Knowledge catalogue
arXiv:2610.21113v1 Announce Type: cross Abstract: Large-scale multi-agent ecosystems require mechanisms for discovering suitable agents without exposing entire registries to a large language model (LL
arXiv:2507.19839v2 Announce Type: replace-cross Abstract: The modality gap is intrinsic to multimodal alignment, where embeddings from different modalities remain partially separated in a shared repre
arXiv:2610.22003v1 Announce Type: cross Abstract: Modern deep learning compilers compile models lazily and cache artifacts across invocations. Retained compiler state can make a later invocation incor
arXiv:2610.20960v1 Announce Type: new Abstract: Safety in agentic systems is inherently sequential: whether an action is safe depends on prior actions, tool outputs, information disclosures, and inter
arXiv:2610.21081v1 Announce Type: new Abstract: Score-based diffusion methods usually perturb data with Brownian Gaussian noise, corresponding to a light-tailed forward stochastic differential equatio
arXiv:2610.21156v1 Announce Type: new Abstract: Proactive dialogue agents must guide multi-turn interactions toward task goals while adapting to users whose intentions and attitudes evolve. Existing p
arXiv:2609.12286v2 Announce Type: replace Abstract: Introducing large language models (LLMs) into evolutionary computation enables semantic generation of candidates, but the classical problem of maint
arXiv:2602.07640v2 Announce Type: replace Abstract: Out-of-distribution detection methods are often either data-centric, detecting deviations from the training input distribution irrespective of their
arXiv:2610.21900v1 Announce Type: new Abstract: An agent's memory must remain useful as information changes across conversations. A new fact may replace an earlier value without making that value irre
arXiv:2610.21748v1 Announce Type: new Abstract: Spiking neural networks (SNNs) offer a compelling pathway toward energy-efficient artificial intelligence, fundamentally anchored in presynaptic input-d
arXiv:2610.05373v2 Announce Type: replace Abstract: On-policy distillation (OPD) has emerged as an effective post-training paradigm for language models, with recent efforts extending it to block diffu
arXiv:2610.21515v1 Announce Type: new Abstract: Language-conditioned humanoid motion generation enables users to create and refine robot movements through natural language. The central challenge is to
arXiv:2610.21792v1 Announce Type: new Abstract: Imitation learning enables robots to perform complex tasks by leveraging human-collected demonstrations. However, most existing approaches require labor
arXiv:2610.21456v1 Announce Type: new Abstract: Recent multilingual large language models achieve strong translation performance, yet the internal mechanisms responsible for low-resource multilingual
arXiv:2610.21922v1 Announce Type: new Abstract: Many vision-language-action policies rely on action-labeled robot demonstrations and lack an explicit mechanism for predicting future visual states. The
arXiv:2610.22327v1 Announce Type: cross Abstract: Time Series Language Models (TSLMs) enable language-based understanding and reasoning over temporal data, yet a fundamental question remains: what inf
arXiv:2610.21202v1 Announce Type: new Abstract: High-resolution synthesis with Latent Diffusion Models (LDMs) faces a fundamental trade-off between computational cost and generative fidelity. This tra
arXiv:2508.04136v4 Announce Type: replace-cross Abstract: Few-shot fine-grained visual classification (FGVC) aims to discriminate between subtly different categories with very limited data. While Mult
arXiv:2610.21304v1 Announce Type: new Abstract: Current value alignment evaluations for large language models (LLMs) heavily rely on output compliance, overlooking whether LLMs maintain value consiste
arXiv:2610.22368v1 Announce Type: new Abstract: Real-world post-training of generalist robot policies faces two challenges: partial observability makes action selection ambiguous, while offline post-t
arXiv:2610.21306v1 Announce Type: new Abstract: Vision-language-action (VLA) models transfer semantic knowledge acquired through vision-language pretraining to robot control, achieving strong performa
arXiv:2610.21999v1 Announce Type: new Abstract: Embodied Visual Tracking (EVT) is an essential capability for intelligent robots in human-robot collaboration, requiring an agent to follow a designated
arXiv:2610.22232v1 Announce Type: new Abstract: Lossless image compression fundamentally relies on accurate entropy modeling, driving a recent shift toward large language model (LLM)-based methods. Ho
arXiv:2610.22039v1 Announce Type: cross Abstract: Real-world software engineering rarely consists of isolated changes. Features, migrations, and systematic fixes often unfold across multiple pull requ
arXiv:2610.21659v1 Announce Type: cross Abstract: Corporate financial disclosures often unfold over time, with announcements, prepared remarks, and discussion spanning multiple high-frequency return i
arXiv:2610.21139v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used upstream of decision-making to clean data, detect label errors, and reconstruct missing values. Yet
arXiv:2610.21549v1 Announce Type: cross Abstract: Muon replaces a matrix gradient by an approximate polar factor and has shown strong empirical performance in neural-network training. The Isotropic Cu
arXiv:2610.21335v1 Announce Type: new Abstract: Generative low-light enhancers recover plausible texture but may hallucinate structure; restoration networks preserve structure but can leave regions un
arXiv:2610.21422v1 Announce Type: cross Abstract: Understanding video content requires reasoning at multiple levels of granularity, from identifying objects to interpreting their interactions and loca
arXiv:2604.03867v2 Announce Type: replace Abstract: Steering vectors have emerged as a lightweight and effective approach for aligning large language models (LLMs) at inference time, enabling modulati
arXiv:2610.22370v1 Announce Type: new Abstract: Token merging has emerged as an effective training-free strategy for accelerating diffusion models by reducing redundant token computation while minimiz
arXiv:2610.22184v1 Announce Type: cross Abstract: Current humanoid dexterous manipulation remains largely confined to end-effectors and simple grasps, falling short of humans' ability to engage the en
arXiv:2610.19342v2 Announce Type: replace-cross Abstract: Agents are increasingly interacting with other agents that act for different principals (people or organizations on whose behalf they work), s
arXiv:2610.21450v1 Announce Type: cross Abstract: Aerial vision-language navigation (VLN) agents built on large vision-language models are expensive to query. Executing a complete predicted action chu
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arXiv:2610.20120v1 Announce Type: new Abstract: We present several advances on neural operators by viewing the action of operator layers as the minimizers of Bregman regularized optimization problems
arXiv:2603.06135v3 Announce Type: replace-cross Abstract: Distinguishing legitimate critical speech from coordinated conspiracy narratives is a pressing challenge for the integrity of the online infor
arXiv:2610.15765v1 Announce Type: cross Abstract: Large language model agents are emerging as an important new class of users of desktop applications. However, existing desktop interfaces were designe
arXiv:2610.19607v1 Announce Type: cross Abstract: Rising privacy concerns around large language models have accelerated research in private Transformer inference (PTI). Synthesizing the empirical clai
arXiv:2610.19395v1 Announce Type: new Abstract: Robotic vascular intervention can reduce radiation exposure to physicians and improve manipulation stability, but effective guidance remains challenging
arXiv:2610.19183v1 Announce Type: new Abstract: Existing biomimetic lower-limb prosthesis controllers are commonly designed for discrete locomotion modes, requiring separate parameterizations for leve
arXiv:2610.20561v1 Announce Type: new Abstract: Reliable brain-mask transfer into native fluid-attenuated inversion recovery (FLAIR) space can fail when T1-to-FLAIR rigid registration is unstable. We
arXiv:2610.19479v1 Announce Type: new Abstract: Sim-and-real co-training is an effective way to scale robot learning by augmenting limited real-world demonstrations with simulation data. However, exis
arXiv:2610.19896v1 Announce Type: new Abstract: The reintegration of people in the workplace after an upper-limb injury is often a long process. Collaborative robots and humanoids operating in shared
arXiv:2604.03925v2 Announce Type: replace Abstract: Large language models (LLMs) struggle to accumulate evidence across multiple rounds of user interactions, failing to update their beliefs in a manne
arXiv:2610.20474v1 Announce Type: new Abstract: Pretrained vision-language-action models (VLAs) often fail midway through unseen long-horizon tasks. Recursive self-improvement (RSI) offers a remedy, b
arXiv:2610.14643v1 Announce Type: new Abstract: Clinical speech reveals identity through both spoken identifiers and voice, yet its pauses, delivery, and speaker roles can also carry useful clinical e
arXiv:2505.21055v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown impressive reasoning capabilities in interactive decision-making tasks. These agents interact with envi
arXiv:2610.15788v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly deployed as teams, yet they fail in characteristically social ways: information holders stay silent,
arXiv:2610.18603v1 Announce Type: new Abstract: Benchmarks report what an Artificial Intelligence (AI) agent can do on a task suite, but not whether one released version has been engineered with the c
arXiv:2610.19478v1 Announce Type: new Abstract: Agricultural systems make decisions over heterogeneous entity types, textual judgments, and knowledge-question options. Generating a textual answer adds
arXiv:2511.17937v2 Announce Type: replace Abstract: Alignment faking is a form of strategic deception in AI in which models selectively comply with training objectives when they infer that they are in
arXiv:2512.16245v2 Announce Type: replace Abstract: Merging large language models (LLMs) is a practical way to compose capabilities from multiple fine-tuned checkpoints without retraining. Yet standar
arXiv:2610.14487v1 Announce Type: new Abstract: Generative recommendation models based on autoregressive decoding have been adopted as alternatives to classical ranking-based systems. While their mode
arXiv:2610.15380v1 Announce Type: new Abstract: Bridging the structural gap between biological brains and artificial neural networks presents a pathway to altered computational dynamics. This paper in
arXiv:2610.16859v2 Announce Type: replace Abstract: For repository-grounded coding agents, a functionally correct patch is not necessarily an acceptable one: developers also constrain how the task mus
arXiv:2610.14479v1 Announce Type: cross Abstract: While language therapy is the primary treatment for post-stroke aphasia, limited clinical resources often restrict patient access to proper care. Addr
arXiv:2609.33196v2 Announce Type: replace Abstract: Evaluating large language models (LLMs) relies heavily on benchmark scores, yet aggregate metrics can obscure whether individual items serve their i
arXiv:2610.14542v1 Announce Type: new Abstract: Multimodal benchmarks are central to evaluating whether models can reason over information from multiple modalities. However, existing literature has re
arXiv:2610.18083v1 Announce Type: new Abstract: Scaling recursive self-improvement requires additional computation to produce useful research progress in reaching persistent objectives. Intuitively, m