Knowledge catalogue
arXiv:2609.15668v1 Announce Type: cross Abstract: Through pre-training on extensive text and image datasets, current multi-modal large language models (MLLMs) achieve strong performance on general tas
arXiv:2609.14010v1 Announce Type: new Abstract: We present CirrGuide, a deep cascaded framework for cirrhotic liver segmentation and severity classification. Cirrhosis causes progressive structural ch
arXiv:2609.15164v1 Announce Type: new Abstract: When several retrieved sources support the same claim, an answer engine cites some but not others. We call this decision citation allocation and introdu
arXiv:2609.15830v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) can improve access to complex information; however, retrieving evidence alone does not ensure that answers are gr
arXiv:2609.15660v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) grounds a language model's answers on retrieved external knowledge and returns each answer with citations that id
Claude Code writes 100% of its own code, and overall 80% of the code at Anthropic is written with Claude. So… …. At Anthropic, Claude now writes 80% of our code. Engineers ship 8x more code per quarte
arXiv:2609.15319v1 Announce Type: cross Abstract: Frontier models score well on shallow document/chart reading tasks. In a controlled data-room audit, moving evidence into buried conditions reduced ac
arXiv:2604.07487v2 Announce Type: replace Abstract: Large language model agents rely on effective model context to obtain task-relevant information for decision-making. Many existing context engineeri
arXiv:2511.20109v2 Announce Type: replace Abstract: Climate science demands automated workflows to transform comprehensive questions into data-driven statements across massive, heterogeneous datasets.
arXiv:2609.13860v1 Announce Type: new Abstract: Querying clinical trial registries remains a manual and error-prone process, requiring researchers to navigate large volumes of semi-structured data wit
arXiv:2609.13238v1 Announce Type: new Abstract: Maxillofacial report generation from cone beam computed tomography is scored here by a composite objective placing 80% of its weight on a large language
arXiv:2601.00290v3 Announce Type: replace Abstract: Clinical trials constitute a critical yet exceptionally challenging and costly stage of drug development (2.6B per drug), where protocols are encode
arXiv:2609.15227v1 Announce Type: new Abstract: While homographies are fundamental to many computer vision tasks, the majority of conventional estimation techniques provide only point estimates withou
arXiv:2609.14952v1 Announce Type: new Abstract: Dynamic cloud workflow scheduling must balance deadline satisfaction, container utilization, and energy consumption while dealing with stochastic task-e
arXiv:2609.13876v1 Announce Type: new Abstract: Personal robots should adapt their co-speech gesture style to a new user without requiring model retraining. We present a training-free personalization
arXiv:2609.14239v1 Announce Type: new Abstract: Static benchmarks for computer-use agents fix a task set at release and score every system against it once. That makes them reproducible, and it lets th
arXiv:2609.15393v1 Announce Type: cross Abstract: Text-to-Time Series Generation (Text-to-TS) provides a promising paradigm for synthesizing time series from natural language, enabling scenario-specif
arXiv:2609.15009v1 Announce Type: new Abstract: Designing effective memory mechanisms is crucial for advancing LLM-driven Multi-Agent Systems (MAS), helping agents learn together and perform better ov
arXiv:2609.14339v1 Announce Type: new Abstract: Decentralized training enables large-model training over low-end GPUs and internet-grade connections, but communication along both data-parallel and pip
arXiv:2609.14936v1 Announce Type: new Abstract: In field robotics, acquiring independent large-scale reference trajectories more accurate than the evaluated estimates remains an open challenge. The do
arXiv:2609.14646v1 Announce Type: cross Abstract: Multi-turn interactions with LLMs are becoming increasingly common in information-seeking scenarios. However, user queries are often ambiguous and con
arXiv:2609.13651v1 Announce Type: cross Abstract: Robot deployments rarely change one mechanism at a time: cameras, action interfaces, and physical dynamics often shift together. Prior adaptation reci
arXiv:2609.14657v1 Announce Type: cross Abstract: While Vision-Language Models (VLMs) excel at visual reasoning, generating structured, editable Scalable Vector Graphics (SVG) remains a fundamental ch
arXiv:2609.15334v1 Announce Type: cross Abstract: Medical imaging modalities such as ultrasound and X-ray are widely used in clinical practice, where diagnosis follows a structured, evidence-driven wo
arXiv:2609.14019v1 Announce Type: cross Abstract: Generative augmentation is a standard remedy for label scarcity in physiological signal classification, but existing quantum generative models start f
arXiv:2609.13397v1 Announce Type: new Abstract: In rendering, a camera is a sampling operator that maps each finite pixel to a bundle of rays. Different camera models change the geometry of this bundl
arXiv:2601.13885v2 Announce Type: replace-cross Abstract: Computerized Adaptive Testing (CAT) has proven effective for efficient LLM evaluation on multiple-choice benchmarks, but modern LLM evaluation
arXiv:2609.13270v1 Announce Type: cross Abstract: In distributed model predictive control for multi-drone collision avoidance, a fixed prediction horizon forces a compromise: a short horizon is inexpe
arXiv:2609.15254v1 Announce Type: cross Abstract: Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual treat
arXiv:2609.14003v1 Announce Type: cross Abstract: Personal AI agents built on large language models (LLMs) are increasingly given access to a user's private data and communications in order to provide
arXiv:2502.04312v3 Announce Type: replace Abstract: Contrastive learning leverages data augmentation to develop feature representation without relying on large labeled datasets. However, despite its e
arXiv:2609.13516v1 Announce Type: new Abstract: In robotic insertion under uncertain contact, the axial force limit and the appropriate controller gain vary across tasks. As a result, a single fixed g
arXiv:2609.14439v1 Announce Type: new Abstract: Blind image inpainting, aiming to recover contaminated images in the case of unknown masks, is a challenging task. Motivated by the perspective of human
arXiv:2603.04419v3 Announce Type: replace-cross Abstract: Vision-language models produce different object and use descriptions under different persona prompts, but low overlap alone does not identify
arXiv:2609.15716v1 Announce Type: new Abstract: CMDIR extends Manifold-Decomposed Impedance Retargeting (MDIR) to transform fixed-impedance demonstrations into continuous variable-impedance controller
arXiv:2609.14383v1 Announce Type: new Abstract: Real-time pedestrian detection in driving scenes is constrained by three coupled failure modes: tiny targets lose discriminative evidence, occlusion wea
arXiv:2602.08696v3 Announce Type: replace-cross Abstract: Dysarthric speech recognition is limited by high speaker variability and scarce labeled data. Existing synthesis methods often couple speaker
arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MO
arXiv:2311.03154v3 Announce Type: replace Abstract: There are two categories of methods in Federated Learning (FL) for joint training across multiple clients: (i) parallel FL (PFL), where clients trai
arXiv:2510.13134v2 Announce Type: replace Abstract: We study continuous-time dropout in controlled differential equations. We introduce a random-batch approximation of additive vector fields. On each
arXiv:2609.15193v1 Announce Type: new Abstract: Drifting models offer a promising route to faster generative AI: they perform gradual transport during training, while generating new samples in a singl
arXiv:2609.14011v1 Announce Type: new Abstract: Few-shot in-context learning (ICL), the capacity of a model to infer abstract patterns from input-output examples provided in its prompt and apply them
arXiv:2609.14985v1 Announce Type: new Abstract: We show how users can create and manipulate causal virtual worlds with large-language-model (LLM) and large-video-model agents. The approach uses feedba
arXiv:2609.14226v1 Announce Type: new Abstract: Automated radiology report generation has advanced rapidly in diagnostic accuracy, yet generated reports frequently diverge from the stylistic conventio
arXiv:2609.15989v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is a safety strategy where the reasoning of a large language model 'actor' is inspected by a 'monitor' (often another
arXiv:2609.13664v1 Announce Type: new Abstract: Knowledge graphs are increasingly distributed across autonomous organizations that share an entity space but own disjoint subsets of relations, forming
arXiv:2606.15317v2 Announce Type: replace Abstract: Offline models for autonomous robots often fail under time-varying dynamics outside their training distribution. While Koopman operator theory offer
arXiv:2609.13842v1 Announce Type: cross Abstract: Recent advances in speech synthesis and voice conversion have made deepfake speech increasingly realistic, making generalization to unseen spoofing at
arXiv:2512.24688v2 Announce Type: replace Abstract: Relative localization is essential for cooperation in autonomous multi-robot systems. Existing approaches either rely on shared environmental featur
arXiv:2609.13201v1 Announce Type: new Abstract: Training datasets for upcoming LLMs would include a significant amount of AI text/image data generated from current LLMs. In such a scenario, it is impo
arXiv:2609.13942v1 Announce Type: cross Abstract: The CRITICS project addresses science accessibility and literacy by converging advanced Machine Translation (MT) based on Large Language Models (LLMs)
Disclaimer: no AI was used whatsoever to write this post Cautionary tale about chasing cheap tokens. exposé: https://kendell.dev/blog/crofaifalse/ reaction by nahcrof, announcing the shutdown of the s
arXiv:2609.14934v1 Announce Type: cross Abstract: Statistical data fusion combines two panels that share a block of covariates but observe disjoint outcome blocks, and in its traditional form no row o
arXiv:2609.14725v1 Announce Type: new Abstract: Few-step distillation accelerates diffusion models but must balance diversity and fidelity: trajectory-based distillation preserves mode coverage, while
arXiv:2609.14811v1 Announce Type: new Abstract: We introduce Crypto Accounting Bench (CAB), a benchmark for assessing whether frontier and open-weight language models can reconstruct the complete jour
arXiv:2609.13646v1 Announce Type: new Abstract: This work addresses decentralized online Riemannian optimization on Hadamard manifolds. Prior work under geodesic convexity (g-convexity) may require cu
EDIT: Sorry for the unclear title. This model is UkisAI's Swift-Qwen3.8-27B, not a new version of BottleCap AI's 3.6-ThinkingCap. All credit goes to UkisAI for making great fine-tune, and I made this
arXiv:2609.13413v1 Announce Type: new Abstract: We introduce CVSS-X, a large-scale synthetic speech-to-speech translation corpus that extends CVSS by reversing the translation direction. While CVSS tr
arXiv:2609.15234v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have gained significant attention due to their strong language understanding and generation capabilities, demonst
arXiv:2609.13892v1 Announce Type: cross Abstract: We study the recovery of forward and reverse quadratic optimal-transport maps from unpaired samples in high dimensions. We introduce a bidirectional n