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 AI

Jun 30

Neural Subspace Reallocation: Continual Learning as Retrieval-Based Subspace Memory Management

arXiv:2606. 30067v1 Announce Type: cross Abstract: We introduce Neural Subspace Reallocation (NSR), which reframes continual learning as memory management over parameter subspaces.

By Byeong Hoon Yoon

rag fine-tuning efficiency benchmarks

arXiv AI

Jun 30

Field Order Should Not Matter: Permutation-Invariant Embedding Model Fine-Tuning for Structured Metadata Retrieval

arXiv:2606. 30473v1 Announce Type: cross Abstract: We study retrieval over catalogs of structured metadata, where each record is a small schema whose fields answer different kinds of query.

By Aivin V. Solatorio, Olivier Dupriez, Rafael Macalaba

llms rag agents fine-tuning benchmarks

arXiv AI

Jun 30

Unlocking the Visual Record of Materials Science: A Large-Scale Multimodal Dataset from Scientific Literature

arXiv:2606. 29667v1 Announce Type: cross Abstract: The materials science literature encodes decades of experimental knowledge in figures, yet this visual record remains locked away and inaccessible to AI at scale.

By Subham Ghosh, Shubham Tiwari, Mohammad Ibrahim, Abhishek Tewari

llms fine-tuning multimodal safety

arXiv AI

Jun 30

ReMAP-PET: Beyond Visual Understanding -- Learning Region-Guided Metabolic Alignment Semantics from Brain PET

arXiv:2606. 29577v1 Announce Type: cross Abstract: Positron Emission Tomography (PET) reveals brain metabolism and is clinically central to neurodegenerative disease assessment, yet existing 3D brain foundation models treat PET as generic volumetric data, missing the structured regional metabolic information that distinguishes it from structural neuroimaging.

By Dasen Dai, Yanteng Zhang, Shuoqi Li, Yuxiang Wei, Hongjie Yu, Qingxin Zhang, Qizhen Lan, Jagath C. Rajapakse, Vince D. Calhoun

rag fine-tuning safety

arXiv AI

Jun 30

Symbolic Mechanistic Data Attribution: Tracing Training Influence to Learned Behavioral Policies

arXiv:2606. 29171v1 Announce Type: cross Abstract: While existing data attribution methods can identify which training examples build specific mechanistic circuits, they cannot explain how training data shapes the high-level behavioral decisions a model learns to make.

By Reza Habibi, Darian Lee, Magy Seif El-Nasr

llms fine-tuning safety

arXiv AI

Jun 30

Evidence-Based Text-Conditioned 3D CT Synthesis for Ovarian Cancer

arXiv:2606. 28980v1 Announce Type: cross Abstract: Ovarian cancer is frequently diagnosed at an advanced stage, making preoperative contrast-enhanced computed tomography (CT) central to staging and surgical planning; yet the scarcity of annotated imaging data, compounded by privacy regulations, limits the development of generalizable computational models in this domain.

By Francesca Pia Panaccione, Eugenio Lomurno, Francesca Fati, Carlotta Pecchiari, Marina Rosanu, Luigi De Vitis, Lucia Ribero, Gabriella Schivardi, Giovanni Damiano Aletti, Nicoletta Colombo, Maria Francesca Spadea, Francesco Multinu, Matteo Matteucci, Elena De Momi

diffusion fine-tuning multimodal benchmarks safety

arXiv AI

Jun 30

TRACE: Temporal Relationship-Aware Conversational Entrainment Detection in Dyadic Speech

arXiv:2606. 30543v1 Announce Type: cross Abstract: With the proliferation of speech AI agents, understanding emotional entrainment in conversational interaction has become increasingly important.

By Sathvik Manikantan Napa Ugandhar, Hao Zhang, Alison Gunzler, Yuzhe Wang, Thomas Thebaud, Georgi Tinchev, Venkatesh Ravichandran, Laureano Moro-Vel\'azquez

rag agents fine-tuning multimodal

arXiv AI

Jun 30

ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation

arXiv:2606. 28357v1 Announce Type: cross Abstract: Recent advances in multimodal recommenders excel at feature fusion but remain opaque and inefficient decision-makers, lacking explicit reasoning and self-awareness of uncertainty.

By Yihua Zhang, Mingfu Liang, Jiyan Yang, Rong Jin, Wen-Yen Chen, Yiping Han, Huayu Li, Buyun Zhang, Liang Luo, Frank Shyu, Luke Simon, Sijia Liu, Tianlong Chen, Xi Liu

agents fine-tuning efficiency multimodal benchmarks

arXiv AI

Jun 30

Toward Secure and Reliable PDDL Formalization of Large Language Models with Planner-in-the-Loop Feedback

arXiv:2606. 29700v1 Announce Type: new Abstract: Planning often requires symbolic specifications that are both executable and verifiable.

By Jiamei Jiang, Jiajing Zhang, Feifei Mo, Linjing Li, Daniel Zeng

llms agents fine-tuning benchmarks safety

arXiv AI

Jun 30

Assessing the Business Process Modeling Competences of Large Language Models

arXiv:2601. 21787v2 Announce Type: replace-cross Abstract: The creation of Business Process Model and Notation (BPMN) models is a complex and time-consuming task requiring both domain knowledge and proficiency in modeling conventions.

By Chantale Lauer, Peter Pfeiffer, Alexander Rombach, Nijat Mehdiyev

llms fine-tuning benchmarks

arXiv AI

Jun 30

To Reason or to Fabricate: Reasoning Without Shortcuts via Hint-Anchored Pairwise Aggregation

arXiv:2606. 29481v1 Announce Type: cross Abstract: While reinforcement learning (RL) significantly enhances LLM reasoning, its efficacy is severely undermined by Pre-RL data overlap, where RL datasets overlap with pretraining or SFT corpora, causing models to exploit shortcuts by memorizing correct answers and fabricating post-hoc reasoning.

By Jiuheng Lin, Chen Zhang, Yansong Feng

llms reinforcement-learning fine-tuning

arXiv AI

Jun 30

Attribution Graphs and Causal Probing for Mechanistic Discovery and Bias Repair in Multimodal Generative Learning

arXiv:2510. 12957v4 Announce Type: replace-cross Abstract: We treat the internals of generative models as mechanistic objects rather than black boxes.

By Noor Islam S. Mohammad, Ulu\u{g} Bayaz{\i}t

fine-tuning multimodal safety

arXiv AI

Jun 30

Vividh-ASR: A Complexity-Tiered Benchmark and Optimization Dynamics for Robust Indic Speech Recognition

arXiv:2605. 13087v2 Announce Type: replace-cross Abstract: Fine-tuning multilingual ASR models like Whisper for low-resource languages often improves read speech but degrades spontaneous audio performance.

By Kush Juvekar, Kavya Manohar, Aditya Srinivas Menon, Arghya Bhattacharya, Kumarmanas Nethil

fine-tuning multimodal benchmarks

arXiv AI

Jun 30

Early Cue Precision Shapes Visual Shortcut Learning in Controlled Cue-Manipulation Benchmarks

arXiv:2606. 30344v1 Announce Type: cross Abstract: Visual classifiers can achieve high matched-distribution accuracy while relying on low-level cues that fail under conflict or suppression.

By Chanho Park, Woochan Lee, Janyeong Oh, Geongho Gong, Minshu Kim, Yeachan Kwak, Seongim Choi

robotics fine-tuning benchmarks

arXiv AI

Jun 30

Semi-Supervised Sound Event Detection with Conditional Mixup and Embedding-Level Contrastive Loss

arXiv:2606. 29901v1 Announce Type: cross Abstract: Sound event detection (SED) is a core module for acoustic environmental analysis, yet its performance is often limited by scarce labeled data.

By Nian Shao, Xian Li, Xiaofei Li

rag fine-tuning

arXiv AI

Jun 30

Online Data Selection for Instruction Tuning via Gaussian Processes

arXiv:2606. 30077v1 Announce Type: cross Abstract: With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to data quality, quality data selection has emerged as a critical research topic.

By Jun Wang, Quoc Phong Nguyen, Julien Monteil, Vu Nguyen

llms fine-tuning benchmarks data-engineering

arXiv AI

Jun 30

Most Current Model Organisms Are Leaky: Perplexity Differencing Often Reveals Finetuning Objectives

arXiv:2605. 00994v2 Announce Type: replace-cross Abstract: Finetuning can significantly modify the behavior of large language models, including introducing harmful or unsafe behaviors.

By Mohammed Abu Baker, Luca Baroni, Dan Wilhelm

llms agents fine-tuning benchmarks

arXiv AI

Jun 30

When AI Reviews Its Own Code: Recursive Self-Training Collapse in Code LLMs

arXiv:2606. 28438v1 Announce Type: cross Abstract: Recursive self-training can degrade neural generative models when generated data is reused without fresh human data or external quality control.

By Xinyuan Song, Zekun Cai, Liang Zhao

llms fine-tuning benchmarks

arXiv AI

Jun 30

Distributionally Robust Reinforcement Learning with Human Feedback

arXiv:2503. 00539v2 Announce Type: replace-cross Abstract: Reinforcement learning from human feedback (RLHF) has evolved to be one of the main methods for fine-tuning large language models (LLMs).

By Debmalya Mandal, Paulius Sasnauskas, Goran Radanovic

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 30

KrishokChat: A Citation-Grounded Dataset and Benchmark for Bengali Agricultural Advisory

arXiv:2606. 29243v1 Announce Type: new Abstract: We present KrishokChat, the first citation-grounded Bengali agricultural instruction-tuning dataset for crop advisory in low-resource settings.

By Khan Raiyan Ibne Reza, Omar Ibne Shahid

rag fine-tuning benchmarks safety

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