Knowledge catalogue
arXiv:2604.20795v1 Announce Type: new Abstract: This paper presents a hybrid architecture for intelligent systems in which large language models (LLMs) are extended with an external ontological memory
arXiv:2604.19780v1 Announce Type: new Abstract: Scaling test-time compute via extended reasoning has become a key paradigm for improving the capabilities of large language models (LLMs). However, exis
arXiv:2501.18873v4 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) has emerged as a powerful approach for aligning generative models, but its reliance on learned rew
arXiv:2512.15146v3 Announce Type: replace Abstract: Test-time reinforcement learning mitigates the reliance on annotated data by using majority voting results as pseudo-labels, emerging as a complemen
arXiv:2510.11423v3 Announce Type: replace-cross Abstract: Community Notes, the crowd-sourced misinformation governance system on X (formerly Twitter), allows users to flag misleading posts, attach con
arXiv:2604.20606v1 Announce Type: cross Abstract: Vision Mamba, as a state space model (SSM), employs a zero-order hold (ZOH) discretization, which assumes that input signals remain constant between s
arXiv:2604.20348v1 Announce Type: cross Abstract: Language Models (LLMs) have emerged as powerful reasoning engines for embodied control. In particular, In-Context Learning (ICL) enables off-the-shelf
btw in talking to friends the best framing for how to discuss GPT-Image-2-Thinking taking multiple tens of mins for generation and being able to oneshot QR codes and diagrams and logos and foods and f
arXiv:2602.15861v2 Announce Type: replace-cross Abstract: Text analysis of tabular data relies on two core operations: summarization for corpus-level theme extraction and tagging for row-level labelin
arXiv:2602.20181v2 Announce Type: replace-cross Abstract: Residential energy retrofit initiation is often stalled by an expertise gap, where homeowners lack the technical literacy required for structu
arXiv:2510.18471v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) excel at code generation by learning from vast code corpora, a fundamental semantic gap remains between the
arXiv:2604.19750v1 Announce Type: cross Abstract: Recent advances in Large Language Model (LLM)-based agents have shown remarkable progress in code generation. However, current agent methods mainly re
arXiv:2604.19771v1 Announce Type: cross Abstract: LLM agents lack persistent memory, causing conversations to reset each session and preventing personalization over time. We present Lyzr Cognis, a uni
arXiv:2604.20027v1 Announce Type: cross Abstract: For state-of-the-art image understanding, Vision Transformers (ViTs) have become the standard architecture but their processing diverges substantially
arXiv:2604.20720v1 Announce Type: cross Abstract: Large language models (LLMs) often exhibit performance disparities across languages, with naive multilingual fine-tuning frequently degrading performa
arXiv:2604.20358v1 Announce Type: new Abstract: The Composed Image Retrieval (CIR) task provides a flexible retrieval paradigm via a reference image and modification text, but it heavily relies on exp
arXiv:2506.19977v2 Announce Type: replace Abstract: Understanding which parts of the retrieved context contribute to a large language model's generated answer is essential for building interpretable a
arXiv:2604.20658v1 Announce Type: new Abstract: Multi-agent systems built from teams of large language models (LLMs) are increasingly deployed for collaborative scientific reasoning and problem-solvin
arXiv:2604.20763v1 Announce Type: cross Abstract: Retrieval quality is the primary bottleneck for accuracy and robustness in retrieval-augmented generation (RAG). Current evaluation relies on heuristi
arXiv:2604.20447v1 Announce Type: new Abstract: Named Entity Recognition (NER) is a key component in industrial information extraction pipelines, where systems must satisfy strict latency and throughp
arXiv:2604.19776v1 Announce Type: new Abstract: Tuberculosis (TB) is one of the world's deadliest infectious diseases, and in South Africa, it contributes a significant burden to the country's health
arXiv:2604.20098v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently hallucinate, limiting their reliability in critical applications. Conformal Prediction (CP) addresses this by ca
arXiv:2510.03013v3 Announce Type: replace Abstract: We propose a distributional framework for offline Inverse Reinforcement Learning (IRL) that jointly models uncertainty over reward functions and ful
arXiv:2604.19859v1 Announce Type: cross Abstract: Edge-scale deep research agents based on small language models are attractive for real-world deployment due to their advantages in cost, latency, and
arXiv:2604.20306v1 Announce Type: cross Abstract: Medical Visual Question Answering (MedVQA) aims to generate clinically reliable answers conditioned on complex medical images and questions. However,
arXiv:2604.20183v1 Announce Type: new Abstract: Large Language Models (LLMs) often struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but confl
arXiv:2604.19980v1 Announce Type: new Abstract: This paper presents a model-based reinforcement learning (RL) framework for optimal closed-loop control of nonlinear robotic systems. The proposed appro
arXiv:2604.20012v1 Announce Type: cross Abstract: Vision-Language-Action Models (VLAs) inherit their visual and linguistic capabilities from Vision-Language Models (VLMs), yet most VLAs are built from
arXiv:2604.20083v1 Announce Type: cross Abstract: Active learning (AL) has emerged as a crucial methodology for minimizing labeling costs in deep learning by selecting the most valuable samples from a
Evals ~= Environments…they’re one of the best investments a team can make for improving agents Step 0: Turn On Tracing for Agents Step 1: Point compute at Traces to understand agent behavior, segment
arXiv:2508.17761v3 Announce Type: replace Abstract: In safety-critical applications data-driven models must not only be accurate but also provide reliable uncertainty estimates. This property, commonl
arXiv:2604.20544v1 Announce Type: cross Abstract: The efficacy of Large Vision-Language Models (LVLMs) is critically dependent on the quality of their training data, requiring a precise balance betwee
arXiv:2604.19762v1 Announce Type: new Abstract: The Voynich Manuscript (VMS) exhibits a script of uncertain origin whose grapheme sequences have resisted linguistic analysis. We present a systematic a
arXiv:2604.19755v1 Announce Type: new Abstract: Anti-money laundering (AML) transaction monitoring generates large volumes of alerts that must be rapidly triaged by investigators under strict audit an
arXiv:2604.20726v1 Announce Type: cross Abstract: This work explores the role of prompt design and judge selection in LLM-as-a-Judge evaluations of free text legal question answering. We examine wheth
arXiv:2506.23323v5 Announce Type: replace Abstract: Open-vocabulary semantic segmentation (OVSS) aims to segment objects from arbitrary text categories without requiring densely annotated datasets. Al
arXiv:2604.20735v1 Announce Type: new Abstract: Accurate condition monitoring of industrial equipment requires inferring latent degradation parameters from indirect sensor measurements under uncertain
arXiv:2604.20825v1 Announce Type: cross Abstract: Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed client
arXiv:2604.20736v1 Announce Type: new Abstract: Semi-supervised node classification is a foundational task in graph machine learning, yet state-of-the-art Graph Neural Networks (GNNs) are hindered by
arXiv:2604.20141v1 Announce Type: new Abstract: We introduce Fourier Weak SINDy, a minimal noise-robust and interpretable derivative-free equation learning method that combines weak-form sparse equati
arXiv:2604.20191v1 Announce Type: cross Abstract: Interpretable driver attention prediction is crucial for human-like autonomous driving. However, existing datasets provide only scene-level global gaz
arXiv:2604.19840v1 Announce Type: new Abstract: Graph-theoretic approaches offer simplicity, interpretability, and low computational cost for molecular property prediction. Among these, the model prop
arXiv:2604.20659v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capabilities of Large Language Models (LLMs) by leveraging direct out
arXiv:2604.20155v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has revolutionized real-time rendering, its performance degrades significantly under sparse-view extrapolation, manif
arXiv:2604.19786v1 Announce Type: new Abstract: Evaluating humor in large language models (LLMs) is an open challenge because existing approaches yield isolated, incomparable metrics rather than unifi
arXiv:2604.20328v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning significantly elevates the complex problem-solving capabilities of multimodal large language models (MLLMs). However, a
arXiv:2604.19770v1 Announce Type: new Abstract: We present a hybrid multi-phase page matching algorithm for automated comparison of Japanese building permit document sets. Building permit review in Ja
Ethan Mollick posted on X about having early access to GPT-5.5, commenting positively on its capabilities and noting that the Pro version is particularly strong. He indicated that a full detailed writ
arXiv:2510.02215v3 Announce Type: replace Abstract: Training large-scale recommendation models under a single global objective implicitly assumes homogeneity across user populations. However, real-wor
arXiv:2604.20677v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in socially sensitive settings, raising concerns about fairness and biases, particularly across i
arXiv:2604.20100v1 Announce Type: new Abstract: Robotic autonomy in open-world environments is fundamentally limited by insufficient data diversity and poor cross-embodiment generalization. Existing r
arXiv:2604.19821v1 Announce Type: new Abstract: Large language model (LLM) agents augmented with external tools often struggle as number of tools grow large and become domain-specific. In such setting
arXiv:2505.07527v5 Announce Type: replace Abstract: The advantage function is a central concept in RL that helps reduce variance in policy gradient estimates. For language modeling, Group Relative Pol
arXiv:2508.01575v2 Announce Type: replace Abstract: Long-term time series forecasting (LTSF) underpins critical applications from energy management to weather prediction, yet achieving reliable multi-
arXiv:2502.12911v3 Announce Type: replace Abstract: Generating SQLs from user queries is a long-standing challenge, where the accuracy of initial schema linking significantly impacts subsequent SQL ge
arXiv:2604.20487v1 Announce Type: cross Abstract: Large language models (LLMs) encode knowledge in parametric weights, making it costly to update or extend without retraining. Retrieval-augmented gene
arXiv:2604.19815v1 Announce Type: new Abstract: Drug repurposing is often framed as a candidate identification task, but existing approaches provide limited guidance for distinguishing biologically pl
arXiv:2604.20652v1 Announce Type: new Abstract: Large language models trained on human feedback may suppress fraud warnings when investors arrive already persuaded of a fraudulent opportunity. We test
arXiv:2604.20048v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to describe, evaluate and interpret places, yet it remains unclear whether they do so from a cultural
arXiv:2506.02276v2 Announce Type: replace Abstract: Stochastic Interpolants (SI) is a powerful framework for generative modeling, capable of flexibly transforming between two probability distributions