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Thursday 15 October
- PaperProgress Reward Model for Reinforcement Learning via Large Language Models
Traditional reinforcement learning (RL) algorithms face significant limitations in handling long-term tasks with sparse rewards. Recent advancements have leveraged large
- PaperName-Bias in Large Language Models
Names carry demographic indicators such as age, gender, and race, which makes them a subtle but strong target of bias in large language models (LLMs). In this work, we pr
- PaperRethinking Successive Rejects Algorithms
In the best-arm identification problem, an algorithm adaptively selects arms to find the arm of the largest mean reward. Successive Rejects (SR) is a simple fixed-budget
- PaperMissed Detections and False Alarms in a CNN Technosignature Detector on Breakthrough Listen Data
Radio technosignature searches rely on machine learning detectors to shrink the candidate pool before a human looks at it. They are validated on held-out data from the tr
- PaperDecoding Naturalistic Speech from fMRI with Temporal Modeling and Multimodal Alignment
Reconstructing what a person perceives from their brain activity is a stringent test of whether neural recordings carry meaningful information. Naturalistic speech offers
- PaperEffectiveness of Hidden-State Probes for Localizing Reasoning Failures
Verifying the final answer of a long reasoning trace may show that the trace is wrong but it does not distinguish three cases. The problem may lie beyond the model's capa
- PaperLearnable Splines for Unstructured Neural Operators
Neural operators approximate solution operators of partial differential equations at a fraction of the cost of numerical solvers. Efficient grid-based architectures such
- PaperMomentum, Commentary Tone, and Earnings Surprise in LLM Financial Forecasts
Which inputs drive a language model's financial forecast? We construct synthetic earnings announcements that independently reverse 20-day momentum, non-informational comm
- PaperNull Controls Are Necessary but Not Sufficient: Evaluating Acceptance Guards for Scientific Discovery and Interpretability
Automated discovery systems propose scientific explanations and test which to accept. Does passing a test establish the intended claim? We develop four executable operati
- PaperEvent-STVG: Spatio-Temporal Video Grounding for Long-Form Event Reasoning
Spatio-Temporal Video Grounding (STVG) aims to jointly localize a target in space and time given a natural-language query. Despite continued advances, existing STVG bench
- PaperWEAVE: Weaving Real-World Code Evolution into Dependency-Structured Tasks for Benchmarking and Training Code Agents
Real-world software engineering rarely consists of isolated changes. Features, migrations, and systematic fixes often unfold across multiple pull requests (PRs), requirin
- PaperETH: Data-Balanced Heterogeneous Pipeline Parallelism for Training Large Models
Current heterogeneity-aware training systems for large models balance workloads by assigning unequal numbers of layers across pipeline stages, a strategy aimed at equaliz
- PaperDesigning Experiments for External Decision Makers
Standard Bayesian experimental design (BED) methods assume the experimenter alone will act on the data, choosing the best action given their own beliefs and goals. In pra
- PaperGraphACM: Graph-based Action Consistency Measurement for Uncertainty Quantification in LLM Agents
Behavioral consistency across repeated executions provides an informative signal for assessing the reliability of large language model (LLM) agents. However, existing app
- PaperSelection Is Not Learning: Choosing Model Futures
Self-evolving language models train on tasks they generate, so every round must decide which updated model the next round inherits. Current loops such as R-Zero, Agent0,
- PaperAugment the Cache, Replace the Activation: A 2x2 Study of Internal-State Sharing
Multi-agent systems built from frozen language models communicate through text, which discards the internal state the sender already computed and makes the receiver pay t
- PaperNot All Unlabeled Data Are Equal: Unlabeled Data Selection for Semi-Supervised QSAR Modeling with Large Chemical Repositories
Semi-supervised learning (SSL) is particularly attractive in domains where labels are expensive to obtain, while unlabeled examples are readily available. Quantitative St
- PaperDemoStream: Batched Task-and-Motion-Plan Optimization for Automated Learning from Demonstration
Learning from demonstration is an effective strategy for endowing robots with manipulation capabilities but requires training on datasets comprised of massive amounts of
- PaperPriming Base Models for Agentic Post-Training: An Open Midtraining Recipe
Frontier models, general-purpose and domain-specific alike, now include a midtraining stage between pretraining and post-training, but little has been published about how
- PaperWhen Models Misread Ink: Auditing VLMs for Arabic Calligraphy Recognition
Arabic calligraphy is a high-stakes cultural visual domain for Muslim communities, yet VLM evaluations rarely test its fine-grained script traditions. We audit 12 VLMs on
- PaperCARA: Causally Guided Retrieval Agent for Question Answering
Retrieval-augmented generation over text-rich knowledge graphs requires identifying evidence that contributes to answering a question, rather than merely matching its tex
- PaperClose Enough is Nowhere Near: Fidelity-Consumability Decoupling in KV Caches
Multi-agent LLM systems communicate in text, which is lossy and wasteful: the sender throws away the reading it already did. A growing literature instead transmits the KV
- PaperGraphBEAT: Graph-based Bucketized Event Aggregation Over Time for Early Risk Stratification of Gastrointestinal Cancers
Longitudinal electronic health records (EHRs) contain complementary diagnostic and laboratory information about patient health. However, irregularity, sparsity, heterogen
- PaperFrom Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems
Large language models (LLMs) provide a convenient interface for translating natural language commands into task plans for cyber-physical systems (CPSs). However, two gaps
- PaperVisual Sanad: Auditing Vision-Language Models for False Acceptance of Qur'anic Citations
Vision-language models (VLMs) are increasingly used to interpret religious, educational, and culturally sensitive visual content, yet their reliability on citation verifi
- PaperStateful Testing for Deep Learning Compilers
Modern deep learning compilers compile models lazily and cache artifacts across invocations. Retained compiler state can make a later invocation incorrect even when it is
- PaperAn Agentic ENF Framework for Forensic Video Authentication Against Generative AI
Generative models now synthesise video that is hard to distinguish from genuine footage, and learned detectors that key on a generator's artefacts degrade sharply on unse
- PaperSparse Autoencoders Can Induce Local Geometry for Mitigating Catastrophic Forgetting
Catastrophic forgetting remains a challenge in supervised continual learning, as adapting to new tasks causes representation drift and loss of previously learned informat
- PaperInterfaces and Modules: A Scalable Representation for Weight-Space Learning across Architectures
Weight-space learning across architectures requires representations that respect parameter symmetries and scale to large networks. Existing equivariant tensor encoders re
- 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
- Paperspora: A Unified Multimodal Dataset and Platform for Spatial Proteomics
Spatial proteomics (SP) captures the molecular composition and spatial organization of tissues at single-cell resolution, opening a direct view of the cellular ecosystems
- 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
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.