Knowledge catalogue
arXiv:2605.08281v1 Announce Type: new Abstract: Implicit neural representations (INRs) encode images as neural-network weights, making image classification a problem of weight-space classifiability. A
arXiv:2605.10684v1 Announce Type: cross Abstract: Data selection studies the problem of identifying high-quality subsets of training data. While some existing works have considered selecting the subse
arXiv:2605.10858v1 Announce Type: new Abstract: Today's driving world models can generate remarkably realistic dash-cam videos, yet no single model excels universally. Some generate photorealistic tex
Pritam Biswas / Reuters: Isomorphic Labs, an AI-powered drug discovery spinoff from Google DeepMind, raised 2.1B led by Thrive, after raising 600M in its first round in March 2025 — Isomorphic Labs, a
arXiv:2412.18798v3 Announce Type: replace-cross Abstract: Transformer-based models have achieved remarkable success in multivariate time series forecasting (MTSF) by capturing long-range dependencies.
'It is categorically *not* a victory for pure LLMs; it’s a victory for borrowing from classical AI and CS to move *beyond* pure LLMs.' ☝️ Compound AI Systems alllll the way downnn 🤩🤯🤩 Claude Code (sti
Elon Musk advocates for transparency and reproducibility in scientific or analytical work, emphasizing that third parties should be able to replicate results using identical data and that credible ext
arXiv:2605.08686v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems often rely on a controller to coordinate a pool of heterogeneous models, yet existing controllers are typ
arXiv:2603.02676v2 Announce Type: replace-cross Abstract: Large language models suffer from content effects in reasoning tasks, particularly in multi-lingual contexts. We introduce a novel method that
arXiv:2605.10551v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have achieved strong results in molecular property prediction, but polymers present distinct challenges: labeled datasets a
'It’s pretty easy to change models these days... what creates more lock-in is when state starts to accumulate behind these APIs... memory has a lot of gravity.' - @hwchase17, Co-Founder & CEO, @LangCh
It’s release day!!! Suicidal Empathy is out into the world. In less than 24 hours, the long wait will be over. Suicidal Empathy to be released tomorrow! https://www.amazon.com/gp/aw/d/0063446537/ref=t
@ivanfioravanti @Prince_Canuma @huggingface this is a fully local stack to get your Reachy Mini running (with vision/tts/stt) on a mac. Uses mlx-vlm, mlx-audio, gemma4 It contains some examples to run
arXiv:2605.08384v1 Announce Type: new Abstract: In this work, we introduce frozen-encoder model composition, a novel approach to multimodal embedding models. We build on the VLM-style architecture, in
arXiv:2605.10159v1 Announce Type: new Abstract: jNO (jax Neural Operators) is a JAX-native library for neural operators and foundation models with unified support for both data-driven and physics-info
arXiv:2605.09954v1 Announce Type: cross Abstract: Articulated objects used in simulation and embodied AI are typically specified by geometry and kinematic structure, but lack the fine-grained dynamica
Join us for a live build session exploring Hermes Agent integrated with ComfyUI. If you're already comfortable navigating ComfyUI workflows and want to understand what an agent layer brings to the tab
arXiv:2605.10178v1 Announce Type: cross Abstract: Adaptive behavior requires the brain to transition between distinct contexts while maintaining representations of prior experience. The ability to rec
arXiv:2601.22143v2 Announce Type: replace-cross Abstract: Audio-Visual Foundation Models, which are pretrained to jointly generate sound and visual content, have recently shown an unprecedented abilit
Just ordered Reachy! Can't wait to do some fun projects with my kids. Reachy is mad, but RAM costs + tariffs are forcing our hand. Prices will go up on June 1st! Still at the early bird price until th
arXiv:2605.09635v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in K-12 education, yet existing benchmarks such as C-Eval, CMMLU, GaokaoBench, and EduEval mainly eva
arXiv:2605.08587v1 Announce Type: cross Abstract: Long-context language modeling remains central to modern sequence modeling, but the quadratic cost of Transformer attention makes scaling computationa
arXiv:2605.09572v1 Announce Type: cross Abstract: Sign language production from symbolic notation offers a scalable route to accessible sign animation. We present KANMultiSign, a multi-scale sequence
arXiv:2605.08175v1 Announce Type: cross Abstract: While significant progress has been made in Video Question Answering and cross-modal understanding, causal reasoning about how visual dynamics drive m
arXiv:2605.09132v1 Announce Type: new Abstract: Vision--language models (VLMs) show promise for clinical decision support in radiology because they enable joint reasoning over radiological images and
arXiv:2605.10727v1 Announce Type: new Abstract: We propose kernel-gradient drifting, a one-step generative modeling framework that replaces the fixed Euclidean displacement direction in drifting model
arXiv:2605.09440v1 Announce Type: cross Abstract: Clinical reports are often fragmented across healthcare institutions because privacy regulations and data silos limit direct information sharing. When
arXiv:2605.09877v1 Announce Type: cross Abstract: We present Key-Value Means ('KVM'), a novel block-recurrence for attention that can accommodate either fixed-size or growing state. Equipping a strong
arXiv:2512.11321v3 Announce Type: replace Abstract: Facial animation is a core component for creating digital characters in Computer Graphics (CG) industry. A typical production workflow relies on spa
arXiv:2605.08635v1 Announce Type: new Abstract: Reconstructing dynamic 3D scenes from blurry monocular videos is challenging as motion-induced blur entangles object motion and geometry, hindering geom
arXiv:2605.09386v1 Announce Type: cross Abstract: Metric-induced discrete flow matching (MI-DFM) exploits token-latent geometry for discrete generation, but its practical use is limited by two issues:
arXiv:2605.09213v1 Announce Type: cross Abstract: We study causal self-attention dynamics -- a toy model for decoder Transformers -- which we interpret as a non-exchangeable interacting particle syste
arXiv:2605.09487v1 Announce Type: new Abstract: Modern embodied agents achieve impressive performance, but their task knowledge is often stored in neural weights, latent state, or prompt-bound memory,
arXiv:2601.11258v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) face the 'knowledge cutoff' challenge, where their frozen parametric memory prevents direct internalization of ne
arXiv:2605.10253v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) is a widely adopted paradigm for enhancing LLMs in medical applications by incorporating expert multimodal knowle
arXiv:2605.09735v1 Announce Type: cross Abstract: Static-graph LLM decoders provide predictable launches, fixed tensor shapes, and low submission overhead, but online decoding exposes highly irregular
arXiv:2605.08806v1 Announce Type: new Abstract: Existing 2D-3D lifting human pose estimation methods have achieved strong performance. But the utilization of historical pose representations across net
arXiv:2605.08156v1 Announce Type: cross Abstract: Zero-shot recognition aims to classify an image by selecting the most compatible label description from a set of candidate classes without any task-sp
arXiv:2605.09299v1 Announce Type: cross Abstract: Reconstructing 3D fluid velocity fields from sparse 2D video observations is a highly ill-posed inverse problem, demanding both transport consistency
arXiv:2605.09994v1 Announce Type: cross Abstract: Modern Large Foundation Model (LFM) training has transformed the data pipeline from a static ingestion layer into a dynamic component that must co-evo
arXiv:2410.14022v2 Announce Type: replace-cross Abstract: Human dexterity arises from combining high-level task reasoning with finger-level dexterity control and physical compliance at the muscle and
arXiv:2605.09060v1 Announce Type: new Abstract: Multilingual vision-language models exhibit systematic performance gaps across languages, but the mechanism remains ambiguous: cross-language divergence
arXiv:2605.09751v1 Announce Type: new Abstract: Trainable input embedding tables are a standard component of modern language models. We ask whether they are actually necessary at the input interface.
arXiv:2602.09297v2 Announce Type: replace Abstract: Transformers update token representations through multi-head attention and residual connections as X leftarrow X + sum_{i} P^{(i)}XW_{V_i}W_{o_i}, w
arXiv:2605.08755v1 Announce Type: new Abstract: Large reasoning models (LRMs) reach competition-level math and coding accuracy via long autoregressive decoding, making per-token decoding cost a primar
arXiv:2602.01015v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly embedded in AI-based tutoring systems. Can they faithfully model novice reasoning and metacognitive ju
arXiv:2605.09009v1 Announce Type: cross Abstract: Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities, yet their potential for sequential decision-making remains
arXiv:2605.08626v1 Announce Type: cross Abstract: Large language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM ser
arXiv:2605.09494v1 Announce Type: cross Abstract: Unmanned underwater vehicles (UUVs) operate persistently in communication-constrained environments, thus requiring high-level autonomous fault-toleran
arXiv:2605.08975v1 Announce Type: new Abstract: Reasoning-based end-to-end (E2E) autonomous driving has recently emerged as a promising approach to improving the interpretability of driving decisions
arXiv:2605.08732v1 Announce Type: cross Abstract: Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these space
arXiv:2605.08496v1 Announce Type: new Abstract: Current adversarial robustness methods for large language models require extensive datasets of harmful prompts (thousands to hundreds of thousands of ex
arXiv:2605.08605v1 Announce Type: cross Abstract: We introduce the Lattice Deduction Transformer (LDT), a recurrent transformer that approximates logically sound deduction by projecting its latent sta
arXiv:2605.08279v1 Announce Type: cross Abstract: Learning predictive world models from visual observations is a core problem in embodied AI, with applications to model-based reinforcement learning an
arXiv:2605.06366v2 Announce Type: replace Abstract: Diffusion language models (DLMs) have recently emerged as competitive alternatives to autoregressive (AR) language models, yet differences in their
arXiv:2605.09204v1 Announce Type: new Abstract: Backpropagation is inherently sequential across depth, creating an O(K)-deep dependency chain that bottlenecks parallel training. While parallel-scan fo
arXiv:2605.09053v1 Announce Type: new Abstract: Online topological planning has become an effective paradigm for Vision-Language Navigation in Continuous Environments (VLN-CE), but existing methods st
arXiv:2605.08489v1 Announce Type: new Abstract: Accurate modeling of nonlinear vehicle dynamics is essential for high-speed autonomous racing, where controllers operate at the handling limits. Model-b
arXiv:2605.09806v1 Announce Type: cross Abstract: Large reasoning models, such as OpenAI o1 and DeepSeek-R1, tend to become increasingly verbose as their reasoning capabilities improve. These inflated
arXiv:2605.09295v1 Announce Type: new Abstract: Text-to-SQL translates natural language questions into executable SQL queries, enabling intuitive database access for non-experts. While large language