Knowledge catalogue
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: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 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:2610.20627v1 Announce Type: new Abstract: Vision-language models are increasingly used for driving-scene understanding, yet their defensive reasoning under weakly structured Global South traffic
On October 16, 2026, China’s Ministry of Commerce and Ministry of Science and Technology released a draft revision to the Catalog of Technologies Prohibited and Restricted from Export. It proposes add
arXiv:2610.21165v1 Announce Type: new Abstract: Long-context capability has become essential for large language models (LLMs) to handle complex tasks, yet continuously expanding the context window doe
arXiv:2601.21132v2 Announce Type: replace Abstract: I demonstrate that large language models can infer ethnicity from names with accuracy exceeding that of Bayesian Improved Surname Geocoding (BISG) w
arXiv:2610.21594v1 Announce Type: new Abstract: Looped Transformers improve parameter efficiency by repeatedly applying the same Transformer layers. However, generating each token still involves seque
arXiv:2610.20973v1 Announce Type: new Abstract: Geo-foundation models (GFMs) are typically evaluated by downstream task predictive performance, revealing little about which physical features they are
arXiv:2610.21286v1 Announce Type: new Abstract: We investigate whether variation in information density across chain-of-thought (CoT) reasoning steps predicts answer correctness and can serve as a con
arXiv:2610.22099v1 Announce Type: cross Abstract: Masked diffusion language models expose an inference-time choice: which positions become visible after each denoising pass. Many existing decoding met
arXiv:2610.22068v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can act unsafely when individually benign images and texts are interpreted jointly. Beyond implicit reasoning r
arXiv:2610.19478v1 Announce Type: new Abstract: Agricultural systems make decisions over heterogeneous entity types, textual judgments, and knowledge-question options. Generating a textual answer adds
arXiv:2610.16713v1 Announce Type: cross Abstract: Teacher rationales can help a smaller model answer health professions questions, but they can also reinforce errors. We examined when they help and ho
arXiv:2511.00110v2 Announce Type: replace Abstract: We propose a novel cognitively-inspired prompting method to stabilize and interpret the physical simulation produced by autoregressive image generat
arXiv:2610.17831v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) are appealing for code generation because they can update many tokens in parallel, yet many are optimized for
arXiv:2610.19743v1 Announce Type: new Abstract: Discriminative models are trained with task-specific objectives such as classification, detection, or representation learning, yet their practical utili
arXiv:2610.19683v1 Announce Type: cross Abstract: World models trained via latent dynamics offer a scalable path for embodied planning, yet existing methods suffer from representational collapse, comp
arXiv:2610.18201v1 Announce Type: cross Abstract: Soil organic carbon (SOC) is an indicator of agricultural soil condition and carbon storage, but numerical outputs from large language models (LLMs) r
arXiv:2610.19975v1 Announce Type: new Abstract: Looped Mixture-of-Experts (MoE) models combine parameter reuse with sparse computation to improve performance under matched budgets. Prior work demonstr
arXiv:2308.12241v2 Announce Type: replace-cross Abstract: Recently, the fast development of Large Language Models (LLMs) such as ChatGPT has significantly advanced NLP tasks by enhancing the capabilit
Over the last few years, we have made significant progress on prompt injection for Claude models. As agents do more, it is increasingly urgent that we solve it across the industry. Really excited to p
arXiv:2601.08689v3 Announce Type: replace Abstract: Large Language Models (LLMs) have shown strong capabilities in finance-oriented question answering, yet their evaluation on professional quantitativ
arXiv:2610.15259v1 Announce Type: new Abstract: Low-bit quantization reduces the cost of serving reasoning models but can substantially degrade long chains of thought. Each step reads cached keys and
arXiv:2610.20174v1 Announce Type: cross Abstract: Reinforcement Learning on Mixed-domain preference data (Mix-RL) has become the mainstream approach for training general-purpose Generative Reward Mode
arXiv:2610.17976v1 Announce Type: new Abstract: Do vision-language models use depth information to judge which object is closer, or rely on image-plane cues? We investigate this question through contr
arXiv:2610.14209v1 Announce Type: new Abstract: The most accurate agents executing user-provided tasks are driven by large, cloud-hosted models, which solve them by reading the private data of the use
arXiv:2608.01008v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can memorize and disclose sensitive information, motivating machine unlearning to remove designated knowled
Benchmarks are becoming the core competency of any company. Our company mission is to build the world’s best models for document processing. This also means that we have to build the world’s best benc
arXiv:2506.17585v4 Announce Type: replace-cross Abstract: Large language models can answer questions correctly and still hallucinate their citations. Most citation systems address this by retrieving d
arXiv:2610.15146v1 Announce Type: new Abstract: Spiking neural networks target energy-efficient language modeling through sparse, binary activations, but the efficiency-accuracy tradeoff of spiking la
arXiv:2610.15640v1 Announce Type: new Abstract: Small language models (SLMs) used as on-device agents must balance accuracy and latency as their context grows. Mixed precision helps manage this tradeo
arXiv:2610.14497v1 Announce Type: new Abstract: EEG foundation models (EFMs) have improved EEG representation learning by transferring knowledge from large-scale multi-subject pretraining to downstrea
arXiv:2511.11984v2 Announce Type: replace Abstract: Fine-grained glomerular subtyping is essential for kidney biopsy interpretation, yet it presents a harder setting than the broad organ- or disease-l
arXiv:2610.14541v1 Announce Type: new Abstract: Emerging strong Vision-Language Models (VLMs) have advanced navigation foundation models. However, these models still have unsatisfactory performance on
arXiv:2610.14254v1 Announce Type: new Abstract: Text-to-motion generation typically relies on dataset-specific skeletal representations, with models trained separately for benchmarks with different jo
arXiv:2610.13956v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) learn world models by predicting future representations rather than reconstructing observations. Variat
arXiv:2610.15638v1 Announce Type: new Abstract: Knowledge graph (KG) foundation models (KGFMs) predict links on unseen graphs zero-shot. KGFMs have been pioneered by ULTRA which builds a relation grap
arXiv:2610.14091v1 Announce Type: new Abstract: Training on broad, unfiltered data enables high-quality image generation, but can also teach models concepts that should not be generated; restricting t
arXiv:2610.17596v1 Announce Type: new Abstract: Vision-language models are increasingly used to identify and describe places, yet their recognition and knowledge of specific cultures remain poorly und
arXiv:2610.16860v1 Announce Type: new Abstract: How the relationship between image-text aligned and vision-only models changes across development remains unknown. We applied time-resolved representati
A gap that has been fixed in our methodology that highlighted a decline was not including local models (GGUF, AWQ, ONNX, etc). Earlier, these were far harder to track and closer to a rounding error. N
arXiv:2610.13821v1 Announce Type: new Abstract: A useful language model should know when to answer and when to say 'I don't know.' Can we improve that decision by training the model itself or by train
With the vLLM Semantic Router team we released Decision 3.0 (d3), our new SOTA decision model family ranging from 0.6B to 27B, Apache-2.0, all reading text, images and video. How it works - Built on Q
arXiv:2610.13337v1 Announce Type: cross Abstract: Attention sinks are tokens or positions that consistently receive disproportionate attention mass, often beyond what their semantic content would sugg
arXiv:2610.13208v1 Announce Type: new Abstract: Time-series foundation models use the same network depth for every input, although forecasting windows differ in structure and difficulty. We test wheth
arXiv:2610.12989v1 Announce Type: new Abstract: We investigate whether language models contain internal components that contribute to representing narrativity, the degree to which a text exhibits narr