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Friday 16 October
- PaperNo-Regret Language Generation
We introduce no-regret generation, which requires a generator to make a sublinear number of mistakes uniformly over all demonstration sequences and compatible target lang
- PaperDeep LLM layers expose passage-level context in EEG during silent reading
In a previous report we showed that a decoder can recover open-vocabulary word-level information from 19-channel EEG recorded during silent reading, by aligning short EEG
- PaperPresenceBench: An Exploratory Evaluation of Large Language Model Adaptation to User State
Presence-aware intelligence is the capacity of an AI system's intelligence layer to adapt appropriately to the functional state of the person it serves (here readiness, f
- PaperDeconstructing Multi-Task Active Learning: The Paradox of Gradient Conflict and Orthogonal Decomposition
Existing multi-task active learning (MTL-AL) methods predominantly rely on linear scalarization of heterogeneous objectives such as uncertainty, diversity, and gradient c
- PaperStereo LWIR-RGB Automatic Labeling for Low-Light Camera-Based VRU Detection
VRU detection in low-light conditions is a key component in ensuring VRU safety during the most collision prone time of the day. Currently, advances in low-light detectio
- PaperGRAFT-EEG: Gauge-Aware Physical Field Transport for Cross-Dataset Motor-Imagery EEG
Cross-dataset EEG decoding often treats montage, reference, and active-sensor changes as representation shift, although they also alter the observation operator. We study
- PaperThetreStageAI: Multi-View Retrieval-Augmented Generation for Structured Bengali Theatre Production Planning
Staging a theatre performance requires coordinating dialogue, actor movement, stage directions, lighting focus, cue timing, and equipment constraints within the same prod
- PaperGEMS: Molecular Structure Elucidation as Learned Search over Isomer Graphs
Identifying small-molecule structures from molecular formulas and tandem mass spectra remains a bottleneck in untargeted metabolomics and natural-product discovery. Even
- PaperLUMINR: Intensity-conditioned Meta-learned INRs for Accelerated Medical Image Registration
Implicit neural representations (INRs) have been applied to deformable image registration, where a lightweight network maps a spatial coordinate to a deformation vector.
- PaperThe Sign Code: The Hidden Binary Nature of Deep Networks
Every neuron in a trained network outputs a number that can be separated into its sign (positive or negative) and its magnitude (absolute size). We call the set of all ne
- PaperBeyond Action Prediction: Learning Search-Free Planning in Latent Space
Latent world models learn action-conditioned state transitions in a compact representation space, providing a predictive model for visual planning and control. However, p
- PaperTrajectory-Triggered Sparse Collaborative Decoding
Dense assistance methods such as speculative decoding repeatedly verify draft tokens with a target model, keeping target computation coupled to generation. We study spars
- PaperSIT-Eval: Situation-Based Interactive Turn-by-Turn Evaluation of Conversational Agents
The rapid proliferation of conversational AI agents necessitates evaluation methods that assess performance across diverse dialogue scenarios, utilizing multiple situatio
- PaperHow Many, but How Sure? Class Competition as a Confidence Proxy in Multi-Class Density-Based Counting
Multi-class density estimators can count objects in crowded scenes, but their class-wise outputs offer no indication of how reliably density mass has been allocated. We i
- PaperNot Every Mention Is a Claim: Rethinking Object Hallucination Detection in Vision-Language Models
Object hallucination in vision-language models (VLMs) is usually measured by checking each object a caption names against the image. Most benchmarks and detectors alike t
- PaperADA 2025: A Multimodal Dataset for Water-Deficit Stress Detection in Sugar Beets
Research and development into artificial intelligence (AI) and machine learning (ML) applications in crop management are hindered by a lack of suitable public datasets. W
- PaperWhen Magnitude Suffices: Reconstructing Time-Domain-Gated Microwave Signals Without Phase on a Thoracic Phantom
Time-domain gating separates the arrivals that form a microwave S-parameter measurement. It requires the measurement of complex values, so this application almost always
- PaperLearning to Discriminate Scene Structures Makes Self-Supervised Depth Learning Scalable
Self-supervised monocular depth estimation is appealing for its potential to scale with unlabeled data, yet existing methods often degrade when trained on diverse scenes.
- PaperFrom Trajectories to Differentiated Skills: Cognitive Differentiation for Self-Improving Agents
Large language model (LLM) agents tackle complex tasks through reasoning and interaction with external environments. Agent skills provide persistent, transferable capabil
- PaperRL-Guided Temporal Localization for Dual-Channel Retrieval in Long-Horizon Agent Memory
Time-aware retrieval is crucial for long-horizon LLM agents to effectively leverage ever-growing long-term memory. Existing methods focus on lexical or semantic similarit
- PaperWhat Remains Without Test Labels? Re-evaluating a Graph-Level Anomaly Detection Benchmark
Graph-level anomaly detectors are trained only on normal graphs, yet benchmarks may use test labels for model selection. We examine whether UB-GOLD results reused by late
- PaperBoundary Detection for High-Dimensional Point Clouds
We introduce two new methods for boundary detection in high-dimensional point cloud data, intended to work in situations with non-uniform density, high ambient dimension,
- PaperSemPurify: Semantics-Aware Data Purification for Black-box Backdoor Defense
Backdoor attacks have emerged as a critical security threat in machine learning, where adversaries embed triggers within poisoned training samples to manipulate a model's
- PaperGliomaINK: A Tumor and Anatomy Conditioned Flow Matching Framework for 3D MRI Sequence Synthesis in Glioma Patients
Background: Incomplete multiparametric MR examinations limit glioma characterization and downstream AI tools. Generative models can synthesize missing MRI sequences, but
- PaperEnhancing RAG-based Medical VQA with Gated Multimodal Representations
Medical Vision-Language Models (Med-LVLMs) have shown strong performance in Medical Visual Question Answering (MVQA), yet they may still produce factually inconsistent re
- PaperAnalyzing Occupational Gender Bias across the LLM Training Lifecycle
Post-training is widely assumed to reduce the social biases language models inherit from pretraining, yet direct evidence for this assumption remains mixed. In this paper
- PaperMARS: Memory-Aware Routing for Session-Level LLM Assistants
Long-term LLM assistants must repeatedly decide what prior context to expose and which model should answer as an interaction evolves. Existing memory systems and model ro
- PaperCLEAR: Cooperative Latent Flow for Visual Counterfactual Explanations with Accurate Region Preservation
Visual counterfactual explanations (VCEs) should alter a classifier's prediction while remaining realistic and preserving decision-irrelevant content. Image-space methods
- PaperDarwin-Claw: Environment-Adaptive Experience Consolidation for Recursively Self-Evolving Agents
While Large Language Model (LLM) agents have demonstrated remarkable capabilities across complex tasks, they predominantly operate in isolated sessions, lacking the abili
- PaperBMuon: Budgeting Muon's Spectral Geometry for Structured Compressibility
Muon has emerged as a strong optimizer for neural network training and admits an interpretation as steepest descent under a spectral-norm geometry. Yet we find that Muon-
- PaperA general recipe for data-adaptive anytime-valid envelopes for iterative algorithms
Most convergence guarantees for iterative stochastic algorithms are stated at a fixed iteration count and do not justify stopping at a data-dependent time. Recent work ha
- PaperFrom Adult Renal Masses to Pediatric Wilms Tumors: Evaluating Cross-Domain Generalization of Kidney Lesion Segmentation on MRI
Deep learning segmentation models for pediatric oncology are often trained on small, single-center cohorts, whereas much larger datasets exist for adult diseases, such as
- PaperBeyond Expected Values: Risk-Sensitive Planning with Distributional Monte-Carlo Tree Search
Monte-Carlo Tree Search (MCTS) typically stores only scalar mean estimates, which makes it poorly suited for tail-risk objectives such as conditional value-at-risk (CVaR)
- PaperLiveSync: Iterative Self-Training for Lip-Sync Generation
Audio-driven lip synchronization aims to match target speech through localized mouth and jaw articulation while preserving subject identity, head pose, and surrounding fa
- PaperBeyond Latent Personalization: Integrating HRV-Conditioned Personalization via LLM for Interpretable Stress Detection
Stress detection based on heart rate variability (HRV) has received increasing attention. However, existing personalized approaches integrate personal attributes with HRV
- PaperSkillBrowser: Selective Skill Exposure for Scalable Skill-Using Agents
Large language model agents increasingly rely on skills to extend their capabilities in complex tasks. However, scalable skill use requires Selective Skill Exposure (SSE)
- PaperReason-guided Human Reward Modeling for Preference-based Reinforcement Learning
Reinforcement learning (RL) policies optimized for task rewards may produce behaviors that are efficient but misaligned with human expectations, safety preferences, or in
- PaperCausal Discovery beyond Conditional Independence: Identifiability, Algorithms, and Statistical Inference for Directed Acyclic and Cyclic Graphs
Learning causal structure from data requires assumptions about mechanisms, noises, observation schemes, and interventions. Historically, causal discovery has relied prima
- PaperExecution Before Memory: Diagnosing Memory Failures in Video World Models
A memory test for a video world model should say whether the scene was set, whether the asked change was carried out, and whether the last frame shows the result. Current
- PaperConvexLLM: Scaling Convex-Hull Speech Interfaces to General-Purpose Audio Intelligence
Speech and audio language models connect continuous acoustic representations to decoders pretrained primarily on text data. Most such models use MLP or Q-Former interface
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.