arXiv Machine Learning
Aug 4
Inference-Time Policy Alignment for Fair Reinforcement Learning
arXiv:2608. 00175v1 Announce Type: new Abstract: Deep reinforcement learning (RL) agents achieve strong performance by optimizing scalar reward functions.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv Machine Learning
Aug 4
Hierarchical Solomonoff Induction: An Unbounded Machine Learning Model
arXiv:2608. 01005v1 Announce Type: new Abstract: Solomonoff Induction, or SolInd, provides an ideal unbounded model of a priori sequence prediction but cannot naturally describe extrapolation from a given training dataset, as performed by Large Language Models.
arXiv Machine Learning
Aug 4
Evaluating Forecasting Techniques for Hardware Errors on a Large-scale HPC System
arXiv:2608. 01648v1 Announce Type: new Abstract: Hardware error logs in high-performance computing (HPC) systems provide early signals of abnormal behavior, yet there remain challenges in effectively forecasting these errors using modern predictive methods.
arXiv Machine Learning
Aug 4
WorldDynCache: Risk-Controlled Latent Dynamics Approximation for Diffusion World Model
arXiv:2608. 01845v1 Announce Type: new Abstract: Diffusion world models generate high-quality futures, but re- peated transformer evaluations make inference prohibitively slow.
arXiv Machine Learning
Aug 4
Geometry-Guided Layerwise FFN Width Allocation in Transformers
arXiv:2608. 02064v1 Announce Type: new Abstract: Feed-forward networks (FFNs) account for a large fraction of Transformer parameters, yet their hidden width is usually constant across depth.
arXiv Machine Learning
Aug 4
MedSAM2-Anatomy: Training-Free Inference-Time Optimization for Musculoskeletal Segmentation
arXiv:2608. 00195v1 Announce Type: cross Abstract: High-resolution 3D segmentation of hip and shoulder anatomy from CT and MRI is essential for surgical planning, yet frozen segmentation models often fail under domain shift.
arXiv Machine Learning
Aug 4
onepot-Bench 0: towards lab-aware in silico chemistry benchmarks
arXiv:2608. 02595v1 Announce Type: new Abstract: Language models are playing an increasingly important role in laboratory science, performing tasks such as experiment planning, execution, and post-hoc analysis.
arXiv Machine Learning
Aug 4
Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams
arXiv:2608. 00012v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are increasingly used to interpret Earth observation data, yet their capability to support real-world disaster emergency response remains insufficiently evaluated.
arXiv Machine Learning
Aug 4
GeoArbiter: Verifiability-Guided Grounding for Remote-Sensing Multimodal LLMs
arXiv:2608. 00877v1 Announce Type: new Abstract: Remote-sensing multimodal large language models (MLLMs) often assert facts that imagery cannot establish, such as a facility's identity or function.
arXiv Machine Learning
Aug 4
Geometric Analysis of Token Selection in Multi-Head Attention
arXiv:2602. 01893v2 Announce Type: replace-cross Abstract: We present a geometric framework for analysing multi-head attention in large language models (LLMs).
arXiv Machine Learning
Aug 4
A Comparative analysis of Layer-wise Representational Capacity in AR and Diffusion LLMs
arXiv:2603. 07475v4 Announce Type: replace-cross Abstract: Autoregressive (AR) language models build representations incrementally via left-to-right prediction, while diffusion language models (dLLMs) are trained through full-sequence denoising.
arXiv Machine Learning
Aug 4
HarnessCompass: Guiding Automatic Harness Evolution toward Generalizable and Effective Agent Harnesses
arXiv:2608. 01918v1 Announce Type: new Abstract: Harness design plays a critical role in agent performance by shaping how large language models (LLMs) perceive, reason over, and act within executable environments.
arXiv Machine Learning
Aug 4
Spatiotemporal Proximal Causal Inference under Hidden Confounding and Interference
arXiv:2608. 01352v1 Announce Type: new Abstract: Estimating causal effects from real-world spatiotemporal data is challenging due to hidden confounders and interference.
arXiv Machine Learning
Aug 4
Unleashing the Potential of Large Language Models: A Blueprint for Real-Time, Enterprise-Ready Deployments
arXiv:2608. 00419v1 Announce Type: new Abstract: Large language models deployed in real-time, regulated settings face knowledge staleness, catastrophic forgetting, hallucination, and weak feedback loops.
arXiv Machine Learning
Aug 4
MemoryForge: Synthesize Lifelong Memory for Human-Like LLM Agents
arXiv:2608. 00007v1 Announce Type: cross Abstract: Equipping Large Language Models (LLMs) with human-like personas is crucial for agentic applications, such as role-play and user simulation.
arXiv Machine Learning
Aug 4
Distill What the Student Can See: Fisher-Projected On-Policy Distillation for Vision-Language Models
arXiv:2608. 01263v1 Announce Type: new Abstract: On-policy distillation (OPD) samples trajectories from the current student policy and minimizes token-level divergence between student and teacher next-token distributions at prefixes along those trajectories.
arXiv Machine Learning
Aug 4
SpatioLM: Towards General Physical Spatial Intelligence in Vision-Language Models
arXiv:2608. 01899v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) perform well on commonsense reasoning tasks but struggle with visual spatial reasoning.
arXiv Machine Learning
Aug 4
Semantic Alignment of AI Models: Concept Collapse, Checkpoint Dynamics, and Cross-Lingual Transfer
arXiv:2608. 01585v1 Announce Type: cross Abstract: Language model benchmarking is a difficult task.
arXiv Machine Learning
Aug 4
(How) Learning Rates Regulate Catastrophic Overtraining
arXiv:2604. 13627v2 Announce Type: replace Abstract: Supervised fine-tuning (SFT) is a common first stage of LLM post-training, teaching the model to follow instructions and shaping its behavior as a helpful assistant.
arXiv Machine Learning
Aug 4
ReasonCast: Towards Explainable Time Series Forecasting with Reasoning
arXiv:2608. 01875v1 Announce Type: cross Abstract: Most time series (TS) models are specialized for a single task, either understanding (i.