Knowledge catalogue
arXiv:2609.19670v1 Announce Type: new Abstract: Direct forecasting has become a standard paradigm for multivariate time-series forecasting because it predicts the full future horizon in a single pass.
arXiv:2609.19801v1 Announce Type: new Abstract: Executable environments enable LLM agents to learn from the consequences of their actions. For embodied agents, those consequences extend beyond whether
arXiv:2609.20649v1 Announce Type: cross Abstract: Learning predictive models of contact-rich dexterous manipulation requires dense tactile interaction, but such data are costly to scale on real robots
arXiv:2609.20304v1 Announce Type: new Abstract: A deployed control policy can conceal consequential dynamics changes: an actuator may lose effectiveness without affecting the current task when the pol
arXiv:2609.20715v1 Announce Type: cross Abstract: Agent trajectories record what an agent does and what happens next. Yet standard supervised fine-tuning (SFT) applies loss only to agent-authored acti
arXiv:2609.19830v1 Announce Type: new Abstract: Reinforcement learning for LLM agents involves two distinct optimization di- mensions: how environment feedback is exploited within a trajectory, and ho
arXiv:2609.20008v1 Announce Type: cross Abstract: Gromov--Wasserstein optimal transport (GW-OT) extends classical optimal transport by introducing structure-aware transport cost. This is particularly
arXiv:2409.18457v2 Announce Type: replace Abstract: This paper addresses a special Perspective-n-Point (PnP) problem: estimating the optimal pose to align 3D and 2D shapes in real-time without corresp
arXiv:2609.19399v1 Announce Type: cross Abstract: Computing Nash equilibria of simulation-based cybersecurity games with policy-space response oracles (PSRO) is bottlenecked by payoff estimation: ever
arXiv:2609.20821v1 Announce Type: new Abstract: Embedding spaces define notions of semantic similarity and distance. We study whether those embeddings reflect physical measurements of mass, distance,
even the anti-woke media sure loves this narrative. doesn’t make it true. 'I worked at OpenAI for two years before resigning in 2024. Fears that AI will cause human extinction are real. You should tak
arXiv:2609.19863v1 Announce Type: cross Abstract: Navigation world models plan by foresight, predicting the future that each candidate action sequence produces and selecting the best, rather than mapp
Four-byline alert: 🚨 “.. People who have discussed the subject with Mr. Trump .. say his outbursts are rooted in worries that if the artificial intelligence boom .. slows down, the market could crash
arXiv:2609.19413v1 Announce Type: cross Abstract: Robot manipulation policies are improving quickly, and real-robot evaluation remains the standard evidence for that progress. It still relies on a hum
arXiv:2609.19447v1 Announce Type: new Abstract: Robots that follow natural-language instructions in everyday indoor environments must act on incomplete human utterances. Instructions often omit essent
arXiv:2609.19452v1 Announce Type: cross Abstract: Robots are moving out of the structured factory floor and into unstructured environments such as disaster sites, planetary surfaces, and agricultural
arXiv:2609.20016v1 Announce Type: cross Abstract: The EU AI Act (Regulation 2024/1689) imposes technical obligations on high-risk AI providers, yet Articles 8-15 were drafted for predictive AI and lea
arXiv:2609.19850v1 Announce Type: new Abstract: Actor-critic architecture has been widely used in continuous robot control. However, they rely on learning a value network, introducing additional compu
arXiv:2609.19956v1 Announce Type: new Abstract: Tree-based Monte-Carlo Tree Search (MCTS) duplicates the same state when it is reached through different trajectories, which can waste simulations in st
arXiv:2609.20659v1 Announce Type: cross Abstract: Large-scale vision-language-action (VLA) models provide powerful priors for robot manipulation, yet adapting them to a specific deployment remains cha
arXiv:2609.20474v1 Announce Type: new Abstract: Agent harnesses supply planning guidance, organize execution, and check completion. We study how these components affect success, erroneous acceptance,
arXiv:2609.19962v1 Announce Type: new Abstract: Placing a grasped book into a tight shelf is a compact but difficult contact-rich control problem: millimetre-scale pose error can turn a geometrically
arXiv:2609.19846v1 Announce Type: cross Abstract: As generalist robot policies gain vision and language from web-scale pretraining, demonstrations remain costly to collect and tied to the robot that r
arXiv:2609.20300v1 Announce Type: new Abstract: Current offline reinforcement learning (ORL) algorithms tend to overfit the training dataset and exhibit poor in-distribution generalization and robustn
arXiv:2609.19414v1 Announce Type: new Abstract: Goal-conditioned reinforcement learning aims to learn policies that reach specified goals, but remains challenging in offline settings with sparse rewar
arXiv:2609.20268v1 Announce Type: new Abstract: Online reinforcement learning (RL) algorithms frequently exhibit poor sample efficiency and unstable learning dynamics, stemming from systematic critic
arXiv:2609.20615v1 Announce Type: cross Abstract: Robots inspecting an assembly must determine which parts are present and whether they are correctly installed. During egocentric assembly assistance,
It ain’t independent if you are partnering with them and chose the partner. We’re partnering with Accenture on independent evaluation of frontier AI—part of our recent commitment to embed evaluators a
It is also possible that every oxygen atom in the room I'm in may migrate to a corner, thereby suffocating me. I think the major takeaway is that @polynoamial is wasting his time on entirely foolish t
arXiv:2609.20200v1 Announce Type: new Abstract: Ride-sharing platforms must continuously decide which open requests to bundle into shared trips and which idle vehicles should serve them. The dominant
arXiv:2609.20278v1 Announce Type: cross Abstract: Text, knowledge graphs, and hypergraphs all have elements that play distinct roles within relation instances, structure that is lost when data is flat
arXiv:2609.19954v1 Announce Type: cross Abstract: This work proposes a real-time 6 Degree-of-Freedom (6 DoF) tracking algorithm for monocular laparoscopic surgery. It provides alignment between intra-
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:2609.20669v1 Announce Type: cross Abstract: 3D diffusion policies are strong at generating geometrically grounded actions from current observations, but successful manipulation requires not only
arXiv:2609.20404v1 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:2609.20558v1 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:2609.19796v1 Announce Type: new Abstract: Robotic manipulation under partial observability requires spatial information that extends beyond the current view. Geometry-aware RGB features describe
arXiv:2609.19521v1 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:2609.20056v1 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:2609.19974v1 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:2609.20747v1 Announce Type: cross Abstract: Reinforcement learning constitutes a promising approach owing to its potential for superhuman performance and self-learned policies. However, its appl
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: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
arXiv:2607.14962v2 Announce Type: replace-cross Abstract: Text-to-image (T2I) models can synthesize realistic, prompt-aligned images, yet samples generated for the same prompt often cover only a small
arXiv:2609.20195v1 Announce Type: cross Abstract: Audio-language models increasingly generate confident music descriptions that are unsupported by the input audio. We present, to our knowledge, the fi
arXiv:2609.19511v1 Announce Type: cross Abstract: Feature importance is central to interpretable machine learning, but the term 'importance' encompasses several fundamentally different notions of rele
arXiv:2609.19768v1 Announce Type: new Abstract: Multivariate ocean forecasting must exploit shared evolution in a coupled ocean system while adapting to the heterogeneous statistical and dynamical cha
arXiv:2609.19709v1 Announce Type: new Abstract: Batched multi-armed bandits update on a service's own schedule, and the usual implementation carries each arm's absolute reward rate from one update to
arXiv:2609.20566v1 Announce Type: cross Abstract: Animal demonstrations provide quadruped robots with natural and distinctive gait styles that are difficult to specify through hand-crafted rewards. Ho
Oops Anthropic IPO moved back to November. (Maybe second delay? And OpenAI IPO moved back to 2027. Making those S-1’s look good given the circumstances is hard work. *ANTHROPIC PLANS TO STAGE ITS IPO
arXiv:2609.19176v1 Announce Type: cross Abstract: Multi-organ segmentation is often challenged by partially annotated datasets and domain shifts across different imaging sources. To address these limi
arXiv:2609.19226v1 Announce Type: cross Abstract: AI-mediated platforms coordinate work through LLM agents acting for different principals. In these workflows, privacy loss can be created before a fin
arXiv:2609.19203v1 Announce Type: new Abstract: AI applications have shifted from single, monolithic foundation models (FM) to compound agentic systems. Yet today's stacks remain fragmented: even as p
arXiv:2601.00728v5 Announce Type: replace Abstract: We propose a reinforcement learning (RL) framework for xy{responsive} precision tuning for linear solvers, which can be extended to general algorith
arXiv:2602.04620v3 Announce Type: replace Abstract: GRPO-style reinforcement learning (RL)-based LLM fine-tuning algorithms have recently gained popularity. Relying on heuristic trust-region approxima
arXiv:2609.19729v1 Announce Type: new Abstract: Autoregressive (AR) video generation degrades over long horizons due to an overlooked train-inference discrepancy we term KV eviction mismatch: models t
arXiv:2609.20538v1 Announce Type: new Abstract: An AI evaluation can be perfectly reproducible and still support the wrong claim. This risk is acute in closed-loop systems: policy determines visited s