Knowledge catalogue
arXiv:2608.06400v1 Announce Type: new Abstract: Reward models are central to learning from human preferences, yet identifying what drives their predictions remains challenging. Recent sparse Mixture-o
arXiv:2608.06751v1 Announce Type: cross Abstract: Artist-grounded image generation requires more than appending an artist name to a prompt. Image models often respond to artist names through canonical
arXiv:2608.07038v1 Announce Type: cross Abstract: Human-Oriented Binary Reverse Engineering (HOBRE) aims to transform decompiled pseudocode into a more human-friendly representation, thereby reducing
arXiv:2608.07220v1 Announce Type: new Abstract: Mixed strategy equilibrium predicts i.i.d play: past actions should not help predict future decisions. Human players, however, systematically depart fro
arXiv:2608.06412v1 Announce Type: cross Abstract: Understanding which surfaces in a scene are physically accessible to a given tool is fundamental for robotic interaction, yet 3D perception systems ty
arXiv:2608.06727v1 Announce Type: new Abstract: Transformer models for high-dimensional omics analysis process thousands of genes or pathways, although only a subset requires deep computation. Mixture
arXiv:2608.07440v1 Announce Type: new Abstract: Agentic coding faces growing problems of affordability and wasted tokens. We introduce Blast Radius, a predictive memory management layer that estimates
arXiv:2608.06940v1 Announce Type: new Abstract: LLM judge panels are a standard evaluation tool, but prior work reports highly correlated panel errors: nine judges provide roughly the effective inform
arXiv:2608.07056v1 Announce Type: new Abstract: A skill is a naturallanguage document that steers a frozen agent whose weights cannot be updated so any capability the agent lacks must be supplied in p
arXiv:2608.06559v1 Announce Type: new Abstract: Contextual bandits offer a natural framework for sample-efficient personalization, but practical deployment remains difficult under sparse, biased inter
arXiv:2408.12792v2 Announce Type: replace Abstract: Event detection turns long recordings into a sparse set of ranked timestamps. Yet many sequence models are trained for samplewise segmentation and o
arXiv:2608.06860v1 Announce Type: cross Abstract: Hyperdimensional computing (HDC) is an approach from the cognitive science literature for solving information processing tasks using data represented
arXiv:2608.06637v1 Announce Type: cross Abstract: Robust aggregation methods are widely used in federated learning to mitigate the impact of adversarial client behavior. Distance-based aggregation rul
arXiv:2608.07045v1 Announce Type: cross Abstract: High-quality demonstrations for dexterous robot manipulation are costly and difficult to collect, whereas monocular human videos provide a scalable so
arXiv:2412.07377v5 Announce Type: replace Abstract: We introduce CADSpotting, an effective method for panoptic symbol spotting in large-scale architectural CAD drawings. Existing approaches often stru
arXiv:2608.06961v1 Announce Type: new Abstract: Early-stage molecular design is an iterative process, not just a task of generating molecules. Researchers turn broad goals into design strategies, refi
arXiv:2608.06908v1 Announce Type: cross Abstract: We propose Zero-phase Component Analysis (ZCA) whitening as a geometric pre-processing step for the Word Embedding Association Test (WEAT). WEAT is a
arXiv:2608.06967v1 Announce Type: new Abstract: Current evaluations do not isolate whether text-only language models can originate visual concepts before image generation. Fluent visual prose can hide
arXiv:2608.06501v1 Announce Type: new Abstract: Creative capabilities of MLLMs matter in design, communication, education, and human--AI collaboration, yet remain difficult to evaluate because explici
arXiv:2608.07256v1 Announce Type: new Abstract: Canonicalizing 3D object orientation is fundamental to 3D understanding and analysis. Existing approaches often rely on geometric cues, although 3D cano
arXiv:2608.07157v1 Announce Type: new Abstract: Sub-model federated learning lets resource-constrained clients train width-reduced versions of a global model, but existing methods allocate capacity by
arXiv:2608.06756v1 Announce Type: new Abstract: Vision-language models are increasingly serving as the reasoning core of embodied agents. Robot execution is inherently iterative: each action reshapes
arXiv:2608.07036v1 Announce Type: cross Abstract: Automated UML generation from sketches and images is gaining renewed attention with the rise of large language models and multimodal AI. However, repr
arXiv:2604.00310v2 Announce Type: replace-cross Abstract: Multimodal large-language models (MLLMs) often experience degraded safety alignment when harmful queries exploit cross-modal interactions. Mod
arXiv:2608.06557v1 Announce Type: cross Abstract: The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges
arXiv:2608.06597v1 Announce Type: cross Abstract: A scientific theory of deep learning, comprising learning dynamics and statistical properties of learned models, is rapidly gaining attention. One of
arXiv:2608.06952v1 Announce Type: cross Abstract: Impersonation is a fundamental security threat in face recognition systems (FRSs). While the security of FRSs has been challenged by various attack ve
arXiv:2608.06871v1 Announce Type: new Abstract: Complex systems, core objects of study in artificial life, model diverse phenomena through nonlinear, feedback-driven interactions that produce emergent
arXiv:2608.06659v1 Announce Type: new Abstract: This paper shows that latent-space predictive pretraining can provide a scalable route to foundation models for spatial transcriptomics. Existing spatia
arXiv:2608.06516v1 Announce Type: cross Abstract: Lightweight connectors make frozen multimodal encoders composable at the representation level. Deployment exposes a second problem at the level of tas
arXiv:2608.06419v1 Announce Type: cross Abstract: In this paper, we address the certification of datadriven feedforward control for periodic tracking of unknown nonlinear systems under partial state m
arXiv:2501.15790v2 Announce Type: replace Abstract: Synthetic minority oversampling is typically designed and evaluated against a predictive objective, generating samples that improve downstream class
arXiv:2608.06640v1 Announce Type: cross Abstract: The widespread integration of AI coding assistants offers undeniable boosts to engineering velocity. Yet, recent studies point to a growing trade-off,
I've been running Gemma 4 E4B with oMLX and I can't find any chat interfaces that directly send the audio file to the model instead of running the audio through a separate STT layer. I can confirm the
arXiv:2504.17584v2 Announce Type: replace-cross Abstract: Attention-FC Disaggregated (AFD) LLM inference systems offload memory-bound Attention operations to memory-rich accelerators (e.g., CPUs, HBM-
Catherine Thorbecke / Bloomberg: Chinese AI labs account for nine of Artificial Analysis' top 10 text-to-video models, gaining global adoption and potentially an edge in building world models — New la
Claude Haiku is my current least favorite model - it hallucinates wildly, and is out-performed now by other similarly priced models like GPT-5.6-Luna Even worse: it seems to still be used by the Claud
arXiv:2608.07423v1 Announce Type: cross Abstract: Low-latency, low-compute speech enhancement is essential for wearable devices with real-time communication requirements, but strict computational cons
arXiv:2606.16048v3 Announce Type: replace Abstract: Reconstructing dense 3D scenes from sparse LiDAR point clouds (LiDAR scene completion) is a fundamental challenge in autonomous driving, where diffu
arXiv:2604.07492v2 Announce Type: replace-cross Abstract: Message Passing Neural Networks have recently become the most popular approach to graph machine learning tasks; however, their receptive field
arXiv:2608.07424v1 Announce Type: new Abstract: Test-time scaling is often implemented by spending more compute along one axis: sampling more solutions, extending a chain of thought, or applying a str
arXiv:2608.06691v1 Announce Type: new Abstract: Real-time human action recognition on Internet-of-Things (IoT) edge devices requires models that capture rich spatio-temporal cues within strict latency
Coding isn't yet another application domain -- it's the meta-skill required for AI to automatically develop its own training material, via symbolic world models. That's how the RSI loop actually kicks
arXiv:2608.07458v1 Announce Type: cross Abstract: Recent optimization studies on Retrieval-Augmented Generation (RAG) have exploited chunk-level KV cache reuse to avoid processing long retrieved conte
I've been comparing Cline / Kilo / Qwen Code lately since they all handle long-task state differently. Cline: has Focus Chain, a markdown file kept outside the conversation that gets reinjected on a c
arXiv:2603.20115v2 Announce Type: replace Abstract: Small protein-family alignments often contain a subset of interest but not enough labeled data to train a conditional generator. We condition a trai
arXiv:2608.06532v1 Announce Type: new Abstract: LVLMs are increasingly used to read financial charts, tables, and documents, where a single misread figure can move a decision and the most authoritativ
Configure and run Fireworks training jobs from your coding agent. Install the Training Skill in one line, describe your goal, and Claude Code, Codex, or Cursor helps choose the method, validate data,
arXiv:2608.06977v1 Announce Type: new Abstract: It is well established that large language models (LLMs) are sensitive to prompt framing, reflecting patterns in their training data or prior prompts. I
arXiv:2608.07434v1 Announce Type: new Abstract: Event boundaries in continuous video are ambiguous: re-annotate the same query-video pair and independent annotators mark moments that overlap by less t
arXiv:2608.07183v1 Announce Type: new Abstract: Multimodal fusion architectures typically assume all modalities are available at inference, yet sensor failures, acquisition variability, and cost const
arXiv:2608.06495v1 Announce Type: new Abstract: Construction accident narratives contain rich causal information, but the evidence is often implicit, long-span, and distributed. We introduce Construct
arXiv:2608.06824v1 Announce Type: cross Abstract: A central task in virtual cell modeling is predicting single-cell transcriptional responses to unseen genetic perturbations and drug combinations, and
arXiv:2608.06878v1 Announce Type: new Abstract: Layout-guided multi-instance generation is essential for controllable image synthesis in Multi-Modal Diffusion Transformers (MM-DiTs). However, integrat
arXiv:2409.18804v3 Announce Type: replace-cross Abstract: Denoising Diffusion Probabilistic Models (DDPM) are powerful state-of-the-art methods used to generate synthetic data from high-dimensional da
Defensive cybersecurity startup Corma Labs Ltd. today announced it has raised 60 million in seed funding to build a foundation model purpose-built for security defense. Founded in 2025, Corma runs off
arXiv:2608.06674v1 Announce Type: new Abstract: Adversarial vulnerabilities remain a major concern for the safe deployment of neural networks, particularly in object detection, a core task embedded in
arXiv:2607.16999v2 Announce Type: replace-cross Abstract: The Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents. Existing framew
arXiv:2602.20710v2 Announce Type: replace Abstract: Inspecting Chain-of-Thought reasoning is among the most common means of understanding why an LLM produced its output. But well-known problems with C
arXiv:2608.06811v1 Announce Type: cross Abstract: Resolving a real software issue with a large language model (LLM) agent is a long repair episode, often tens to hundreds of steps spanning exploration