Academic
Academic
Training Is Everything: Artificial Intelligence, Copyright, and Fair Training
To learn how to behave, the current revolutionary generation of AIs must be trained on vast quantities of published images, written works, and sounds, many …
BLooP: Zero-Shot Abstractive Summarization using Large Language Models with Bigram Lookahead Promotion
arXiv:2603.11415v1 Announce Type: new Abstract: Abstractive summarization requires models to generate summaries that convey information in the source document. While large language models can generate …
Speculative Decoding Scaling Laws (SDSL): Throughput Optimization Made Simple
arXiv:2603.11053v1 Announce Type: new Abstract: Speculative decoding is a technique that uses multiple language models to accelerate infer- ence. Previous works have used an experi- …
DocSage: An Information Structuring Agent for Multi-Doc Multi-Entity Question Answering
arXiv:2603.11798v1 Announce Type: new Abstract: Multi-document Multi-entity Question Answering inherently demands models to track implicit logic between multiple entities across scattered documents. However, existing Large …
Mind the Sim2Real Gap in User Simulation for Agentic Tasks
arXiv:2603.11245v1 Announce Type: new Abstract: As NLP evaluation shifts from static benchmarks to multi-turn interactive settings, LLM-based simulators have become widely used as user proxies, …
A Semi-Decentralized Approach to Multiagent Control
arXiv:2603.11802v1 Announce Type: new Abstract: We introduce an expressive framework and algorithms for the semi-decentralized control of cooperative agents in environments with communication uncertainty. Whereas …
RewardHackingAgents: Benchmarking Evaluation Integrity for LLM ML-Engineering Agents
arXiv:2603.11337v1 Announce Type: new Abstract: LLM agents increasingly perform end-to-end ML engineering tasks where success is judged by a single scalar test metric. This creates …
Deactivating Refusal Triggers: Understanding and Mitigating Overrefusal in Safety Alignment
arXiv:2603.11388v1 Announce Type: new Abstract: Safety alignment aims to ensure that large language models (LLMs) refuse harmful requests by post-training on harmful queries paired with …
Scaling Laws for Educational AI Agents
arXiv:2603.11709v1 Announce Type: new Abstract: While scaling laws for Large Language Models (LLMs) have been extensively studied along dimensions of model parameters, training data, and …
Examining Users' Behavioural Intention to Use OpenClaw Through the Cognition--Affect--Conation Framework
arXiv:2603.11455v1 Announce Type: new Abstract: This study examines users' behavioural intention to use OpenClaw through the Cognition--Affect--Conation (CAC) framework. The research investigates how cognitive perceptions …