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Monday 19 October
- PaperSleepPairNet: Learning Multimodal Pediatric Sleep Representations with Random-Pair Cross-Attention
Analyzing physiological sleep signals is important for assessing sleep quality and detecting sleep-related disorders. Learning inter-modal relationships across physiologi
- PaperEgoXperience: Learning World-Grounded Egocentric Representations from Human Experience
Reconstructing human activity from egocentric video requires more than recovering individual 3D components: the observer's motion, surrounding geometry, and hand-object i
- PaperGlobal Time-Aware Conditioning (GTC): A Mechanism for Time-Aware Pediatric Sleep Representation Learning
Sleep physiology evolves throughout the night, with changes in sleep cycles, respiratory stability, and cardiovascular dynamics. However, self-supervised learning (SSL) m
- PaperHilbert-Scale Stability of Posterior Diffusion Priors for Ill-Posed Signal Recovery
Indirect and noisy observations are difficult to invert when the forward operator smooths out the unknown signal. In this case, the unstable modes are precisely the modes
- PaperConducting Reliable Keyword Estimation with Unreliable ASR in Police Audio Data
Extracting insights from large audio data sets using human transcription is expensive and time-consuming. Off-the-shelf Automatic Speech Recognition (ASR) systems offer a
- PaperDegree Bounds for Separating Polynomial and Neural Invariants for Point Clouds
Neural networks that respect the symmetries of point clouds commonly compute high-order representations of the rotation group to improve their expressivity. Such models a
- PaperA Probabilistic, Rotation-Preserving Kernel Operator Surrogate for Continuum-Robot Design Spaces
We develop an uncertainty-aware, operator-learning-based surrogate for tendon-driven continuum robots (TDCRs) that generalizes across robot design and actuation while pre
- PaperITU-DESC: a missingness-aware machine-learning dementia risk score in the UK Biobank cohort
Background: Existing dementia risk scores rely on fixed, pre-selected features and linear models, or ignore bias from informative missingness in cohort data. Here we intr
- PaperBERT Is Already a Denoiser: Cheaply Unlocking Iterative Generation in Masked Language Models
Masked language models (MLMs) such as BERT are typically used for representation learning despite their denoising pretraining. We test whether this capability supports co
- PaperBombadil: A Foundation Model for Plant Electrophysiology
Living plants, like animals, use electrical signals to carry information about their physiological state across their body, from roots to branches. These signals can be r
- PaperConcerns in Benchmarking Jailbreaks: An Empirical Study of Attack Success Rates
Jailbreak methods are commonly compared using Attack Success Rate (ASR), but these comparisons depend on how responses are generated and judged. In this paper, we examine
- PaperBeyond Attack Success Rate: Cost-Aware Metric for Evaluating Iterative LLM Jailbreaks
Attack success rate (ASR) evaluates jailbreaks against large language models without accounting for repeated calls to the victim model. Yet each additional call gives the
- PaperEGiME: Entropy-Guided Model Evaluation in Few-Shot Learning
The feature-extraction quality of pre-trained models sets the ceiling of downstream transfer learning, yet few-shot model selection remains difficult: supervised evaluati
- PaperScaling Vision Foundation Models for Toxicologic Pathology
Here, we present TOX-FM, a family of non-clinical PFMs trained on large-scale diverse histopathology datasets of over one million whole slide images consisting of over 60
- PaperDrive2Gauss: FeedForward 4D Gaussian Reconstruction from Multimodal Generative Driving Videos
Generating reusable driving environments requires both controllable future observations and an explicit representation that supports rendering from different viewpoints,
- PaperTeaching AI Agent to Play Poker Strategically Through Preference Optimization
A poker agent can make a legal move and still make a strategically poor one. We ask whether compact AI agents can exhibit strategic behavior under hidden information and
- PaperDenseHap: Dense Hand Haptic Estimation from Monocular Images
As dexterous robotic manipulation advances, scalable annotations of human hand-object interactions are becoming increasingly important. Furthermore, previous hand contact
- PaperWhatIf-Bench: Benchmarking Intervention-Aware Time Series Forecasting
Recent time series foundation models (TSFMs) can consume covariates in-context, extending zero-shot forecasting to problems driven by external factors. This enables scena
- PaperSafeWall: A Risk-Aware Job Walltime Recommendation for HPC Scheduling
In high-performance computing (HPC), users must submit resource requirements for their jobs, with user-supplied walltime being a critical parameter. However, these estima
- PaperReward Objectives for Robust (PO)MDPs: A Law-Preserving Equivalence with (Partially Observable) Games
Robust partially observable Markov decision processes model agents that act under partial observability while adversarial environment resolves uncertainty in the transiti
- PaperPrismWAM: Decoupling State Representations in World Action Models
World Action Models (WAMs) typically represent the world through latent embeddings from pretrained video models, which entangle heterogeneous aspects of the world, from h
- PaperMRI Reconstruction Risk Estimation from Inference Dynamics
Iterative MRI reconstructors produce intermediate estimates before returning a final image. We study these inference trajectories as a model-internal representation of re
- PaperBetaMem: Learning to Remember a Changing World for Embodied Agents
Long-horizon embodied agents face temporal confusion and incomplete or distorted records, recurring errors from fixed memory rules, and growing storage and retrieval cost
- PaperTraining Natural Schemers: RL Can Amplify Oversight-Conditional Behavior
One of the central concerns for the safety of advanced AI is scheming: when a model strategically complies under oversight but pursues its own goals when unobserved. Curr
- PaperHoli-World: Building Holistic World Intelligence from Human-Ego Experience
Human egocentric experience offers a rich foundation for learning how the world is organized and how it changes through interaction. Realizing this potential at scale req
- PaperAgentic Orchestration of EDC Data Space Retrieval and Natural Language Driven Data Processing
Participating in a data space still requires the Dataspace Protocol (DSP) negotiation flow to be hand-coded against each provider's access policy. We present a multi-agen
- PaperDoes Multi-Agent Collaboration Reduce Hallucination in Clinical Tasks?
Multi-agent LLM systems improve performance on clinical tasks, and that improvement is often read as evidence of a more trustworthy system. However, the two are not the s
- PaperBreaking Diffusion: Parallel Reasoning with Draft Refinement
Drafting an answer allows a reasoning model to reconsider each decision in the context of the others. Yet repeated diffusion denoising does not ensure that individually p
- PaperBR-MoE: Budgeted Semantic Release for Privacy-Preserving Traffic Analytics
Traffic cameras support useful analysis, but their outputs can reveal private information. Replacing images with text reduces direct visual exposure. It does not place a
- PaperSmall Average Error, Wide Individual Error: General-Purpose LLMs Estimating Energy Intake from Meal Photos in a Feeding Trial
Photo-based estimation of food energy could ease dietary assessment, and general-purpose multimodal large language models (LLMs) make such estimates easy to obtain. Most
- PaperReAnchor: Reusing Rejected Verifier States for Diffusion Drafting
Block diffusion drafters accelerate speculative decoding by generating multiple draft tokens in a single forward pass. However, each pass produces only a fixed-length blo
- PaperA Semantic Knowledge Graph Construction Pipeline for Vehicle Intention Prediction in nuScenes
Knowledge graphs (KGs) are increasingly used in autonomous driving (AD) to make traffic scene entities and relations explicit for prediction and reasoning. Existing traff
- PaperA Fisher-Geometric View of Multi-Teacher On-Policy Distillation
Consolidating specialized foundation models into a unified generalist is a central goal of post-training. Two approaches are in common use: model merging, which is fast b
- PaperBeyond Behavioral Accuracy: Evaluating Individual-Level Fidelity in LLM Investor Simulation
Large language models (LLMs) are increasingly used as user simulators, but can they faithfully reproduce an individual's behavior and decision logic over time? We investi
- PaperInterpretable Face Contrast Modeling for Smartphone Portrait Rendering
The quality of smartphone portrait photography has become increasingly important to both manufacturers and users. Recent devices use complex computational pipelines to im
- PaperToward Structured Financial Foundation Models
Foundation models promise reusable capabilities and step-change performance gains across tasks. In finance, large-scale pretraining of domain-specialized foundation model
- PaperRouterWorld: Scaling Conversational AI with Latency-Aware LLM Routing
Conversational AI systems built on Large Language Models (LLMs) require routing mechanisms that balance answer quality and response latency at scale. However, existing LL
- PaperLearn the Task, Not the Schedule: Reducing Spurious Cross-Arm Coupling in Bimanual Imitation Learning
A bimanual demonstration specifies both what the robot should do and one particular schedule for when each arm should act. Standard imitation learning does not distinguis
- PaperScopeMem: Latent Memory That Learns Where to Revise Frozen GUI Agents
Latent memory is gaining ground as a lightweight history supplement for GUI agents. It supplies history to a frozen vision-language model via prefix vectors trained with
- PaperMitigating Reward Hacking in Preference Reward Models with Evolving Penalties
Reinforcement learning for language models has moved from preference reward models to verifiable rewards, which sustain much longer optimization. Preference models are pr
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.