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Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

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arXiv AI
Sep 30

Less Uniform Discrete Diffusion is More Powerful and Scalable

The paper introduces Less Uniform Diffusion (LUDI), a framework that improves uniform diffusion language models by using a less uniform loss and per-token time embeddings to guide reverse transitions and enable confidence-based few-step sampling. Experiments demonstrate that LUDI provides cleaner supervision, enhances few-step generation, and scales to a 7B model (LUDI-7B) that achieves a 3-token-per-step speedup over autoregressive decoding while matching masked diffusion baselines. The work suggests that UDLMs still have untapped potential for complex generation tasks.

By Kaibo Wang, Ding Ding, Fangyu Ding, Zijin Feng, Han Shi, Haili Bai, Jiacheng Sun, Yang Xiang
llmsragdiffusion
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arXiv AI
Sep 30

One Geometry, Different Outcomes: Readout-Dependent Effects of the Modality Gap in Vision-Language Models

The paper investigates how the modality gap— the separation between image and text representations in contrastive vision‑language models—affects different downstream tasks. By showing that a single dominant direction accounts for most of the image‑text mean separation, the authors explain why reducing or removing this gap can improve zero‑shot classification, degrade retrieval, or restore performance depending on the task. The study provides a geometric framework that clarifies when and why gap interventions should be applied in vision‑language systems.

By Aditya Sharma, Divya Saxena
llmsragmultimodalsafety
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arXiv AI
Sep 30

AdaKerNet: Neural Kernel Decoding for Task-Adaptive Prediction with Multimodal Large Models

AdaKerNet is a task‑adaptive neural kernel decoder that operates on frozen multimodal representations from large foundation models, without requiring access to the models’ parameters. It learns Lipschitz‑controlled multimodal features, a reference kernel providing a soft structural prior, and a lightweight nonlinear predictor that deforms this structure. Experiments on four multimodal large language models and diverse input modalities show consistent improvements over baseline decoders, achieving up to 41% error reduction in scarce‑label settings.

By Konstantinos D. Polyzos, Eleni Oikonomou, Tara Javidi
llmsragfine-tuningmultimodal
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arXiv AI
Sep 30

AgentBug-Smith: Automatically Reproducing Real-World Harness Bugs in Agentic Systems

arXiv:2609.37864v1 Announce Type: cross Abstract: Agent harness bugs exhibit unique characteristics and remain challenging for state-of-the-art software agents to repair. Progress in this area is fur...

By Yiming Cheng (The University of Chicago), Alfin Wijaya Rahardja (Fudan University), Mengshi Zhang (TensorBlock, Inc), Zihao Chen (TensorBlock, Inc), Zhenpeng Chen (Tsinghua University), Yiling Lou (University of Illinois Urbana-Champaign)
llmsragagentsbenchmarks
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