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Thursday 15 October
- PaperData-Efficient ConvNeXt Pretraining for High-Resolution Sentinel-1 GRD SAR Representation Learning
Labeled Sentinel-1 Ground Range Detected (S1GRD) datasets remain scarce, limiting supervised deep learning for land-surface remote sensing, while self-supervised pretrain
- PaperVesperMUSE: Multi-Modal Fusion Vision Transformer for Image Semantic Segmentation Under Diverse Light Conditions
Semantic segmentation is critical for the reliable navigation and scene understanding of autonomous systems, yet its performance degrades severely in low-light environmen
- PaperGRID-Planner: Graph-Structured Relational Reward Shaping for Diffusion Planning
Autonomous driving planning requires selecting trajectories that remain safe, rule-compliant, and comfortable under complex multi-agent interactions, yet robust closed-lo
- PaperOntoAct: Hypothesis-Based Neuro-Symbolic Agents through Ontological Abstraction
Learning from experience requires recovering reusable knowledge and determining when it applies to a new situation. We introduce OntoAct, a hypothesis-based neuro-symboli
- PaperDeep Learning of Topological Equivalence Classes
We demonstrate an application of deep learning to the problem of identifying whether two generating vectors correspond to topologically equivalent finite group actions on
- PaperP2TPO: Structure-Aligned Page-to-Token Credit Assignment for Full-Page Document Parsing
End-to-end full-page document parsing must preserve the content and structure of heterogeneous elements, yet otherwise accurate outputs can contain local errors and omiss
- PaperLearn Only Where You Can't Compute: Structured Corrections to Gaussian Process Posteriors
Physics-informed Gaussian processes (GPs) provide a principled framework for scientific inference by expressing uncertainty over empirical measurements, boundary conditio
- PaperTime to Fake It: Continual Machine Learning Intensifies Unfairness in Deepfake Detection
Deepfake detectors safeguard against misinformation, rendering their failures as much a societal as a technical concern. As generators constantly evolve, detectors traine
- PaperUncertainty-Aware Federated Learning for Joint Communication and Computation in XR Systems
The convergence of communication and computation, empowered by artificial intelligence (AI) enhanced capabilities, emerges as a key enabler for realizing the promising fu
- PaperWhy Shallow Neural Networks Learn Sinusoidal Components First: An Orthogonal Polynomial Dynamics Framework
Neural networks often learn low-frequency components before high-frequency ones --- a phenomenon called spectral bias or termed F-principle. Many numerical studies have d
- PaperChronoSheaf: Joint Spatio-Temporal Spectral Filtering on Cellular Sheaves
Sheaf neural networks generalize graph neural networks by assigning each edge a learned linear restriction map that specifies how the local representations of adjacent no
- PaperSelectPFN: Amortized Bayesian Variable Selection for Sparse Nonlinear Regression with Prior-Data Fitted Networks
Tabular foundation models based on prior-data fitted networks (PFNs) predict on a new data set in a single forward pass, but they cannot report which inputs their predict
- PaperFollowing Delegation Traces to Study Human Oversight in Interaction with Multimodal AI
As multimodal AI systems are becoming our everyday thought partners, the shift towards studying how users transfer control to these systems becomes especially urgent. In
- PaperLearning Speech Attribute Representations via Multi-Teacher Distillation
Recent post-training approaches have shown that self-supervised speech encoders can be adapted to learn a single utterance-level speech attribute through knowledge distil
- PaperAgentic-Priors: Using Large Language Models to Address the Cold Start Problem in Preference Elicitation
An agent that learns a user's preferences from their choices faces a cold start problem: before any choices are observed, it must rely solely on a prior. Informative prio
- PaperFlow-CLIP: Image-Conditioned Residual Scoring for CompositionalImage-Text Matching
Vision--language pre-training has enabled substantial progress in image--text retrieval and zero-shot recognition by learning transferable cross-modal representations. Ho
- PaperThe Lagrangian Structure of Cost-Penalized Active Feature Acquisition
Active Feature Acquisition (AFA) sequentially selects features to maximize predictive accuracy under an acquisition budget. A natural heuristic greedily maximizes Expecte
- PaperBridgeAfford: Bridging Query-Target Gap for Generalizable 3D Affordance Grounding
Language-guided 3D affordance grounding aims to localize functional regions on 3D objects according to interaction intents. However, existing methods exhibit poor general
- PaperCodeHealth Predicts the Token Cost of Agentic Code Review: A Pre-Registered Study of 465 Agent-Authored Pull Requests
Context: Organizations increasingly delegate code review to multi-agent large language model (LLM) pipelines billed per token. Low code quality raises human maintenance e
- PaperLearning Rewards in Strategic Games
Understanding the objectives that drive agents' behavior is crucial in strategic environments, where multiple agents interact and adapt to one another. This challenge ari
- PaperCoWorld Reasoner: Agentic Multimodal Reasoning with the Evolving Video World Model-Driven Harness
As Vision-Language Models (VLMs) advance, they are increasingly expected to perform spatiotemporal reasoning over motion, actions, and view changes beyond what the input
- PaperThe Tilted Sampling Problem: Optimism, Posterior Sampling, and Logarithmic Regret
Many modern learning systems are used not to select a single best action, but to sample diverse high-quality outputs. Examples include entropy-regularized reinforcement l
- PaperSeeing is Believing: Benchmarking and Mitigating the Interference of Language Priors on Visual Perception in MLLMs
Language priors in MLLMs can override visual evidence, impairing visual perception. Existing benchmarks assume shared priors across models and rely solely on accuracy. Ho
- PaperIn-Context Learning Before Specialisation: Theory of Learning in a Solvable Topic Model
Sequence models can generalise in fundamentally distinct ways: extrapolating structures seen in training, or using their prompt to adapt to novel tasks. The prevailing st
- PaperTEM: Temporal State Tracking Memory for Large Language Model Agents
An agent's memory must remain useful as information changes across conversations. A new fact may replace an earlier value without making that value irrelevant to a later
- PaperSelf-Calibrated Subspace Robust PCA for Parameter-Free Removal of Mixed Noise and Line Stripes in Hyperspectral Images
Hyperspectral images (HSIs) are often corrupted by Gaussian noise, impulses and stripes whose intensities vary across bands. The representative coefficient correlated tot
- PaperSurgStreamer: proactive, request-driven monitoring of surgical video streams with structured state and a reusable reasoning layer
Surgical teams could benefit from assistants that watch the video for requested events, such as "tell me when the clip applier appears during Calot triangle dissection",
- PaperGraph Dirichlet Energy for Fitting-Free Bivariate Causal Inference
We propose the Graph Dirichlet Energy Causal Inference (GDECI) criterion for distinguishing cause from effect in bivariate settings. For a candidate direction $X\to Y$, w
- PaperAnvil: Scaling Embodied Spatial Intelligence through Knowledge Recirculation
Embodied spatial intelligence requires more than visual recognition: models must localize entities, reason about their relations, and express this knowledge in action-rel
- PaperLearning Lean Feedback for Efficient Proof-Candidate Evaluation on GPUs
Training language models for formal theorem proving requires large numbers of verifier calls. Slow checks and timeouts often leave GPUs waiting and withhold rewards from
- PaperProvably Efficient Risk-Sensitive Offline Reinforcement Learning via Optimized Certainty Equivalents
Risk-sensitive reinforcement learning (RL) addresses decision-making under uncertainty and adverse outcomes. Learning risk-sensitive policies from fixed datasets is impor
- PaperLethe: Link Inference Attacks For Evaluation of Edge Unlearning Methods
Graph Unlearning (GU) aims to remove the influence of specific training data from a Graph Neural Network without retraining anew. While most methods address removal of no
- PaperFrom Sketch to Scene: Relightable Garment Reconstruction from Marked Sketches
Sketching is one of the most natural ways for people to express visual ideas. Unlike the text prompts that most generative systems rely on today, a sketch captures spatia
- PaperDynamic ordering in sequence generation in Transformers: experiments on low-resource Tagging scenarios
This paper introduces TagInsert, a Transformer-based model that challenges the common left-to-right (L2R) generation paradigm in Transformers, for sequence labelling task
- PaperSeeing Both the Forest and the Trees: Reusing Holistic 3D Priors for Part-Decomposed Generation
Generating 3D assets with explicit parts requires two properties at once: geometric detail inside each part and structural coherence across the whole object. Recent image
- PaperTemporal Periodic Image Registration with Implicit Neural Representations
Implicit Neural Representations (INRs) have recently gained popularity for their ability to model functions using simple and lightweight networks. These methods have also
- PaperPhysics-Aware Distillation of a Grid Foundation Model for Real-Time Edge Inference
Grid foundation models (GridFMs) are graph transformers pretrained on a masked power-flow objective, and evaluate power flow orders of magnitude faster than an iterative
- PaperLocal Epochs as Optimal Stopping: Theory and Evidence in Federated Learning
How long should each client train before communicating? Federated learning usually answers with a fixed, hand-tuned number of local epochs, yet an extra epoch helps only
- PaperCompiling Multi-Threshold Operators for Quantized Non-Monotonic and Fine-Granular Activations in Custom-Tailored DNN Accelerators
Aggressive quantization of neural networks can expose activation functions as bottlenecks for efficient hardware acceleration. Implementing quantized activations as multi
- PaperA Closed Form Depth-Width Law for Neural Collapse in Deep Linear Networks
How depth and width of neural networks shape learned representations is usually studied only after training. We show that, in a tractable feature-learning setting, this d
Headlines and links come from the labs' own blogs, Hacker News and the GDELT news index, and from the sources our index already follows. We show a short excerpt and always link to the original. A launch reported by a news site rather than the lab is marked “Reported” until the lab itself announces it.