Asynchronous reinforcement learning can improve language-model post-training throughput by decoupling response generation from policy optimization, bu
arXiv:2606.02724v1 Announce Type: cross Abstract: Audio-visual speaker tracking aims to localize and track active speakers by leveraging auditory and visual cues, enabling fine-grained, human-centric
arXiv:2606.03920v1 Announce Type: new Abstract: Understanding a video requires more than recognizing isolated moments, as humans continuously track entities, states, and events over time. This capacit
arXiv:2606.03604v1 Announce Type: new Abstract: When asked what a meme or sarcastic post means, Large Vision Language Models (LVLMs) tend to describe what the image shows rather than what the author i
arXiv:2603.19250v2 Announce Type: replace Abstract: Evaluating language models in streaming environments is critical, yet underexplored. Existing benchmarks either focus on single complex events or pr