Knowledge catalogue
arXiv:2610.00571v1 Announce Type: cross Abstract: Large reasoning models (LRMs) have achieved substantial improvements in solving complex mathematical problems, but often produce lengthy, repetitive,
arXiv:2610.01064v1 Announce Type: cross Abstract: Retrieving the right tables is a prerequisite for Text-to-SQL over realistic databases. Dense table retrievers rank schema elements independently, but
arXiv:2609.33289v2 Announce Type: replace Abstract: Autonomous large language model (LLM) agents operating in multi-product markets must make sequential decisions under information asymmetry and resou
arXiv:2610.00609v1 Announce Type: new Abstract: Legal research is a core and time-consuming legal workflow. Lawyers must identify controlling authority, verify that it remains valid, reconcile statute
arXiv:2610.01494v1 Announce Type: new Abstract: Sheaf Neural Networks generalize scalar-weighted message passing by replacing scalar edge weights with linear transport maps between local feature space
arXiv:2610.00888v1 Announce Type: cross Abstract: Multi-token prediction (MTP) improves the throughput of autoregressive generation by enabling the language model to draft multiple next tokens per for
arXiv:2610.01506v1 Announce Type: new Abstract: As agents evolve from single-tool systems into modular, composite architectures, skills are becoming an important mechanism for capability development a
arXiv:2610.02002v1 Announce Type: cross Abstract: Large Language Model (LLM) agents now take part in organizational work, where many authors record decisions across documents over months. Because a re
arXiv:2610.00544v1 Announce Type: new Abstract: Streaming world models should render a place consistently across repeated visits. Directly supervising such revisits requires training samples that capt
arXiv:2610.02001v1 Announce Type: new Abstract: Small open-weight models (2-9B) run on ordinary laptops, but under cloud-scale agent harnesses they rarely complete real tasks: tool prefill overflows t
arXiv:2610.01352v1 Announce Type: new Abstract: Open multimodal reasoning models have benefited from large-scale reasoning supervision, yet reliable post-training remains challenging due to uneven dat
arXiv:2610.02153v1 Announce Type: new Abstract: Long-horizon autoregressive video generation is limited by a finite context window. When an object or scene falls out of context, its fine-grained visua
arXiv:2609.33149v2 Announce Type: replace Abstract: A common principle of effective learning is to practice material that is neither already mastered nor too difficult to permit progress. We ask how t
arXiv:2610.00381v1 Announce Type: new Abstract: Medical models are judged not only on correctness, but on whether reported confidence matches actual accuracy. Generalist multimodal medical models have
arXiv:2610.01842v1 Announce Type: new Abstract: A time series world model (TSWM) predicts a controlled system's state from its observed history and planned actions and exogenous inputs. Current approa
arXiv:2610.00518v1 Announce Type: new Abstract: One-step generative models construct a static generator through iterative training-time transport. Existing transport objectives primarily assess distri
arXiv:2610.01162v1 Announce Type: new Abstract: Reliable video world models could provide scalable predictive environments for robot learning, planning, and evaluation. However, generated robot videos
arXiv:2607.17384v3 Announce Type: replace Abstract: This paper provides an experimentally verified formal law for calculating the uplift that diversity of thought provides in Large Language Model (LLM
Last month I posted a Qwen3.8-27B LoRA that makes it talk like a person instead of an assistant. It got a lot more attention than I expected: 700+ upvotes, 248 comments and 44k downloads since. I read
arXiv:2610.01344v1 Announce Type: cross Abstract: Reinforcement learning offers the prospect of a reusable sequential decision-making mechanism for spacecraft trajectory design, motivating policy inte
Hi folks, I'm a long-time lurker and big fan of this subreddit and a massive self-hosting fan (also outside of AI). I doubt many people will disagree with me here because I see the same arguments bein
The results are in, no thinking / instruct mode for new models degrade performance more than on old models such as 3.6 vs 3.8, where 3.6 takes the lead on several coding benches in instruct mode. I wo
arXiv:2610.00968v1 Announce Type: cross Abstract: Causal representation learning aims to discover robust features by exploiting the causal structure underlying data generation. Existing methods requir
arXiv:2610.00106v1 Announce Type: cross Abstract: Deep learning and large language models (LLMs) are rapidly gaining adoption in semiconductor design, driving demand for training datasets. Most effort
arXiv:2610.00041v1 Announce Type: cross Abstract: Agent frameworks increasingly delegate work by forking sub-agents; a common default makes the child inherit the parent's full working context. We meas
arXiv:2610.02191v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated striking capabilities on frontier mathematical problems, it remains unclear whether they possess th
arXiv:2610.00341v1 Announce Type: cross Abstract: As Large Multimodal Models (LMMs) transition toward natively unified architectures, evaluating their safety in synergistic harmful image-text generati
arXiv:2610.00994v1 Announce Type: new Abstract: Existing synthetic image evaluators typically provide only a scalar quality score and do not identify the image regions that support it. We introduce VI
arXiv:2610.02000v1 Announce Type: new Abstract: Adverse conditions such as rain, snow, fog, and dust remain challenging for camera-based perception in autonomous driving. We study multi-class weather
arXiv:2610.01243v1 Announce Type: cross Abstract: Vision-language models (VLMs) increasingly act as judges that pick the best of several generated images, so their choices decide what users see. Such
arXiv:2610.02117v1 Announce Type: cross Abstract: On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a frozen
arXiv:2609.39564v1 Announce Type: new Abstract: Delegating complete application development to coding agents requires preserving the intended design rather than simply producing plausible outputs thro
arXiv:2609.39188v1 Announce Type: new Abstract: Developmental psychology holds that certain priors are given to infants prior to experience rather than induced from data, and that the influence of suc
arXiv:2609.38878v1 Announce Type: cross Abstract: A large audio language model (LALM) turns a minute of speech into 750-1,500 tokens and prefills every one. Image-token pruning often cuts after the la
arXiv:2609.33676v2 Announce Type: replace Abstract: LLM agents increasingly take consequential actions through interactions with users, policies, and external tools. Auditing these agents requires aut
arXiv:2609.39843v1 Announce Type: cross Abstract: Density estimation is a fundamental problem in statistics and machine learning. In this work, we introduce BayesNDE, a neural density estimator based
arXiv:2609.39387v1 Announce Type: new Abstract: Neural operators increasingly benefit from pretraining on numerical simulations, yet adapting them for real-world prediction remains challenging. We int
arXiv:2609.39072v1 Announce Type: cross Abstract: Emotion recognition in conversation has been widely studied, but applying Large Language Models (LLMs) to continuous dimensional emotion evaluation in
arXiv:2609.39111v1 Announce Type: new Abstract: Human intelligence relies heavily on learned intuition: recognising patterns and judging situations without explicitly unfolding every intermediate step
arXiv:2609.38340v1 Announce Type: new Abstract: When a materials LLM answers a question about crystal structure, does it reason from the structure or copy an answer already printed in its input? Accur
arXiv:2609.38642v1 Announce Type: new Abstract: Chart editing requires cross-modal edit grounding, realizing a requested visual change in the code that draws it, with necessary related updates and wit
arXiv:2609.39914v1 Announce Type: cross Abstract: Traditional simulations of parametric partial differential equations (PDEs) rely on repetitive computations for each parameter, which makes high-fidel
arXiv:2609.33665v2 Announce Type: replace Abstract: Automatically generated environments provide a scalable source of interaction data for training general agents. However, existing approaches mainly
arXiv:2609.39924v1 Announce Type: cross Abstract: Vision-language models face a fundamental scaling bottleneck: the number of visual tokens grows with both temporal duration and spatial resolution, ma
arXiv:2609.39154v1 Announce Type: cross Abstract: Deep research tasks require agents to navigate large knowledge spaces, synthesize evidence across many sources, and adapt their plans as findings emer
arXiv:2603.26164v2 Announce Type: replace-cross Abstract: Data-centric training has emerged as a promising direction for improving large language models (LLMs) by optimizing not only model parameters
arXiv:2609.40124v1 Announce Type: new Abstract: Bias has long been studied in social psychology and cognitive science, where decades of research have produced a body of validated interventions that re
This is the first of a two-part series exploring how Google Cloud is bringing the foundational values of a high-performance parallel filesystem–TB/s throughput, sub-ms latency at high client scale, an
arXiv:2401.13937v3 Announce Type: replace Abstract: Video object segmentation is a fundamental research problem in computer vision. Recent techniques have often applied attention mechanism to object r
arXiv:2609.39148v1 Announce Type: new Abstract: AI agents can externalize what they learn from past tasks into reusable skills, such as procedures, checklists, code, or other executable artifacts, tha
arXiv:2609.38879v1 Announce Type: cross Abstract: Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protei
arXiv:2609.39710v1 Announce Type: new Abstract: Scientific abstracts mix contributions with background, motivation, and meta-language, so tools that read them as-is cannot separate what a field produc
arXiv:2609.39198v1 Announce Type: new Abstract: While Vision-Language-Action (VLA) models excel in static tasks, they struggle in dynamic environments where objects are in motion (e.g., conveyor belt
arXiv:2609.39841v1 Announce Type: new Abstract: Reconstructing dynamic driving scenes from recorded sensor data supports closed-loop evaluation of autonomous driving systems by synthesizing observatio
arXiv:2609.38193v1 Announce Type: cross Abstract: Patient world models and clinical agents aim to predict changes in patients' health and support clinical work. Developing these systems requires relia
arXiv:2609.38182v1 Announce Type: cross Abstract: Avatar-based multimodal empathetic response generation has emerged as a pivotal capability in human-centric systems, aiming to recognize user emotions
arXiv:2609.38415v1 Announce Type: cross Abstract: This technical report presents an alignment evaluation developed and performed by the UK AI Security Institute for assessing whether advanced AI syste
arXiv:2609.38406v1 Announce Type: new Abstract: Access to real-world information is often noisy and fragmented. Constructing a coherent narrative from such fragments requires models to reconstruct mis
arXiv:2609.38976v1 Announce Type: new Abstract: Demographic fairness gaps in automatic speech recognition are almost always reported from a single training run. We fine-tune the Q-former projector and
arXiv:2609.39228v1 Announce Type: new Abstract: We present FYAN, a human--AI harness for document-level mathematical formalization. Rather than treating theorems in isolation, FYAN coordinates an end-