Knowledge catalogue
arXiv:2609.01406v1 Announce Type: new Abstract: Multimodal models are increasingly the default option for perception at the network edge, yet they are trained almost entirely in the datacenter, becaus
arXiv:2609.00441v1 Announce Type: new Abstract: Effective manager-employee communication is critical for retaining high performers and developing underperformers, yet training managers in these skills
arXiv:2609.00590v1 Announce Type: new Abstract: The next generation of mobile networks is envisioned as fully AI-native, with AI-RAN architectures embedding small language models (SLMs) to perform rea
arXiv:2609.00808v1 Announce Type: cross Abstract: As far-right actors increasingly exploit online platforms to disseminate ideology and mobilize supporters, civil society organizations (CSOs) play a v
arXiv:2609.00361v1 Announce Type: cross Abstract: Toxic language in digital workplaces such as pejoratives, sarcasm, condescension, and subtle incivility can erode trust, morale, and collaboration. Ex
arXiv:2609.00666v1 Announce Type: new Abstract: InfRared Small Target Detection (IRSTD) is a prominent and challenging task in computer vision. In recent years, text-guided methods have significantly
arXiv:2502.03726v3 Announce Type: replace Abstract: Text-to-image diffusion models are capable of generating high-quality images, but suboptimal pre-trained text representations often result in these
arXiv:2609.00100v1 Announce Type: new Abstract: Psychometric questionnaires contain rich item-level information, yet it remains unclear whether different representation learning objectives recover the
arXiv:2609.00708v1 Announce Type: cross Abstract: Differentially private (DP) synthesis has been extensively studied for tabular and image data separately, yet many real-world datasets contain images
arXiv:2609.00206v1 Announce Type: cross Abstract: Despite their growing use in video moderation, multimodal large language models (MLLMs) exhibit a compositional safety blind spot: videos composed of
arXiv:2609.00345v1 Announce Type: new Abstract: Human mobility is central to urban planning, transportation, public health, and emergency response, yet fine-grained trajectory data are often proprieta
arXiv:2507.20881v3 Announce Type: replace Abstract: Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted consider
arXiv:2606.17506v2 Announce Type: replace Abstract: Evaluations of social bias in LLMs largely focus on whether models generate or imply biased content. However, as LLMs are increasingly used as judge
arXiv:2606.19101v2 Announce Type: replace-cross Abstract: We investigate a structure-first approach to dynamical learning in which the organization of stateful interactions is prescribed explicitly ra
arXiv:2609.01322v1 Announce Type: new Abstract: In many settings, studying causal questions based on text data requires adjusting for confounding information within texts. Yet there is a tradeoff in c
arXiv:2609.01596v1 Announce Type: cross Abstract: Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We pres
arXiv:2510.03899v3 Announce Type: replace-cross Abstract: Balancing resource efficiency and fairness is critical in networked systems that support modern learning applications. We introduce the Fair M
Jen Zhu (@jenzhuscott) noted on Twitter that “fear marketing failed. Here comes the pump.” Following this, Watcher.Guru shared a new statement: OpenAI CEO Sam Altman declared that AI will become the l
arXiv:2609.00831v1 Announce Type: new Abstract: Token aggregation converts token-level representations into fixed-dimensional sample representations, but most pooling methods operate only in the origi
arXiv:2609.00875v1 Announce Type: new Abstract: Satellite mega-constellations are emerging as large-scale sensing, communication, and computation fabrics, yet their learning architectures remain large
arXiv:2609.00051v1 Announce Type: cross Abstract: Despite extensive alignment efforts, Large Language Models (LLMs) remain vulnerable to generating unsafe content under adversarial prompting, yet the
arXiv:2609.01422v1 Announce Type: new Abstract: Post-training large language models usually applies a single training recipe to all samples, even though the model's own rollouts reveal different sampl
arXiv:2508.04080v2 Announce Type: replace Abstract: Standard large language model prompting treats geospatial inference as independent, instance-wise prediction, ignoring the fundamental spatial depen
arXiv:2609.01491v1 Announce Type: cross Abstract: The growing rate at which LLM agents interact with one another raises key questions about language evolution in multi-LLM-agent settings, with implica
arXiv:2609.01558v1 Announce Type: new Abstract: Training Physics-Informed Neural Networks (PINNs) requires jointly optimizing physics residual and initial/boundary condition loss terms, which often in
arXiv:2602.14028v2 Announce Type: replace Abstract: While Group Relative Policy Optimization (GRPO) offers a powerful framework for LLM post-training, its effectiveness in open-ended domains like Mach
arXiv:2603.10359v2 Announce Type: replace Abstract: Distilling reasoning capabilities from Large Reasoning Models (LRMs) into smaller models is typically constrained by the limitations of rejection sa
arXiv:2609.00727v1 Announce Type: cross Abstract: Audio language models are designed to understand speech, yet it remains unclear whether they capture how something is said beyond what is said. We pre
arXiv:2606.16285v2 Announce Type: replace Abstract: Long-horizon agents rely on memory mechanisms to compress interaction history, but optimizing memory writing faces a distinct credit assignment chal
arXiv:2607.18114v2 Announce Type: replace-cross Abstract: Modern LLMs are alarmingly susceptible to surprisingly simple immaterial changes of input prompts: a casual hint, an incorrectly labeled few-s
arXiv:2609.00334v1 Announce Type: new Abstract: Across law, education, policy analysis, and public moral argumentation, LLM outputs are being used often for work that requires interpretations to be ju
arXiv:2609.00494v1 Announce Type: new Abstract: LLM-based factuality judges provide scalable evaluation signals, but their metrics are often systematically biased relative to human judgments. We study
arXiv:2609.00002v1 Announce Type: new Abstract: World models enable language-model agents to predict environment dynamics and plan before acting. In text environments, the model must learn symbolic ac
I highly encourage anyone interested in AI safety to read this. I remember discussing the importance of observability with my mates at @BlueDotImpact last year. This feels more important than ever URG
If this is true, OpenAI seems to be violating one of the few redlines that exist in the AI industry. Absolutely do not train your models like this - what is going on?? Really huge and extremely concer
In case you're wondering while this steaming POS is being floated now, it's because SoftBank needs to refinance the 40B bridge loan it used to invest 30B into OpenAI before it matures in March 2027...
arXiv:2609.00904v1 Announce Type: new Abstract: Whether large language models (LLMs) can control their own internal representations matters for both machine metacognition and AI safety. A recent study
in other words, generative AI is poised to become a complete clusterfuck. All three pillars of a safety case look about to fall. We are rather likely to have highly capable, poorly monitorable, dubiou
arXiv:2609.01339v1 Announce Type: new Abstract: Road traffic noise remains a major environmental challenge, yet most speed management strategies are static and do not respond to short-term variations
arXiv:2609.00608v1 Announce Type: new Abstract: LLM-based user simulators are increasingly used to evaluate autonomous agents at scale, in place of costly human evaluations. Despite this promise, thes
arXiv:2511.21638v2 Announce Type: replace Abstract: Practical LLM agents often operate over multi-turn conversations where success is determined only after the full interaction ends. Most multi-turn R
arXiv:2609.01168v1 Announce Type: new Abstract: Text-to-image (T2I) models remain vulnerable to jailbreak attacks that elicit Not-Safe-For-Work (NSFW) content, despite increasingly being guarded by he
arXiv:2606.20563v2 Announce Type: replace Abstract: Creating 3D visual illusions, a single 3D mesh that reveals entirely different semantics from various viewing angles, is a fascinating but tough cha
arXiv:2408.12845v3 Announce Type: replace-cross Abstract: This paper considers a novel variant of the online fair division problem involving multiple agents in which a learner sequentially observes an
arXiv:2609.00908v1 Announce Type: new Abstract: Action chunking is a standard execution strategy in modern Vision-Language-Action (VLA) frameworks, but fixed execution horizons impose a trade-off betw
Legible chain of thought was extremely useful for our independent investigation into the OpenAI/Hugging Face incident. Developments like this may make third-party auditing much more difficult and make
arXiv:2609.00222v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as judges for subjective tasks, where annotators disagree and the relevant question is not only how a
arXiv:2609.00454v1 Announce Type: new Abstract: Pretrained transformer-based language models achieve strong performance across a wide range of NLP tasks but remain limited in encoding geo-locational s
arXiv:2609.01257v1 Announce Type: new Abstract: As LLM-based human simulators are increasingly used for policy, evaluation, and training, they must faithfully reproduce real behavioral patterns. While
arXiv:2609.01595v1 Announce Type: cross Abstract: We develop a framework for mechanism design with AI agents whose alignment (preferences) and capabilities (feasible actions and information) are unkno
arXiv:2511.01706v3 Announce Type: replace-cross Abstract: Natural Language Explanations (NLEs) describe how Large Language Models (LLMs) make decisions by drawing on external Context Knowledge (CK) an
Nobody can predict the future with perfect accuracy, but I am increasingly confident that by the end of decade, generative AI (at least we know it) will be viewed as on balance a tragic, costly mistak
arXiv:2609.00771v1 Announce Type: new Abstract: Robotic throwing enables fast object transport and extends a robot's reachable workspace beyond traditional pick-and-place. While prehensile (grasp-base
arXiv:2609.01147v1 Announce Type: cross Abstract: Text-rich visual inputs require models that can read, retrieve, and compress language directly in pixel space, yet existing pixel-text encoders strugg
arXiv:2609.00987v1 Announce Type: cross Abstract: Recent advances in generative AI are raising ethical concerns regarding the originality of generated content and the potential replication of training
arXiv:2609.00813v1 Announce Type: new Abstract: While reinforcement learning has enabled LLM-based search agents to invoke external tools, existing methods train under fixed budgets and cannot adapt w
arXiv:2506.19388v2 Announce Type: replace Abstract: Tissue deformation recovery based on stereo endoscopic images is crucial for tool-tissue interaction analysis and benefits surgical navigation and a
Georgia Wells / Wall Street Journal: OpenAI is facing a new round of lawsuits from witnesses and victims of the February mass shooting in a Canadian town; the suspect spent days talking to ChatGPT — N
arXiv:2507.22052v3 Announce Type: replace Abstract: We present Ov3R, a novel framework for open-vocabulary semantic 3D reconstruction from RGB video streams, designed to advance Spatial AI. The system
arXiv:2609.01073v1 Announce Type: new Abstract: The LLM-as-a-judge (LLMaJ) framework offers a cost-effective and reproducible solution for automatic evaluation. However, current evaluation practices t