Knowledge catalogue
arXiv:2609.01431v1 Announce Type: cross Abstract: Optimal hyperparameter scaling laws describe how the best hyperparameters for large language model (LLM) training change with model and data scale, en
arXiv:2609.00479v1 Announce Type: new Abstract: For emerging scientific research domains, local Small Language Models (SLMs) are becoming more attractive, as they offer stronger privacy control and mo
arXiv:2609.00189v1 Announce Type: new Abstract: Goal-directed optimization is essential for steering molecular generators to propose candidates with desired properties. However, it is often implemente
Elon vient de poster un truc simple : presque tout le discours sur l’IA se passe sur X. Ce n’est pas un détail d’audience. C’est le signe que la noosphère est en train de devenir réelle. Teilhard de C
arXiv:2609.00551v1 Announce Type: cross Abstract: Multimodal memory offers a scalable interface for long-video question answering, but existing methods often retrieve captions, frames, transcripts, su
arXiv:2609.00946v1 Announce Type: cross Abstract: Conditional independence tests (CITs) test for conditional dependence between two random objects X and Y given a third random object Z. Existing CITs
arXiv:2609.01281v1 Announce Type: cross Abstract: Vision-language-action (VLA) models map visual observations and language instructions directly to robot actions, but long-horizon tasks require more t
arXiv:2609.00310v1 Announce Type: new Abstract: Emotional labor is the effortful management of emotional displays to meet social or professional expectations. Personality traits have been correlated w
arXiv:2507.20881v3 Announce Type: replace Abstract: Endoscopic depth estimation is a critical technology for improving the safety and precision of minimally invasive surgery. It has attracted consider
arXiv:2606.23382v2 Announce Type: replace-cross Abstract: Transformer language models have become established tools for modeling human sentence processing, with measures such as surprisal and attentio
arXiv:2609.00581v1 Announce Type: new Abstract: Ensuring factuality remains a critical challenge for deploying LLMs in high-stakes settings. Existing hallucination detectors usually operate at a singl
arXiv:2609.00023v1 Announce Type: cross Abstract: In this paper, we introduce ES-AHD, a novel framework that fundamentally integrates Evolution Strategy (ES) into Large Language Model (LLM)-driven Aut
arXiv:2609.00738v1 Announce Type: new Abstract: Inference-time search with large language models (LLMs) often concentrates on a small set of structurally or semantically similar trajectories, leaving
arXiv:2609.00032v1 Announce Type: new Abstract: Mathematical communities work with different objects, invariants, and tools, so transferring a problem across them is expensive and often skipped. We pr
arXiv:2609.01404v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are strong perceivers of images and video. We ask how far that reach extends into acting: dropping an MLLM di
arXiv:2606.17506v2 Announce Type: replace Abstract: Evaluations of social bias in LLMs largely focus on whether models generate or imply biased content. However, as LLMs are increasingly used as judge
arXiv:2508.06374v4 Announce Type: replace Abstract: With the surge of large language models (LLMs) and their ability to produce customized output, style-personalized text generation--'write like me'--
arXiv:2609.00487v1 Announce Type: cross Abstract: Frontier language models that refuse harmful single-turn prompts often comply when the same intent is reached gradually over many turns, making multi-
arXiv:2609.00994v1 Announce Type: new Abstract: Recent extensions of 3D Gaussian Splatting (3DGS) enable real-time novel view synthesis in dynamic scenes by learning time-conditioned Gaussian deformat
arXiv:2609.01526v1 Announce Type: new Abstract: Scientific agents must learn not only how to reason, but also what to believe. However, existing LLM agents typically express scientific hypotheses in f
arXiv:2609.00279v1 Announce Type: cross Abstract: This work shows that diffusion models learned with standard denoising loss can provide effective global MCMC proposals for complex high-dimensional ta
arXiv:2609.01355v1 Announce Type: cross Abstract: Scenario optimization, conformal prediction, and related distribution-free certification methods use finite samples to construct decisions or predicti
arXiv:2609.01344v1 Announce Type: new Abstract: Multimodal coding and editing systems must map a visible or semantic referent to the exact executable object that can be edited. A wrong reference may s
arXiv:2609.00629v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) achieve strong performance on image-grounded text generation and visual question answering. However, it remains dif
arXiv:2609.00473v1 Announce Type: cross Abstract: Manual annotation remains a major bottleneck in ultrasound (US) bone segmentation, where experts typically iteratively refine rough brush masks rather
arXiv:2606.19101v2 Announce Type: replace-cross Abstract: We investigate a structure-first approach to dynamical learning in which the organization of stateful interactions is prescribed explicitly ra
arXiv:2504.10214v2 Announce Type: replace Abstract: Pretrained model-based incremental object detection (PTMIOD) leverages the rich detection priors of pretrained detectors to learn new categories inc
arXiv:2609.01294v1 Announce Type: new Abstract: Deep-research agents answer complex questions by interacting with search and browsing tools, yet they often search along a single evolving trajectory. O
arXiv:2609.01245v1 Announce Type: cross Abstract: Reinforcement learning is a natural way to post-train LLM agents for long-horizon interactive tasks judged only by end-of-task verification, yet a sha
arXiv:2609.00474v1 Announce Type: cross Abstract: LLMs are increasingly deployed as orchestrators that coordinate specialized subagents to solve complex tasks through natural language. However, in man
arXiv:2609.00539v1 Announce Type: new Abstract: Animals' body morphology shapes the gait patterns they can perform, where mechanical resonance reduces the need for active control. By tuning posture an
arXiv:2609.01322v1 Announce Type: new Abstract: In many settings, studying causal questions based on text data requires adjusting for confounding information within texts. Yet there is a tradeoff in c
ExtractBench is now live on @Kaggle. It tests schema-guided document extraction on the documents most likely to break downstream agents and workflows, including long record lists, noisy scans, handwri
Fable 5.1 didn't come with document OCR benchmarks, so we benchmarked it on ParseBench: a comprehensive parsing benchmark that's comprised of 2k+ real-world documents across finance, legal, insurance,
arXiv:2609.01596v1 Announce Type: cross Abstract: Real-world robotic assembly at sub-millimeter tolerances demands spatial precision, compliant interaction, and robustness to contact failures. We pres
arXiv:2510.03899v3 Announce Type: replace-cross Abstract: Balancing resource efficiency and fairness is critical in networked systems that support modern learning applications. We introduce the Fair M
arXiv:2508.04698v2 Announce Type: replace Abstract: LLM-powered conversational assistants are often deployed in a one-size-fits-all manner, which fails to accommodate individual user preferences. Rece
arXiv:2609.00097v1 Announce Type: cross Abstract: The development of long-context Large Language Models (LLMs) is constrained by the memory bandwidth bottleneck and quadratic complexity of the attenti
Jen Zhu (@jenzhuscott) noted on Twitter that “fear marketing failed. Here comes the pump.” Following this, Watcher.Guru shared a new statement: OpenAI CEO Sam Altman declared that AI will become the l
arXiv:2609.00404v1 Announce Type: cross Abstract: Robot capability marketplaces, the 'app store for robot skills,' are emerging as the deployment vector for LLM-driven robot fleets. The default cloud-
arXiv:2402.16934v2 Announce Type: replace-cross Abstract: Federated learning has emerged as a decentralized approach for training high-performance models without accessing user data. Despite its effec
arXiv:2609.00745v1 Announce Type: new Abstract: Multi-view human reconstruction has been extensively studied under simplified settings, yet robust and efficient multi-person reconstruction in unconstr
arXiv:2609.00543v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems rely on external corpora that may contain outdated, contradictory, noisy, or unreliable documents, introduc
World Labs Inc., the high-profile and well-funded artificial intelligence startup co-founded by the renowned computer vision pioneer Fei-Fei Li, has just dropped Atlas, which promises to be a game-cha
arXiv:2609.00961v1 Announce Type: new Abstract: Conversational agents like chatbots and voice assistants are trained to understand and respond to user intents. On encountering an utterance with an int
arXiv:2609.01027v1 Announce Type: new Abstract: Out-of-distribution (OOD) detection predicts whether a test image belongs to none of the predefined classes. To evaluate this task, benchmarks need imag
arXiv:2609.01006v1 Announce Type: new Abstract: Scientific methodology figures are essential for communicating complex methods clearly, yet creating them remains labor-intensive and typically requires
Finally, a good paper testing if graph memory actually beats flat retrieval for long-term agents. (bookmark this one) Researchers extract each conversational turn into typed nodes and attributed edges
arXiv:2609.01198v1 Announce Type: new Abstract: Repeated banking interactions require assistants to maintain complete, current, and traceable customer records as life changes emerge incidentally in ro
This was my first serious attempt at tuning a local LLM. I started because Qwen3.8-27B IQ3 was fast on my RTX 5080 but the coding quality disappointed me, and the Q4 profiles I tried in LM Studio were
Two of the ways that exist (im sure there are more) to train a LoRA on H3 well are on two different trainers. Fizgig's is Optimised Likeness Learning: I've found the stable core of H3's identity lives
arXiv:2609.00831v1 Announce Type: new Abstract: Token aggregation converts token-level representations into fixed-dimensional sample representations, but most pooling methods operate only in the origi
arXiv:2609.00129v1 Announce Type: cross Abstract: The performance of artificial intelligence (AI) and machine learning (ML) models degrades when the problem they were trained on drifts. This is a near
arXiv:2609.00659v1 Announce Type: new Abstract: Cooperative perception allows a drone fleet to combine observations from multiple viewpoints. However, existing systems typically fix their feature-shar
arXiv:2508.10349v2 Announce Type: replace-cross Abstract: To fine-tune large language models (LLMs) over private data, federated learning (FL) has emerged as a promising paradigm. However, the prohibi
arXiv:2601.19094v3 Announce Type: replace-cross Abstract: Learning algorithmic computation often requires explicit relational intermediate states, yet many graph processors maintain their primary stat
arXiv:2609.00377v1 Announce Type: cross Abstract: We present FoldingAgent, an agentic framework for inferring explicit parametric folding programs directly from origami demonstration videos. Our frame
arXiv:2609.00788v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have extended the capability of large language models (LLMs) to process more contextual multimodal information,
arXiv:2609.00089v1 Announce Type: new Abstract: Foundation models promise accurate forecasts with little or no task-specific training, but whether they can replace models designed specifically for ele
arXiv:2609.00475v1 Announce Type: cross Abstract: Angle-encoded quantum kernels on tabular data collapse when the feature map is wider than the intrinsic dimension of the data. We propose the correlat