Knowledge catalogue
arXiv:2605.24018v1 Announce Type: new Abstract: Large language models (LLMs), have shown strong potential in scientific discovery, yet existing methods still face substantial challenges in the design
Agentic artificial intelligence startup Canyon Code stepped out of the shadows today after closing on a 5 million pre-seed funding round. Its mission is to give enterprises the granular controls requi
arXiv:2508.12479v2 Announce Type: replace-cross Abstract: Min-max optimization arises in many domains such as game theory, adversarial machine learning, etc. For these problems, gradient-based methods
arXiv:2508.09801v3 Announce Type: replace-cross Abstract: Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive tech
arXiv:2605.24913v1 Announce Type: cross Abstract: Retinal imaging provides a non-invasive window into systemic microvascular health and has emerged as a potential biomarker for systemic diseases. Howe
arXiv:2605.24912v1 Announce Type: cross Abstract: Background: Type 2 diabetes mellitus (T2DM) is increasingly recognised as a systemic disease characterised by coordinated dysfunction across metabolic
arXiv:2605.25856v1 Announce Type: cross Abstract: Large Language Model interfaces are increasingly verbose, exposing intermediate reasoning traces alongside final answers. Traces are framed as transpa
arXiv:2502.10311v3 Announce Type: replace-cross Abstract: Most commonly used non-linear machine learning methods are closed-box models, uninterpretable to humans. The field of explainable artificial i
arXiv:2605.24678v1 Announce Type: new Abstract: Speech and language technologies offer valuable opportunities for supporting mental health assessment through objective and interpretable cues. We prese
arXiv:2605.25931v1 Announce Type: new Abstract: We systematically investigate all 25 public ARC-AGI-3 games and find that every one is reachable through non-intelligent strategies: 10 in a single blin
arXiv:2605.24996v1 Announce Type: new Abstract: Cognitive distortions, distorted patterns of thinking, have been increasingly studied in computational mental health research. Although they are related
arXiv:2605.25477v1 Announce Type: cross Abstract: The ability to efficiently and reliably learn new tasks has been a foundational challenge in robotics. Vision-Language-Action (VLA) models have demons
arXiv:2605.25813v1 Announce Type: new Abstract: Embodied Question Answering (EQA) connects perception, reasoning, and interaction within embodied environments. However, existing datasets and benchmark
arXiv:2605.24173v1 Announce Type: cross Abstract: Memorization in large language models has been studied almost exclusively through prefix-conditioned extraction, a natural choice for autoregressive m
arXiv:2605.25582v1 Announce Type: cross Abstract: Reinforcement learning for large language models faces a fundamental trade-off between sample efficiency and asymptotic performance: strictly on-polic
arXiv:2601.03327v3 Announce Type: replace-cross Abstract: Wildfires are highly imbalanced natural hazards in both space and severity, making the prediction of extreme events particularly challenging.
arXiv:2602.06717v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is commonly based on group sampling to estimate advantages and stabilize policy updates.
arXiv:2602.16229v2 Announce Type: replace Abstract: Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning. Latent actions p
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:2510.22827v3 Announce Type: replace-cross Abstract: Evaluating text-to-image (T2I) systems requires judging not only whether an image matches a prompt, but also whether socially salient attribut
arXiv:2605.23970v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as automatic judges for summarization and dialogue evaluation. Prior work has documented biases such
arXiv:2605.24286v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning is useful for monitoring language models only when the reasoning trace faithfully reflects the computation that produ
arXiv:2605.25052v1 Announce Type: new Abstract: Chains of thought (CoTs) have become central in interpreting and auditing behaviors of large language models. Yet growing evidence suggests that these t
arXiv:2510.14925v4 Announce Type: replace Abstract: High-confidence errors in large language models are often treated as fragile failures. We study an alternative: some errors may be false fixed point
arXiv:2605.24710v1 Announce Type: new Abstract: We establish four structural results for feature learning in wide two-layer neural networks under the Maximal Update Parametrization (muP). First, we pr
arXiv:2605.24057v1 Announce Type: cross Abstract: Neural networks acquire structured representations at specific moments during training, yet identifying these transitions typically relies on retrospe
arXiv:2603.05143v3 Announce Type: replace Abstract: Understanding reasoning in large language models is complicated by evaluations that conflate multiple reasoning types. We isolate analogical reasoni
arXiv:2605.24062v1 Announce Type: cross Abstract: Human-body communication (HBC) is a promising physical substrate for wearable body-area networks because it can localize communication around the body
arXiv:2501.19389v4 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) on resource-constrained clients remains a challenging problem. Recent works have fused low-rank adaptation
arXiv:2605.24386v1 Announce Type: cross Abstract: Fermi-Dirac machines were proposed recently as an approach to solving semidefinite optimization problems on quantum computers. Here, we reinterpret th
arXiv:2603.06218v2 Announce Type: replace Abstract: Accurate physics simulation is essential for robotic learning and control, yet analytical simulators often fail to capture complex contact dynamics,
arXiv:2510.10921v3 Announce Type: replace-cross Abstract: Fine-grained vision-language understanding requires precise alignment between visual content and linguistic descriptions, a capability that re
Financial Times: Filing: Monzo reports its “refer a friend” payouts grew 40% YoY to £29.5M for the 12 months ending March 2026, as part of a broader £143M marketing spend — London-based fintech record
arXiv:2605.24192v1 Announce Type: cross Abstract: The neural-network denoising functions which form the backbone of image diffusion models are remarkably consistent in their generalization behaviour a
arXiv:2605.25645v1 Announce Type: cross Abstract: We present the first end-to-end demonstration of fine-tuning and serving Google's Gemma 4 31B model on TPU hardware, providing an empirical comparison
arXiv:2602.02605v2 Announce Type: replace-cross Abstract: Evaluating true metacognition in Large Language Models (LLMs) is difficult due to biases and heuristics. This paper presents a framework to me
arXiv:2510.01384v4 Announce Type: replace Abstract: A natural desideratum for generative models is self-correction--detecting and revising low-quality tokens at inference. While Masked Diffusion Model
arXiv:2605.25816v1 Announce Type: cross Abstract: Personally identifiable information (PII) detection systems are frequently trained within narrow source or domain boundaries, limiting coverage when d
Firecrawl, a web scraping and data extraction tool, has been integrated into the Vercel Marketplace, enabling developers to easily incorporate its capabilities into their Vercel-hosted projects. This
Fireworks is coming to Tech Week, and we're doing it across multiple cities. Boston is up first. We're co-hosting a Founder Skybar Social with @fin_ai and @TrustVanta. Follow along via #BOSTechWeek In
Yohei Nakajima announced the first arXiv citation of ActiveGraph, positioning it as a complementary runtime tool. The announcement included a working bridge example in the repository, indicating pract
arXiv:2605.25258v1 Announce Type: cross Abstract: Recommender systems generally optimises user engagement, but this approach is dangerous in mental health contexts. When vulnerable users show signs of
arXiv:2511.03548v2 Announce Type: replace Abstract: Understanding the generalization behavior of learning algorithms is a central goal of learning theory. A recently emerging explanation is that learn
arXiv:2602.09620v2 Announce Type: replace Abstract: Constraint Answer Set Programming (CASP) is a hybrid paradigm that enriches Answer Set Programming (ASP) with numerical constraint processing, a cru
arXiv:2605.25717v1 Announce Type: new Abstract: Most of the world's offshore wind resource lies in waters too deep for fixed-bottom foundations, making floating offshore wind turbines (FOWTs) essentia
arXiv:2507.07644v4 Announce Type: replace Abstract: We introduce FloorplanQA, a diagnostic benchmark for evaluating spatial reasoning in large language models (LLMs). FloorplanQA is grounded in struct
arXiv:2506.09199v2 Announce Type: replace-cross Abstract: Integrating Low-Rank Adaptation (LoRA) into federated learning offers a promising solution for parameter-efficient fine-tuning of Large Langua
arXiv:2605.24503v1 Announce Type: cross Abstract: As AI-powered compliance monitoring becomes increasingly important in public governance and industrial safety, the ability to provide verifiable evide
for ActiveGraph, i'm using Cofounder by @intelligenceco as my... cofounder I'm working on the repo, doing research. Cofounder built the website, manages newsletter/blog, and when I do a new blog or ha
Forecasting Scientific Progress with Artificial Intelligence https://arxiv.org/abs/2605.22681 Turns out AI is just as bad at forecasting biology and physics breakthroughs as we are. To be fair, most b
arXiv:2306.02216v3 Announce Type: replace Abstract: Federated Learning (FL) enables collaborative model training across distributed clients while preserving user privacy. Recently, Federated Unlearnin
arXiv:2605.26097v1 Announce Type: new Abstract: Models trained on a new task typically degrade on prior tasks, a phenomenon known as forgetting. Traditionally, mitigating forgetting has required repla
arXiv:2605.26007v1 Announce Type: new Abstract: Dementia detection from spontaneous speech offers a scalable approach to cognitive screening, yet NLP systems remain predominantly English-centric. This
arXiv:2605.24432v1 Announce Type: new Abstract: Large Language Model (LLM) interactions are typically underspecified, with users clarifying all necessary details across multiple conversational turns.
arXiv:2605.25371v1 Announce Type: new Abstract: We present the first approach to build hierarchical task-driven 3D scene graphs of arbitrary indoor or outdoor environments using an uncalibrated monocu
arXiv:2605.24278v1 Announce Type: new Abstract: We present an improved neural field architecture for solving partial differential equations (PDEs). Current physics-informed neural networks (PINNs) pro
arXiv:2502.01184v2 Announce Type: replace-cross Abstract: Molecular representation learning methods typically tokenize molecules as individual atoms or use rigid, rule-based fragment decompositions, l
arXiv:2507.06038v4 Announce Type: replace-cross Abstract: Building on our previous work on Fredholm Neural Networks (Fredholm NNs/ FNNs) for solving integral equations, we extend the framework to inve
Clem Delangue advocates for the release of a 100 billion parameter Gemma 4 Mixture of Experts model, suggesting that Gemini Flash 3.5's release creates an opportunity for this larger model to be made
arXiv:2603.16105v3 Announce Type: replace-cross Abstract: Post-training model compression is essential for enhancing the portability of Large Language Models (LLMs) while preserving their performance.