Fine-tuning and adaptation

LoRA, PEFT, instruction tuning and domain adaptation — adapting a pretrained model without paying to train one.

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arXiv Machine Learning

Jul 1

Criticality-Constrained Iterative Pruning for Energy-Efficient Spiking Neural Networks via Combined Importance Scoring

arXiv:2606. 30676v1 Announce Type: cross Abstract: Deploying spiking neural networks (SNNs) on neuromorphic hardware demands aggressive synaptic pruning while preserving temporal computation integrity.

By Muhammad Hamza

fine-tuning efficiency

arXiv Machine Learning

Jul 1

Fora: From Weight-Space to Function-Space Protection in Capability-Preserving Fine-Tuning

arXiv:2606. 31092v1 Announce Type: new Abstract: Full fine-tuning adapts large language models to new tasks but can erode capabilities they already possess.

By Rui Zhou, Tianci Xie

llms fine-tuning benchmarks

arXiv AI

Jul 1

Teaching LLMs String Matching, Backtracking, and Error Recovery to Deduce Bases and Truth Tables for the Combinatorially Exploding Bit Manipulation Puzzles

arXiv:2606. 23672v2 Announce Type: replace Abstract: This paper presents our algorithmic innovations for the NVIDIA Nemotron Model Reasoning Challenge, focusing on Bit Manipulation Puzzles.

By Prateek Agnihotri, Sanchit Jain, Prabhat Agnihotri, Aditya Prasad, Shubham Jain

llms agents nlp robotics fine-tuning safety

arXiv AI

Jul 1

LeCropFollow: Latent Space Planning for Navigation in Unstructured Crop Fields

arXiv:2606. 31941v1 Announce Type: cross Abstract: Unstructured navigational features, such as irregular planting or discontinuities, remain the primary failure mode for under-canopy agricultural robots.

By Felipe Tommaselli, Francisco Affonso, Arthur Pompeu, Gianluca Capezzuto, Arun Narenthiran Sivakumar, Girish Chowdhary, Marcelo Becker

diffusion reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jul 1

Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models

arXiv:2606. 31603v1 Announce Type: cross Abstract: Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.

By Nikolai R\"ohrich, Julian Glei{\ss}ner, Ahmed H. A. Ibrahim, Silvan Mertes, Tobias Huber

agents diffusion computer-vision fine-tuning efficiency safety

arXiv AI

Jul 1

PGUDA: Pressure-Guided Unsupervised Domain Adaptation with Cross-Modal Knowledge Distillation for sEMG-Based Gesture Recognition

arXiv:2606. 31349v1 Announce Type: cross Abstract: Surface electromyography (sEMG)-based gesture recognition has emerged as a promising technology for natural human-computer interaction.

By Yurui Liu, Xiao-Cong Zhong, Qisong Wang, Xuefu Wang, Dan Liu, Jinwei Sun

fine-tuning efficiency multimodal benchmarks

arXiv AI

Jul 1

Probing Stylistic Appropriation using Large Language Models: An Evaluation Framework for Copyright Infringement under EU Law

arXiv:2606. 31250v1 Announce Type: cross Abstract: Large language models (LLM) trained on web-scale corpora generate output that may infringe copyright, yet existing technical safeguards focus narrowly on verbatim memorisation.

By Noah Scharrenberg, Chang Sun

llms fine-tuning

arXiv AI

Jul 1

Learning from Failure: Inference-Time Self-Improvement for Computer-Use Agents

arXiv:2606. 31270v1 Announce Type: cross Abstract: Computer-use agents, which leverage multimodal large language models (MLLMs) to operate computers and complete tasks, have attracted significant attention for their utility and versatility.

By Xueqiao Sun, Xiaohan Wang, Ludwig Schmidt, Serena Yeung-Levy, Yuhui Zhang

llms agents fine-tuning multimodal benchmarks

arXiv AI

Jul 1

The Label Imitation Game: Turing Test Network for Zero-Shot Pseudo-Label Pruning

arXiv:2606. 30875v1 Announce Type: cross Abstract: Foundation model pseudo-labeling - labeling data strictly via zero-shot inference - enables massive scale, but performance is undermined by hallucinations that evade standard thresholds.

By Brent A. Griffin, Jason J. Corso

llms computer-vision fine-tuning efficiency multimodal benchmarks safety

arXiv AI

Jul 1

Knowledge Distillation from Large Reasoning Models to Compact Student Models: A Case Study on the John O Bryan Mathematics Competition

arXiv:2606. 31048v1 Announce Type: cross Abstract: This paper investigates knowledge distillation from a large reasoning model (DeepSeek-R1) to a compact student model (Qwen2.

By Gaurab Baral, Aaditya Khanal, Yangyang Tao, Junxiu Zhou

agents fine-tuning efficiency benchmarks

arXiv AI

Jul 1

From Search to Synthesis: Training LLMs as Zero-Shot Workflow Generators

arXiv:2606. 30704v1 Announce Type: cross Abstract: Large language models (LLMs) excel across a wide range of tasks, yet their instance-specific solutions often lack the structural consistency needed for reliable deployment.

By Gan Luo, Zihan Qin, Bin Dong, Wotao Yin

llms nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 1

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design

arXiv:2606. 28578v1 Announce Type: cross Abstract: Closed-loop materials discovery iterates between proposing candidate structures and evaluating their properties, and property evaluation dominates the cost.

By Sk Md Ahnaf Akif Alvi, Jan Janssen, Danny Perez, Douglas Allaire, Raymundo Arroyave

rag diffusion reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 1

Self-Study Reconsidered: The Hidden Fragility of Learning from Self-Generated QA

arXiv:2606. 32002v1 Announce Type: new Abstract: Language models are increasingly taught from synthetic question--answer (QA) supervision: a model generates questions about a document, answers them from the same text, and the resulting pairs are used to fine-tune, distill, or compress knowledge into another model.

By Ekaterina Alimaskina, Denis Shveykin, Gleb Molodtsov, Igor Shalygin, Alexey Kadeishvili, Aleksandr Beznosikov

llms fine-tuning

arXiv AI

Jul 1

Beyond the Library: An Agentic Framework for Autoformalizing Research Mathematics

arXiv:2606. 31134v1 Announce Type: new Abstract: While Large Language Models (LLMs) have demonstrated exceptional capabilities in mathematical reasoning, they frequently produce subtle errors that evade human detection.

By Arshia Soltani Moakhar, Iman Gholami, Max Springer, Mahdi JafariRaviz, MohammadTaghi Hajiaghayi

llms agents fine-tuning

arXiv AI

Jun 30

Inference-Time Diversity in RL-Trained Lean Theorem Provers: A Diagnostic Study

arXiv:2601. 16172v3 Announce Type: replace Abstract: RL-trained Lean theorem provers mode-collapse at inference time: on miniF2F-test with DeepSeek-Prover-V1.

By Zachary Burton

reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 30

ITSPACE: Monotone Gaussian Optimal Transport Updates

arXiv:2606. 30523v1 Announce Type: new Abstract: Covariance matrices serve as compact descriptors of feature distributions in many machine-learning pipelines, including domain adaptation and Gaussian embeddings.

By Woojoo Na, Jennifer Dy

rag fine-tuning benchmarks safety

arXiv AI

Jun 30

T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation

arXiv:2606. 30011v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) deployed in real-world systems typically have fixed weights, often leading to degraded performance under distribution shifts.

By Huy Truong, Alexander Lazovik, Victoria Degeler

fine-tuning benchmarks

arXiv AI

Jun 30

Geo-Expert: Towards Expert-Level Geological Reasoning via Parameter-Efficient Fine-Tuning

arXiv:2605. 24844v2 Announce Type: replace Abstract: While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evolution, current AI in Earth sciences predominantly targets surface remote sensing and GIS.

By Chenyou Guo, Zongqi Liu, Yizhou Zhang, Zhaorui Jiang, Ze Liu

llms fine-tuning benchmarks

arXiv AI

Jun 30

MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting

arXiv:2606. 28670v1 Announce Type: cross Abstract: We introduce MACROCAST, a lightweight Time Series Foundation Model (TSFM) for real-time macroeconomic forecasting.

By Andrea Carriero, Davide Pettenuzzo, Shubhranshu Shekhar

fine-tuning benchmarks safety

arXiv AI

Jun 30

SearchSkill: Teaching LLMs to Use Search Tools with Evolving Skill Banks

arXiv:2605. 09038v3 Announce Type: replace Abstract: Teaching language models to use search tools is not only a question of whether they search, but also of whether they issue good queries.

By Jinchao Hu, Meizhi Zhong, Kehai Chen, Min Zhang

llms nlp fine-tuning benchmarks

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