Knowledge catalogue
arXiv:2606.03421v1 Announce Type: new Abstract: Specular glare on reflective floors, glass boundaries, and glossy indoor surfaces frequently corrupts active-stereo RGB-D depth measurements, producing
arXiv:2606.02606v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed as continuously evolving services, where frequent base-model updates may invalidate previously
arXiv:2606.02765v1 Announce Type: cross Abstract: Model dimension (d_{model}) is a fundamental hyperparameter in transformer language models, yet its role in setting the geometric limits of feature re
arXiv:2606.03019v1 Announce Type: cross Abstract: Copyleft, as implemented in licenses such as the GNU General Public License, was a legal hack that used copyright to guarantee user freedom by tying t
arXiv:2606.02852v1 Announce Type: new Abstract: Accurate short-term forecasting of residential energy load and indoor temperature is essential for home energy management systems, grid-level demand res
arXiv:2411.15851v2 Announce Type: replace Abstract: While vision-language models like CLIP have shown remarkable success in open-vocabulary tasks, their application is currently confined to image-leve
arXiv:2606.03736v1 Announce Type: cross Abstract: Resource-constrained pricing controllers can make fixed-price inference impossible: the controller's resource state may remove the target price neighb
arXiv:2606.03057v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for molecular tasks, but it remains unclear which molecular representation to use. We present a sys
arXiv:2606.03125v1 Announce Type: new Abstract: Deep learning proxies for Alternating Current Optimal Power Flow (ACOPF) lack systematic methods for determining architectural size. This paper conducts
arXiv:2606.03817v1 Announce Type: new Abstract: Idioms can be analysed in terms of their decomposability, the extent to which constituent meanings contribute to the figurative whole. Decomposability i
arXiv:2606.03465v1 Announce Type: cross Abstract: Post-training compression is essential for deploying large language models (LLMs) under tight resource constraints. Tensor decompositions have emerged
arXiv:2606.03784v1 Announce Type: new Abstract: Embodied chain-of-thought (CoT) aims to bridge linguistic reasoning and robotic control, but its effective form and integration strategy remain underexp
arXiv:2502.08834v4 Announce Type: replace-cross Abstract: Deep generative models based on neural differential equations have become state-of-the-art for many generation tasks. These models rely on ODE
arXiv:2510.16392v3 Announce Type: replace Abstract: Personalized and continuous interactions are critical for LLM-based conversational agents, yet finite context windows and static parametric memory h
arXiv:2606.03234v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has become the dominant approach for improving mathematical reasoning in large language models, ye
arXiv:2606.03074v1 Announce Type: new Abstract: Diffusion models achieve high-fidelity radio map construction through iterative denoising, yet their sampling cost limits practicality in dynamic wirele
arXiv:2512.21235v3 Announce Type: replace Abstract: Imitation learning from human demonstrations has become a dominant approach for training autonomous robot policies. However, collecting demonstratio
arXiv:2606.03312v1 Announce Type: cross Abstract: While household robots are often evaluated based on task completion, everyday domestic environments involve value-conflicting situations in which robo
arXiv:2606.03069v1 Announce Type: cross Abstract: Generalized segmentation of medical images prevents performance degradation when different imaging devices and clinical protocols are used across mult
arXiv:2606.03344v1 Announce Type: cross Abstract: Model merging composes specialized capabilities into a single LLM by aggregating task vectors sourced from unverified public platforms, exposing a cri
Routing and post-training open-source models won't only give you more accurate systems but also meaningfully faster and cheaper systems as most companies are currently learning (in addition to giving
arXiv:2606.02876v1 Announce Type: new Abstract: Randomized smoothing (RS) uses a smoothed classifier to provide architecture-agnostic certificates of ell_2 classification robustness, but its dependenc
arXiv:2606.03648v1 Announce Type: cross Abstract: Adapting foundation large language models to a user's task or preferred style through fine-tuning can result in compromising the model's safety. Previ
arXiv:2606.03301v1 Announce Type: new Abstract: We introduce SagaQA, a long-form video benchmark for multi-hop reasoning over full-length TV series. Existing video reasoning benchmarks often emphasize
arXiv:2606.03544v1 Announce Type: new Abstract: Self-improving language agents are typically evaluated in isolation: an agent attempts a task, receives feedback, and iteratively refines its own behavi
arXiv:2511.13663v2 Announce Type: replace-cross Abstract: How to construct globally sound abstract interpreters to safely approximate program behaviors remains a bottleneck in abstract interpretation.
arXiv:2606.02927v1 Announce Type: new Abstract: Normalization layers such as BatchNorm and LayerNorm have long been considered essential for stable training in deep networks. This work demonstrates th
Sam Altman swearing to tell the whole truth to the US Senate an hour or so before he lied his fanny off about caring about artists and creators and wanting them to get a fair shake. Hey OpenAI, when a
arXiv:2606.03406v1 Announce Type: new Abstract: Reliable correspondence estimation is a fundamental problem in image processing, underpinning applications such as Structure from Motion, visual localiz
arXiv:2606.03219v1 Announce Type: new Abstract: African languages have very little labelled data, and it is unclear if augmenting the quantity of annotation data reliably enhances downstream performan
arXiv:2606.02610v1 Announce Type: cross Abstract: Ocean general circulation models (OGCMs) are essential to climate science but computationally expensive, limiting ensemble size and forcing scenarios.
The pharmaceutical industry’s research and development marathon — stretching 10 to 12 years from hypothesis to approval — is set to shrink as AI-driven drug development compresses that timeline. The s
@saranormous codex is agi man https://youtu.be/cFNI2FORAc0 oneshotted this, no notes https://x.com/TheTuringPost/status/2061901518522188251?s=20 Love that in this high-tech age of AI @swyx still uses
This episode features Satya Nadella, CEO of Microsoft, in a crossover special between the 'No Priors' and 'Latent Space' podcasts, likely discussing Microsoft's AI strategy, product developments, and
LangSmith's LLM Gateway provides real-time cost visibility and spend tracking capabilities, allowing users to monitor their language model usage costs aggregated by workspace. This feature helps elimi
arXiv:2606.02909v1 Announce Type: cross Abstract: Gradient observations can substantially improve Gaussian process (GP) surrogates, particularly in high-dimensional settings where function evaluations
arXiv:2606.03517v1 Announce Type: cross Abstract: Training quantum neural networks (QNNs) on quantum hardware is currently bottlenecked by the cost of gradient estimation: standard parameter-shift met
arXiv:2511.02986v2 Announce Type: replace-cross Abstract: Computational modeling of single-cell gene expression is crucial for understanding cellular processes, but generating realistic expression pro
arXiv:2606.02886v1 Announce Type: cross Abstract: Deep learning weather models now match numerical weather prediction accuracy while running orders of magnitude faster, but produce deterministic forec
Databricks Genie enables enterprise conversational intelligence across multiple industries through partner technology and functional solutions, allowing organizations to interact with data and systems
arXiv:2505.08222v3 Announce Type: replace-cross Abstract: Autonomous vehicles (AVs) offer a cost-effective solution for scientific missions such as underwater tracking. Reinforcement learning (RL) has
Carina Hong discusses how Axiom Math is scaling beyond informal AI approaches to create more rigorous, systematic methods for AI-assisted mathematics education and problem-solving. The episode likely
🆕Scaling Past Informal AI https://latent.space/p/axiom @axiommathai founder & CEO @CarinaLHong explains why math may be the missing path from code agents to AGI, why verified AI is about scaling brill
arXiv:2602.00423v3 Announce Type: replace Abstract: Single-cell integration workflows often construct low-dimensional cell embeddings and then refine them with post-hoc methods to reduce batch effects
arXiv:2606.02951v1 Announce Type: cross Abstract: Deploying language-driven agents in robotics requires evaluations that reflect real-world task demands: natural-language instructions with reproducibl
arXiv:2606.02740v1 Announce Type: cross Abstract: Gradient boosted decision trees require a stopping rule to avoid overfitting. The standard rule monitors a validation loss and stops if the loss fails
arXiv:2606.03906v1 Announce Type: new Abstract: Simultaneous measurement of multiple omics modalities in single cells enables researchers to gain a more comprehensive understanding of cellular states
arXiv:2606.03027v1 Announce Type: new Abstract: Text embeddings are fundamental to many downstream applications, making robustness important for real-world NLP. However, most recent state-of-the-art e
arXiv:2606.03284v1 Announce Type: new Abstract: Frontier LLMs perform well in Western contexts, but remain poorly tested on underrepresented cultures such as those in Southeast Asia (SEA). Existing NL
arXiv:2606.03940v1 Announce Type: cross Abstract: In robotics systems, vast amounts of visual data are easily captured at high resolution using low-cost, low-power hardware. Yet, limited bandwidth and
The Hacker News: Security firm Calif says it used OpenAI's Codex to discover HTTP/2 Bomb, a remote DoS exploit affecting web servers like Nginx, Apache HTTPD, and Microsoft IIS — Cybersecurity researc
arXiv:2606.03371v1 Announce Type: new Abstract: Multimodal retail agents should not only recognize what a customer is doing, but also decide whether and how to assist before an explicit request is mad
arXiv:2606.02735v1 Announce Type: cross Abstract: Generalization remains a central bottleneck for vision-language-action (VLA) models: under distractors, appearance shifts, and semantically similar ta
arXiv:2606.02745v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) are promising general-purpose robot policies, but adapting them to new tasks typically requires costly task-speci
arXiv:2606.03875v1 Announce Type: new Abstract: Autonomous systems require robust Multi-Object Tracking and Segmentation (MOTS) to operate reliably in dynamic environments, ensuring consistent object
arXiv:2606.02638v1 Announce Type: cross Abstract: Recent advances in neural song generation have enabled high-quality synthesis from lyrics and global textual prompts. However, most systems fail to mo
arXiv:2606.03399v1 Announce Type: new Abstract: While large language models (LLMs) are increasingly used for clinical applications, many existing pipelines require sending raw sensitive health informa
arXiv:2606.03963v1 Announce Type: cross Abstract: Deep reinforcement learning has shown strong potential for enabling autonomous robots to learn complex navigational tasks. However, its practical use
arXiv:2602.02890v2 Announce Type: replace Abstract: Model soups are strange and strangely effective combinations of parameters. They take a model (the stock), fine-tune it into multiple models (the in
Sema4.ai Inc. a startup that provides tools for building and managing artificial intelligence agents, today announced a massive revamp of its platform, with big changes coming to every layer of the ag