Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv Machine Learning

Jul 28

MioFFAn: an Annotation Software for Formula Formalization with LLM Automation Capabilities

arXiv:2607. 22552v1 Announce Type: cross Abstract: The automatic translation of mathematical expressions in scientific literature into executable symbolic code (a process we refer to as Formula Formalization) is hindered by a severe scarcity of high-quality, ground-truth datasets specialized for technical scientific domains.

By Nicolas Sibuet, Horacio Saggion, Riccardo Rossi

llms nlp

arXiv Machine Learning

Jul 28

Benchmarking LLMs for Verilog Design Flows

arXiv:2607. 22759v1 Announce Type: cross Abstract: Large language models (LLMs) show promise in code generation, but their capabilities to produce correct, synthesizable hardware description language (HDL) code still remain to be properly benchmarked.

By Angshuman Chakravertty, Rahul Koshti, Buddhi Prakash Sharma, Vinay Chamola

llms benchmarks

arXiv Machine Learning

Jul 28

Similarity Is Not Logic: Factored Inference for Dual-Encoder Vision-Language Models

arXiv:2607. 23052v1 Announce Type: cross Abstract: Dual-encoder vision-language models (VLMs) expose a similarity interface that enables zero-shot retrieval but fails compositional constraints: queries like "umbrella and no person" retrieve images containing both, even when concept detection is reliable.

By Sultan Alshehri, Zhantao Yang, Han Zhang, Marios Savvides

llms rag fine-tuning multimodal

arXiv Machine Learning

Jul 28

SMART: LLM-Augmented Hybrid Retrieval for Dynamic Product Ads

arXiv:2607. 23121v1 Announce Type: cross Abstract: Dynamic Product Ads (DPA) require retrieving relevant items from multi-million product catalogs, balancing two competing objectives: retargeting (re-surfacing known interests) and prospecting (discovering new categories).

By Congfei Zhang, Jingxiao Ma, Xiaodong Liu, Hsiang-wei Chao, Siman Wang, Ge Liu, Shantanu Aggarwal, Vincent Zhang, Meghana Missula, Rachel Liao, Zichu Li, Xiao Bai, Yunzhi Zhou, Yajun Wang, Zhe Liu, Jinchao Li, Yu Zhang

llms rag

arXiv Machine Learning

Jul 28

BHARATI: Morphology-Aware Tokenizers for Classical Indian Languages with Subword Fertility Analysis

arXiv:2607. 23319v1 Announce Type: cross Abstract: Standard subword tokenization algorithms such as Byte-Pair Encoding (BPE) and SentencePiece are trained predominantly on modern language corpora and produce inefficient segmentations when applied to classical Indian languages.

By Poornima Kumaresan, Pavithra Muruganantham, Lakshmi Rajendran, Santhosh Sivasubramani

llms nlp benchmarks

arXiv Machine Learning

Jul 28

IKS-Instruct: A 24,000-Example Multilingual Dataset for Teaching Language Models Indian Knowledge Systems

arXiv:2607. 23322v1 Announce Type: cross Abstract: Instruction tuning has become the standard method for adapting large language models to follow human intent, yet existing instruction datasets are dominated by English-language general-knowledge tasks and lack coverage of specialized pedagogical domains.

By Shwetha Singaravelu, Gayathri Muruganantham, Lakshmi Rajendran, Santhosh Sivasubramani

llms fine-tuning

arXiv Machine Learning

Jul 28

To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion

arXiv:2607. 23492v1 Announce Type: cross Abstract: Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets such as NSFW content or copyrighted styles, while preserving model utility on benign concepts.

By Shaswati Saha, Rajasekhar Anguluri, Manas Gaur

llms diffusion multimodal

arXiv Machine Learning

Jul 28

SpecFormer: Mitigating Embedding and Attention Collapse via Spectral-Aware Transformer for Recommendation

arXiv:2607. 24025v1 Announce Type: cross Abstract: Transformer architectures have achieved remarkable success across diverse domains; however, directly applying their standard self-attention mechanism to recommendation often yields suboptimal performance, sometimes even trailing behind well-designed simple recommendation models.

By Yu Cui, Yi Xu, Jiahao Wang, Hao Zhang, Yu Zhang, Xiaoyi Zeng, Can Wang, Jinxin Hu, Jiawei Chen

llms rag benchmarks safety

arXiv Machine Learning

Jul 28

Catalyst Diffusion Transformer: Generative Inverse Design of Heterogeneous Catalysts

arXiv:2607. 24272v1 Announce Type: cross Abstract: The vast chemical design space and complex, interdependent design variables make catalyst discovery for targeted properties highly labor- and resource-intensive.

By Hayoung Doo, Dong Hyeon Mok, Seoin Back, Jonggeol Na

llms diffusion

arXiv Machine Learning

Jul 28

MMOE: Modernizing Diffusion Transformers with Efficient Expert Design

arXiv:2607. 24665v1 Announce Type: cross Abstract: Modern large language models scale successfully by pairing capacity growth with efficiency, keeping per-token and deployment costs under control as capacity grows.

By Yanhao Jia, Jiepeng Wang, Haibin Huang, Chi Zhang, Erik Cambria, Xuelong Li

llms diffusion efficiency

arXiv Machine Learning

Jul 28

Beyond Scale and Generation: Understanding Language Model-based Entity Matching

arXiv:2607. 24688v1 Announce Type: cross Abstract: Entity matching identifies records that refer to the same real-world entity.

By Zeyu Zhang, Xue Li, Iacer Calixto, Paul Groth, Sebastian Schelter

llms rag fine-tuning benchmarks

arXiv Machine Learning

Jul 28

FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time

arXiv:2604. 16648v2 Announce Type: replace Abstract: Tandem mass spectrometry is prominent in scientific discovery workflows for identifying unknown small molecules, yet high-throughput structural elucidation remains challenging.

By Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar, Magdalena Lederbauer, Shuiwang Ji, Runzhong Wang, Connor W. Coley

llms diffusion efficiency benchmarks

arXiv Machine Learning

Jul 28

Steering grids for sparse-autoencoder features: when a top-context label names an activation regime rather than a causal axis

arXiv:2605. 03160v2 Announce Type: replace Abstract: The standard protocol for interpreting sparse-autoencoder (SAE) features labels each feature from its top-activating contexts and validates the label by steering that single feature at a typical magnitude.

By Michael A. Riegler, Birk Sebastian Frostelid Torpmann-Hagen

llms

arXiv Machine Learning

Jul 28

Transfer learning for conflict and duplicate detection in software requirement pairs

arXiv:2301. 03709v3 Announce Type: replace-cross Abstract: Consistent and holistic expression of software requirements is important for the success of software projects.

By Garima Malik, Savas Yildirim, Mucahit Cevik

llms nlp fine-tuning

arXiv Machine Learning

Jul 28

MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback

arXiv:2604. 23107v2 Announce Type: replace-cross Abstract: Causal effect estimation from observational data requires careful adjustment for confounding.

By Lei Wang, Debashis Ghosh

llms agents benchmarks

arXiv AI

Jul 28

EviBack: Search-Agent Reinforcement Learning via Evidence-Constrained Teacher Backoff

arXiv:2607. 23955v1 Announce Type: new Abstract: Reinforcement learning enables Agentic RAG systems to learn multi-turn search from verifiable outcome rewards, but all- zero rollout groups provide no comparative signal and may hide useful search behavior.

By Xiao Ma, Zhiquan Hu, Yi Wei, Chenchen Zhao, Yijun Chen, Jicheng Zhao, Yuming Li Chuang Dai

llms rag agents reinforcement-learning benchmarks

arXiv AI

Jul 28

cMoLLM at Scale: Horizontal Scaling Laws for Mixture-of-LLMs

arXiv:2607. 22577v1 Announce Type: new Abstract: Scaling large language models (LLMs) has driven their success, yet dense Transformers couple capacity and computation: every parameter is activated for every token, making training and inference costs grow linearly with model size-a critical bottleneck as models approach trillion-parameter regimes.

By Xin Yang, Yemin Wang, Mingda Liu, Letian Li, Shuaishuai Cao, Zhengxiao He, Ryan Dong

llms

arXiv AI

Jul 28

Hierarchical Group-Conditional Conformal Risk Control for Selective Prediction in Language Models

arXiv:2607. 24562v1 Announce Type: new Abstract: Large language models serve heterogeneous populations structured by domain, topic difficulty, and linguistic style.

By Murilo Salem, Lu\'isa B\"ohm, Daniel Pontes, Anderson Ferrugem

llms efficiency benchmarks

arXiv AI

Jul 28

Eviction as Estimation: A Fixed-Lag Smoothing View of Test-Time Memory, and When Measuring Beats Accumulating

arXiv:2607. 24667v1 Announce Type: new Abstract: A language model with a bounded working memory must repeatedly decide which stored items to keep.

By Maruthi Vemula, Neeraj Praneeth Gajula

llms nlp benchmarks

arXiv AI

Jul 28

ERUnderstand: Evaluating Vision-Language Models on Structured ER Diagrams

arXiv:2607. 24707v1 Announce Type: new Abstract: Entity-Relationship Diagrams (ERDs) are central to conceptual database design, yet they are typically available only as rendered images rather than machine-readable schemas, limiting AI-assisted database engineering.

By Ali Ansari, Yasmin Mohammadi, Farnoush Nili, Parsa Esmaeilkhani, Longin Jan Latecki, Eduard Dragut

llms multimodal benchmarks

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