Knowledge catalogue
arXiv:2609.37052v1 Announce Type: new Abstract: Omnimodal large language models (Omni-LLMs) encode audio and visual streams into temporally interleaved token sequences for multimodal reasoning. Howeve
arXiv:2609.37773v1 Announce Type: new Abstract: Artificial Intelligence Virtual Cells (AIVCs) are envisioned as scientific agents that simulate cellular responses, explain underlying mechanisms, and s
arXiv:2609.36659v1 Announce Type: new Abstract: The strong generalization performance of on-policy post-training paradigms has motivated studies of their parameter update behaviors. However, these stu
arXiv:2609.34879v2 Announce Type: replace Abstract: Tool agents use large language models to act through external tools, yet successfully executed calls can still leave user requests unfulfilled. Tool
arXiv:2609.37011v1 Announce Type: cross Abstract: Federated learning enables multiple clients to collaboratively train models without sharing their private data. However, the lack of visibility into l
arXiv:2609.37221v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed to solve complex scientific and practical problems via iterative optimization. However, dynamical
arXiv:2609.37986v1 Announce Type: new Abstract: Recovering articulated 4D representations of animals from monocular videos remains challenging due to the large diversity of quadruped morphologies and
arXiv:2609.36374v1 Announce Type: new Abstract: Feed-forward 3D reconstruction models have achieved impressive performance by scaling model and dataset size, but their cost excludes most research grou
arXiv:2609.35865v1 Announce Type: cross Abstract: Single-token typed-decision models answer a schema question by reading the logits of a few one-letter answer codes at a single position: they are fast
arXiv:2609.37685v1 Announce Type: new Abstract: Language-aligned and self-supervised visual encoders offer complementary strengths in semantic abstraction and spatial detail. Harnessing this complemen
arXiv:2609.36521v2 Announce Type: new Abstract: Physical-field reconstruction and forecasting depend on both measurement density and spatial layout, yet evaluation under a single observation pattern d
PDFs aren't built for machines: text shows up as glyphs with coordinates, tables as line segments instead of table structures. @jerryjliu0, CEO of LlamaIndex, digs into what that means for AI agents i
I have a long list of my own domain specific eval questions that I run to validate which models I can rely on: coding, coding (numpy/pandas), data analytics decision making, local RAG, and voice assis
I just bought the GPUs after deliberating and debating for over two years. With costs not coming down any time soon and me just wanting to get this massive todo-box ticked, I decided to just YOLO it;
arXiv:2609.36322v1 Announce Type: cross Abstract: Chunked KV-cache compression reduces the memory and attention costs of long-context inference by compressing windows of consecutive tokens into fewer
arXiv:2609.36388v1 Announce Type: new Abstract: An activation-steering coefficient sets intervention strength, but requesting a particular degree of persona expression requires a behavioral scale. We
arXiv:2609.36087v1 Announce Type: cross Abstract: Clinicians and neuroscientists have long analyzed intracranial electroencephalography (iEEG) through directly measurable physiological characteristics
arXiv:2609.37609v1 Announce Type: new Abstract: Magnetohydrodynamics (MHD) is central to plasma modeling in astrophysics, space science, fusion, and engineering, but resolving multiscale MHD dynamics
arXiv:2609.37525v1 Announce Type: new Abstract: Physical neural networks and analog in-memory computing could reduce the energy cost of neural network training. Realizing this potential, however, requ
arXiv:2609.37487v1 Announce Type: new Abstract: Precipitation nowcasting, generating future radar fields from past observations, is critical for flood warning and disaster response. It is also a deman
I can't seem to find what's wrong. I'm using Pi for my llama-server and sometimes it just stops processing for some reason and stuck after tool call. Logs seems to think that it's finished its job whi
arXiv:2609.37485v1 Announce Type: new Abstract: Bi-temporal change understanding, which localizes and characterizes what changed between two satellite images, is central to disaster response and envir
arXiv:2609.38180v1 Announce Type: new Abstract: Existing 3D part decomposition methods do not necessarily partition the original shape into non-overlapping parts that collectively cover the entire sha
arXiv:2609.37712v1 Announce Type: cross Abstract: Optical Character Recognition (OCR) is evolving from plain-text transcription toward general visual intelligence, requiring models to recognize, local
arXiv:2609.37351v1 Announce Type: cross Abstract: Test-time compute scaling has emerged as a cornerstone of advanced machine reasoning, yet performing iterative deliberation directly within continuous
arXiv:2609.37935v1 Announce Type: cross Abstract: Deep Support Vector Data Description (Deep SVDD) has become a prominent framework for unsupervised anomaly detection by learning latent representation
arXiv:2609.37321v1 Announce Type: cross Abstract: Power markets are a natural testbed for multi-agent reinforcement learning (MARL), where multiple self-interested participants repeatedly submit bids.
arXiv:2609.36052v1 Announce Type: new Abstract: Power system operation is a safety-critical sequential decision-making problem, making it a natural testbed for reinforcement learning (RL). However, ex
arXiv:2609.36923v1 Announce Type: new Abstract: Existing reactive Graphical User Interface (GUI) agents often fail in long-horizon, dynamic scenarios, where unexpected disturbances trigger attention-d
arXiv:2609.37470v1 Announce Type: new Abstract: A probability used for a decision should refer to the same event across equivalent requests. We introduce probability contracts, a benchmark connecting
arXiv:2609.38070v1 Announce Type: new Abstract: As the chain-of-thought reasoning capabilities of large language models improve, evaluating and calibrating their reasoning confidence is becoming incre
arXiv:2609.37631v1 Announce Type: cross Abstract: Transformers are typically trained from random initialization, requiring all their capabilities to emerge from large-scale optimization. Recent work s
arXiv:2609.37632v1 Announce Type: cross Abstract: Time series imputation has progressed from statistical and deep learning approaches to diffusion-based models, which have shown strong recent performa
arXiv:2609.36684v1 Announce Type: new Abstract: Embodied agents now take on ever longer tasks. For long tasks, knowing only whether a task finally succeeds or fails says little; the steps along the wa
arXiv:2609.36891v1 Announce Type: new Abstract: Panoptic segmentation in forest environments is bottlenecked not by semantic quality but by instance separation; existing unsupervised panoptic approach
arXiv:2609.35928v1 Announce Type: cross Abstract: Multi-agent LLM systems increasingly mix models from several providers, yet exposing each agent's underlying model identity to its peers significantly
arXiv:2609.36760v1 Announce Type: cross Abstract: Multi-Head Latent Attention (MLA) enables expressive multi-head attention with compact caches for its content and decoupled RoPE paths, yet cache memo
I'm far from being an expert, but for me Qwen is not a Turbo Model so it cannot be as good as Krea 2 Turbo. Also their dataset seems to be not good, it's hard to make doodles, cartoon styles, and its
I think most people are sleeping on this inference engine. I tried multiple llama.cpp forks and none of them comes close to the inference speed of Strata. Initial version had some bugs with kv cache,
arXiv:2609.36794v1 Announce Type: new Abstract: Signal features derived from photoplethysmography (PPG) require different signal durations to characterize. Existing PPG foundation models treat duratio
arXiv:2609.35549v2 Announce Type: replace Abstract: Rare-disease diagnosis is a long-tail reasoning problem: phenotypes are incomplete, individual disorders are sparsely documented, and relevant evide
arXiv:2609.37530v1 Announce Type: cross Abstract: Vision-language-action (VLA) models typically operate on RGB images produced by a fixed camera image signal processor (ISP), leaving the imaging pipel
arXiv:2609.35694v2 Announce Type: replace Abstract: Continuous diffusion generates complete reasoning solutions through iterative refinement in latent space. We introduce the Continuous Embedding Diff
arXiv:2609.37889v1 Announce Type: new Abstract: Multimodal continual instruction tuning (MCIT) aims to enable multimodal large language models to acquire new capabilities from sequential tasks while p
arXiv:2609.37292v1 Announce Type: new Abstract: Physical active vision allows robots to change their viewpoint when task-relevant observations become unreliable, yet existing manipulation benchmarks p
arXiv:2609.36329v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) determines not only how many parameters are trained, but also which local directions a model can move in, so simi
Wulver v0.5 is a full fine-tune of Krea 2 Raw (the whole 12.8B DiT) for anime and anthro characters, including scenes where several characters interact. We made it at Vaelico, an independent studio. C
arXiv:2609.37469v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) grounds large language models in external sources, but retrieved passages often name the right entities without pro
arXiv:2609.36525v1 Announce Type: cross Abstract: Distributed inference has become an indispensable part of deploying medical models under practical latency, memory, and throughput constraints. Althou
arXiv:2609.32965v2 Announce Type: replace Abstract: Multiple agents may often conflict in an organization: for example, one coding agent changes an interface in a repository, but another continues to
arXiv:2609.36654v1 Announce Type: cross Abstract: Large language models make weight storage and memory traffic major inference costs, motivating low-precision formats that represent each weight with o
arXiv:2609.35917v1 Announce Type: new Abstract: Graph neural networks are increasingly applied to road-level crash prediction, but the stability of their reported gains has received little scrutiny. W
arXiv:2609.32490v2 Announce Type: replace Abstract: LLM-based multi-agent systems (MASs) have shown strong potential for solving complex tasks, but most assume that task requirements are sufficiently
arXiv:2609.36208v1 Announce Type: cross Abstract: Input encodings can restrict which measured contrasts a predictor can jointly reproduce, even when no single contrast is forced to vanish. We compute
arXiv:2609.35864v1 Announce Type: new Abstract: SEC 10-K filings contain substantial financial information that is not consistently captured in structured datasets, creating a missing-data problem aff
arXiv:2609.37669v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is increasingly used to enhance Large Language Model (LLM)-based software vulnerability detection by grounding pr
arXiv:2609.37554v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as high-level planners in robot navigation, but their outputs may become unreliable when instructio
arXiv:2609.37633v1 Announce Type: cross Abstract: The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards t
arXiv:2609.36605v1 Announce Type: new Abstract: Understanding ongoing robot manipulation requires models to interpret visual observations in relation to interaction history and task progress. We intro
arXiv:2609.37583v1 Announce Type: new Abstract: Vision-language models can coordinate long-horizon robot manipulation, yet successful task reasoning still depends on whether local physical interaction