Knowledge catalogue
arXiv:2608.05539v1 Announce Type: new Abstract: Recent vision-language models (VLMs) can generate executable CAD programs from images, but existing methods mainly target coarse, general-purpose 3D obj
arXiv:2608.05802v1 Announce Type: new Abstract: On-Policy Distillation (OPD) is emerging as a promising alternative to reinforcement learning for LLM post-training, yet its effectiveness in multilingu
arXiv:2608.06296v1 Announce Type: new Abstract: On-policy (Self-)Distillation (OPD / OPSD) has shown strong potential for post-training large language models (LLMs). However, existing methods still re
arXiv:2608.06182v1 Announce Type: cross Abstract: We study stochastic extragradient (SEG) methods for solving monotone variational inequality problems (VIPs) over a feasible set. Although extragradien
arXiv:2510.22899v2 Announce Type: replace Abstract: We investigate the role of network architecture in shaping the inductive biases of modern score-based generative models. To this end, we introduce t
arXiv:2608.05741v1 Announce Type: cross Abstract: Large language models (LLMs) can generate fluent and convincing text at scale, creating growing risks for misinformation dissemination, educational mi
arXiv:2512.14751v3 Announce Type: replace-cross Abstract: Finetuning pretrained large language models (LLMs) has become the standard paradigm for developing downstream applications. However, its secur
arXiv:2608.05240v1 Announce Type: cross Abstract: One-bit post-training quantization represents each weight using only its sign, requiring all deployment contexts to share the same binary weight matri
arXiv:2608.05707v1 Announce Type: new Abstract: Frame selection is essential for applying Large Multimodal Models (LMMs) to long videos due to severe frame redundancy and limited context windows. Sinc
arXiv:2604.01170v2 Announce Type: replace-cross Abstract: While test-time scaling has enabled large language models to solve highly difficult tasks, state-of-the-art results come at exorbitant compute
OpenAI says it is pausing 'internal activities' around an in-development AI model, Astra, because it doesn't yet meet new security standards the company is putting in place. The announcement follows i
arXiv:2608.05990v1 Announce Type: new Abstract: Autonomous agents choose actions using scores that may not reflect experimental success. We developed OPERA, an operator-residual framework for optical
arXiv:2608.05944v1 Announce Type: cross Abstract: We report operational experience full-fine-tuning a 32.76B-parameter dense model (Qwen3-32B) on 16 x NVIDIA B300 (two nodes, FSDP / ZeRO-3) -- among t
arXiv:2409.12788v3 Announce Type: replace Abstract: Recently there has been a surge of interest in optimal decision tree (ODT) methods that globally optimize accuracy directly, in contrast to traditio
arXiv:2608.06337v1 Announce Type: cross Abstract: A monotone adversary observes an i.i.d. labeled sample and appends a finite number of further examples of its choice, every one of them labeled correc
arXiv:2608.05263v1 Announce Type: new Abstract: Multi-agent orchestration frameworks are moving from demos to production, yet benchmarks typically report task accuracy without diagnosing why a pipelin
arXiv:2608.05804v1 Announce Type: new Abstract: 3D human registration has historically been treated as a regression task, assuming a unique ground-truth alignment exists between the template and an in
arXiv:2608.06264v1 Announce Type: new Abstract: The development of deep learning over the past decade has revolutionized medical imaging segmentation, allowing the extraction of precise descriptors fr
arXiv:2608.05206v1 Announce Type: new Abstract: Otter is a 15.3M-parameter human chess AI that predicts human move selection by modeling play as a time-aware, sequential process rather than treating e
Outstanding cost-to-performance from DeepSeek GPT-5.6 Luna (Max) performance for a 1/4th of the cost on ARC-AGI DeepSeek V4 Flash from @deepseek_ai on ARC-AGI (Verified): - ARC-AGI-2: 61.4%, 0.04/task
arXiv:2608.05843v1 Announce Type: new Abstract: Restoring high-fidelity remote sensing imagery from extreme low-light degradation is indispensable for reliable Earth observation and downstream machine
arXiv:2608.06030v1 Announce Type: new Abstract: Extracting data from visualizations has long challenged computer vision, with current research focused on bar, line, and pie charts, among other low-dim
arXiv:2608.06146v1 Announce Type: new Abstract: End-to-end document parsers provide a unified interface, but serialize page layouts and regional contents into one autoregressive sequence. This formula
High-performance inference of NVIDIA's Parakeet TDT 0.6B V2 English transcription model, in the browser. Check out the live demo: https://parakeet.narcotic.sh/ A fully custom, dependancy-free implemen
arXiv:2603.10711v3 Announce Type: replace Abstract: Real-time solution of nonlinear optimal control problems remains challenging on embedded robotic hardware, where conventional solvers often rely on
arXiv:2604.04444v2 Announce Type: replace Abstract: Open-vocabulary object detection (OVOD) enables models to detect any object category, including unseen ones. Benefiting from large-scale pre-trainin
arXiv:2208.08211v2 Announce Type: replace-cross Abstract: Recently, as the demand for cleaning robots has steadily increased, therefore household electricity consumption is also increasing. To solve t
arXiv:2608.05586v1 Announce Type: new Abstract: Autonomous robot navigation requires the rapid generation of obstacle-free regions for trajectory planning. However, existing corridor generators strugg
arXiv:2608.06126v1 Announce Type: new Abstract: An adequate diagnostic quality of radiographs is essential for reliable diagnoses and treatment planning. The patient's pose during radiography is one o
arXiv:2608.05218v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) enables fast, photorealistic talking-head rendering, yet accurate lip articulation remains elusive: mouth motion is often o
arXiv:2512.17426v2 Announce Type: replace-cross Abstract: We consider sparse signal reconstruction via minimization of the smoothly clipped absolute deviation (SCAD) penalty, and develop one-step repl
arXiv:2606.14695v2 Announce Type: replace-cross Abstract: Language Models (LMs) have shown remarkable potential as role-playing chatbots, delivering consistent, stylized interactions when given a spec
arXiv:2608.05876v1 Announce Type: new Abstract: User requests serve as research specifications for deep research agents, shaping what evidence to seek and how to synthesize it. In personalized deep re
arXiv:2608.05419v1 Announce Type: cross Abstract: Models trained by empirical risk minimization on data containing spurious correlations achieve high average accuracy while failing on subpopulations w
arXiv:2601.21124v2 Announce Type: replace-cross Abstract: Current multimodal LLMs process audio as a mono stream, ignoring the rich spatial information essential for embodied AI. Existing spatial audi
arXiv:2608.05720v1 Announce Type: new Abstract: We propose PhyLatent, a dynamics-relevant training objective for JointEmbedding Predictive Architecture (JEPA) world models. Our key observation is that
arXiv:2608.05336v1 Announce Type: cross Abstract: Molecular representations are essential for the evaluation of molecular similarity and the development of structure-property relationships. Despite th
arXiv:2606.14750v2 Announce Type: replace-cross Abstract: Recent advances in pixel-based text modeling show that representing text as images enables models to exploit visual cues for language understa
arXiv:2604.17359v2 Announce Type: replace-cross Abstract: Language models asked to simulate psychiatric patients produce cases that survive inspection one at a time and populations that match no real
I'm considering replacing a single RTX 3090 with two ASRock AMD Pro R9700s for about 2900 new out of the door. That would move me from 24GB to 64GB VRAM. Yes yes, CUDA/ROCm, but the real problem is po
arXiv:2608.06123v1 Announce Type: new Abstract: Measuring political bias in large language models (LLMs) remains challenging as it can manifest through subtle differences in framing, argumentation, an
arXiv:2606.13227v2 Announce Type: replace Abstract: Post-training methods such as supervised fine-tuning (SFT) and preference optimization typically align language models toward a single global assist
arXiv:2608.05162v1 Announce Type: new Abstract: Pooling is a consequential but under-examined design choice in decoder-only concept representation work: practitioners must collapse token-level hidden
arXiv:2608.05188v1 Announce Type: cross Abstract: Despite ever-increasing sophistication in language model (LM) pre- and post-training pipelines, many important failures persist: models overcondition
arXiv:2608.05341v1 Announce Type: new Abstract: Vision-Language Models (VLMs) for radiology report generation are typically trained on retrospective clinical reports, which suffer from omission noise:
arXiv:2608.05199v1 Announce Type: cross Abstract: Autonomous security agents operate as staged pipelines, such as classifying network traffic and then attributing attacks to a specific technique. Spli
arXiv:2608.05256v1 Announce Type: new Abstract: Pre-commitment posture, the assignment of military assets to theater locations before conflict scenarios resolve, is a critical and formally unsolved pr
arXiv:2608.05666v1 Announce Type: new Abstract: We introduce Potential Matching Optimal Transport (PMOT), a potential-flow framework for general p-cost optimal transport with c_p(x,y)=|x-y|^p. PMOT pa
arXiv:2608.05234v1 Announce Type: new Abstract: Building reliable applications that leverage large language models (LLMs) remains a significant challenge. While LLMs offer impressive capabilities acro
arXiv:2511.17241v2 Announce Type: replace Abstract: Understanding and predicting user behavior on social media platforms is crucial for content recommendation and platform design. While existing appro
arXiv:2608.05797v1 Announce Type: cross Abstract: Task difficulty dictates an agent's likelihood of success, and estimating it without rollouts means forecasting this directly from a task description
arXiv:2608.06170v1 Announce Type: cross Abstract: Hierarchical 3D scene graphs are a promising representation for high-level spatial reasoning in autonomous mobile platforms. However, existing extract
arXiv:2608.06240v1 Announce Type: cross Abstract: Unpaired image-to-image translation must decide, per image, what to change and what to preserve without paired supervision. Many diffusion-based unpai
arXiv:2608.05249v1 Announce Type: cross Abstract: Real-world multimodal instructions often bundle multiple requirements with unequal importance, yet most multimodal training data still reduce instruct
arXiv:2608.06015v1 Announce Type: cross Abstract: Deep Reinforcement Learning (RL) is notoriously sample inefficient. One contributing factor is that RL agents are typically initialized from scratch,
arXiv:2608.05225v1 Announce Type: new Abstract: Research agents can increasingly search literature, propose hypotheses, generate code, run experiments, and draft manuscripts from a single topic. Howev
arXiv:2507.00673v2 Announce Type: replace-cross Abstract: Image segmentation is central to automated medical image analysis, enabling precise identification of anatomical structures and pathological r
arXiv:2608.05686v1 Announce Type: cross Abstract: Quantum error correction protects logical information only when every physical operation remains below the fault-tolerance threshold, a condition that
arXiv:2608.05326v1 Announce Type: cross Abstract: Autoregressive large language model inference is increasingly constrained by the memory footprint of the Key-Value (KV) cache. A dominant line of work
arXiv:2608.05230v1 Announce Type: cross Abstract: The daily allocation of the finite 24-hour time budget is strongly associated with physical, mental, and cognitive health. While predictive models can