Tootfinder

Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions
Frederic Kirstein, Sonu Kumar, Terry Ruas, Bela Gipp
https://arxiv.org/abs/2509.15901

Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions
Meeting summarization with large language models (LLMs) remains error-prone, often producing outputs with hallucinations, omissions, and irrelevancies. We present FRAME, a modular pipeline that reframes summarization as a semantic enrichment task. FRAME extracts and scores salient facts, organizes them thematically, and uses these to enrich an outline into an abstractive summary. To personalize summaries, we introduce SCOPE, a reason-out-loud protocol that has the model build a reasoning trace …

Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision
Dulhan Jayalath, Shashwat Goel, Thomas Foster, Parag Jain, Suchin Gururangan, Cheng Zhang, Anirudh Goyal, Alan Schelten
https://arxiv.org/abs/2509.14234

Compute as Teacher: Turning Inference Compute Into Reference-Free Supervision
Where do learning signals come from when there is no ground truth in post-training? We propose turning exploration into supervision through Compute as Teacher (CaT), which converts the model's own exploration at inference-time into reference-free supervision by synthesizing a single reference from a group of parallel rollouts and then optimizing toward it. Concretely, the current policy produces a group of rollouts; a frozen anchor (the initial policy) reconciles omissions and contradictions to…

Advancing Real-World Parking Slot Detection with Large-Scale Dataset and Semi-Supervised Baseline
Zhihao Zhang, Chunyu Lin, Lang Nie, Jiyuan Wang, Yao Zhao
https://arxiv.org/abs/2509.13133

Advancing Real-World Parking Slot Detection with Large-Scale Dataset and Semi-Supervised Baseline
As automatic parking systems evolve, the accurate detection of parking slots has become increasingly critical. This study focuses on parking slot detection using surround-view cameras, which offer a comprehensive bird's-eye view of the parking environment. However, the current datasets are limited in scale, and the scenes they contain are seldom disrupted by real-world noise (e.g., light, occlusion, etc.). Moreover, manual data annotation is prone to errors and omissions due to the complexity o…

An arithmetic measure of width for convex bodies
Jes\'us A. De Loera, Brittney Marsters, Christopher O'Neill
https://arxiv.org/abs/2509.04726 https://

An arithmetic measure of width for convex bodies
We introduce the arithmetic width of a convex body, defined as the number of distinct values a linear functional attains on the lattice points within the body. Arithmetic width refines lattice width by detecting gaps in the lattice point distribution and always provides a natural lower bound. We show that for large dilates of a convex body, the attained values form an arithmetic progression with only a bounded number of omissions near the extremes. For rational polytopes, we show that the arith…

Can Synthetic Query Rewrites Capture User Intent Better than Humans in Retrieval-Augmented Generation?
JiaYing Zheng, HaiNan Zhang, Liang Pang, YongXin Tong, ZhiMing Zheng
https://arxiv.org/abs/2509.22325

Can Synthetic Query Rewrites Capture User Intent Better than Humans in Retrieval-Augmented Generation?
Multi-turn RAG systems often face queries with colloquial omissions and ambiguous references, posing significant challenges for effective retrieval and generation. Traditional query rewriting relies on human annotators to clarify queries, but due to limitations in annotators' expressive ability and depth of understanding, manually rewritten queries often diverge from those needed in real-world RAG systems, resulting in a gap between user intent and system response. We observe that high-quality …

Optimal Control Meets Flow Matching: A Principled Route to Multi-Subject Fidelity
Eric Tillmann Bill, Enis Simsar, Thomas Hofmann
https://arxiv.org/abs/2510.02315 https://

Optimal Control Meets Flow Matching: A Principled Route to Multi-Subject Fidelity
Text-to-image (T2I) models excel on single-entity prompts but struggle with multi-subject descriptions, often showing attribute leakage, identity entanglement, and subject omissions. We introduce the first theoretical framework with a principled, optimizable objective for steering sampling dynamics toward multi-subject fidelity. Viewing flow matching (FM) through stochastic optimal control (SOC), we formulate subject disentanglement as control over a trained FM sampler. This yields two architec…

Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation
Wenyuan Chen, Fateme Nateghi Haredasht, Kameron C. Black, Francois Grolleau, Emily Alsentzer, Jonathan H. Chen, Stephen P. Ma
https://arxiv.org/abs/2509.22565

Retrieval-Augmented Guardrails for AI-Drafted Patient-Portal Messages: Error Taxonomy Construction and Large-Scale Evaluation
Asynchronous patient-clinician messaging via EHR portals is a growing source of clinician workload, prompting interest in large language models (LLMs) to assist with draft responses. However, LLM outputs may contain clinical inaccuracies, omissions, or tone mismatches, making robust evaluation essential. Our contributions are threefold: (1) we introduce a clinically grounded error ontology comprising 5 domains and 59 granular error codes, developed through inductive coding and expert adjudicati…

SeqVLM: Proposal-Guided Multi-View Sequences Reasoning via VLM for Zero-Shot 3D Visual Grounding
Jiawen Lin, Shiran Bian, Yihang Zhu, Wenbin Tan, Yachao Zhang, Yuan Xie, Yanyun Qu
https://arxiv.org/abs/2508.20758

SeqVLM: Proposal-Guided Multi-View Sequences Reasoning via VLM for Zero-Shot 3D Visual Grounding
3D Visual Grounding (3DVG) aims to localize objects in 3D scenes using natural language descriptions. Although supervised methods achieve higher accuracy in constrained settings, zero-shot 3DVG holds greater promise for real-world applications since eliminating scene-specific training requirements. However, existing zero-shot methods face challenges of spatial-limited reasoning due to reliance on single-view localization, and contextual omissions or detail degradation. To address these issues, …