Knowledge catalogue
arXiv:2502.16184v3 Announce Type: replace Abstract: The EU Artificial Intelligence Act (AIA) establishes different legal principles for different types of AI systems. While prior work has sought to cl
Sam Altman: “Concentration of power with AI is a terrifying thing.” 'A lot of the talk about safety concerns is well-founded, and then a lot of it is about people that just really, even if it's slight
arXiv:2503.14549v3 Announce Type: replace-cross Abstract: How can a cheap but biased sequential, finite-horizon sampler over a discrete space be corrected so that its terminal output follows a prescri
arXiv:2607.24300v1 Announce Type: new Abstract: Self-improving agents accumulate capability by repeatedly rewriting procedural policies, controllers, or heuristic rules. They typically rely on self-au
arXiv:2607.23125v1 Announce Type: new Abstract: Post-training enables vision-language models (VLMs) to understand human instructions and perform various downstream tasks. Current post-training methods
arXiv:2512.12713v2 Announce Type: replace-cross Abstract: Control policies are often implemented with fixed-capacity multilayer perceptrons trained by backpropagation, which require architecture selec
arXiv:2501.19060v4 Announce Type: replace Abstract: Vision-language models (VLMs), such as CLIP, adapt effectively to downstream tasks through prompt tuning, but fine-tuning can misalign predictive co
arXiv:2502.05925v2 Announce Type: replace-cross Abstract: Backpropagation (BP) has long been the predominant method for training neural networks due to its effectiveness. However, numerous alternative
arXiv:2607.24025v1 Announce Type: cross Abstract: Transformer architectures have achieved remarkable success across diverse domains; however, directly applying their standard self-attention mechanism
arXiv:2607.22758v1 Announce Type: cross Abstract: The integration of iterative LLMs within multi-agent diagnostic frameworks requires a rigorous quantitative reevaluation of underlying communication t
arXiv:2607.23284v1 Announce Type: new Abstract: Automated sleep staging is increasingly used in large-scale studies to derive sleep-architecture endpoints: total sleep time, REM latency, sleep efficie
arXiv:2607.22649v1 Announce Type: new Abstract: Following complex instructions with multiple explicit constraints remains a fundamental challenge for large language models (LLMs). Existing alignment m
arXiv:2607.23046v1 Announce Type: new Abstract: Recent high-resolution Multimodal Large Language Models (MLLMs) generate thousands of visual tokens per input, leading to a visual token explosion that
arXiv:2607.22563v1 Announce Type: new Abstract: Industrial agent benchmarks require realistic evaluation scenarios that integrate telemetry, failure modes, maintenance records, and domain standards. H
arXiv:2607.23991v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly controlled through system prompts that specify roles, styles, formats, and safety requirements. However,
arXiv:2607.24515v1 Announce Type: new Abstract: The challenge of Machine Translation for low resource languages such as Tamil is primarily caused by the restricted amount of parallel data for these la
arXiv:2510.12993v3 Announce Type: replace Abstract: Large Language Models (LLMs) can generate highly persuasive disinformation, yet little is known about how effectively they personalise it across lan
arXiv:2607.24611v1 Announce Type: new Abstract: Deep learning models have achieved state-of-the-art performance in several computer vision tasks. However, they experience severe performance degradatio
The administration is very consistent: All aspects of foreign involvement in AI are banned: people (students and employees), hardware and models. And funding is down to a drip and capital allocation i
Michael Nuñez / VentureBeat: The Agentic AI Foundation updates MCP with a fully stateless architecture, a hardened authentication model, a formal 12-month deprecation policy, and more — The Model Cont
arXiv:2607.23108v1 Announce Type: new Abstract: While scaling laws for imitation learning have primarily focused on generalization in open-world settings, the relationship between data and precision i
arXiv:2607.23050v1 Announce Type: new Abstract: Neural scaling laws describe how loss decreases as models, data, and compute grow, but they do not answer a prior question: for a fixed task, what is th
arXiv:2607.24720v1 Announce Type: cross Abstract: Multi-turn long-horizon planning is critical for foundation model agents, yet how to fundamentally improve it remains unclear. Existing models are tra
arXiv:2607.23181v1 Announce Type: new Abstract: Vision-and-Language Navigation in continuous environments (VLN-CE) requires an agent to ground language in egocentric observations and plan in unseen sc
arXiv:2607.24563v1 Announce Type: new Abstract: Security Operations Centers increasingly rely on automated mapping of Cyber Threat Intelligence reports to MITRE ATT&CK, yet extractor outputs remain fa
arXiv:2607.23333v1 Announce Type: cross Abstract: We revisit the regret loss framework introduced in Park et al. (2025), which uses decision-theoretic regret as a direct loss function for training mod
arXiv:2607.24336v1 Announce Type: new Abstract: City-scale autonomous vehicle fleet coordinators are typically optimized for aggregate travel time, yet fleet averages conceal how delay is distributed
arXiv:2504.17647v2 Announce Type: replace Abstract: Safety-critical whole-body robot control demands reactive methods that ensure collision avoidance in real-time. Complementarity constraints and cont
arXiv:2607.23940v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) solve differential equations by minimizing the residual of a nonlinear operator over a neural parameterization
arXiv:2607.22716v1 Announce Type: new Abstract: In this paper, we show for the first time that visual token pruning enhances the robustness of Multimodal Large Language Models (MLLMs), mitigating vuln
arXiv:2607.22868v1 Announce Type: new Abstract: Runtime guardrails act before irreversible tool calls, but their guarantees depend on what policy state is representable, what a judge observes, and whe
arXiv:2607.23271v1 Announce Type: cross Abstract: Contrastive vision-language models such as CLIP map semantically opposite phrases (e.g., 'a dog' vs. 'not a dog') to nearly identical embeddings, rend
arXiv:2607.23469v1 Announce Type: cross Abstract: Photonic-crystal surface-emitting lasers (PCSELs) can combine high-power operation with narrow-divergence surface emission, but optimizing coupled par
arXiv:2607.24010v1 Announce Type: new Abstract: Active RAG systems decide when to retrieve external knowledge during generation, making them a budget-sensitive case of agentic RAG and self-adaptive re
arXiv:2607.23624v1 Announce Type: cross Abstract: Third-party API routers have become a common layer that unifies access across increasingly diverse LLM providers. In coding-agent workflows, high-auto
You patch security holes by intentionally finding them. If the models refuse to do it, how can companies protect themselves against rogue AIs, whether they are Chinese or OpenAI/Anthropic themselves?
It doesn't solve any problems. Just a few paragraphs above, he says he fears that authoritarian states (he names China, and possibly others) can use their models to do evil stuff. And surely enough, m
Why is this breaking news? And what the heck does it actually mean? Has anyone actually defined it? Is it anything more than a ruse to distract from a disturbing hack and falling confidence in AI? BRE
arXiv:2607.22595v1 Announce Type: new Abstract: Mechanistic interpretability (MI) has emerged as a powerful approach for analyzing and intervening in inference computations, with a growing number of a
arXiv:2607.22656v1 Announce Type: cross Abstract: Objective: This paper investigates the gendered structure of speaker addressee relationships in film dialogue, asking not merely who speaks, but who i
arXiv:2607.21632v1 Announce Type: new Abstract: Traditional benchmarks for LLMs primarily rely on static datasets and objective scoring metrics, which often fail to capture differences in response qua
arXiv:2607.21960v1 Announce Type: new Abstract: This paper demonstrates how deep reinforcement learning (DRL) enables adaptive locomotion of snake-like robots in dynamically changing viscous environme
arXiv:2607.21725v1 Announce Type: new Abstract: General-purpose robots need to reason about their actions, combining perception, world knowledge, planning, success detection, recovery, and low-level c
arXiv:2607.21646v1 Announce Type: new Abstract: Ensuring safety in reinforcement learning under nonstationarity requires determining whether a learning system can safely adapt to forecasted environmen
arXiv:2607.21804v1 Announce Type: cross Abstract: Lossless acceleration schemes, such as speculative decoding, promise significant inference speedups by relying on dynamic token-level alignment betwee
arXiv:2607.21619v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved impressive performance, but their safety alignment remains vulnerable to jailbreak attacks. Exist
arXiv:2607.22241v1 Announce Type: new Abstract: Recent advances in video diffusion models have spurred interest in human-object interaction (HOI) video generation, which demands fine-grained control o
創業以来、オープンソースコミュニティから多くを学び、また研究成果の公開を通じてそこに貢献してきました。オープンなエコシステムが健全なAI産業と技術主権を支える重要な基盤の一つであると考えており、その発展を支持します。 このたび、Sakana AIは、オープンウェイトAIモデルに関する公開書簡 「Open Weights and American AI Leadership」に署名しました。 書簡は
arXiv:2601.17483v2 Announce Type: replace Abstract: Training modern neural networks is increasingly fragile, with rare but severe destabilizing updates often causing irreversible divergence or silent
arXiv:2607.22264v1 Announce Type: new Abstract: Autoregressive foundation models trained on tokenized electronic health records (EHRs) can support zero-shot clinical prediction, yet most operate on st
For the enterprise, the promise of agentic AI is much more than just a better chatbot. It is software agents that execute business tasks end-to-end across people, business workflows, data, and systems
arXiv:2607.21839v1 Announce Type: cross Abstract: Privacy-preserving machine learning auditing protocols allow auditors to assess models for properties such as accuracy or fairness, without revealing
arXiv:2607.21623v1 Announce Type: new Abstract: We present EaaS, a cloud-native reference architecture that operationalizes AI evaluation methods as six stateless Kubernetes microservices: conformal p
arXiv:2509.10058v2 Announce Type: replace Abstract: Accurate color alignment in text-to-image (T2I) generation is critical for applications such as fashion, product visualization, and interior design,
arXiv:2607.22078v1 Announce Type: new Abstract: As autonomous driving systems move toward real-world deployment, interpretable, behavior-level decision-making is essential for safety, trust, and regul
arXiv:2607.22062v1 Announce Type: cross Abstract: This paper addresses the modeling and synthesis of constrained robotic system behaviors using Petri nets (PNs). It investigates how to construct model
arXiv:2607.22012v1 Announce Type: new Abstract: Off-Policy Evaluation and Learning (OPE/L) in contextual bandits is rapidly gaining popularity in real systems because new policies can be evaluated and
arXiv:2601.12311v2 Announce Type: replace-cross Abstract: The emergence of 6G-enabled vehicular metaverses enables Autonomous Vehicles (AVs) to operate across physical and virtual spaces through space
arXiv:2607.22446v1 Announce Type: new Abstract: Recent radiance field methods represent scenes with 2D primitives that offer surface alignment and efficient rasterization, from Gaussian disks to trian
arXiv:2607.22249v1 Announce Type: new Abstract: Nociception is a protective biological mechanism that links harmful stimulation to a reaction. This paper investigates artificial nociception for a robo