Knowledge catalogue
arXiv:2604.10316v1 Announce Type: new Abstract: Background: Large Language Models (LLMs) are transforming artificial intelligence applications in healthcare due to their ability to understand, generat
arXiv:2604.11572v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) have demonstrated strong potential for embodied AI, yet their deployment on resource-limited robots remains challen
arXiv:2604.11299v1 Announce Type: cross Abstract: In recent years, rapid advances in Multimodal Large Language Models (MLLMs) have increasingly stimulated research on ancient Chinese scripts. As the e
arXiv:2604.11154v1 Announce Type: new Abstract: New multi-modal large language models (MLLMs) are continuously being trained and deployed, following rapid development cycles. This generative AI frenzy
arXiv:2604.09606v1 Announce Type: new Abstract: Traditional benchmarks for large language models (LLMs), such as HELM and AIR-BENCH, primarily assess safety risk through breadth-oriented evaluation ac
arXiv:2509.04334v4 Announce Type: replace Abstract: Geographic reasoning is a fundamental cognitive capability that requires models to infer plausible locations by synthesizing visual evidence with sp
arXiv:2604.11401v1 Announce Type: new Abstract: Recent semantic 3D Gaussian Splatting (3DGS) methods primarily rely on 2D foundation models, often yielding ambiguous boundaries and limited support for
arXiv:2604.11133v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly being explored for clinical question answering and decision support, yet safe deployment critically requir
arXiv:2510.02311v2 Announce Type: replace Abstract: We study the task of predicting dynamic physical properties from videos. More specifically, we consider physical properties that require temporal in
arXiv:2501.06416v3 Announce Type: replace-cross Abstract: Designing a reinforcement learning from human feedback (RLHF) algorithm to approximate a human's unobservable reward function requires assumin
arXiv:2604.09841v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly adapted through domain-specific fine-tuning, yet it remains unclear whether this improves reasoning bey
arXiv:2604.10326v1 Announce Type: cross Abstract: Large language models remain vulnerable to jailbreak attacks -- inputs designed to bypass safety mechanisms and elicit harmful responses -- despite ad
arXiv:2406.13621v2 Announce Type: replace Abstract: Commonsense reasoning often requires both textual and visual knowledge, yet Large Language Models (LLMs) trained solely on text lack visual groundin
arXiv:2604.07382v2 Announce Type: replace-cross Abstract: The geometric structure of latent representations in large language models (LLMs) is an active area of research, driven in part by its implica
arXiv:2511.18373v2 Announce Type: replace Abstract: Vision Language Models (VLMs) perform well on standard video tasks but struggle with physics-related reasoning involving motion dynamics and spatial
MiniMax M2.7 is a large language model now available for local deployment through LM Studio, notable for its strong performance in agentic tool calling tasks. The model requires a substantial minimum
arXiv:2505.23606v4 Announce Type: replace-cross Abstract: Unified generation models aim to handle diverse tasks across modalities -- such as text generation, image generation, and vision-language reas
arXiv:2604.11543v1 Announce Type: cross Abstract: Novelty is a core requirement in academic publishing and a central focus of peer review, yet the growing volume of submissions has placed increasing p
arXiv:2512.22142v2 Announce Type: replace-cross Abstract: The foundation-model ecosystem remains highly centralized because training requires immense compute resources and is therefore largely limited
arXiv:2602.21428v2 Announce Type: replace Abstract: Medical Vision Language Models (VLMs) can change their answers when clinicians rephrase the same question, a failure mode that threatens deployment
arXiv:2604.11399v1 Announce Type: cross Abstract: Multimodal adaptation equips large language models (LLMs) with perceptual capabilities, but often weakens the reasoning ability inherited from languag
arXiv:2604.09548v1 Announce Type: cross Abstract: Older adults commonly experience chronic conditions such as pain and sleep disturbances and may consider cannabidiol for symptom management. Safe use
arXiv:2604.11711v1 Announce Type: new Abstract: Occlusion, where target structures are partially hidden by surgical instruments or overlapping tissues, remains a critical yet underexplored challenge f
arXiv:2604.11200v1 Announce Type: cross Abstract: Changes in input distribution can induce shifts in the average predictions of machine learning models. Such prediction shifts may impact downstream bu
arXiv:2604.03873v2 Announce Type: replace-cross Abstract: Black-box knowledge distillation for large language models presents a strict trade-off. Simple off-policy methods (e.g., sequence-level knowle
arXiv:2504.06313v5 Announce Type: replace Abstract: This paper investigates how popular text-to-image (T2I) models, DALL-E 3 and Gemini 3 Pro Preview, depict people from 206 nationalities when prompte
arXiv:2505.17682v2 Announce Type: replace-cross Abstract: Predicting user behavior is essential for intelligent assistant services, yet deep learning models often struggle to capture long-tailed behav
We've tested new OSS models the moment they're released for a while at Lindy. Inference is our #1 cost by a lot (more than payroll) — cutting it by 2-5x would be transformative. Last year, OSS models
arXiv:2604.10079v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) is the standard approach for adapting large language models (LLMs) to downstream tasks. However, we observe a persistent fa
arXiv:2604.08964v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently become a promising alternative to autoregressive large language models (ARMs). Semi-autoregressive
Natsuki Yamamoto / Nikkei Asia: Sources: SoftBank, Sony, Honda, and six other Japanese companies launch a new AI company to develop a 1T-parameter foundation model for “physical AI” by 2030 — TOKYO —
arXiv:2602.22918v2 Announce Type: replace Abstract: Vision-language models (VLMs) can read text from images, but where does this optical character recognition (OCR) information enter the language proc
I think Muse Spark came in far better than most were expecting as the first new model attempt from Meta, especially given the fact that it has been a year since Llama 4 with no models at all (and that
Tesla's Model Y is featured in a post from the Tesla Japan X (formerly Twitter) account, likely promoting the vehicle to Japanese consumers with a cherry blossom theme suggested by the flower emoji. T
OpenAI should probably bite the bullet and just name their next set of models something more human sounding. Everyone anthropomorphizes their AIs anyway, and 'Claude' is an easier name to refer to tha
The `'Failed to recognize model type!'` error in ForgeNeo (stable-diffusion-webui-forge) is a `ValueError` raised by the backend loader (`backend/loader.py`) when the application cannot identify th...
This r/StableDiffusion thread discusses the community debate around choosing between FLUX.2 [klein] 9B and Qwen Image Edit 2511 as an image editing model, two strong open-source contenders in the spac
arXiv:2604.07583v1 Announce Type: new Abstract: Real-world categorization is severely hampered by class imbalance because traditional ensembles favor majority classes, which lowers minority performanc
arXiv:2604.07166v1 Announce Type: cross Abstract: Although visual foundation models like DINOv2 provide state-of-the-art performance as feature extractors, their complex, high-dimensional representati
arXiv:2412.08637v4 Announce Type: replace Abstract: Identifying the training data samples that most influence a generated image is a critical task in understanding diffusion models (DMs), yet existing
arXiv:2507.10303v2 Announce Type: replace-cross Abstract: Stochastic simulators exhibit intrinsic stochasticity due to unobservable, uncontrollable, or unmodeled input variables, resulting in random o
arXiv:2604.07991v1 Announce Type: new Abstract: Recent advances in world models have demonstrated strong capabilities in simulating physical reality, making them an increasingly important foundation f
arXiv:2510.26083v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) excel at general language tasks but struggle in specialized domains. Specialized Generalist Models (SGMs) address
arXiv:2604.06562v1 Announce Type: new Abstract: Small language models (SLM) are increasingly used as interactive decision-making agents, yet most decision-oriented evaluations ignore emotion as a caus
arXiv:2604.06996v1 Announce Type: cross Abstract: LLM-as-a-judge has become the de facto approach for evaluating LLM outputs. However, judges are known to exhibit self-preference bias (SPB): they tend
arXiv:2604.07592v1 Announce Type: new Abstract: Embodied-AI agents must reason about how objects move and interact in 3-D space over time, yet existing smaller frontier Large Language Models (LLMs) st
arXiv:2602.22683v2 Announce Type: replace Abstract: The rapid advancement of AI-powered smart glasses-one of the hottest wearable devices-has unlocked new frontiers for multimodal interaction, with Vi
arXiv:2604.06427v1 Announce Type: cross Abstract: The viability of chain-of-thought (CoT) monitoring hinges on models being unable to reason effectively in their latent representations. Yet little is
arXiv:2604.06613v2 Announce Type: cross Abstract: Modern reasoning models continue generating long after the answer is already determined. Across five model configurations, two families, and three ben
arXiv:2512.08410v2 Announce Type: replace Abstract: Due to excessive memory overhead, most Multimodal Large Language Models (MLLMs) can only process videos of limited frames. In this paper, we propose
arXiv:2604.08362v1 Announce Type: new Abstract: The emergence of Large Language Models (LLMs) has illuminated the potential for a general-purpose user simulator. However, existing benchmarks remain co
arXiv:2511.18787v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) perform well on multimodal benchmarks but lag behind humans and specialized models on visual perception tasks like dep
I was unable to retrieve the content of the specified Reddit URL directly, as my web search tool does not fetch raw URLs or Reddit threads directly, and I've exhausted my search attempts for this t...
The specific X (Twitter) post at the provided URL could not be retrieved — the content is not publicly indexed in search results, and direct access to X posts requires authentication or is otherwis...
The founder of a $4B inference company says that if you're building agents, foundational models could become your IP. According to @lqiao, 90% of the world's data is still private and locked inside ap
Users are running Google's Gemma 4 models locally via Ollama and accessing them on their phones through Open WebUI, reporting a positive experience. Since Ollama doesn't run natively on iOS or And...
In this post, we'll walk you through a complete implementation of model fine-tuning in Amazon Bedrock using Amazon Nova models, demonstrating each step through an intent classifier example that achiev
JEPA world models + Hierarchical Planning is a massive step for long-horizon robotics. A classic failure mode I’ve faced with planning with world models: flat planning often 'cheats.' For example, in
arXiv:2608.11216v1 Announce Type: new Abstract: World modeling is an unsettled field: architectures, training objectives, and state representations interact in complex ways, and no single recipe domin
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