Knowledge catalogue
arXiv:2605.26290v1 Announce Type: new Abstract: Temporal signed networks (TSNs) model the time evolution of cooperative and adversarial relationships that arise in applications such as social media an
arXiv:2602.04931v2 Announce Type: replace-cross Abstract: Geometric analyses of large language model (LLM) representations reveal structured variation across depth but remain fundamentally correlation
arXiv:2605.26371v1 Announce Type: new Abstract: Hierarchical Reinforcement Learning (HRL) promises to solve long-horizon Reinforcement Learning (RL) tasks more efficiently than non-hierarchical counte
arXiv:2601.22384v2 Announce Type: replace-cross Abstract: Graphs provide a natural representation of relational structure that arises across diverse domains. Despite this ubiquity, graph structure is
arXiv:2502.06567v2 Announce Type: replace-cross Abstract: Quantizing machine learning models has demonstrated its effectiveness in lowering memory and inference costs while maintaining performance lev
Model-Harness-Task fit! it’s clear that RL post-training produces a model-harness fit via tool shapes and prompting as models are trained with the harness in the loop. Mentioned this in a previous Lan
arXiv:2602.10104v2 Announce Type: replace-cross Abstract: Scaling action-controllable world models is limited by the scarcity of action labels. While latent action learning promises to extract control
arXiv:2605.26194v1 Announce Type: new Abstract: Clinical time-series learning is routinely constrained by small, heterogeneous cohorts and protocol drift, while its downstream use spans both classific
arXiv:2605.27318v1 Announce Type: new Abstract: Video spatial reasoning requires accumulating viewpoint-dependent evidence over time while retaining information useful to the question being asked. Exi
arXiv:2605.26519v1 Announce Type: new Abstract: Recent feed-forward geometry foundation models have demonstrated impressive generalization by recovering depth and poses in a single forward pass. Howev
arXiv:2603.01327v2 Announce Type: replace-cross Abstract: Large language models (LLMs) exhibit strong performance on self-contained programming tasks. However, they still struggle with repository-leve
arXiv:2507.02628v2 Announce Type: replace Abstract: The reliability of clinical artificial intelligence (AI) depends on high-quality data, yet Electronic Health Records are often inconsistent with exi
Activegraph's website, newsletter, and marketing are all run on Cofounder! [technical blog post] “Evidence Compilation Before Semantic Memory: ActiveGraph on LongMemEval-S” —— 🔍85.6% QA accuracy and 8
arXiv:2605.23938v1 Announce Type: new Abstract: Large language models (LLMs) increasingly fuse heterogeneous inputs in ubiquitous systems. Yet, how LLMs implicitly allocate authority when sensor measu
arXiv:2605.26086v1 Announce Type: new Abstract: Large language model agents are increasingly envisioned as always-on personal assistants with access to anything relevant in the user's digital world. Y
arXiv:2602.18640v2 Announce Type: replace Abstract: Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product r
arXiv:2605.24879v1 Announce Type: new Abstract: Large language models (LLMs) are trained on vast datasets that may contain sensitive information. Differential privacy (DP), the de facto standard for f
arXiv:2602.03983v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for generalist robotic control. Built upon vision-language model (
arXiv:2605.24911v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) have recently achieved strong zero-shot forecasting performance through large-scale pretraining and retrieval-au
arXiv:2605.23922v1 Announce Type: cross Abstract: The EU Artificial Intelligence Act (AIA) establishes a lifecycle governance regime for high-risk AI systems built around ex-ante conformity assessment
arXiv:2605.24330v1 Announce Type: new Abstract: Transformers and deep state space models (SSMs) sit at opposite ends of a basic design choice: attention routes each query through a growing key-value (
arXiv:2509.13608v2 Announce Type: replace Abstract: As Large Multimodal Models (LMMs) become integral to daily digital life, understanding their safety architectures is a critical problem for AI Align
// Language Models Need Sleep // Let your agents 'sleep', folks. On a serious note, this is a fascinating paper on getting the most from long-horizon agents. Here is the problem with agents today: Att
arXiv:2605.25179v1 Announce Type: new Abstract: Audio-language models (ALMs) are increasingly used for audio captioning, question answering, and open-ended audio understanding, but their inference cos
arXiv:2605.24020v1 Announce Type: cross Abstract: Advancements at the intersection of computer vision and natural language processing are crucial for applications like assistive tech, multimedia query
arXiv:2605.24941v1 Announce Type: cross Abstract: Modern LLM agents combine long-term memory for personalization with tool-calling interfaces for taking actions in the world -- a combination underpinn
arXiv:2605.25701v1 Announce Type: cross Abstract: Large language models (LLMs) can serve as the semantic-matching engine of a content-based publish/subscribe broker for agentic AI across the edge-clou
The way we work has fundamentally changed. From automated agents to sophisticated AI services, Generative AI (GenAI) has become a daily tool for a vast majority of employees. But with this rapid adopt
arXiv:2603.09943v2 Announce Type: replace Abstract: Computational pathology demands both visual pattern recognition and dynamic integration of structured domain knowledge, including taxonomy, grading
ran my first benchmark this weekend (longmemeval) mostly to test activegraph, learned a lot! - this is a stepping stone to show the event based agent system works. the AI convinced me not to start wit
arXiv:2605.25171v1 Announce Type: new Abstract: In most existing AI humor research, humor was treated as either 'present' or 'not present.' We explore the concept of humor as a social interaction with
arXiv:2509.05614v3 Announce Type: replace-cross Abstract: Pruning is a typical acceleration technique for compute-bound models by removing computation on unimportant values. Recently, it has been appl
System scaling is the next real bottleneck in agentic AI. If you build agent orchestration layers, this is a clean map of where the engineering leverage actually sits. The labs own the model. You own
arXiv:2605.25510v1 Announce Type: new Abstract: Children increasingly have access to Large Language Models (LLMs), which may expose them to responses that are developmentally inappropriate or require
arXiv:2605.23918v1 Announce Type: cross Abstract: The AI inference industry keeps models loaded in GPU memory around the clock to avoid cold-start latency, implicitly treating idle power as a fixed co
arXiv:2605.25208v1 Announce Type: new Abstract: Emotion-Cause Pair Extraction (ECPE) was introduced to explain why an emotion occurs, but this goal is now often reduced to binary pair/non-pair predict
arXiv:2408.08399v3 Announce Type: replace Abstract: Electricity Consumption Profiles (ECPs) are crucial for operating and planning power distribution systems, especially with the increasing number of
arXiv:2605.25313v1 Announce Type: cross Abstract: World models for partially observed environments must imagine multiple compatible hidden futures and steer between them under counterfactual actions.
arXiv:2510.06672v3 Announce Type: replace Abstract: Reinforcement learning algorithms such as GRPO have driven recent advances in large language model (LLM) reasoning. While scaling the number of roll
arXiv:2605.22924v1 Announce Type: new Abstract: Serving personalized content on mobile devices has traditionally required pooling sensitive user data on centralized servers, a practice increasingly at
arXiv:2605.23328v1 Announce Type: new Abstract: Emotion Recognition in Conversation is a core component of affective computing, while current resources of sign language emotion datasets primarily focu
arXiv:2605.22897v1 Announce Type: new Abstract: A persistent challenge in machine learning for scientific applications is jointly achieving prediction and understanding. Statistical models excel on st
arXiv:2605.23304v1 Announce Type: new Abstract: Hazard, as an abstract concept, is typically defined through cognitive-level logical reasoning rather than concrete examples. In contrast, existing haza
arXiv:2605.22892v1 Announce Type: cross Abstract: Modelling claim frequency and severity for non-life insurance pricing predominantly relies on generalised linear models, with gradient-boosted machine
arXiv:2605.23470v1 Announce Type: cross Abstract: Predicting how a dynamical unit evolves over time - how an individual ages, an epidemic spreads, or a physical system degrades - typically requires de
arXiv:2605.22829v1 Announce Type: cross Abstract: Multimodal Retrieval-Augmented Generation (RAG) has emerged as an effective paradigm for enhancing Large Language Models (LLMs) with external knowledg
arXiv:2605.22981v1 Announce Type: cross Abstract: Fill-in-the-middle (FIM) is a pretraining objective widely used to equip causal language models with infilling ability, yet its effect on verbatim mem
Dropped this morning by the Vatican: Magnifica Humanitas of His Holiness Pope Leo XIV on Safeguarding the Human Person in the Time of Artificial Intelligence. This is a very interesting document. It's
arXiv:2605.22850v1 Announce Type: cross Abstract: Prefix KV caching has become a key mechanism in LLM serving: it reduces time to first token (TTFT) by avoiding redundant computation across requests t
arXiv:2605.23668v1 Announce Type: cross Abstract: Although large language model (LLM) conversational systems process millions of multi-turn dialogues daily, they remain fundamentally reactive: they re
arXiv:2605.23068v1 Announce Type: new Abstract: Reliable visual understanding in robot-assisted and minimally invasive surgery (RMIS/MIS) demands more than accurate masks: in clinical practice, clinic
arXiv:2605.22876v1 Announce Type: new Abstract: Existing neural solvers for Multi-Objective Combinatorial Optimization Problems (MOCOPs) commonly adopt decomposition-based strategies that scalarize an
arXiv:2605.23025v1 Announce Type: new Abstract: World models represent a paradigm shift in generative AI, pursuing predictive understanding and controllable simulation of environments in a structured
arXiv:2605.21550v1 Announce Type: new Abstract: Electricity load peak forecasting (ELPF), simultaneously predicting peak timing and intensity, is a prerequisite for effective grid scheduling and risk
arXiv:2605.22099v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm for grounding large language model (LLM) outputs in retrieved evidence, thereby
arXiv:2605.22714v1 Announce Type: cross Abstract: Large language models are routinely used as automated evaluators: to review code, moderate content, or score outputs, often with many items passing th
arXiv:2605.22697v1 Announce Type: new Abstract: Robustness to domain changes is a key capability for effective deployment of human action recognition systems in real-world scenarios, where action cate
arXiv:2605.22269v1 Announce Type: new Abstract: Long streaming video QA remains challenging due to growing visual tokens and limited reasoning length of large language models (LLMs). KV-caching stores
arXiv:2605.22487v1 Announce Type: new Abstract: Recent advances in Multilingual Large Language Models (MLLMs) have significantly enhanced cross-lingual conversational capabilities, yet modeling cultur
arXiv:2605.22542v1 Announce Type: new Abstract: Coffee and tea share many properties, yet they evoke strikingly different situations, atmospheres, and affective associations. These situated dimensions