Knowledge catalogue
arXiv:2506.02955v3 Announce Type: replace-cross Abstract: Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation
arXiv:2610.12851v1 Announce Type: cross Abstract: This work studies an entropy-based estimator of Kullback-Leibler (KL) divergence constructed from normalized importance weights. We prove a universal
arXiv:2610.13692v1 Announce Type: cross Abstract: Standard Policy Gradient (PG) algorithms suffer from high estimation variance because they rely on Monte Carlo updates. While off-policy variance redu
arXiv:2610.12813v1 Announce Type: new Abstract: LLM agents increasingly operate over sensitive resources, such as private documents and mutable system states, which can create serious safety and secur
arXiv:2610.13252v1 Announce Type: cross Abstract: Can a vision model learn what words mean from pixels alone? We show that the two largest DINOv3 vision transformers organize words rendered in images
arXiv:2610.12733v1 Announce Type: new Abstract: Indoor navigation to task-relevant targets remains difficult when the target is small, occluded, or visually indistinct. Meanwhile, smart appliances, mo
arXiv:2610.12832v1 Announce Type: new Abstract: Pretrained vision-language models (VLMs) already exhibit strong visual, spatial and reasoning capabilities relevant to robotic manipulation. The key cha
We need a name for the kind of people that conflate 2022 pure LLMs that were in fact a tiny bit like stochastic parrots (not my phrase, and not one I ever endorsed) with 2026 systems that couple those
arXiv:2610.13247v1 Announce Type: cross Abstract: Open audio-language models are used as judges of generated music: given a clip and its text prompt, the model rates the clip, and the judge is validat
arXiv:2610.12995v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) remains a widely used approach for post-training large language models, because fixed demonstrations can directly introdu
arXiv:2610.13465v1 Announce Type: cross Abstract: Multimodal LLMs can fail to read text embedded in an image's coarse structure (text a human recovers by squinting) and report that no text is present.
arXiv:2610.13404v1 Announce Type: new Abstract: Linear models can predict well in complex domains even when underlying mechanisms are nonlinear. We argue that this can occur because the complexity of
arXiv:2610.13687v1 Announce Type: cross Abstract: Audio-visual dataset distillation aims to synthesize a small dataset from a large paired audio-visual dataset, while nearly preserving the training ut
arXiv:2610.13786v1 Announce Type: cross Abstract: Discrete graph diffusion models (GDMs) corrupt the graph structure by adding noise independently to nodes and edges under a Markovian noise model. In
Introducing Multi-Agent Self-Supervision (MASS): Recursive self-improvement through collective intelligence Blog: https://pub.sakana.ai/mass/ As AI systems tackle increasingly open-ended research prob
CBS News: A look at a 1,700-member Slack run by Medicare agency CMS where Microsoft, OpenAI, and other companies help shape policy on AI apps and medical records access — A cadre of elite techies, fin
Reasoning From Scratch: Reinforcement Learning with Verifiable Rewards (RLVR) round 2. Covering clipped policy ratios, KL loss term, format rewards, and other GRPO tips & tricks. 00:00 Introduction an
arXiv:2610.12465v1 Announce Type: cross Abstract: General-purpose robots must perform a wide range of tasks from agile locomotion to dexterous manipulation. While sim-to-real reinforcement learning (R
arXiv:2610.12113v1 Announce Type: cross Abstract: Off-policy actor--critic methods control overestimation bias by taking the minimum of two critics. This uses the same aggregation rule everywhere, reg
arXiv:2610.12404v1 Announce Type: new Abstract: Close-proximity multi-arm manipulation requires collision models that are both geometrically accurate and differentiable enough for real-time optimizati
arXiv:2610.12420v1 Announce Type: new Abstract: Reinforcement learning in safety-critical domains requires maximizing task performance while strictly adhering to safety constraints. Existing safe rein
arXiv:2610.11140v1 Announce Type: cross Abstract: Clinical diagnosis is inherently sequential: clinicians escalate from cheap to costly tests only when additional evidence is expected to resolve diagn
arXiv:2610.11354v1 Announce Type: new Abstract: Rule-governed contextual decision tasks require models to apply specified rules to case-specific context and evidence. Written rules can leave gaps in d
arXiv:2610.11724v1 Announce Type: new Abstract: Multimodal meme understanding is increasingly used to analyze socially sensitive content, yet existing models often exhibit biased behavior when interpr
arXiv:2610.11060v1 Announce Type: cross Abstract: World-action models have recently improved autonomous driving by jointly learning future scene prediction and trajectory generation. Most existing app
arXiv:2603.08706v2 Announce Type: replace Abstract: Imitation learning (IL) teaches language-model agents to reproduce expert actions but not to distinguish them from plausible mistakes. Self-reflecti
Automated evals can score every agent session, but they do not automatically reflect what your team considers a good outcome. That takes human judgment. The post Alignment is a human-AI workflow: how
arXiv:2610.11213v1 Announce Type: new Abstract: Long-term Time Series Forecasting (LTSF) tasks, which leverage the current data sequence as input to predict the future sequence, have become increasing
arXiv:2610.12083v1 Announce Type: cross Abstract: LLM-as-a-Judge is the standard paradigm for NLP evaluation, yet its systemic reliability remains poorly understood despite being widely treated as a d
Emily Forlini / Fortune: Anthropic is setting up a “presidential engagement” program for the 2028 US elections that will offer AI policy education to candidates in both parties — Anthropic is setting
arXiv:2610.12386v1 Announce Type: cross Abstract: The prevailing approach to improving robot foundation models (RFMs) relies on larger models, more robot demonstrations, and costly training at scale.
Assorted things I've been mulling over regarding agents in the economy: • Companies are in some sense the original superintelligence. It's often the case that they can use their amortized cognitive su
arXiv:2610.11792v1 Announce Type: cross Abstract: We present AuraLuxMuse, a novel system for automated aesthetic stage lighting design that integrates expert knowledge, representation learning, and pr
arXiv:2610.11128v1 Announce Type: new Abstract: A verified reference solution provides a correct trajectory for training a reasoning model. Alternatively, a prefix of the reference can guide the model
arXiv:2607.10169v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a dominant paradigm for enhancing LLMs' reasoning capabilities. However, RL algorithms with PPO-Clip ar
arXiv:2610.11461v1 Announce Type: cross Abstract: Natural-language style descriptions provide an interpretable interface between large language models (LLMs) and controllable text-to-speech (TTS). How
arXiv:2610.10651v1 Announce Type: cross Abstract: This paper exposes the ergodic ceiling and thermodynamic inefficiency of current deep learning, which converges to a statistical average of historic h
arXiv:2510.15866v2 Announce Type: replace Abstract: The clinical adoption of biomedical vision-language models is hindered by prompt optimization techniques that produce either uninterpretable latent
arXiv:2507.21037v3 Announce Type: replace Abstract: Cross-subject motor-imagery electroencephalography (MI-EEG) decoding remains challenging because substantial inter-subject variability can cause bot
arXiv:2610.10742v1 Announce Type: cross Abstract: AI systems increasingly rely on Large Language Models (LLMs) as core reasoning engines, making them targets for prompt injection and jailbreaks. Guard
arXiv:2610.11483v1 Announce Type: new Abstract: Diffusion-based driving planners capture diverse behaviors but can generate unsafe trajectories under distribution shift. We propose BridgeGuard, a safe
arXiv:2610.12016v1 Announce Type: new Abstract: World models for control must capture which aspects of the environment respond to the agent's actions and which are relevant to reward. Generative world
arXiv:2610.12362v1 Announce Type: new Abstract: We consider policy optimization for online episodic tabular Markov decision processes (MDPs) with adversarial losses and bandit feedback. Policy optimiz
arXiv:2512.22454v2 Announce Type: replace Abstract: Electrical substations are a significant component of an electrical grid. Indeed, the assets at these substations (e.g., transformers) are vulnerabl
arXiv:2610.12156v1 Announce Type: new Abstract: To stream long videos while maintaining visual quality and temporal consistency, Self Forcing mitigates exposure bias through self-rollout training on s
arXiv:2610.11663v1 Announce Type: new Abstract: Deep reinforcement learning agents reach strong performance in real-time strategy games but can be brittle against opponents outside their training dist
arXiv:2610.11096v1 Announce Type: cross Abstract: Visual tracking across the air-water interface is challenged by splashes, bubbles, refraction, reflections, and abrupt appearance changes that can tem
arXiv:2610.12107v1 Announce Type: new Abstract: Generalized referring expression segmentation (GRES) requires dynamically balancing high-level semantics for identifying a variable number of language-s
arXiv:2610.12431v1 Announce Type: new Abstract: Diffusion models can represent complex, multimodal trajectory distributions, but extracting uncertainty from them typically requires costly Monte Carlo
arXiv:2610.10641v1 Announce Type: cross Abstract: Large language models (LLMs) offer promising potential for next-visit diagnosis prediction, owing to their ability to integrate longitudinal clinical
arXiv:2610.12467v1 Announce Type: cross Abstract: Text-conditioned motion generators produce trackable whole-body motion, but they have no notion of scene-dependent safety: the same action may target
arXiv:2610.11085v1 Announce Type: new Abstract: Offline multi-task reinforcement learning (Offline MTRL) heavily depends on the quality and distribution of pre-collected data. However, existing method
arXiv:2610.11505v1 Announce Type: new Abstract: Humanoid robots possess the structural capability to traverse complex terrains. However, achieving stable t raversal without relying on perceived inform
arXiv:2610.11748v1 Announce Type: cross Abstract: U-Net inference for brain-tumor segmentation requires billions of multiply-accumulate operations, motivating hardware that can reduce computation dyna
arXiv:2610.12470v1 Announce Type: cross Abstract: While learning dexterous manipulation from a single human video offers a promising alternative to costly robot demonstrations, many recent methods pre
arXiv:2610.12094v1 Announce Type: cross Abstract: Particle filters are a standard tool for nonlinear state estimation, but their resampling step is discrete, preventing gradient-based learning in vari
arXiv:2610.11934v1 Announce Type: cross Abstract: Industrial environments are increasingly characterized by the tight interaction among physical processes, communication infrastructures, and intellige
arXiv:2610.11012v1 Announce Type: new Abstract: Powerful misaligned AI models might recognize alignment evaluations and strategically behave well on them, rendering direct audits uninformative. Howeve
arXiv:2610.12355v1 Announce Type: cross Abstract: Spatial reasoning remains a persistent weakness of vision-language models (VLMs), because RGB inputs do not directly provide geometric evidence. Exist
arXiv:2610.11510v1 Announce Type: new Abstract: Large language models (LLMs) often default to Modern Standard Arabic (MSA) when generating Arabic, even when prompted with dialectal Arabic. A natural e