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 …
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 …
LLM-Assisted Causal Structure Disambiguation and Factor Extraction for Legal Judgment Prediction
arXiv:2603.11446v1 Announce Type: new Abstract: Mainstream methods for Legal Judgment Prediction (LJP) based on Pre-trained Language Models (PLMs) heavily rely on the statistical correlation between …
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- …
PACED: Distillation at the Frontier of Student Competence
arXiv:2603.11178v1 Announce Type: new Abstract: Standard LLM distillation wastes compute on two fronts: problems the student has already mastered (near-zero gradients) and problems far beyond …
Improving LLM Performance Through Black-Box Online Tuning: A Case for Adding System Specs to Factsheets …
arXiv:2603.11340v1 Announce Type: new Abstract: In this paper, we present a novel black-box online controller that uses only end-to-end measurements over short segments, without internal …
AI Knows What's Wrong But Cannot Fix It: Helicoid Dynamics in Frontier LLMs Under High-Stakes …
arXiv:2603.11559v1 Announce Type: new Abstract: Large language models perform reliably when their outputs can be checked: solving equations, writing code, retrieving facts. They perform differently …