arXiv:2502.06574v3 Announce Type: replace Abstract: Semivalue-based data valuation uses cooperative-game theory intuitions to assign each data point a value reflecting its contribution to a downstream task. Still, those values depend on the practitioner’s choice of utility, raising the question: How robust is semivalue-based data valuation to changes in the utility? This issue is critical when […]
SKETCH: Semantic Key-Point Conditioning for Long-Horizon Vessel Trajectory Prediction
arXiv:2601.18537v1 Announce Type: cross Abstract: Accurate long-horizon vessel trajectory prediction remains challenging due to compounded uncertainty from complex navigation behaviors and environmental factors. Existing methods often struggle to maintain global directional consistency, leading to drifting or implausible trajectories when extrapolated over long time horizons. To address this issue, we propose a semantic-key-point-conditioned trajectory modeling framework, […]
High-Fidelity Longitudinal Patient Simulation Using Real-World Data
arXiv:2601.17310v1 Announce Type: new Abstract: Simulation is a powerful tool for exploring uncertainty. Its potential in clinical medicine is transformative and includes personalized treatment planning and virtual clinical trials. However, simulating patient trajectories is challenging because of complex biological and sociocultural influences. Here, we show that real-world clinical records can be leveraged to empirically model […]
One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment
arXiv:2601.18731v1 Announce Type: cross Abstract: Alignment of Large Language Models (LLMs) aims to align outputs with human preferences, and personalized alignment further adapts models to individual users. This relies on personalized reward models that capture user-specific preferences and automatically provide individualized feedback. However, developing these models faces two critical challenges: the scarcity of feedback from […]
Phase Transition for Budgeted Multi-Agent Synergy
arXiv:2601.17311v1 Announce Type: new Abstract: Multi-agent systems can improve reliability, yet under a fixed inference budget they often help, saturate, or even collapse. We develop a minimal and calibratable theory that predicts these regimes from three binding constraints of modern agent stacks: finite context windows, lossy inter-agent communication, and shared failures among similar agents. Each […]
Mutagenesis screen to map the functions of parameters of Large Language Models
arXiv:2408.11494v4 Announce Type: replace Abstract: Large Language Models (LLMs) have significantly advanced artificial intelligence, excelling in numerous tasks. Although the functionality of a model is inherently tied to its parameters, a systematic method for exploring the connections between the parameters and the functionality are lacking. Models sharing similar structure and parameter counts exhibit significant performance […]
FASTR: Reimagining FASTQ via Compact Image-inspired Representation
arXiv:2601.17184v1 Announce Type: new Abstract: Motivation: High-throughput sequencing (HTS) enables population-scale genomics but generates massive datasets, creating bottlenecks in storage, transfer, and analysis. FASTQ, the standard format for over two decades, stores one byte per base and one byte per quality score, leading to inefficient I/O, high storage costs, and redundancy. Existing compression tools can […]
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
arXiv:2509.02547v4 Announce Type: replace Abstract: The emergence of agentic reinforcement learning (Agentic RL) marks a paradigm shift from conventional reinforcement learning applied to large language models (LLM RL), reframing LLMs from passive sequence generators into autonomous, decision-making agents embedded in complex, dynamic worlds. This survey formalizes this conceptual shift by contrasting the degenerate single-step Markov […]
Implementing Tensor Logic: Unifying Datalog and Neural Reasoning via Tensor Contraction
arXiv:2601.17188v1 Announce Type: new Abstract: The unification of symbolic reasoning and neural networks remains a central challenge in artificial intelligence. Symbolic systems offer reliability and interpretability but lack scalability, while neural networks provide learning capabilities but sacrifice transparency. Tensor Logic, proposed by Domingos, suggests that logical rules and Einstein summation are mathematically equivalent, offering a […]
ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs
arXiv:2510.22590v2 Announce Type: replace Abstract: In today’s rapidly expanding data landscape, knowledge extraction from unstructured text is vital for real-time analytics, temporal inference, and dynamic memory frameworks. However, traditional static knowledge graph (KG) construction often overlooks the dynamic and time-sensitive nature of real-world data, limiting adaptability to continuous changes. Moreover, recent zero- or few-shot approaches […]
Interpreting Agentic Systems: Beyond Model Explanations to System-Level Accountability
arXiv:2601.17168v1 Announce Type: new Abstract: Agentic systems have transformed how Large Language Models (LLMs) can be leveraged to create autonomous systems with goal-directed behaviors, consisting of multi-step planning and the ability to interact with different environments. These systems differ fundamentally from traditional machine learning models, both in architecture and deployment, introducing unique AI safety challenges, […]
LLM for Large-Scale Optimization Model Auto-Formulation: Bridging Flexibility and Standardization via Agentic Workflow
arXiv:2601.09635v2 Announce Type: replace Abstract: Large-scale optimization is a key backbone of modern business decision-making. However, building these models is often labor-intensive and time-consuming. We address this by proposing LEAN-LLM-OPT, a LightwEight AgeNtic workflow construction framework for LLM-assisted large-scale OPTimization auto-formulation. LEAN-LLM-OPT takes as input a problem description together with associated datasets and orchestrates a […]