Knowledge catalogue
arXiv:2609.34933v1 Announce Type: new Abstract: Memorization has been proposed as a mechanism to explain how language models fit the tail of their training distributions, but its training dynamics are
arXiv:2609.34911v1 Announce Type: cross Abstract: Modern robot policies predict a chunk of future actions from a single observation, execute only a prefix, and discard the rest before replanning. Choo
arXiv:2609.34325v1 Announce Type: new Abstract: Vision Transformers (ViTs) incur quadratic self-attention cost in the number of tokens. Most token-reduction methods adapt token identities within a pre
arXiv:2609.32453v1 Announce Type: new Abstract: Robotic manipulation is inherently history-dependent, yet most pretrained robotic policies condition on only the current observation or a short temporal
arXiv:2606.18307v2 Announce Type: replace Abstract: Data selection for instruction tuning is usually studied for efficiency, reaching strong performance with a fraction of the training data. We ask wh
arXiv:2609.33172v1 Announce Type: cross Abstract: World-Action models (WAMs) trained on static demonstrations often fail to manipulate moving targets even when they possess the required manipulation s
arXiv:2412.16633v4 Announce Type: replace Abstract: LLM-based robots use Large Language Models (LLMs) as planners to translate natural language instructions into policies such as grasp(), move_to(), a
arXiv:2609.34893v1 Announce Type: new Abstract: Learning-based manipulation policies relying on RGB cameras often suffer from degraded observations under extreme exposure. Event cameras mitigate this
arXiv:2609.33962v1 Announce Type: cross Abstract: Subject-independent motor-imagery (MI) EEG decoding can exhibit subject-level failures even when average performance appears acceptable: under subject
arXiv:2503.10439v4 Announce Type: replace Abstract: Exemplar-free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper,
arXiv:2609.33762v1 Announce Type: cross Abstract: LLM agents resend their whole conversation on every turn, and most of it was already processed on the previous turn. Serving systems avoid recomputing
arXiv:2609.35368v1 Announce Type: new Abstract: Exo-to-egocentric video generation aims to synthesize what a person sees from their own viewpoint given third-person footage and a target head trajector
arXiv:2609.31751v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have advanced rapidly in static visual understanding, yet remain unreliable when judging how an egocentric task is progres
arXiv:2609.33444v1 Announce Type: cross Abstract: A nascent family of methods that forgoes the policy gradient and reweights a supervised regression instead has garnered momentum in reinforcement lear
arXiv:2609.34373v1 Announce Type: new Abstract: Rate-limited multi-agent teams raise three questions the emergent-communication literature has answered only empirically: what an optimal message should
arXiv:2609.31905v1 Announce Type: new Abstract: We present an end-to-end QP-based policy framework that enables systematic policy construction with minimal domain-specific design, while preserving the
arXiv:2609.32661v1 Announce Type: new Abstract: Maximum common edge subgraph (MCES) matching finds a partial vertex correspondence between two labeled graphs that preserves as many labeled edges as po
arXiv:2609.34018v1 Announce Type: cross Abstract: Simulation-trained manipulation policies can exploit privileged state information to learn effective contact-rich behaviours, but deployment requires
Every state ag and DA w jurisdiction should be launching investigations NEW: Employees at OpenAI had raised security alarms months before the Hugging Face incident and related A.I. cyberattacks — thei
arXiv:2509.15880v2 Announce Type: replace Abstract: Geometry-grounded vision models, such as VGGT, have emerged as robust visual encoders, providing essential geometric priors for robotic manipulation
arXiv:2609.34672v1 Announce Type: new Abstract: Fine-grained visual understanding requires models to recognize small details within complex images. Multimodal on-policy self-distillation (OPSD) addres
arXiv:2609.34114v1 Announce Type: new Abstract: Fairness, as a fundamental social norm, continues to pose a longstanding puzzle regarding its emergence. Traditional game-theoretic models largely rely
arXiv:2609.34146v1 Announce Type: new Abstract: Continuous cuffless blood pressure (BP) monitoring from photoplethysmography (PPG) has strong potential for wearable health and telemonitoring, but accu
arXiv:2609.35575v1 Announce Type: new Abstract: The real-world performance of current vision-language-action models is fundamentally constrained by the limited coverage of expert demonstrations and th
arXiv:2609.34175v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies are typically adapted using successful demonstrations, which provide direct action supervision but rarely cover fa
arXiv:2609.31890v1 Announce Type: new Abstract: Content personalization systems in financial services must ensure fair exposure across diverse offerings-a requirement driven by contractual obligations
arXiv:2609.33248v1 Announce Type: new Abstract: Patient knowledge graphs from bedside monitoring should type their relations and state whether the data support their signs. In anesthesia and intensive
arXiv:2609.34673v1 Announce Type: new Abstract: Few-step generative models can generate high-fidelity samples within a few function evaluations. Despite this efficiency, generated samples may not exhi
arXiv:2609.32353v1 Announce Type: new Abstract: Visual token reduction is an effective way to accelerate multimodal large language models (MLLMs), but performance deteriorates rapidly under extremely
arXiv:2609.32069v1 Announce Type: new Abstract: Vision--language--action (VLA) models provide strong priors for robotic manipulation but are typically deployed as frozen policies, unable to improve fr
arXiv:2609.33973v1 Announce Type: new Abstract: Manipulating a Rubik's Cube with a single dexterous hand is a challenging test of sustained, contact-rich control: the hand must execute successive laye
arXiv:2609.34791v1 Announce Type: cross Abstract: We study finite-time concentration and convergence rates for projected two-time-scale stochastic approximation driven by a controlled Markov chain. Th
arXiv:2609.35377v1 Announce Type: new Abstract: Diffusion models trained on a finite dataset first learn to generate novel, high-quality samples and only much later collapse onto their training set. W
arXiv:2609.34009v1 Announce Type: new Abstract: Feedback-based on-policy self-distillation has emerged as a promising approach for enabling foundation models, more specifically Large Language Models (
arXiv:2609.35138v1 Announce Type: new Abstract: Latent world models predict future states for goal-directed planning using action chunks spanning multiple primitive steps. Existing methods typically u
arXiv:2609.34488v1 Announce Type: new Abstract: Looped architectures scale computation by reusing the same parameters across recurrent steps, and recent work shows that they substantially improve deep
arXiv:2609.33129v1 Announce Type: new Abstract: As the bridge between protein modality and discrete modeling, protein structure tokenization still largely relies on heavily engineered training objecti
arXiv:2609.32773v1 Announce Type: cross Abstract: Monetary-policy announcements and central-bank communications play a central role in foreign exchange markets, yet their qualitative, unstructured for
arXiv:2602.00974v2 Announce Type: replace Abstract: Label-supervised manifold alignment bridges the gap between unsupervised and correspondence-based paradigms by leveraging shared label information t
arXiv:2609.32323v1 Announce Type: new Abstract: We introduce FoundDSR, a generalizable foundation model for robust depth reconstruction across unseen data distributions using RGB-D pairs. FoundDSR beg
arXiv:2605.05660v2 Announce Type: replace Abstract: Multi-objective optimization (MOO) has received growing attention in applications that require learning under multiple criteria. However, most exist
arXiv:2609.35375v1 Announce Type: new Abstract: Scaling up robotic manipulation is primarily bottlenecked by the scarcity of real-world robot data. While recent approaches leverage human video demonst
arXiv:2609.35491v1 Announce Type: new Abstract: Few-step autoregressive video generation commonly relies on Distribution Matching Distillation (DMD), requiring a bidirectional diffusion teacher and an
arXiv:2609.34371v1 Announce Type: new Abstract: On-policy distillation (OPD) specializes pretrained video diffusion models through teacher supervision along the student's own generation trajectory. Al
arXiv:2609.34414v1 Announce Type: new Abstract: Predicting the next state is a core paradigm of World Models for modeling physical dynamics, emphasizing prediction fidelity. As World Models evolve int
arXiv:2609.35426v1 Announce Type: new Abstract: Reinforcement Learning-based post-training of Large Language Models (LLM) has been successfully applied to improve their reasoning capabilities. Existin
arXiv:2609.34207v1 Announce Type: cross Abstract: Functional data are intrinsically infinite-dimensional, and often exhibit phase variation, where corresponding events occur at different times across
arXiv:2609.33981v1 Announce Type: new Abstract: Exploration--exploitation is a central trade-off in bandit learning. While classical algorithms such as upper confidence bound methods and Thompson Samp
arXiv:2609.31715v1 Announce Type: new Abstract: Agentic recognition requires visual perception to move beyond static scene understanding and produce structured scene representations that support the p
arXiv:2609.35189v1 Announce Type: new Abstract: Post-training foundation video models on heterogeneous reward-weighted data usually assume that all data categories induce compatible updates. This assu
arXiv:2609.34233v1 Announce Type: new Abstract: Humanoid avatars extend human physical presence beyond the body, enabling people to participate in social, service, and labor activities through remotel
arXiv:2609.32596v1 Announce Type: new Abstract: Feed-forward models regress dense 3D point trajectories directly from monocular video, yet the residual after global alignment is substantial and lacks
arXiv:2609.34550v1 Announce Type: new Abstract: Vision-Language-Action (VLA) fine-tuning pairs images with actions at every step, yet typically provides only a task-level language instruction, leaving
Gary Marcus tweeted that generative AI will change the web. A reply from MMitchell (mmitchell_ai) echoes this idea and asks whether the internet’s content has deteriorated compared to four years ago,
arXiv:2609.32222v1 Announce Type: new Abstract: Raw 3D point clouds are a core geometric representation. Establishing their ownership is challenging because point sets are irregular, unstructured, and
arXiv:2609.32788v1 Announce Type: new Abstract: Deep learning has transformed symbolic music generation by borrowing the training paradigms of large language models, with systems such as NotaGen now p
arXiv:2609.34564v1 Announce Type: new Abstract: Post-training quantization (PTQ) efficiently compresses Vision Transformers (ViTs) without retraining, yet suffers severe accuracy degradation at low bi
arXiv:2609.32056v1 Announce Type: new Abstract: Achieving precise control over multiple properties without sacrificing chemical validity remains a central challenge in molecular inverse design. Existi
arXiv:2609.35545v1 Announce Type: new Abstract: Epidemic policy planning often requires coordination between geographical regions, taking into account mobility-driven spillovers and how to make use of
arXiv:2609.35084v1 Announce Type: new Abstract: Group-based reinforcement learning (RL) has advanced large language models (LLMs) and is increasingly extending to agentic tasks, where sparse terminal