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Tuesday 20 October
- PaperUncovering Audio-Text Alignment in Large Audio-Language Models through Cross-Modal Concept Neurons
While the language model backbone is pretrained on text alone, large audio-language models (LALMs) can, to a great extent, recognize acoustic attributes, such as emotion,
- PaperForeword: Pre-Generation Prediction and Characterisation of LLM Test Failures using the Jacobian Lens
Testing LLM-based software is expensive: each input needs several sampled responses, each judged by a costly oracle. Further, every prompt or model change calls for re-te
- PaperPerceives but Doesn't Brake: A Perception-Action Binding Gap in Reasoning Driving VLAs
Reasoning-augmented vision-language-action (VLA) driving models can perceive and reason about safety-critical hazards yet fail to translate that evidence into safe action
- PaperCAPS: Clinically-Verifiable Anatomy-Prioritized Synthesis for Missing MRI Imputation in Brain Tumor Segmentation
Multi-parametric MRI is the gold standard for neuro-oncology, yet acquiring complete 4-modality protocols may be unfeasible in real-world clinical practice. When handling
- PaperCoincidence-Maximizing Customer Clustering Using Smart Meter Data
Electricity distribution planning depends on peak-demand coincidence within customer groups, yet similarity-based clustering of smart-meter data does not optimize it dire
- PaperTemporal Evidence-Parameter Co-Alignment for LLM Reasoning over Temporal Knowledge Graphs
Large Language Models (LLMs) often struggle with temporally grounded reasoning because their static parametric memory is misaligned with the dynamic evolution of real-wor
- PaperAfrica-Papers: A 10-Billion-Token Multi-Domain Academic Corpus for African Foundation Language Models
Large Language Models (LLMs) depend fundamentally on the diversity, depth, and factual rigor of their pre-training corpora. However, contemporary foundational pre-trainin
- PaperDocLayout-YOLO: Axis-Attentive Document Layout Detector with Mesh-Guided Synthetic Pretraining
Accurate and efficient document layout analysis is critical for modern document parsing systems. However, although the YOLO framework represents a leading paradigm for th
- PaperFrom Code Generator to Engineering Companion: A Plant-aware Agent for PLC Programming
Programmable Logic Controller (PLC) coding remains a manual, IDE-centric task that demands constant context switching between documentation, project files, and code edito
- PaperPredicting silent failures of learned inference under restricted access: what measurement physics and prior information explain
Learned priors are used to reconstruct regions that measurements barely reach, and to report uncertainty there. They can fail silently: the reported interval excludes the
- PaperCurriculum Learning for Generative Flexible Docking
Flexible docking is a highly practical task, aiming to predict the joint configuration of a protein-ligand complex from a protein apo structure and a ligand SMILES string
- PaperFixed Sets and the Hardness of Computing Correlated Equilibria
We show that computing an approximate correlated equilibrium of a polytope game is PPAD-complete, and thus polynomial-time equivalent to finding a Nash equilibrium. This
- PaperNear-Optimal Regret Bounds for UCB-Style Gaussian Process Bandits via Data Thinning
Gaussian process upper confidence bound (GP-UCB) has been widely studied, though its known regret upper bound is suboptimal. To obtain near-optimal regret upper bounds, e
- PaperCausal Discovery for Linear Models with Latent Confounding via Multi-domain Identifiable Representation Learning
Causal discovery with latent confounding is particularly challenging, but a common setting in real-world data. In this work, we focus on linear causal models with latent
- PaperOccupational Uniqueness and Wage Growth
Changes in occupational task content may have different economic implications depending on whether the tasks gained or lost are occupation-specific or already widespread
- PaperOV-Foam: How 3D Representations Shape Semantic Feature Lifting
Existing feature lifting approaches for open-vocabulary segmentation rely on simple backprojection to assign features to primitives. In the case of 3D Gaussian splatting
- PaperGAMF-Net: Grouped Availability-Aware Multimodal Fusion for Smartphone Skin Lesion Classification
Smartphone clinical images provide a low-cost data source for computer-aided skin lesion classification in resource-constrained settings, but images alone cannot fully ca
- PaperDe-Diffusing WAMs: Action-Sufficient Direct Prediction for Robot Control
Cascade world-action models (WAMs) use predicted visual futures to condition robot actions, but inherit costly iterative inference from diffusion-pretrained experts. We a
- PaperInterruption-Aware Recurrent Reinforcement Learning for UAS Navigation Under Temporary Overrides
Safe autonomous navigation of UASs requires tactical deconfliction to address conflicts that strategic coordination cannot prevent. This task is challenging under partial
- PaperPointForward V2: Point-Aligned Interaction for Feedforward Driving Reconstruction
Feedforward reconstruction of dynamic driving scenes requires integrating observations across multiple views and time steps into a coherent 3D representation. PointForwar
- PaperReliability-Guided Vision-Language Decoding with Text-Aware Skip Refinement for Medical Image Segmentation
Medical image segmentation remains challenging when targets exhibit limited visual distinction, irregular structure, or substantial variation across imaging modalities. M
- PaperRobust Representation Learning for Spiking Neural Networks based on Conditional Information Bottleneck
Spiking Neural Networks (SNNs) have shown great potential for low-power neuromorphic computing due to their event-driven processing and spatiotemporal sparsity, but they
- PaperWeak-$L^α$ Noise, Strong Guarantees: Gradient Clipping under Affine Heavy Tails
Gradient clipping is a key tool for obtaining high-probability convergence guarantees for stochastic first-order methods under heavy-tailed noise. Existing analyses typic
- PaperReading Too Much Into It? Interpretive Preferences and Commitment in LLM Mentalisation under Ambiguity
Large Language Models (LLMs) are increasingly used for interpersonal advice, potentially shaping how users interpret ambiguous social encounters. Existing Theory of Mind
- PaperA Semi-Analytical Inference Framework for Structural Causal Models
Structural Causal Models (SCMs) provide a principled framework for observational, interventional, and counterfactual reasoning, all of which rely on probabilistic inferen
- PaperPolyscene: Scenario Planning as Diversity Planning
Scenario planning is a foresight practice in which analysts construct a small set of plausible futures and prepare an organisation for each. Each scenario is a narrative
- PaperClawMMU: Protection-Coherent Virtual Memory for Stateful LLM Agents
AI agents keep long-term memory, much of which comes from web pages and tool results. Agent harnesses, the software that runs the model, do not record who may act on this
- PaperBAIT: Can LLMs Find What Biases a Reader? A Benchmark for Cognitive Bias Triggers in News
Persuasive writing exploits cognitive biases, recruiting a reader's mental shortcuts to shape belief, often without the reader noticing. We call the linguistic features e
- PaperLatticeRank: Spacing-Ranked Consistency for Native-Grid 3D Medical Image Segmentation
Variable voxel spacing changes more than image resolution: it alters the discrete sampling lattice, tensor geometry, and anatomical evidence available to a 3D segmentatio
- PaperFrom Complex Histories to Simple Predictors: Predictive Dimension in Dynamic Graph Learning
Existing continuous-time graph learning methods rely heavily on complex neural architectures to encode ever-expanding interaction histories. However, the future condition
- PaperWildAIGC-Bench: Can AI Image Detectors Handle the Wild? A Benchmark of Generated and Edited Images in Online Circulation
AI-generated images linked to news events and online claims can mislead audiences, enable fraud, or cause public controversy. We introduce WildAIGC-Bench, built through r
- PaperMoDDiT: Simple Token Routing in Diffusion Transformers
Real-time video generation, interactive world models, and on-device image synthesis are almost a reality. Diffusion Transformers (DiT) achieve excellent sample quality, s
- PaperInsightRAG: Aggregating Distributed Scholarly Judgments for Scientific Survey Generation
The rapid growth of scientific literature makes survey writing increasingly challenging, motivating automatic survey generation. Yet, existing systems must search an open
- PaperIECG: Weakly-Supervised Interpretability-Enhancing Foundation Model for Electrocardiogram
Electrocardiography (ECG) is a cornerstone of cardiac diagnosis, yet most machine learning methods for ECG analysis lack interpretability, limiting their clinical adoptio
- PaperPULSE: Personalized Opportunistic Learning for Wearable Devices
Wearable devices increasingly support personalized health and well-being applications, but post-deployment data can differ substantially from those used for training, whi
- PaperSlot-JEPA: Object-Structured Prediction Interfaces for Latent World Models
As a prototypical latent world model, Joint-Embedding Predictive Architectures (JEPAs) provide strong semantic representations. However, dense patch representations expos
- PaperMuPACE: Learning to Pace Muon for LoRA Fine-Tuning
Learning rate schedules are typically fixed before training begins, yet when fine-tuning large language models with low-rank adapters (LoRA), the appropriate scale varies
- PaperContextual Candour: Role-Aware Graduated Disclosure for LLMs over Mixed-Sensitivity Contexts
LLMs increasingly operate over mixed-sensitivity contexts, where they must reason over information ranging from public to confidential while disclosing only what the user
- PaperSame Judgment, Different Yes: Measuring Multilingual Judgment Profiles in Language Models
Multilingual evaluations often score a model's opening yes or no, a token that also carries model and language answering conventions. We ask when different ways of elicit
- PaperQuantifying the Threat: A Bayesian Probabilistic Framework for Heavy Metal Health Risks among Children in the Densu Basin, Ghana
Groundwater in Ghana's Densu River Basin remains a key domestic water source, but multi-metal contamination threatens children's health. Previous deterministic hazard ind
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.