Knowledge catalogue
arXiv:2605.10789v1 Announce Type: new Abstract: Accurate quantification of forest coverage and combustible biomass (fuel load) is critical for wildfire risk assessment and ecosystem management. Howeve
arXiv:2605.08857v1 Announce Type: new Abstract: Recent advances in uncertainty quantification for time series forecasting show that conformal prediction can provide reliable prediction intervals, yet
arXiv:2602.09016v2 Announce Type: replace Abstract: Reconstructing a structured vector-graphics representation from a rasterized floorplan image is typically an important prerequisite for computationa
arXiv:2605.09457v1 Announce Type: cross Abstract: While Graph Neural Networks (GNNs) have demonstrated significant efficacy in node classification tasks, where predictions rely on local neighborhood i
arXiv:2605.08937v1 Announce Type: new Abstract: Static mapping is fundamental to robot navigation, providing a persistent geometric prior and a consistent reference for long-term autonomy. However, dy
arXiv:2605.09652v1 Announce Type: cross Abstract: We extend RDEx-CSOP with 3 changes that target stagnation & late-stage variance, plus minor parameter tuning. The second scale factor in the standard
arXiv:2605.08317v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong performance across diverse tasks, but their inference with long input contexts is bottlenecked by memor
arXiv:2605.10611v1 Announce Type: cross Abstract: This paper proposes a jailbreaking prompt detection method for large language models (LLMs) to defend against jailbreak attacks. Although recent LLMs
arXiv:2605.09012v1 Announce Type: new Abstract: Large language models are increasingly capable at closed-world mathematical reasoning, but research assistance also requires source-grounded use of the
Reachy is mad, but RAM costs + tariffs are forcing our hand. Prices will go up on June 1st! Still at the early bird price until then though if you were looking for an excuse to get one now: http://hf.
arXiv:2605.10430v1 Announce Type: cross Abstract: Estimating heterogeneous treatment effects with machine learning has attracted substantial attention in both academic research and industrial practice
Really amazing dissection. LLMs hitting rarely. Tools list is amazing. 🤩🤯🤩 Claude Code (still not AGI but biggest advance since GPT-4) is the most neurosymbolic thing I have ever seen in my life. 53 s
arXiv:2505.20381v4 Announce Type: replace Abstract: Referring Multi-Object Tracking (RMOT) aims to track targets specified by language instructions. However, existing RMOT paradigms heavily rely on ex
arXiv:2604.01527v3 Announce Type: replace-cross Abstract: Production deployment of AI coding agents requires fast, reproducible evaluation signals. Existing industrial practices trade off speed and fi
arXiv:2605.08713v1 Announce Type: cross Abstract: In recent years, autonomous parking has made significant advances, yet parking tasks still face challenges in extreme scenarios such as mechanical and
arXiv:2605.08625v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) excel at numerical forecasting but operate as black boxes lacking qualitative reasoning. Conversely, applying LL
arXiv:2605.08776v1 Announce Type: new Abstract: Reasoning-centric large language models (LLMs) achieve strong performance by generating intermediate reasoning trajectories, but often incur excessive t
arXiv:2605.08142v1 Announce Type: cross Abstract: Reasoning in large language models is predominantly evaluated through labeled benchmarks, conflating task performance with the quality of internal inf
arXiv:2605.10805v1 Announce Type: new Abstract: Reasoning-capable large language models (LLMs) have recently been adopted as automated judges, but their benefits and costs in LLM-as-a-Judge settings r
arXiv:2511.00371v2 Announce Type: replace Abstract: In Socratic debugging, instructors guide students towards identifying and fixing a bug on their own, instead of providing the bug fix directly. Most
arXiv:2605.10781v1 Announce Type: cross Abstract: Self-distillation has emerged as a powerful framework for post-training LLMs, where a teacher conditioned on extra information guides a student withou
arXiv:2605.08688v1 Announce Type: new Abstract: We establish, from the point of view of Explainable AI (XAI), connections between Consistency-Based Diagnosis (CBD), on one side, and Actual Causality a
arXiv:2605.10612v1 Announce Type: cross Abstract: Graph neural networks are increasingly adopted in trigger systems for collider experiments, where strict latency and throughput constraints render dep
arXiv:2605.08454v1 Announce Type: cross Abstract: Recovering continuous-time dynamics from discrete observations is difficult because local supervision (e.g., pointwise regression targets, derivative
arXiv:2512.24601v3 Announce Type: replace Abstract: We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive
IBM Corp. subsidiary Red Hat today is unveiling a broad set of product and partnership announcements aimed at helping enterprises put artificial intelligence into operation, modernize infrastructure a
arXiv:2605.09067v1 Announce Type: new Abstract: Purpose: To develop and evaluate cross-sequence transfer learning for automatic femoral cartilage segmentation, testing bidirectional transfer between d
arXiv:2605.09614v1 Announce Type: new Abstract: Long chain-of-thought (CoT) reasoning improves large vision--language models, but visual information often fades during generation, limiting long-horizo
arXiv:2605.08315v1 Announce Type: new Abstract: Existing LLM-based policy optimizers see only scalar rewards: that a policy scored 0.45, but not whether the agent got stuck in a loop, fell into a hole
arXiv:2604.03883v2 Announce Type: replace-cross Abstract: Ride-hailing and autonomous mobility-on-demand operators reposition idle supply before future demand is fully observed. We study a retrieval-c
arXiv:2605.06300v2 Announce Type: replace Abstract: Deep networks with continuous piecewise affine activations induce polyhedral partitions of the input space, making the number of realized affine reg
arXiv:2605.10385v1 Announce Type: cross Abstract: Guided-diffusion black-box optimization (BO) has shown strong empirical performance on structured design problems such as molecules and crystals, but
arXiv:2605.10475v1 Announce Type: cross Abstract: We address the problem of maximizing Gain from Trade (GFT) in repeated buyer-seller exchanges subject to global budget balance constraints. While this
arXiv:2505.10872v4 Announce Type: replace-cross Abstract: Robot task planning decomposes human instructions into executable action sequences that enable robots to complete a series of complex tasks. A
arXiv:2605.10759v1 Announce Type: cross Abstract: Diffusion and flow-matching models scale because pretraining is supervised regression: a clean sample is noised analytically, and a model regresses ag
arXiv:2605.08098v1 Announce Type: new Abstract: Kirigami is an increasingly useful fabrication method to produce shape-programmable metamaterial structures. However, inverse design remains difficult b
arXiv:2605.08378v1 Announce Type: cross Abstract: Reinforcement learning has become a powerful paradigm for improving the capability of intelligent systems, but its practical deployment faces two cent
arXiv:2605.09305v1 Announce Type: cross Abstract: Interactive assessments generate sequential process data that are not well handled by conventional item response models. Existing MDP-based measuremen
arXiv:2507.07969v4 Announce Type: replace-cross Abstract: We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward task
arXiv:2605.09262v1 Announce Type: cross Abstract: Reinforcement Learning has significantly advanced the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet the resulting policies r
arXiv:2506.04289v3 Announce Type: replace Abstract: Transformer-based models have demonstrated remarkable reasoning abilities, but the mechanisms underlying relational reasoning remain poorly understo
arXiv:2605.09420v1 Announce Type: cross Abstract: In this study, we tackle Generalized Category Discovery (GCD) via a Relational Retrieval perspective, explicitly coupling labeled and unlabeled data t
arXiv:2605.10317v1 Announce Type: cross Abstract: Knowledge graph embedding (KGE) models typically represent each relation as an operator on entity embeddings. In this work, we identify three structur
arXiv:2605.09008v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) prompting symbolized a huge improvement of reasoning capabilities of Large Language Models (LLMs). However, scaling up test-tim
arXiv:2605.10218v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) offer a promising route to parallel and efficient text generation, but improving their reasoning ability require
arXiv:2602.12606v2 Announce Type: replace Abstract: Relational deep learning (RDL) has emerged as a powerful paradigm for learning directly on relational databases by modeling entities and their relat
arXiv:2605.10706v1 Announce Type: new Abstract: We present a new class of efficient attention mechanisms applying universal 3D Relative Positional Encoding (RPE) methods given by arbitrary integrable
arXiv:2512.20012v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications, assisting with tasks including
arXiv:2605.08639v1 Announce Type: new Abstract: Load imbalance is a long-standing challenge in Mixture-of-Experts (MoE) training and is exacerbated in reinforcement learning (RL) for LLMs, where hot e
arXiv:2605.09024v1 Announce Type: new Abstract: Virtual production (VP) use LED walls to provide both background imagery and image-based lighting. While this enables on-set compositing, it couples lig
arXiv:2605.10870v1 Announce Type: new Abstract: Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive crit
arXiv:2605.10414v1 Announce Type: new Abstract: Rotary Positional Encoding (RoPE) is widely used in modern large language models. However, when sequences are extended beyond the range seen during trai
arXiv:2605.09259v1 Announce Type: cross Abstract: Timbre transfer aims to modify the timbral identity of a musical recording while preserving the original melody and rhythm. While single-instrument ti
arXiv:2605.08871v1 Announce Type: cross Abstract: Large-scale machine learning models are trained on clusters of machines that exhibit heterogeneous performance due to hardware variability, network de
arXiv:2605.08739v1 Announce Type: new Abstract: A converged 3D Gaussian Splatting (3DGS) model may approximate the target scene while remaining poorly parameterized for further optimization. We identi
arXiv:2605.09239v1 Announce Type: new Abstract: Large language models fail at counting repeated tokens despite strong performance on broader reasoning benchmarks. These failures are commonly attribute
arXiv:2605.08197v1 Announce Type: cross Abstract: Most causal benchmarks for language models score local answers or graph structure. We introduce ReplaySCM, a 1,300 item benchmark for executable causa
Replit is going to London ✈️ @posthog CEO, @james406, and @amasad are coming together for a fireside chat, hosted with our friends at @meetgranola. May 21st. Save your spot ⠕ https://luma.com/amjad-ja
arXiv:2605.09410v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models remain brittle in long-horizon, contact-rich manipulation because success-only imitation provides little supervisi
arXiv:2505.18269v5 Announce Type: replace Abstract: We study the problem of selecting a subset from a large action space shared by a family of bandits. In many natural situations, while the nominal se