Papers by Nils Lukas

5 papers
SD-E2: Semantic Exploration for Reasoning Under Token Budgets (2026.findings-eacl)

Copied to clipboard

Challenge: Small language models struggle with complex reasoning because exploration is expensive under tight compute budgets.
Approach: They propose a framework that makes exploration explicit by optimizing semantic diversity in generated reasoning trajectories.
Outcome: The proposed framework surpasses Qwen2.5-3B-Instruct and strong GRPO baselines on GSM8K and improves on the harder AIME benchmark to 13.28% vs. base 6.74%.
Forest Before Trees: Latent Superposition for Efficient Visual Reasoning (2026.acl-long)

Copied to clipboard

Challenge: Recent latent reasoning methods suffer from a bandwidth bottleneck . explicit textual rationales suffer from premature semantic collapse .
Approach: They propose a new paradigm that reformulates visual deduction via Dynamic Windowed Alignment Learning.
Outcome: The proposed paradigm achieves state-of-the-art performance among latent reasoning methods surpassing the strong baseline Monet by 5.03% on average.
Detecting AI-Generated Video: A Vision–Language Dual-View Survey (2026.findings-acl)

Copied to clipboard

Challenge: realism of AI-generated Videos (AIGC-V) rendering artifact-centric detection insufficient, authors argue . a vision–language dual-view taxonomy is proposed to systematize this rapidly evolving field .
Approach: They propose a Vision–Language Dual-View taxonomy to systematize AIGC-V detection . they propose realism of AI-generated Videos is rendering traditional inspection insufficient .
Outcome: The proposed model aims to show that the existing methods are consistent with real-world facts.
SPIRIT: Patching Speech Language Models against Jailbreak Attacks (2025.emnlp-main)

Copied to clipboard

Challenge: Speech language models (SLMs) enable natural interactions via spoken instructions, which more effectively capture user intent by detecting nuances in speech.
Approach: They propose post-hoc patching defenses to intervene during inference by modifying the SLM’s activations that improve robustness up to 99% with negligible impact on utility and without any re-training.
Outcome: The proposed defenses improve robustness up to 99% with negligible impact on utility and (ii) without any re-training.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations