Knowledge catalogue
I spent the last 2 months trying to prevent this. If OpenAI offered a fig leaf, Google said 'imagine we offered a fig leaf.' Google affirms it can't veto usage, commits to modify safety filters at gov
arXiv:2604.22817v1 Announce Type: cross Abstract: Recent advances in speech-aware language models have coupled strong acoustic encoders with large language models, enabling systems that move beyond tr
In this informal poll, only a fifth of all coders and software engineers have gone over full time to vibe coding. Most use vibe coding; few use it exclusively. Most still do some hand coding. Amateurs
arXiv:2602.23024v2 Announce Type: replace Abstract: Mobile manipulation is a fundamental capability for general-purpose robotic agents, requiring both coordinated control of the mobile base and manipu
arXiv:2604.23446v1 Announce Type: new Abstract: Industrial maintenance environments increasingly rely on AI systems to assist operators in understanding asset behavior, diagnosing failures, and evalua
arXiv:2604.23095v1 Announce Type: new Abstract: Indoor environments lack the spatial intelligence infrastructure that GPS provides outdoors; first responders arriving at unfamiliar buildings typically
arXiv:2604.22966v1 Announce Type: cross Abstract: Each major technological revolution inverts a particular scarcity and rebuilds institutions around the shift. The near-consensus diagnosis of the AI r
arXiv:2604.22832v1 Announce Type: cross Abstract: Microscopy-based phenotypic profiling is scalable for drug discovery but lacks the mechanistic depth of transcriptomics, which remains costly and scar
arXiv:2604.24114v1 Announce Type: new Abstract: Curriculum learning helps language models tackle complex reasoning by gradually increasing task difficulty. However, it often fails to generate consiste
arXiv:2508.01191v5 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting has been shown to be effective in eliciting structured reasoning (i.e., CoT reasoning) from large language models (
arXiv:2603.06589v2 Announce Type: replace-cross Abstract: Model calibration and debiasing are fundamental yet operationally expensive challenges in large-scale recommendation systems. Existing approac
arXiv:2508.10945v2 Announce Type: replace Abstract: Potholes on the roads are a serious hazard and maintenance burden. This poses a significant threat to road safety and vehicle longevity, especially
arXiv:2604.24031v1 Announce Type: new Abstract: The encoder-decoder framework has become widely popular nowadays. In this model, the encoder extracts informative visual features from an input image, a
arXiv:2604.23056v1 Announce Type: cross Abstract: We propose a simple yet effective alternative to reward normalization in policy gradient reinforcement learning by integrating a 1D Kalman filter for
arXiv:2604.23320v1 Announce Type: new Abstract: The Convolutional Neural Networks (CNNs) have been the dominant and effective approach for general computer vision tasks. Recently, Kolmogorov-Arnold ne
arXiv:2604.23257v1 Announce Type: cross Abstract: Software engineering (SE) organizations operate in a knowledge-intensive domain where critical assets -- architectural expertise, design rationale, an
arXiv:2604.23513v1 Announce Type: new Abstract: In high-conflict mixed-traffic scenarios involving human-driven and autonomous vehicles, most existing autonomous driving systems default to overly cons
arXiv:2604.23957v1 Announce Type: new Abstract: Proactive watermarking offers a promising approach for deepfake tamper detection and localization in short-form videos. However, existing methods often
arXiv:2604.23360v1 Announce Type: new Abstract: Learning from demonstration is widely used for robot navigation, yet it suffers from a fundamental limitation: demonstrations consist predominantly of s
arXiv:2604.23125v1 Announce Type: new Abstract: Real-world data often exhibit long-tailed distributions with numerous noisy labels, substantially degrading the performance of deep models. While prior
arXiv:2604.23000v1 Announce Type: new Abstract: In behavioral cloning (BC), policy performance is fundamentally limited by demonstration data quality. Real-world datasets contain trajectories of varyi
arXiv:2604.24681v1 Announce Type: new Abstract: Human videos contain rich manipulation priors, but using them for robot learning remains difficult because raw observations entangle scene understanding
arXiv:2604.23855v1 Announce Type: new Abstract: We present a deployed system that automates end-to-end customer support workflows inside an enterprise Business Process Management (BPM) platform. The a
arXiv:2509.00084v2 Announce Type: replace-cross Abstract: Test-time scaling (TTS) has gained widespread attention for enhancing LLM reasoning. Existing approaches such as Best-of-N and majority voting
arXiv:2405.20642v3 Announce Type: replace Abstract: Machine learning has become increasingly popular in informing data-driven policy-making. Policies influence behavior in individuals or populations,
arXiv:2604.23950v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in visual understanding and reasoning, but they also impose significant
arXiv:2603.20806v2 Announce Type: replace Abstract: Multi-label fundus diagnosis requires features that capture both fine-grained lesions and large-scale retinal structure. Many multi-scale medical vi
arXiv:2604.00829v3 Announce Type: replace-cross Abstract: Adapting pretrained language models (LMs) into vision-language models (VLMs) can degrade their native linguistic capability due to representat
arXiv:2512.10551v2 Announce Type: replace-cross Abstract: The commercialization of LLM applications is the next frontier in online advertising, with LLM-native advertising emerging as a promising para
arXiv:2604.23902v1 Announce Type: new Abstract: Traffic signal control is a critical task in intelligent transportation systems, yet conventional fixed-time and rule-based methods often struggle to ad
arXiv:2604.23578v1 Announce Type: cross Abstract: Human daily behavior unfolds as complex sequences shaped by intentions, preferences, and context. Effectively modeling these behaviors is crucial for
arXiv:2603.03269v2 Announce Type: replace Abstract: Feedforward geometric foundation models achieve strong short-window reconstruction, yet scaling them to minutes-long videos is bottlenecked by quadr
arXiv:2604.22896v1 Announce Type: cross Abstract: Indoor positioning is an essential technology for a wide range of applications in GNSS-denied environments, including indoor navigation and IoT system
arXiv:2510.17590v2 Announce Type: replace Abstract: We present MERIT, an inference-time modular framework for multimodal misinformation detection that decomposes verification into four specialized mod
arXiv:2604.24178v1 Announce Type: cross Abstract: Multi-Objective Alignment aims to align Large Language Models (LLMs) with diverse and often conflicting human values by optimizing multiple objectives
arXiv:2604.24374v1 Announce Type: new Abstract: Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Repr
arXiv:2604.23788v1 Announce Type: new Abstract: Appreciating multi-figure paintings requires understanding how characters relate through subtle cues like gaze alignment, gesture, and spatial arrangeme
arXiv:2604.24280v1 Announce Type: new Abstract: We present a framework using Relative Entropy Inverse Reinforcement Learning (RE-IRL) to recover investor reward functions from observed investment acti
arXiv:2604.23264v1 Announce Type: new Abstract: Text-to-motion generation aims to generate 3D human motions that are tightly aligned with the input text while remaining physically plausible and rich i
arXiv:2604.22881v1 Announce Type: cross Abstract: Generative recommendation (GR) offers superior modeling capabilities but suffers from prohibitive inference costs due to the repeated encoding of long
arXiv:2604.24186v1 Announce Type: cross Abstract: Diagnostic prediction and clinical reasoning are critical tasks in healthcare applications. While Large Language Models (LLMs) have shown strong capab
arXiv:2508.07555v3 Announce Type: replace Abstract: We consider a remote inference system with multiple modalities, where a multimodal machine learning (ML) model performs real-time inference using fe
arXiv:2508.07220v2 Announce Type: replace-cross Abstract: Learning stochastic functions from partially observed context-target pairs requires models that are expressive, uncertainty-aware, and strongl
arXiv:2604.22837v1 Announce Type: cross Abstract: SAM-based dense trackers provide strong short-term mask propagation but remain fragile under long occlusion, fast motion, viewpoint change, and distra
arXiv:2604.24191v1 Announce Type: new Abstract: Omnimodal understanding entails a massive, highly redundant search space of cross-modal interactions, demanding focused and deliberative reasoning. Curr
arXiv:2512.09297v3 Announce Type: replace Abstract: Learning dexterous bimanual manipulation policies critically depends on large-scale, high-quality demonstrations, yet current paradigms face inheren
OpenAI, which squandered its tremendous lead and is now missing its projections, is in trouble. There’s no two ways about it. 'OpenAI Chief Financial Officer Sarah Friar has told other company leaders
arXiv:2604.24242v1 Announce Type: new Abstract: OpenPodcar2 is a robust, ROS2-interfaced, low-cost, open source hardware and software, autonomous vehicle platform based on an off-the-shelf, hard-canop
arXiv:2604.23540v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved remarkable generative capabilities, yet accurately aligning complex textual prompts with synthesized layout
arXiv:2502.04274v4 Announce Type: replace Abstract: End-to-end representation learning has become a powerful tool for estimating causal quantities from high-dimensional observational data, but its eff
arXiv:2604.24348v1 Announce Type: new Abstract: The evolution of Multimodal Large Language Models (MLLMs) has shifted the focus from text generation to active behavioral execution, particularly via OS
OpenAI outlines its commitment to implementing safety measures and responsible practices in the development and deployment of AI systems to protect users and communities. The statement likely covers O
arXiv:2604.23412v1 Announce Type: new Abstract: While annotated corpora are crucial in the field of natural language processing (NLP), those containing copyrighted material are difficult to exchange a
arXiv:2604.24384v1 Announce Type: new Abstract: Automated vehicles (AVs) are commonly programmed to yield unconditionally to pedestrians in the interest of safety. However, this design choice can give
arXiv:2604.22971v1 Announce Type: cross Abstract: The TRUST democratic discourse analysis pipeline exposes its large language model (LLM) components to peer model identity through multiple structural
arXiv:2604.23600v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in persona-driven applications such as education, customer service, and social platforms, where m
arXiv:2604.23574v1 Announce Type: new Abstract: Existing image-to-video generation methods often produce physically implausible motions and lack precise control over object dynamics. While prior appro
arXiv:2604.24171v1 Announce Type: new Abstract: Current visual text generation models struggle with the trade-off between text accuracy and overall image coherence. We find that achieving high text ac
arXiv:2509.25424v5 Announce Type: replace-cross Abstract: Reinforcement learning fine-tuning (RLFT) is a dominant paradigm for improving pretrained policies for downstream tasks. These pretrained poli
arXiv:2604.04177v2 Announce Type: replace Abstract: As large language models (LLMs) are increasing integrated into fact-checking pipelines, formal logic is often proposed as a rigorous means by which