Knowledge catalogue
arXiv:2609.19717v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable reasoning capabilities by utilizing chain-of-thought (CoT) as a scratchpad for intermediate stag
arXiv:2510.21736v2 Announce Type: replace-cross Abstract: This study introduces a novel control framework for adaptive cruise control (ACC) in automated driving, leveraging neural networks and physics
arXiv:2609.19721v1 Announce Type: new Abstract: Clinical coding agents repeatedly encounter the same failure modes, including unsupported codes, missed documented conditions, specificity errors, and p
arXiv:2609.19906v1 Announce Type: cross Abstract: Closed-loop robot policies require observation processing, state management, and situation-dependent branching, making them costly to design and tune
arXiv:2609.19786v1 Announce Type: new Abstract: The inverse problem of reconstructing optical properties, specifically absorption and scattering coefficients, in layered biological media from time-dom
arXiv:2609.20669v1 Announce Type: cross Abstract: 3D diffusion policies are strong at generating geometrically grounded actions from current observations, but successful manipulation requires not only
arXiv:2609.19288v1 Announce Type: new Abstract: Learning in recurrent neural networks can fundamentally reshape their underlying dynamics, transforming initially chaotic activity into stable task-depe
arXiv:2609.20404v1 Announce Type: new Abstract: Agricultural soils are a major untapped carbon sink. Carbon farming is emerging as a promising practice for tapping this potential. Smallholder farmers,
arXiv:2609.19894v1 Announce Type: new Abstract: Parking is a routine yet safety-critical task for autonomous vehicles operating in urban environments. However, cluttered and weakly structured parking
arXiv:2609.19272v1 Announce Type: new Abstract: Research in humanoid robotics has achieved rapid progress in locomotion, and recent results have pushed the boundary on autonomous navigation. We demons
arXiv:2609.20558v1 Announce Type: new Abstract: Roofing requires workers to coordinate locomotion, balance, and work-related body motions on pitched surfaces, creating a challenging application for hu
arXiv:2609.19357v1 Announce Type: cross Abstract: We propose a framework for restricted inference on random dot product graphs whose latent positions lie on an unknown low-dimensional support manifold
arXiv:2609.20252v1 Announce Type: cross Abstract: High-quality representations are essential for a wide range of downstream tasks. Dedicated embedding models are explicitly optimized for representatio
arXiv:2609.20165v1 Announce Type: cross Abstract: Low Earth orbit (LEO) satellite constellations are poised to become a cornerstone of the sixth-generation (6G) Internet of Things (IoT), providing tru
arXiv:2609.19417v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (RAG) is widely used for multimodal, cross-document question answering. However, building corpus-level graphs
arXiv:2609.19796v1 Announce Type: new Abstract: Robotic manipulation under partial observability requires spatial information that extends beyond the current view. Geometry-aware RGB features describe
arXiv:2609.20581v1 Announce Type: new Abstract: Discrete diffusion, including remasking and uniform-state samplers, generate a sequence by writing multiple token positions per step, drawing each from
arXiv:2609.19377v1 Announce Type: cross Abstract: Recovering numerical series from line plots requires accurate axis calibration and reliable curve extraction. We present LinePilot Digitizer (LinePilo
arXiv:2609.19515v1 Announce Type: new Abstract: Verifier-based selection improves LLM performance by generating multiple candidate solutions and using a verifier to select the most promising one. Howe
arXiv:2609.20318v1 Announce Type: cross Abstract: Testing Autonomous Driving Systems (ADS) requires realistic safety-critical scenarios, but collecting such data from real-world driving is costly and
arXiv:2509.03405v2 Announce Type: replace Abstract: Language models (LMs) increasingly drive real-world applications that require world knowledge. However, the internal processes through which models
arXiv:2609.20129v1 Announce Type: cross Abstract: Deployment-time safety methods for large language models (LLMs) are predominantly supervised and assume access to unsafe training data. Nevertheless,
Jordan Novet / CNBC: London-based AI infrastructure startup Nscale files for a US IPO, reports H1 2026 revenue up 1,252% YoY to 140.6M, with a net loss of 1.02B, up from $368.9M — Nscale, a cloud prov
arXiv:2609.19814v1 Announce Type: cross Abstract: Landmark mathematical formalizations have taken specialist teams years to complete. We present FormalFlow, a system that coordinates AI proving agents
arXiv:2609.19521v1 Announce Type: new Abstract: Recurrent-depth Transformers apply shared computation repeatedly, but differ in how they retain information across steps. We compare a Block Universal T
arXiv:2609.19688v1 Announce Type: new Abstract: We present LYRIC, a generative flow-matching controller for language-driven physics-based contact-rich interaction control, that enables simulated chara
arXiv:2609.19374v1 Announce Type: new Abstract: Small clinical tabular datasets require interpretable machine learning because deep learning is often impractical and ensemble models can be difficult t
Thanks for the exceptional support (https://www.reddit.com/r/LocalLLaMA/comments/1wijo3e/i_literally_built_the_jev_architecture_one_year/) and for the dozens of requests to make a generic model, run b
arXiv:2609.20056v1 Announce Type: new Abstract: Hierarchical robotic systems executing long-horizon manipulation tasks must make high-level semantic decisions that orchestrate stochastic low-level ski
arXiv:2609.20670v1 Announce Type: new Abstract: Robots that localize a radio transmitter need more than a point estimate: in cluttered rooms, one measurement is often consistent with several transmitt
arXiv:2609.19391v1 Announce Type: new Abstract: LLM coding agents now generate complex programs at a scale that makes thorough human review increasingly difficult, raising the risk of safety and secur
arXiv:2609.19385v1 Announce Type: cross Abstract: Cardiovascular disease (CVD) remains the leading cause of death among women, yet cardiovascular risk assessment often relies on clinical variables tha
arXiv:2609.20068v1 Announce Type: new Abstract: This paper builds a theoretical bridge between the economic notion of marginal utility and two machine-learning constructs, matrix factorization and the
arXiv:2511.22924v3 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based Multi-Agent Systems (MAS) are susceptible to linguistic attacks that can trigger cascading failures across th
arXiv:2609.19944v1 Announce Type: new Abstract: Large language models (LLMs) have been applied to causal discovery, but candidate-graph generation rarely treats premature omission of potentially relev
arXiv:2609.19974v1 Announce Type: cross Abstract: Long-horizon robot manipulation requires not only stable local visuomotor control, but also continuous target tracking and reliable task progress asse
arXiv:2609.20082v1 Announce Type: cross Abstract: Tool learning enables large language models (LLMs) to use external tools for tasks beyond parametric knowledge. Reinforcement learning can optimize to
arXiv:2609.20048v1 Announce Type: new Abstract: Herbicide-based weed control is increasingly unsustainable due to rising weed resistance and the adverse environmental impacts of chemical use. While me
🚀 Meet Qwen3.8-Omni-Flash, Qwen's first omni-modal model built around agentic capabilities! Native audio-video understanding, reasoning, and tool use come together in one model: understand the content
arXiv:2609.19207v1 Announce Type: cross Abstract: Autoregressive transformer decoding is constrained by irregular key-value (KV) cache movement on tiled accelerators. Prior compression and DRAM-placem
arXiv:2609.19183v1 Announce Type: cross Abstract: Whether human or large language model (LLM), an agent in a discussion reads only a few of the others' contributions, bounded by cognition, context, or
Jay Peters / The Verge: Meta launches a Mac app for Muse after releasing the AI agent on iOS, Android, and the web earlier this month, allowing it to manage files, pull from apps, more — Follow topics
arXiv:2609.19832v1 Announce Type: new Abstract: Relational table learning has gained increasing attention with the widespread use of relational databases. Existing methods typically rely on deep GNN o
Georgia Hennessy / Business Insider: Meta's personal AI agent Muse climbs to No. 1 among free apps on Apple's US App Store, ahead of ChatGPT; Muse launched on September 8 — Mark Zuckerberg's ambition
Migrate a multi-model healthcare AI agent from self-managed Amazon ECS with AWS Fargate to Amazon Bedrock AgentCore runtime, preserving triple-model orchestration and vector-enhanced knowledge retriev
arXiv:2609.20747v1 Announce Type: cross Abstract: Reinforcement learning constitutes a promising approach owing to its potential for superhuman performance and self-learned policies. However, its appl
MiniMax has open-sourced the terminal version of MiniMax Code: https://github.com/MiniMax-AI/minimax-code How can developers verify the content that encoding proxies read, send, and store? This is a t
MiniMax has released the source code for MiniMax Code’s terminal agent: https://github.com/MiniMax-AI/minimax-code https://preview.redd.it/st9izmm8zaqh1.png?width=3016&format=png&auto=webp&s=5853bc316
arXiv:2609.20353v1 Announce Type: new Abstract: We study repeated contract design when a principal observes outcomes but not the actions that generate them. The principal may use any bounded outcome-c
Reuters: Mistral and other European AI startups accuse US rivals of using safety concerns to entrench their dominance, rejecting Anthropic's calls to pace the frontier — Europe's leading AI company Mi
arXiv:2609.20548v1 Announce Type: cross Abstract: Reinforcement learning agents trained to maximize their own reward in repeated interactions can converge to supra-competitive outcomes resembling expl
arXiv:2609.19683v1 Announce Type: cross Abstract: The deployment of Vision-Language Models (VLMs) on edge devices is severely bottlenecked by memory bandwidth, necessitating aggressive sub-8-bit quant
arXiv:2609.20377v1 Announce Type: new Abstract: Autonomous driving involves coupled decision-making and scene evolution under multi-mode uncertainty. To capture this coupling and uncertainty, we intro
arXiv:2609.19148v1 Announce Type: new Abstract: Ambivalence and hesitancy (A/H) are affective states in which individuals express contradictory signals across facial, vocal, and linguistic channels. A
arXiv:2609.20504v1 Announce Type: cross Abstract: FarmerChat is Digital Green's AI-powered agricultural advisory assistant for smallholder farmers, who access it in their own language through text, vo
arXiv:2609.20389v1 Announce Type: cross Abstract: Offline policy evaluation (OPE) is crucial in high-stakes reinforcement learning applications, where new policies must be assessed reliably before dep
arXiv:2602.01346v2 Announce Type: replace Abstract: While open sourced Vision-Language Models (VLMs) have proliferated, selecting the optimal pretrained model for a specific downstream task remains ch
arXiv:2511.15227v2 Announce Type: replace Abstract: Model-based development accelerates prototyping, enables earlier experimentation, and ensures rigorous validation of system design intents. In multi
arXiv:2609.20181v1 Announce Type: new Abstract: Accurate skin lesion segmentation is crucial for reliable computer-aided dermatological diagnosis, yet existing convolutional and transformer-based mode
arXiv:2609.20709v1 Announce Type: new Abstract: World Action Models (WAMs) improve robot policy learning by incorporating future dynamics, yet explicitly generating future videos at inference introduc