Knowledge catalogue
arXiv:2604.12616v1 Announce Type: new Abstract: The rapid evolution of Vision-Language Models (VLMs) has catalyzed unprecedented capabilities in artificial intelligence; however, this continuous modal
arXiv:2604.12568v1 Announce Type: new Abstract: Conventional deep network training generally optimizes all samples under a largely uniform learning paradigm, without explicitly modeling the heterogene
arXiv:2604.12776v1 Announce Type: new Abstract: Realizing endogenous narrative evolution in LLM-based multi-agent systems is hindered by the inherent stochasticity of generative emergence. In particul
arXiv:2604.12781v1 Announce Type: new Abstract: Recently, detecting AI-generated images produced by diffusion-based models has attracted increasing attention due to their potential threat to safety. A
arXiv:2604.12385v1 Announce Type: new Abstract: Multi-turn dialogue is the predominant form of interaction with large language models (LLMs). While LLM routing is effective in single-turn settings, ex
FSD v14.3.1 review after 10 drives and many hours, here are my thoughts (it’s a great one) - v14.3.1 feels like a big jump even compared to FSD v14.3, even for “just” a point release build. Everything
Generative AI in nutshell: 'If the technology is as formidable as the [tech CEO's] claim, then they could be leading us toward existential disaster; if the technology proves less transformative, and t
arXiv:2604.12630v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have exhibited remarkable performance in various visual tasks, yet still struggle with spatial reasoning. Rec
arXiv:2604.02821v2 Announce Type: replace Abstract: This paper presents a learning-based approach for all-pairs motion planning, where the initial and goal states are allowed to be arbitrary points in
arXiv:2506.01256v4 Announce Type: replace-cross Abstract: Forced alignment is a common tool to align audio with orthographic and phonetic transcriptions. Most forced alignment tools provide only point
arXiv:2206.00939v3 Announce Type: replace-cross Abstract: The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution. Yet, despite some recent progres
arXiv:2604.05643v2 Announce Type: replace Abstract: Extending CoT through RL has been widely used to enhance the reasoning capabilities of LLMs. However, due to the sparsity of reward signals, it can
arXiv:2604.12929v1 Announce Type: new Abstract: We present Grasp in Gaussians (GraG), a fast and robust method for reconstructing dynamic 3D hand-object interactions from a single monocular video. Unl
Grok just hit its highest monthly traffic EVER: over 326 MILLION visits in March alone That’s a massive 61% jump YoY and up 9.3% just since February People love Grok because it's the only AI they can
arXiv:2603.27306v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been proposed as supervisory agents for spacecraft operations, but existing approaches rely on static prompt
arXiv:2511.09780v2 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) has demonstrated wide adoption in the post-training of Large Language Models (LLMs). In GRPO, prompts are
arXiv:2509.19742v4 Announce Type: replace-cross Abstract: Zero-shot Dialog State Tracking (zs-DST) is essential for enabling Task-Oriented Dialog Systems (TODs) to generalize to new domains without co
arXiv:2604.11912v1 Announce Type: cross Abstract: While next-token prediction (NTP) has been the standard objective for training language models, it often struggles to capture global structure in reas
arXiv:2604.12663v1 Announce Type: new Abstract: Existing topic modeling methods, from LDA to recent neural and LLM-based approaches, which focus mainly on statistical coherence, often produce redundan
arXiv:2511.21356v2 Announce Type: replace-cross Abstract: Adversarial Inverse Reinforcement Learning (AIRL) has shown promise in addressing the sparse reward problem in reinforcement learning (RL) by
I find it fascinating that OpenAI's Global Affairs team will denounce AI doomers for being too negative and then, simultaneously, advocate for an Illinois bill that would shield them from liability if
I spent some time trying to distill all the complex factors impacting open models -- economics, capabilities, distribution, policy, etc. -- into a clear list of beliefs. Here they are in full. 1. It’s
arXiv:2505.19134v2 Announce Type: replace-cross Abstract: Human-annotated data plays a vital role in training large language models (LLMs), such as supervised fine-tuning and human preference alignmen
arXiv:2604.12340v1 Announce Type: cross Abstract: We decompose the Kullback--Leibler generalization error (GE) -- the expected KL divergence from the data distribution to the trained model -- of unsup
arXiv:2604.12721v1 Announce Type: new Abstract: Clinical case formulation organizes patient symptoms and psychosocial factors into causal models, often using the 5P framework. However, constructing su
arXiv:2604.12303v1 Announce Type: new Abstract: Batch active learning (BAL) is a crucial technique for reducing labeling costs and improving data efficiency in training large-scale deep learning model
arXiv:2601.10398v3 Announce Type: replace Abstract: In LLM-based text-to-SQL systems, unanswerable and underspecified user queries may generate not only incorrect text but also executable programs tha
arXiv:2604.12413v1 Announce Type: cross Abstract: Dynamic soaring enables sustained flight by extracting energy from wind shear, yet it is commonly understood as a cycle-level maneuver that assumes st
arXiv:2604.13015v1 Announce Type: new Abstract: Humanoid robots promise general-purpose assistance, yet real-world humanoid loco-manipulation remains challenging because it requires whole-body stabili
arXiv:2604.13010v1 Announce Type: cross Abstract: On-policy distillation (OPD) has emerged as an efficient post-training paradigm for large language models. However, standard OPD requires a live teach
arXiv:2604.12286v1 Announce Type: new Abstract: Live Photo captures both a high-quality key photo and a short video clip to preserve the precious dynamics around the captured moment. While users may c
arXiv:2604.12994v1 Announce Type: cross Abstract: Logical vulnerabilities in software stem from flaws in program logic rather than memory safety, which can lead to critical security failures. Although
arXiv:2603.19042v4 Announce Type: replace Abstract: The integration of artificial intelligence (AI) technologies into judicial decision-making, particularly in pretrial, sentencing, and parole context
arXiv:2604.12479v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have significantly improved the alignment of models with general human preferences. However, a major cha
arXiv:2604.12277v1 Announce Type: new Abstract: Pretrained language models often rely on superficial features that appear predictive during training yet fail to generalize at test time, a phenomenon k
arXiv:2604.12537v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have achieved remarkable progress in multimodal understanding, yet their positional encoding mechanisms remain suboptima
arXiv:2604.12237v1 Announce Type: cross Abstract: In drug discovery, molecular optimization aims to iteratively refine a lead compound to improve molecular properties while preserving structural simil
arXiv:2508.17403v3 Announce Type: replace Abstract: A community of researchers appears to think that a machine can be surprised and have introduced various surprise measures, principally the Shannon S
arXiv:2601.06794v2 Announce Type: replace Abstract: Critique-guided reinforcement learning (RL) has emerged as a powerful paradigm for training LLM agents by augmenting sparse outcome rewards with nat
arXiv:2604.05164v2 Announce Type: replace-cross Abstract: As LLM reasoning performance plateau, improving inference-time compute efficiency is crucial to mitigate overthinking and long thinking traces
arXiv:2604.11994v1 Announce Type: new Abstract: We study offline-online reinforcement learning in linear mixture Markov decision processes (MDPs) under environment shift. In the offline phase, data ar
arXiv:2604.12852v1 Announce Type: new Abstract: Collaborative transport requires robots to infer partner intent through physical interaction while maintaining stable loco-manipulation. This becomes pa
arXiv:2604.12986v1 Announce Type: cross Abstract: Autonomous AI agents are rapidly transitioning from experimental tools to operational infrastructure, with projections that 80% of enterprise applicat
arXiv:2507.06448v5 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has proven to be a highly effective strategy for endowing Large Language Models (LLMs) with ro
arXiv:2604.12239v1 Announce Type: new Abstract: Accurate inter-vehicle distance estimation is a cornerstone of Advanced Driver Assistance Systems (ADAS) and autonomous driving. While LiDAR and radar p
🇧🇪 Positive news for FSD Supervised in Belgium! I just received an official response from the cabinet of @MDiependaele , Minister-President of the Flemish Government. A few days ago, the Dutch vehicle
arXiv:2604.12113v1 Announce Type: cross Abstract: Visual Foundation Models (VFMs) such as the Segment Anything Model (SAM) have significantly advanced broad use of image segmentation. However, SAM and
Prediction from a year ago about AI backlash that looks to be on track: Public backlash against AI will so be strong by 2028 that anti-AI sentiment will probably be a major factor in the 2028 US Presi
arXiv:2604.12384v1 Announce Type: new Abstract: Safety alignment in Large Language Models (LLMs) remains highly fragile during fine-tuning, where even benign adaptation can degrade pre-trained refusal
arXiv:2604.12970v1 Announce Type: cross Abstract: Multimodal federated learning enables privacy-preserving collaborative model training across healthcare institutions. However, a fundamental challenge
arXiv:2511.17097v2 Announce Type: replace Abstract: Vision-Language Navigation requires agents to act coherently over long horizons by understanding not only local visual context but also how far they
arXiv:2604.12160v1 Announce Type: new Abstract: Reasoning post-training with reinforcement learning from verifiable rewards (RLVR) is typically studied in centralized settings, yet many realistic appl
arXiv:2604.12418v1 Announce Type: cross Abstract: Autonomous vehicles are increasingly deployed in safety-critical applications, where sensing failures or cyberphysical attacks can lead to unsafe oper
arXiv:2511.10453v3 Announce Type: replace-cross Abstract: Large language models often respond to ambiguous requests by implicitly committing to one interpretation, frustrating users and creating safet
arXiv:2604.12175v1 Announce Type: new Abstract: Recent advances in image editing have heightened the need for reliable Image Editing Quality Assessment (IEQA). Unlike traditional methods, IEQA require
arXiv:2604.12582v1 Announce Type: new Abstract: Recent Video Large Language Models (Video-LLMs) have demonstrated strong capability in video understanding, yet they still suffer from hallucinations. E
arXiv:2604.12753v1 Announce Type: new Abstract: Specular glare on reflective floors and glass surfaces frequently corrupts RGB-D depth measurements, producing holes and spikes that accumulate as persi
arXiv:2604.13016v1 Announce Type: cross Abstract: On-policy distillation (OPD) has become a core technique in the post-training of large language models, yet its training dynamics remain poorly unders
arXiv:2511.09803v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) improves factuality but retrieving for every query often hurts quality while inflating tokens and latency. We p
arXiv:2604.12693v1 Announce Type: new Abstract: Deep learning models often achieve expert-level accuracy in medical image classification but suffer from a critical flaw: semantic incoherence. These hi