Knowledge catalogue
arXiv:2609.14097v1 Announce Type: new Abstract: Subtomogram classification in cryo-electron tomography (cryo-ET) is a challenging problem due to the scarcity of labeled examples. While cryo-ET simulat
arXiv:2609.15400v1 Announce Type: new Abstract: Vessel segmentation in X-ray coronary angiography (XCA) is a fundamental step for quantitative coronary analysis and subsequent assessment of coronary a
arXiv:2609.13807v1 Announce Type: new Abstract: Large language models reason in high-dimensional hidden-state spaces, while users observe only final outputs. We introduce Bypass Observation, a non-int
arXiv:2609.14754v1 Announce Type: cross Abstract: Causal claims about large language model (LLM) internals rest on measurements. Those might include a projection, a cosine, an ablation delta, or an in
arXiv:2609.14963v1 Announce Type: new Abstract: As large language models become capable translators of classical texts, a key challenge is deciding which outputs need expert review when no human refer
arXiv:2609.15561v1 Announce Type: new Abstract: Evaluating the factual correctness of large language models (LLMs) is vital for many applications. But are our evaluation tools themselves trustworthy?
arXiv:2609.13499v1 Announce Type: cross Abstract: Private Evolution (PE) generates high-fidelity synthetic data in federated settings without exposing users' raw data. It aggregates clipped user votes
arXiv:2609.13268v1 Announce Type: new Abstract: Annual reports, diligence packs, and infographic dashboards bury numbers in page images: axes, cell grids, and footnotes that OCR pipelines flatten and
arXiv:2609.14717v1 Announce Type: cross Abstract: Speculative decoding accelerates LLM inference by verifying multiple drafted tokens in parallel, allowing a single target forward pass to accept sever
arXiv:2609.13445v1 Announce Type: new Abstract: Speech-to-speech LLMs like Moshi, and its derivative PersonaPlex, can listen and speak concurrently through full-duplex generation. However, they can be
arXiv:2609.13567v1 Announce Type: new Abstract: Chemotherapy improves survival for some patients with breast cancer, but doctors cannot reliably predict who. Current guidelines rely on recurrence scor
arXiv:2609.13439v1 Announce Type: cross Abstract: As compute scales, models evolve, and training algorithms advance, our ability to explain the increasingly powerful AI systems they enable is eroding.
arXiv:2609.14616v1 Announce Type: cross Abstract: Semantic image communication seeks to preserve task-relevant scene structure under limited channel resources, but carriers are often dense latent tens
arXiv:2609.15100v1 Announce Type: cross Abstract: An image tool can change its underlying generator while retaining its public name, making version attribution from online posts ambiguous. We study th
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:2609.15660v1 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
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:2609.14646v1 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:2609.13397v1 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:2609.15254v1 Announce Type: cross Abstract: Conformal counterfactual prediction constructs prediction sets with finite-sample coverage guarantees for counterfactual outcomes and individual treat
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:2609.15716v1 Announce Type: new Abstract: CMDIR extends Manifold-Decomposed Impedance Retargeting (MDIR) to transform fixed-impedance demonstrations into continuous variable-impedance controller
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:2609.13842v1 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: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:2609.14934v1 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:2609.13646v1 Announce Type: new Abstract: This work addresses decentralized online Riemannian optimization on Hadamard manifolds. Prior work under geodesic convexity (g-convexity) may require cu
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
arXiv:2609.14171v1 Announce Type: new Abstract: Complex-valued signals, such as Magnetic Resonance Imaging (MRI) and audio spectrograms, are almost always modelled as flat two-channel Euclidean data.
arXiv:2609.15070v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) generate text by iteratively denoising a masked sequence, independently predicting multiple tokens at each step. This c
arXiv:2609.15044v1 Announce Type: cross Abstract: Data attribution seeks to quantify how individual training examples shape a model's predictions and underpins problems including data valuation, machi
arXiv:2609.15485v1 Announce Type: new Abstract: Avalanche forecasting requires knowledge of snowpack conditions and recent avalanche activity, but field observations are sparse across large mountain r
arXiv:2609.14821v1 Announce Type: new Abstract: Earth system modeling is shifting from task-specific predictors toward foundation models with general spatiotemporal representation capabilities. Althou
arXiv:2602.00377v3 Announce Type: replace Abstract: Existing knowledge probing methods rely on pre-defined queries, limiting extraction to known concepts. We introduce DecompressionLM, a stateless fra
arXiv:2609.13324v1 Announce Type: cross Abstract: Graph-RAG systems often assume pristine data quality, overlooking the severe impact of perturbations in cloud-native databases. This paper proposes a
arXiv:2609.14129v1 Announce Type: cross Abstract: Ultra-High-Definition (UHD) video presents significant challenges for efficient storage and real-time decoding. Learning-based methods, such as Neural
arXiv:2609.14257v1 Announce Type: new Abstract: Semantic text watermarks encode signals in meaning rather than surface token choices, offering robustness to paraphrasing and other semantic-preserving
arXiv:2609.14715v1 Announce Type: new Abstract: We scale our conventional sub-150M pretraining recipe from 53.5M to 109.7M parameters, holding the method fixed (Qwen3-style decoder with grouped-query
arXiv:2607.16196v2 Announce Type: replace Abstract: Soft plush companions provide a safe and intuitive platform for affective human-robot interaction, but their deformable structure and distributed ta
arXiv:2609.15744v1 Announce Type: new Abstract: Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges. Financial risk early
arXiv:2609.13756v1 Announce Type: new Abstract: One in every five vehicle accidents on the road today is caused simply due to driver fatigue. Fatigue or otherwise drowsiness, significantly reduces the
arXiv:2609.13588v1 Announce Type: new Abstract: Control allocation for multirotors with bidirectional propellers is commonly formulated in signed-thrust variables, where the wrench map is linear. The
arXiv:2609.14642v1 Announce Type: new Abstract: Generation of musculoskeletal gait trajectories conditioned on patient-specific parameters remains a key challenge for wearable robotics and rehabilitat
arXiv:2609.15357v1 Announce Type: new Abstract: Diffusion models generate images through an iterative diffusion process, and recent studies have demonstrated that the intermediate feature maps produce
arXiv:2609.14596v1 Announce Type: new Abstract: Training-free diffusion inverse solvers typically choose between local measurement guidance and costly clean-space posterior updates. Independent poster
arXiv:2609.15975v1 Announce Type: new Abstract: Transformer representations evolve through learned additive transformations that either preserve their current direction or redirect it. We study this e
arXiv:2609.13830v1 Announce Type: new Abstract: We present DiVA, a deeply interactive digital life simulator pioneering a new paradigm for long-term, open-ended interactive experiences within digital
arXiv:2609.14571v1 Announce Type: new Abstract: This study highlights the role of domain-specific pretraining profile (DSPP) in Transformer performance for modeling digital pragmatics in Arabic-Englis
arXiv:2609.15559v1 Announce Type: cross Abstract: Grammatical error correction (GEC) evaluation has traditionally relied on reference or edit overlap, which can penalize valid rewrites that differ fro
arXiv:2609.13228v1 Announce Type: new Abstract: Vision Language Models (VLMs) should rely on visual evidence that directly determines the correct answer, but supervision for grounding visual reasoning
arXiv:2609.15338v1 Announce Type: cross Abstract: Large Language Models (LLMs) primarily perform inference at the token level, resulting in substantial memory overhead and compromised computational ef
arXiv:2609.13533v1 Announce Type: cross Abstract: Most hyperparameter configurations for Evolvable-Substrate HyperNEAT (ES-HyperNEAT) produce networks that stagnate at random-guessing performance, was
arXiv:2507.23600v5 Announce Type: replace Abstract: A single measurement of a chemical mixture, a reaction mixture, a natural extract, or a tissue, records the sum of the profiles of the few component