Datasets:

Yukino13
/

IdeaEval-Corpus

arxiv_id large_stringlengths 10 10	title large_stringlengths 6 240	abstract large_stringlengths 85 3.49k	publication_date large_stringdate 2025-09-01 00:00:00 2026-01-31 00:00:00	primary_category large_stringclasses 5 values	categories listlengths 1 6	s2_paper_id large_stringlengths 40 40 ⌀	s2_citation_count float64 0 527 ⌀	in_ideation_bench bool 2 classes	bench_cohort_ids listlengths 0 3
2509.01033	Seeing through Unclear Glass: Occlusion Removal with One Shot	Images taken through window glass are often degraded by contaminants adhered to the glass surfaces. Such contaminants cause occlusions that attenuate the incoming light and scatter stray light towards the camera. Most of existing deep learning methods for neutralizing the effects of contaminated glasses relied on synth...	2025-09-01	cs.CV	[ "cs.CV", "cs.AI" ]	4c774bc4cea8d1874cfa72ebe09803768fb4dd07	1	true	[ "l1_017_01_w1" ]
2509.01035	We Politely Insist: Your LLM Must Learn the Persian Art of Taarof	Large language models (LLMs) struggle to navigate culturally specific communication norms, limiting their effectiveness in global contexts. We focus on Persian taarof, a social norm in Iranian interactions, which is a sophisticated system of ritual politeness that emphasizes deference, modesty, and indirectness, yet re...	2025-09-01	cs.CL	[ "cs.CL" ]	05bc90856833ef881c2a26dc56c0cfdc18ee01b6	8	false	[]
2509.01042	MatPROV: A Provenance Graph Dataset of Material Synthesis Extracted from Scientific Literature	Synthesis procedures play a critical role in materials research, as they directly affect material properties. With data-driven approaches increasingly accelerating materials discovery, there is growing interest in extracting synthesis procedures from scientific literature as structured data. However, existing studies o...	2025-09-01	cs.LG	[ "cs.LG", "cs.IR" ]	f14100d5b787e5e30c5dd9d80e9c5ae0a2d0ff8d	0	false	[]
2509.01052	FlashAdventure: A Benchmark for GUI Agents Solving Full Story Arcs in Diverse Adventure Games	GUI agents powered by LLMs show promise in interacting with diverse digital environments. Among these, video games offer a valuable testbed due to their varied interfaces, with adventure games posing additional challenges through complex, narrative-driven interactions. Existing game benchmarks, however, lack diversity ...	2025-09-01	cs.AI	[ "cs.AI", "cs.CL", "cs.CV" ]	a641feba82a9f80664d485767e8a81e5457f9260	1	false	[]
2509.01053	A Dynamic Fusion Model for Consistent Crisis Response	In response to the urgent need for effective communication with crisis-affected populations, automated responses driven by language models have been proposed to assist in crisis communications. A critical yet often overlooked factor is the consistency of response style, which could affect the trust of affected individu...	2025-09-01	cs.CL	[ "cs.CL", "cs.AI" ]	9efb2a49f3e16671fa160dc0c8d88f54755971ff	2	false	[]
2509.01055	VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use	Reinforcement Learning with Verifiable Rewards (RLVR) has demonstrated success in enhancing LLM reasoning capabilities, but remains limited to single-turn interactions without tool integration. While recent Agentic Reinforcement Learning with Tool use (ARLT) approaches have emerged to address multi-turn tool interactio...	2025-09-01	cs.AI	[ "cs.AI", "cs.CL", "cs.CV" ]	ca38d3dbf0ca25d4eeeb43becdc50aa45052e22d	45	true	[ "l1_002_03_02_w1" ]
2509.01058	Speaking at the Right Level: Literacy-Controlled Counterspeech Generation with RAG-RL	Health misinformation spreading online poses a significant threat to public health. Researchers have explored methods for automatically generating counterspeech to health misinformation as a mitigation strategy. Existing approaches often produce uniform responses, ignoring that the health literacy level of the audience...	2025-09-01	cs.CL	[ "cs.CL", "cs.AI" ]	e033d1272ef57ee6025ab1522f4cca3e5e91f5e7	1	false	[]
2509.01071	A Unified Low-level Foundation Model for Enhancing Pathology Image Quality	Foundation models have revolutionized computational pathology by achieving remarkable success in high-level diagnostic tasks, yet the critical challenge of low-level image enhancement remains largely unaddressed. Real-world pathology images frequently suffer from degradations such as noise, blur, and low resolution due...	2025-09-01	cs.CV	[ "cs.CV" ]	4e81b88416bcfa917aca85d4a7d34b280e3a1396	0	false	[]
2509.01073	IMU-Enhanced EEG Motion Artifact Removal with Fine-Tuned Large Brain Models	Electroencephalography (EEG) is a non-invasive method for measuring brain activity with high temporal resolution; however, EEG signals often exhibit low signal-to-noise ratios because of contamination from physiological and environmental artifacts. One of the major challenges hindering the real-world deployment of brai...	2025-09-01	cs.LG	[ "cs.LG", "eess.SP", "q-bio.NC" ]	0579063fc7ef821bc64c40055bf481775dad4a5a	0	false	[]
2509.01080	SpectMamba: Integrating Frequency and State Space Models for Enhanced Medical Image Detection	Abnormality detection in medical imaging is a critical task requiring both high efficiency and accuracy to support effective diagnosis. While convolutional neural networks (CNNs) and Transformer-based models are widely used, both face intrinsic challenges: CNNs have limited receptive fields, restricting their ability t...	2025-09-01	cs.CV	[ "cs.CV" ]	37ca6a8821a70aa925895d6568d58bf058a7d6e9	0	false	[]
2509.01081	Assessing Large Language Models on Islamic Legal Reasoning: Evidence from Inheritance Law Evaluation	This paper evaluates the knowledge and reasoning capabilities of Large Language Models in Islamic inheritance law, known as 'ilm al-mawarith. We assess the performance of seven LLMs using a benchmark of 1,000 multiple-choice questions covering diverse inheritance scenarios, designed to test models' ability to understan...	2025-09-01	cs.CL	[ "cs.CL", "cs.AI" ]	33d9a960a0866ff5ee2367fe22496be4cc1e90d0	15	false	[]
2509.01082	RefineStat: Efficient Exploration for Probabilistic Program Synthesis	Probabilistic programming offers a powerful framework for modeling uncertainty, yet statistical model discovery in this domain entails navigating an immense search space under strict domain-specific constraints. When small language models are tasked with generating probabilistic programs, they frequently produce output...	2025-09-01	cs.LG	[ "cs.LG", "cs.PL" ]	0cd41a4fb164fce912b23be0f1a9fe1dfcd298b9	2	false	[]
2509.01084	A Paradigm Gap in Urdu	In this paper, we document a paradigm gap in the combinatorial possibilities of verbs and aspect in Urdu: the perfective form of the -ya: kar construction (e.g. ro-ya: ki: cry-Pfv do.Pfv) is sharply ungrammatical in modern Urdu and Hindi, despite being freely attested in 19th century literature. We investigate this dia...	2025-09-01	cs.CL	[ "cs.CL" ]	24e3bf61e340b3b1d1d38613dccc8bc2951106b0	0	false	[]
2509.01085	Bidirectional Sparse Attention for Faster Video Diffusion Training	Video diffusion Transformer (DiT) models excel in generative quality but hit major computational bottlenecks when producing high-resolution, long-duration videos. The quadratic complexity of full attention leads to prohibitively high training and inference costs. Full attention inefficiency stems from two key challenge...	2025-09-01	cs.CV	[ "cs.CV" ]	b077771038ecff102bf996ef1097f4a2a83d9f62	8	false	[]
2509.01088	Privacy-Preserving Reasoning with Knowledge-Distilled Parametric Retrieval Augmented Generation	The current RAG system requires uploading plaintext documents to the cloud, risking private data leakage. Parametric RAG (PRAG) encodes documents as LoRA parameters within LLMs, offering a possible way to reduce exposure of raw content. However, it still faces two issues: (1) PRAG demands synthesizing QA pairs and fine...	2025-09-01	cs.CL	[ "cs.CL" ]	9721e8349475952f7cef4434693d39411c3090f8	1	false	[]
2509.01090	A Class of Random-Kernel Network Models	We introduce random-kernel networks, a multilayer extension of random feature models where depth is created by deterministic kernel composition and randomness enters only in the outermost layer. We prove that deeper constructions can approximate certain functions with fewer Monte Carlo samples than any shallow counterp...	2025-09-01	cs.LG	[ "cs.LG", "math.FA", "math.NA" ]	f9faaaca2ef3a7a973263870cacfdcbb20038fd3	0	false	[]
2509.01092	REFRAG: Rethinking RAG based Decoding	Large Language Models (LLMs) have demonstrated remarkable capabilities in leveraging extensive external knowledge to enhance responses in multi-turn and agentic applications, such as retrieval-augmented generation (RAG). However, processing long-context inputs introduces significant system latency and demands substanti...	2025-09-01	cs.CL	[ "cs.CL", "cs.AI", "cs.LG" ]	6376d60824754beba1c37380318390da2cbc5b65	1	false	[]
2509.01093	Natural Context Drift Undermines the Natural Language Understanding of Large Language Models	How does the natural evolution of context paragraphs affect question answering in generative Large Language Models (LLMs)? To investigate this, we propose a framework for curating naturally evolved, human-edited variants of reading passages from contemporary QA benchmarks and for analyzing LLM performance across a rang...	2025-09-01	cs.CL	[ "cs.CL", "cs.AI" ]	0f031dbbad29adfd016d8a2cca7475f7d8e2d909	1	false	[]
2509.01095	An End-to-End Framework for Video Multi-Person Pose Estimation	Video-based human pose estimation models aim to address scenarios that cannot be effectively solved by static image models such as motion blur, out-of-focus and occlusion. Most existing approaches consist of two stages: detecting human instances in each image frame and then using a temporal model for single-person pose...	2025-09-01	cs.CV	[ "cs.CV" ]	cdc45d1594e9c2c8019acd7b562242958c14bb99	0	false	[]
2509.01097	PVINet: Point-Voxel Interlaced Network for Point Cloud Compression	In point cloud compression, the quality of a reconstructed point cloud relies on both the global structure and the local context, with existing methods usually processing global and local information sequentially and lacking communication between these two types of information. In this paper, we propose a point-voxel i...	2025-09-01	cs.CV	[ "cs.CV" ]	230cb7730d7e11c5d1d2cf8899490c19803ece04	0	false	[]
2509.01098	CCE: Confidence-Consistency Evaluation for Time Series Anomaly Detection	Time Series Anomaly Detection metrics serve as crucial tools for model evaluation. However, existing metrics suffer from several limitations: insufficient discriminative power, strong hyperparameter dependency, sensitivity to perturbations, and high computational overhead. This paper introduces Confidence-Consistency E...	2025-09-01	cs.LG	[ "cs.LG", "cs.AI", "stat.ML" ]	924e362461697c74115ebd0f3522b8d724c5d221	1	false	[]
2509.01106	Robix: A Unified Model for Robot Interaction, Reasoning and Planning	We introduce Robix, a unified model that integrates robot reasoning, task planning, and natural language interaction within a single vision-language architecture. Acting as the high-level cognitive layer in a hierarchical robot system, Robix dynamically generates atomic commands for the low-level controller and verbal ...	2025-09-01	cs.AI	[ "cs.AI", "cs.CV", "cs.RO" ]	8784c5dc46d470b7fefb3a729adc7fe89db38e5a	13	false	[]
2509.01107	FICGen: Frequency-Inspired Contextual Disentanglement for Layout-driven Degraded Image Generation	Layout-to-image (L2I) generation has exhibited promising results in natural domains, but suffers from limited generative fidelity and weak alignment with user-provided layouts when applied to degraded scenes (i.e., low-light, underwater). We primarily attribute these limitations to the "contextual illusion dilemma" in ...	2025-09-01	cs.CV	[ "cs.CV" ]	0c78c58d9edcfb3b8976e14833ecf744371770cf	3	false	[]
2509.01109	GPSToken: Gaussian Parameterized Spatially-adaptive Tokenization for Image Representation and Generation	Effective and efficient tokenization plays an important role in image representation and generation. Conventional methods, constrained by uniform 2D/1D grid tokenization, are inflexible to represent regions with varying shapes and textures and at different locations, limiting their efficacy of feature representation. I...	2025-09-01	cs.CV	[ "cs.CV" ]	66201eabba33e64dabfdf8d72e2291da7690dd52	3	false	[]
2509.01119	SC-GIR: Goal-oriented Semantic Communication via Invariant Representation Learning	Goal-oriented semantic communication (SC) aims to revolutionize communication systems by transmitting only task-essential information. However, current approaches face challenges such as joint training at transceivers, leading to redundant data exchange and reliance on labeled datasets, which limits their task-agnostic...	2025-09-01	cs.LG	[ "cs.LG", "cs.AI", "eess.SP" ]	36d67bdaedb3f4e835bda9f5b8ada9e423311e77	5	false	[]
2509.01135	Learning Domain- and Class-Disentangled Prototypes for Domain-Generalized EEG Emotion Recognition	Electroencephalography (EEG)-based emotion recognition plays a critical role in affective Brain-Computer Interfaces (aBCIs), yet its practical deployment remains limited by inter-subject variability, reliance on target-domain data, and unavoidable label noise. To address these challenges, we propose a Multi-domain Aggr...	2025-09-01	cs.LG	[ "cs.LG", "cs.AI" ]	3017ebc42804035abcd87e65485f415286f777c9	0	false	[]
2509.01136	Heads or Tails: A Simple Example of Causal Abstractive Simulation	This note illustrates how a variety of causal abstraction arXiv:1707.00819 arXiv:1812.03789, defined here as causal abstractive simulation, can be used to formalize a simple example of language model simulation. This note considers the case of simulating a fair coin toss with a language model. Examples are presented il...	2025-09-01	cs.AI	[ "cs.AI" ]	8f49c7311304717ca55384076443a849e85a7adc	0	false	[]
2509.01139	Nonlinear Performative Prediction	Performative prediction is an emerging paradigm in machine learning that addresses scenarios where the model's prediction may induce a shift in the distribution of the data it aims to predict. Current works in this field often rely on uncontrollable assumptions, such as bounded gradients of performative loss, and prima...	2025-09-01	cs.LG	[ "cs.LG", "stat.ML" ]	66d59accefc7bf35692dd7c09a5dbb32ed8e3f77	1	false	[]
2509.01142	Dream-Coder 7B: An Open Diffusion Language Model for Code	We present Dream-Coder 7B, an open-source discrete diffusion language model for code generation that exhibits emergent any-order generation capabilities. Unlike traditional autoregressive (AR) models that decode strictly left-to-right, Dream-Coder 7B adaptively determines its decoding strategy based on the coding task:...	2025-09-01	cs.CL	[ "cs.CL" ]	4b37680b828ea09aab51a3fa829d7ec166dcfb48	38	false	[]
2509.01144	MetaSSL: A General Heterogeneous Loss for Semi-Supervised Medical Image Segmentation	Semi-Supervised Learning (SSL) is important for reducing the annotation cost for medical image segmentation models. State-of-the-art SSL methods such as Mean Teacher, FixMatch and Cross Pseudo Supervision (CPS) are mainly based on consistency regularization or pseudo-label supervision between a reference prediction and...	2025-09-01	cs.CV	[ "cs.CV" ]	9e6d7b43ca2e0d9a482ccf6a321f9c9844c0ebc0	3	false	[]
2509.01147	Zero-shot Cross-lingual NER via Mitigating Language Difference: An Entity-aligned Translation Perspective	Cross-lingual Named Entity Recognition (CL-NER) aims to transfer knowledge from high-resource languages to low-resource languages. However, existing zero-shot CL-NER (ZCL-NER) approaches primarily focus on Latin script language (LSL), where shared linguistic features facilitate effective knowledge transfer. In contrast...	2025-09-01	cs.CL	[ "cs.CL" ]	3022d9bf970d3ab1bce4d2871ab7166f728d3d4b	0	false	[]
2509.01157	MVTrajecter: Multi-View Pedestrian Tracking with Trajectory Motion Cost and Trajectory Appearance Cost	Multi-View Pedestrian Tracking (MVPT) aims to track pedestrians in the form of a bird's eye view occupancy map from multi-view videos. End-to-end methods that detect and associate pedestrians within one model have shown great progress in MVPT. The motion and appearance information of pedestrians is important for the as...	2025-09-01	cs.CV	[ "cs.CV" ]	e66e08e00b97e8b1cb1c0759f613b56b19ade159	1	false	[]
2509.01158	Joint Information Extraction Across Classical and Modern Chinese with Tea-MOELoRA	Chinese information extraction (IE) involves multiple tasks across diverse temporal domains, including Classical and Modern documents. Fine-tuning a single model on heterogeneous tasks and across different eras may lead to interference and reduced performance. Therefore, in this paper, we propose Tea-MOELoRA, a paramet...	2025-09-01	cs.CL	[ "cs.CL" ]	c0e1bbab802d131db9b2329c2c5d9fcbfa6abb18	0	false	[]
2509.01161	Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers	Accurately predicting early recurrence in brain tumor patients following surgical resection remains a clinical challenge. This study proposes a multi-modal machine learning framework that integrates structural MRI features with clinical biomarkers to improve postoperative recurrence prediction. We employ four machine l...	2025-09-01	cs.LG	[ "cs.LG" ]	f0236836f6deb8fa89ca951f8413afd21522cab9	0	false	[]
2509.01164	A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecture	This paper proposes a novel multimodal deep learning framework integrating bidirectional LSTM, multi-head attention mechanism, and variational mode decomposition (BiLSTM-AM-VMD) for early liver cancer diagnosis. Using heterogeneous data that include clinical characteristics, biochemical markers, and imaging-derived var...	2025-09-01	cs.LG	[ "cs.LG", "eess.IV" ]	b2caa8c4bb2e81a973a78714897c858429691e2f	1	false	[]
2509.01166	Enhancing Large Language Model for Knowledge Graph Completion via Structure-Aware Alignment-Tuning	Knowledge graph completion (KGC) aims to infer new knowledge and make predictions from knowledge graphs. Recently, large language models (LLMs) have exhibited remarkable reasoning capabilities. LLM-enhanced KGC methods primarily focus on designing task-specific instructions, achieving promising advancements. However, t...	2025-09-01	cs.CL	[ "cs.CL", "cs.AI" ]	40235f9308abe8b36e42164125d061f82c2d2123	5	false	[]
2509.01167	TempCore: Are Video QA Benchmarks Temporally Grounded? A Frame Selection Sensitivity Analysis and Benchmark	Vision-language models (VLMs) can ingest only a limited number of video frames, making frame selection a practical necessity. But do current Video QA benchmarks genuinely require temporal frame selection, or can most questions be answered regardless of which frames are shown? We introduce Frame Selection Sensitivity (F...	2025-09-01	cs.CV	[ "cs.CV", "cs.CL", "cs.LG" ]	7a918f8d5856fc31bf826b2c610f2b7442c40491	0	false	[]
2509.01170	ADMP-GNN: Adaptive Depth Message Passing GNN	Graph Neural Networks (GNNs) have proven to be highly effective in various graph learning tasks. A key characteristic of GNNs is their use of a fixed number of message-passing steps for all nodes in the graph, regardless of each node's diverse computational needs and characteristics. Through empirical real-world data a...	2025-09-01	cs.LG	[ "cs.LG", "stat.ML" ]	3760cdfa3a1671f6bbb56d6d1489140daaf0199e	2	false	[]
2509.01177	DynaMind: Reconstructing Dynamic Visual Scenes from EEG by Aligning Temporal Dynamics and Multimodal Semantics to Guided Diffusion	Reconstruction dynamic visual scenes from electroencephalography (EEG) signals remains a primary challenge in brain decoding, limited by the low spatial resolution of EEG, a temporal mismatch between neural recordings and video dynamics, and the insufficient use of semantic information within brain activity. Therefore,...	2025-09-01	cs.CV	[ "cs.CV", "cs.AI", "cs.HC", "eess.SP" ]	6f1bf9dbdd3260b0b9e404926c48a37dbeacf53d	1	false	[]
2509.01181	FocusDPO: Dynamic Preference Optimization for Multi-Subject Personalized Image Generation via Adaptive Focus	Multi-subject personalized image generation aims to synthesize customized images containing multiple specified subjects without requiring test-time optimization. However, achieving fine-grained independent control over multiple subjects remains challenging due to difficulties in preserving subject fidelity and preventi...	2025-09-01	cs.CV	[ "cs.CV", "cs.AI" ]	2fd3bbf8cb92482a03eb2b6d20404000dcffb1b5	0	false	[]
2509.01182	Question-to-Knowledge (Q2K): Multi-Agent Generation of Inspectable Facts for Product Mapping	Identifying whether two product listings refer to the same Stock Keeping Unit (SKU) is a persistent challenge in ecommerce, especially when explicit identifiers are missing and product names vary widely across platforms. Rule based heuristics and keyword similarity often misclassify products by overlooking subtle disti...	2025-09-01	cs.AI	[ "cs.AI", "cs.CL", "cs.HC", "cs.IR", "cs.MA" ]	be7884820bcd2ce70c817a7d99dc2d18ece546df	0	false	[]
2509.01183	SegAssess: Panoramic quality mapping for robust and transferable unsupervised segmentation assessment	High-quality image segmentation is fundamental to pixel-level geospatial analysis in remote sensing, necessitating robust segmentation quality assessment (SQA), particularly in unsupervised settings lacking ground truth. Although recent deep learning (DL) based unsupervised SQA methods show potential, they often suffer...	2025-09-01	cs.CV	[ "cs.CV" ]	65c14595ee72396bc7e5c3f9c2bd67b15aa1a362	0	false	[]
2509.01185	Modular Techniques for Synthetic Long-Context Data Generation in Language Model Training and Evaluation	The ability of large language models (LLMs) to process and reason over long textual inputs is critical for a wide range of real-world applications. However, progress in this area is significantly constrained by the absence of high-quality, diverse, and verifiable long-context datasets suitable for both training and eva...	2025-09-01	cs.CL	[ "cs.CL", "cs.AI" ]	e82b07a4f6062bd586e8fb0a448e49f36a52edb0	0	false	[]
2509.01186	Statutory Construction and Interpretation for Artificial Intelligence	AI systems are increasingly governed by natural language principles, yet a key challenge arising from reliance on language remains underexplored: interpretive ambiguity. As in legal systems, ambiguity arises both from how these principles are written and how they are applied. But while legal systems use institutional s...	2025-09-01	cs.CL	[ "cs.CL", "cs.AI", "cs.CY" ]	c729324d698e4201e5a7dbf36ac2e8a890c6dd7f	4	true	[ "l1_001_02_06_w1" ]
2509.01187	StoxLSTM: A Stochastic Extended Long Short-Term Memory Network for Time Series Forecasting	The Extended Long Short-Term Memory (xLSTM) network has demonstrated strong capability in modeling complex long-term dependencies in time series data. Despite its success, the deterministic architecture of xLSTM limits its representational capacity and forecasting performance, especially on challenging real-world time ...	2025-09-01	cs.LG	[ "cs.LG" ]	3c224f67ad5952c98820f168425c943cf9c2c790	1	false	[]
2509.01190	Efficient Large Language Models with Zero-Shot Adjustable Acceleration	Using Large Language Models (LLMs) in real-world applications presents significant challenges, particularly in balancing computational efficiency with model performance. Optimizing acceleration after fine-tuning and during inference is critical for building efficient architectures. This paper introduces Zero-Shot Adjus...	2025-09-01	cs.CL	[ "cs.CL" ]	20f001de3e77e6756ef7e245b0541e90bfb01db9	0	false	[]
2509.01198	Preserving Vector Space Properties in Dimensionality Reduction: A Relationship Preserving Loss Framework	Dimensionality reduction can distort vector space properties such as orthogonality and linear independence, which are critical for tasks including cross-modal retrieval, clustering, and classification. We propose a Relationship Preserving Loss (RPL), a loss function that preserves these properties by minimizing discrep...	2025-09-01	cs.LG	[ "cs.LG", "cs.AI" ]	499d7d31cb059281996dc139582fac9c42946ea3	0	false	[]
2509.01200	SimulMEGA: MoE Routers are Advanced Policy Makers for Simultaneous Speech Translation	Simultaneous Speech Translation (SimulST) enables real-time cross-lingual communication by jointly optimizing speech recognition and machine translation under strict latency constraints. Existing systems struggle to balance translation quality, latency, and semantic coherence, particularly in multilingual many-to-many ...	2025-09-01	cs.CL	[ "cs.CL", "cs.SD", "eess.AS" ]	a38b7d70f1563d2c02105930351c327e284ef084	2	false	[]
2509.01202	PrediTree: A Multi-Temporal Sub-meter Dataset of Multi-Spectral Imagery Aligned With Canopy Height Maps	We present PrediTree, the first comprehensive open-source dataset designed for training and evaluating tree height prediction models at sub-meter resolution. This dataset combines very high-resolution (0.5m) LiDAR-derived canopy height maps, spatially aligned with multi-temporal and multi-spectral imagery, across diver...	2025-09-01	cs.CV	[ "cs.CV" ]	f798ec80797065430fe6dfbc6279655b5c2400de	0	false	[]
2509.01204	DcMatch: Unsupervised Multi-Shape Matching with Dual-Level Consistency	Establishing point-to-point correspondences across multiple 3D shapes is a fundamental problem in computer vision and graphics. In this paper, we introduce DcMatch, a novel unsupervised learning framework for non-rigid multi-shape matching. Unlike existing methods that learn a canonical embedding from a single shape, o...	2025-09-01	cs.CV	[ "cs.CV" ]	c1679105aebd9bb53f86340d64eb26a0aaa72628	1	false	[]
2509.01206	EndoGMDE: Generalizable Monocular Depth Estimation with Mixture of Low-Rank Experts for Diverse Endoscopic Scenes	Self-supervised monocular depth estimation is a significant task for low-cost and efficient 3D scene perception and measurement in endoscopy. However, the variety of illumination conditions and scene features is still the primary challenges for depth estimation in endoscopic scenes. In this work, a novel self-supervise...	2025-09-01	cs.CV	[ "cs.CV", "cs.AI" ]	7f824d31338555ec0f86ee22ee521df98ccea366	0	false	[]
2509.01209	Measuring Image-Relation Alignment: Reference-Free Evaluation of VLMs and Synthetic Pre-training for Open-Vocabulary Scene Graph Generation	Scene Graph Generation (SGG) encodes visual relationships between objects in images as graph structures. Thanks to the advances of Vision-Language Models (VLMs), the task of Open-Vocabulary SGG has been recently proposed where models are evaluated on their functionality to learn a wide and diverse range of relations. C...	2025-09-01	cs.CV	[ "cs.CV" ]	e8c8f1684cb48b7d224bb4e537ea9692091f124e	0	false	[]
2509.01213	Mitigating Catastrophic Forgetting in Continual Learning through Model Growth	Catastrophic forgetting is a significant challenge in continual learning, in which a model loses prior knowledge when it is fine-tuned on new tasks. This problem is particularly critical for large language models (LLMs) undergoing continual learning, as retaining performance across diverse domains is important for thei...	2025-09-01	cs.CL	[ "cs.CL" ]	bbcbb3fe793628937a695d334bb6694bce850760	3	false	[]
2509.01214	PRINTER:Deformation-Aware Adversarial Learning for Virtual IHC Staining with In Situ Fidelity	Tumor spatial heterogeneity analysis requires precise correlation between Hematoxylin and Eosin H&E morphology and immunohistochemical (IHC) biomarker expression, yet current methods suffer from spatial misalignment in consecutive sections, severely compromising in situ pathological interpretation. In order to obtain a...	2025-09-01	cs.CV	[ "cs.CV", "cs.MM" ]	2e1875a4659fc4aca2df7b5cd0d9dcf4a958ed8e	1	false	[]
2509.01215	POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion	High-quality labeled data is essential for training accurate document conversion models, particularly in domains with complex formats such as tables, formulas, and multi-column text. However, manual annotation is both costly and time-consuming, while automatic labeling using existing models often lacks accuracy in hand...	2025-09-01	cs.CV	[ "cs.CV" ]	377d4ffd34af6ed4381809b7c3b6569be420c22f	21	false	[]
2509.01221	DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression	Large language models (LLMs) excel in general tasks but struggle with domain-specific ones, requiring fine-tuning with specific data. With many open-source LLMs available, selecting the best model for fine-tuning downstream tasks is challenging, primarily focusing on how to quickly identify the optimal LLM. We introduc...	2025-09-01	cs.CL	[ "cs.CL", "cs.AI", "cs.LG" ]	f13eadabe41694acb4993215a55684d08bd65890	0	false	[]
2509.01232	FantasyHSI: Video-Generation-Centric 4D Human Synthesis In Any Scene through A Graph-based Multi-Agent Framework	Human-Scene Interaction (HSI) seeks to generate realistic human behaviors within complex environments, yet it faces significant challenges in handling long-horizon, high-level tasks and generalizing to unseen scenes. To address these limitations, we introduce FantasyHSI, a novel HSI framework centered on video generati...	2025-09-01	cs.CV	[ "cs.CV" ]	495795965fef8777ada7b2c66dc880d3d66b4235	0	false	[]
2509.01235	Geometric origin of adversarial vulnerability in deep learning	How to balance training accuracy and adversarial robustness has become a challenge since the birth of deep learning. Here, we introduce a geometry-aware deep learning framework that leverages layer-wise local training to sculpt the internal representations of deep neural networks. This framework promotes intra-class co...	2025-09-01	cs.LG	[ "cs.LG", "cond-mat.stat-mech", "q-bio.NC" ]	e2291b1f850b64ce81fc3e198d0dd8067b15663b	0	false	[]
2509.01236	Rethinking the Chain-of-Thought: The Roles of In-Context Learning and Pre-trained Priors	Chain-of-Thought reasoning has emerged as a pivotal methodology for enhancing model inference capabilities. Despite growing interest in Chain-of-Thought reasoning, its underlying mechanisms remain unclear. This paper explores the working mechanisms of Chain-of-Thought reasoning from the perspective of the dual relation...	2025-09-01	cs.CL	[ "cs.CL", "cs.AI" ]	74c1d8d31bb3b165223d0c1e0abd2bb64b0f88d4	0	false	[]
2509.01238	Towards Open-World Retrieval-Augmented Generation on Knowledge Graph: A Multi-Agent Collaboration Framework	Large Language Models (LLMs) have demonstrated strong capabilities in web search and reasoning. However, their dependence on static training corpora makes them prone to factual errors and knowledge gaps. Retrieval-Augmented Generation (RAG) addresses this limitation by incorporating external knowledge sources, especial...	2025-09-01	cs.AI	[ "cs.AI" ]	2afb685ea5a3548256ff9923eabe165a94253e6c	0	false	[]
2509.01242	Learning Correlation-aware Aleatoric Uncertainty for 3D Hand Pose Estimation	3D hand pose estimation is a fundamental task in understanding human hands. However, accurately estimating 3D hand poses remains challenging due to the complex movement of hands, self-similarity, and frequent occlusions. In this work, we address two limitations: the inability of existing 3D hand pose estimation methods...	2025-09-01	cs.CV	[ "cs.CV" ]	18235f45265446eebda65b59ad6a6d5f7e19adf9	0	false	[]
2509.01245	Towards Agentic OS: An LLM Agent Framework for Linux Schedulers	Operating system schedulers suffer from a fundamental semantic gap, where kernel policies fail to understand application-specific needs, leading to suboptimal performance. We introduce SchedCP, the first framework that enables fully autonomous Large Language Model (LLM) agents to safely and efficiently optimize Linux s...	2025-09-01	cs.AI	[ "cs.AI", "cs.MA", "cs.OS" ]	3cbe2ead4fc81f91a7219491e9b4a36069368170	0	false	[]
2509.01250	Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views	Point cloud learning, especially in a self-supervised way without manual labels, has gained growing attention in both vision and learning communities due to its potential utility in a wide range of applications. Most existing generative approaches for point cloud self-supervised learning focus on recovering masked poin...	2025-09-01	cs.CV	[ "cs.CV" ]	458e3b6fdeab52f65314b22ef81893fa9f5edda2	2	false	[]
2509.01254	What Expressivity Theory Misses: Message Passing Complexity for GNNs	Expressivity theory, characterizing which graphs a GNN can distinguish, has become the predominant framework for analyzing GNNs, with new models striving for higher expressivity. However, we argue that this focus is misguided: First, higher expressivity is not necessary for most real-world tasks as these tasks rarely r...	2025-09-01	cs.LG	[ "cs.LG" ]	30ebe0e1bbe25f8275b36f36fcf29da7f9c18075	0	false	[]
2509.01257	Multi-Agent Reinforcement Learning for Task Offloading in Wireless Edge Networks	In edge computing systems, autonomous agents must make fast local decisions while competing for shared resources. Existing MARL methods often resume to centralized critics or frequent communication, which fail under limited observability and communication constraints. We propose a decentralized framework in which each ...	2025-09-01	cs.LG	[ "cs.LG", "cs.AI", "cs.NI" ]	c2ba1a832c22120b39fb425e74e64f91900e5ec1	0	false	[]
2509.01259	ReCap: Event-Aware Image Captioning with Article Retrieval and Semantic Gaussian Normalization	Image captioning systems often produce generic descriptions that fail to capture event-level semantics which are crucial for applications like news reporting and digital archiving. We present ReCap, a novel pipeline for event-enriched image retrieval and captioning that incorporates broader contextual information from ...	2025-09-01	cs.CV	[ "cs.CV" ]	d4cb93e97d03201d94d858a6817795cddfca41a0	0	false	[]
2509.01260	Annotation and modeling of emotions in a textual corpus: an evaluative approach	Emotion is a crucial phenomenon in the functioning of human beings in society. However, it remains a widely open subject, particularly in its textual manifestations. This paper examines an industrial corpus manually annotated following an evaluative approach to emotion. This theoretical framework, which is currently un...	2025-09-01	cs.CL	[ "cs.CL" ]	34a094098dea1e9621ad300fd56dcec971b198a0	0	false	[]
2509.01267	Iterative In-Context Learning to Enhance LLMs Abstract Reasoning: The Case-Study of Algebraic Tasks	LLMs face significant challenges in systematic generalization, particularly when dealing with reasoning tasks requiring compositional rules and handling out-of-distribution examples. To address these challenges, we introduce an in-context learning methodology that improves the generalization capabilities of general pur...	2025-09-01	cs.LG	[ "cs.LG" ]	6843326ef68da22238f01d8d55d0d6ac97f5e6e2	0	false	[]
2509.01275	Novel Category Discovery with X-Agent Attention for Open-Vocabulary Semantic Segmentation	Open-vocabulary semantic segmentation (OVSS) conducts pixel-level classification via text-driven alignment, where the domain discrepancy between base category training and open-vocabulary inference poses challenges in discriminative modeling of latent unseen category. To address this challenge, existing vision-language...	2025-09-01	cs.CV	[ "cs.CV" ]	110a7e7844dc2f3c32f675b6246612375fdc0e28	1	false	[]
2509.01277	Communicative Agents for Slideshow Storytelling Video Generation based on LLMs	With the rapid advancement of artificial intelligence (AI), the proliferation of AI-generated content (AIGC) tasks has significantly accelerated developments in text-to-video generation. As a result, the field of video production is undergoing a transformative shift. However, conventional text-to-video models are typic...	2025-09-01	cs.AI	[ "cs.AI" ]	60ef0ab79698db2580eee7d70bad57ed8d3e1b1f	0	false	[]
2509.01279	SAR-NAS: Lightweight SAR Object Detection with Neural Architecture Search	Synthetic Aperture Radar (SAR) object detection faces significant challenges from speckle noise, small target ambiguities, and on-board computational constraints. While existing approaches predominantly focus on SAR-specific architectural modifications, this paper explores the application of the existing lightweight ob...	2025-09-01	cs.CV	[ "cs.CV" ]	8b7cdc6b35323a5c2941294513e20f0338670f5a	0	false	[]
2509.01280	Multi-Representation Adapter with Neural Architecture Search for Efficient Range-Doppler Radar Object Detection	Detecting objects efficiently from radar sensors has recently become a popular trend due to their robustness against adverse lighting and weather conditions compared with cameras. This paper presents an efficient object detection model for Range-Doppler (RD) radar maps. Specifically, we first represent RD radar maps wi...	2025-09-01	cs.CV	[ "cs.CV" ]	83535e026673ec76e30182525320be86100f2db5	0	false	[]
2509.01285	Building surrogate models using trajectories of agents trained by Reinforcement Learning	Sample efficiency in the face of computationally expensive simulations is a common concern in surrogate modeling. Current strategies to minimize the number of samples needed are not as effective in simulated environments with wide state spaces. As a response to this challenge, we propose a novel method to efficiently s...	2025-09-01	cs.LG	[ "cs.LG", "cs.AI" ]	be069ed8f737b03ffdff08aa9d41153cd66d7b24	4	false	[]
2509.01293	Equivariant U-Shaped Neural Operators for the Cahn-Hilliard Phase-Field Model	Phase separation in binary mixtures, governed by the Cahn-Hilliard equation, plays a central role in interfacial dynamics across materials science and soft matter. While numerical solvers are accurate, they are often computationally expensive and lack flexibility across varying initial conditions and geometries. Neural...	2025-09-01	cs.LG	[ "cs.LG", "physics.comp-ph", "physics.flu-dyn" ]	ae6b8a5851872f60b66a6f4da213b586d3ab2eaf	3	false	[]
2509.01299	Cross-Domain Few-Shot Segmentation via Ordinary Differential Equations over Time Intervals	Cross-domain few-shot segmentation (CD-FSS) not only enables the segmentation of unseen categories with very limited samples, but also improves cross-domain generalization ability within the few-shot segmentation framework. Currently, existing CD-FSS studies typically design multiple independent modules to enhance the ...	2025-09-01	cs.CV	[ "cs.CV" ]	b96d88c5935984f14d09225eeb4ad4329af7d919	0	false	[]
2509.01301	Culture is Everywhere: A Call for Intentionally Cultural Evaluation	The prevailing ``trivia-centered paradigm'' for evaluating the cultural alignment of large language models (LLMs) is increasingly inadequate as these models become more advanced and widely deployed. Existing approaches typically reduce culture to static facts or values, testing models via multiple-choice or short-answe...	2025-09-01	cs.CL	[ "cs.CL" ]	329ef81615851ff064d7a5a7886955ec08a3bc31	4	false	[]
2509.01308	GradeSQL: Test-Time Inference with Outcome Reward Models for Text-to-SQL Generation from Large Language Models	Text-to-SQL, the task of translating natural language questions into SQL queries, has significantly advanced with the introduction of Large Language Models (LLMs), broadening database accessibility for a wide range of users. Despite substantial progress in generating valid SQL, current LLMs still struggle with complex ...	2025-09-01	cs.AI	[ "cs.AI", "cs.CL", "cs.DB" ]	799c9dc3bf85bce02839e67a753cae70299a9a5d	0	false	[]
2509.01312	TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering	While large language models (LLMs) have shown promise in the table question answering (TQA) task through prompt engineering, they face challenges in industrial applications, including structural heterogeneity, difficulties in target data localization, and bottlenecks in complex reasoning. To address these limitations, ...	2025-09-01	cs.CL	[ "cs.CL" ]	0e6f896ac61c7bc4945d964937b7ebe92a07299c	7	false	[]
2509.01314	Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization	Large Language Models (LLMs), being generic task solvers, are versatile. However, despite the vast amount of data they are trained on, there are speculations about their adaptation capabilities to a new domain. Additionally, the simple fine-tuning of the model to incorporate knowledge of a new domain is computationally...	2025-09-01	cs.CL	[ "cs.CL" ]	154a694fc3ad1ab88222da17c6ca31fef9a79641	1	false	[]
2509.01317	Guided Model-based LiDAR Super-Resolution for Resource-Efficient Automotive scene Segmentation	High-resolution LiDAR data plays a critical role in 3D semantic segmentation for autonomous driving, but the high cost of advanced sensors limits large-scale deployment. In contrast, low-cost sensors such as 16-channel LiDAR produce sparse point clouds that degrade segmentation accuracy. To overcome this, we introduce ...	2025-09-01	cs.CV	[ "cs.CV" ]	48d8f8998a62ec8272d268db85a1c3d57a0f5f8a	1	false	[]
2509.01319	Towards Trustworthy Vital Sign Forecasting: Leveraging Uncertainty for Prediction Intervals	Vital signs, such as heart rate and blood pressure, are critical indicators of patient health and are widely used in clinical monitoring and decision-making. While deep learning models have shown promise in forecasting these signals, their deployment in healthcare remains limited in part because clinicians must be able...	2025-09-01	cs.LG	[ "cs.LG", "cs.AI" ]	6af63169a834be4598cab0085457cecfa8682188	1	false	[]
2509.01321	Towards High Data Efficiency in Reinforcement Learning with Verifiable Reward	Recent advances in large reasoning models have leveraged reinforcement learning with verifiable rewards (RLVR) to improve reasoning capabilities. However, scaling these methods typically requires extensive rollout computation and large datasets, leading to high training costs and low data efficiency. To mitigate this i...	2025-09-01	cs.LG	[ "cs.LG", "cs.CL" ]	7e4dea45be16b7ffa59d9fdc8dc32dda0b5e2d33	6	true	[ "l1_002_02_04_w1" ]
2509.01322	LongCat-Flash Technical Report	We introduce LongCat-Flash, a 560-billion-parameter Mixture-of-Experts (MoE) language model designed for both computational efficiency and advanced agentic capabilities. Stemming from the need for scalable efficiency, LongCat-Flash adopts two novel designs: (a) Zero-computation Experts, which enables dynamic computatio...	2025-09-01	cs.CL	[ "cs.CL", "cs.AI", "cs.DC", "cs.LG" ]	4f947224264117cc32b1c2cb3dc719ba7f6af3da	32	false	[]
2509.01323	Multitask Battery Management with Flexible Pretraining	Industrial-scale battery management involves various types of tasks, such as estimation, prediction, and system-level diagnostics. Each task employs distinct data across temporal scales, sensor resolutions, and data channels. Building task-specific methods requires a great deal of data and engineering effort, which lim...	2025-09-01	cs.LG	[ "cs.LG", "cs.AI" ]	3de0996c8af24458d2f7a395dacaef3fe790095d	0	false	[]
2509.01324	KoBLEX: Open Legal Question Answering with Multi-hop Reasoning	Large Language Models (LLM) have achieved remarkable performances in general domains and are now extending into the expert domain of law. Several benchmarks have been proposed to evaluate LLMs' legal capabilities. However, these benchmarks fail to evaluate open-ended and provision-grounded Question Answering (QA). To a...	2025-09-01	cs.CL	[ "cs.CL" ]	c6433be28ba39bb06a851d22947507ede0e46577	2	false	[]
2509.01328	Can Large Language Models Master Complex Card Games?	Complex games have long been an important benchmark for testing the progress of artificial intelligence algorithms. AlphaGo, AlphaZero, and MuZero have defeated top human players in Go and Chess, garnering widespread societal attention towards artificial intelligence. Concurrently, large language models (LLMs) have exh...	2025-09-01	cs.CL	[ "cs.CL" ]	73b939f199c135c27395544fa65eae4ca47a57fb	2	false	[]
2509.01329	Globally aware optimization with resurgence	Modern optimization faces a fundamental challenge: local gradient-based methods provide no global information about the objective function $L$ landscape, often leading to suboptimal convergence and sensitivity to initialization. We introduce a novel optimization framework that leverages resurgence theory from complex a...	2025-09-01	cs.LG	[ "cs.LG", "math-ph", "math.OC" ]	aed55d0472480f03b1defe6c8d2690fe9666f5be	0	false	[]
2509.01330	Prior-Guided Residual Diffusion: Calibrated and Efficient Medical Image Segmentation	Ambiguity in medical image segmentation calls for models that capture full conditional distributions rather than a single point estimate. We present Prior-Guided Residual Diffusion (PGRD), a diffusion-based framework that learns voxel-wise distributions while maintaining strong calibration and practical sampling effici...	2025-09-01	cs.CV	[ "cs.CV" ]	a50b913f98aef117a9e3e199ddd98e5f2d87b1f1	1	false	[]
2509.01332	Image Quality Enhancement and Detection of Small and Dense Objects in Industrial Recycling Processes	This paper tackles two key challenges: detecting small, dense, and overlapping objects (a major hurdle in computer vision) and improving the quality of noisy images, especially those encountered in industrial environments. [1, 2]. Our focus is on evaluating methods built on supervised deep learning. We perform an analy...	2025-09-01	cs.CV	[ "cs.CV", "eess.IV" ]	658b6a2bdf6a9c5290d95aa9c9cd1c4440426904	0	false	[]
2509.01338	Conformal Predictive Monitoring for Multi-Modal Scenarios	We consider the problem of quantitative predictive monitoring (QPM) of stochastic systems, i.e., predicting at runtime the degree of satisfaction of a desired temporal logic property from the current state of the system. Since computational efficiency is key to enable timely intervention against predicted violations, s...	2025-09-01	cs.AI	[ "cs.AI" ]	a23ec6c106ea622013e9fccebf59fee57040bd50	1	false	[]
2509.01341	Street-Level Geolocalization Using Multimodal Large Language Models and Retrieval-Augmented Generation	Street-level geolocalization from images is crucial for a wide range of essential applications and services, such as navigation, location-based recommendations, and urban planning. With the growing popularity of social media data and cameras embedded in smartphones, applying traditional computer vision techniques to lo...	2025-09-01	cs.CV	[ "cs.CV", "cs.AI" ]	44b8a3ebcd87748b96c0d2e9d49ea0550ee50add	0	false	[]
2509.01344	AgroSense: An Integrated Deep Learning System for Crop Recommendation via Soil Image Analysis and Nutrient Profiling	Meeting the increasing global demand for food security and sustainable farming requires intelligent crop recommendation systems that operate in real time. Traditional soil analysis techniques are often slow, labor-intensive, and not suitable for on-field decision-making. To address these limitations, we introduce AgroS...	2025-09-01	cs.CV	[ "cs.CV", "cs.LG" ]	9d6ab601f34dbe842dac11dd7058e38fbd343224	5	false	[]
2509.01348	Advanced Torrential Loss Function for Precipitation Forecasting	Accurate precipitation forecasting is becoming increasingly important in the context of climate change. In response, machine learning-based approaches have recently gained attention as an emerging alternative to traditional methods such as numerical weather prediction and climate models. Nonetheless, many recent approa...	2025-09-01	cs.LG	[ "cs.LG", "cs.AI", "physics.ao-ph" ]	45a0331d4113e79efad9dd832ff0e40d9237d2d6	0	false	[]
2509.01350	Error Notebook-Guided, Training-Free Part Retrieval in 3D CAD Assemblies via Vision-Language Models	Effective specification-aware part retrieval within complex CAD assemblies is essential for automated engineering tasks. However, using LLMs/VLMs for this task is challenging: the CAD model metadata sequences often exceed token budgets, and fine-tuning high-performing proprietary models (e.g., GPT or Gemini) is unavail...	2025-09-01	cs.AI	[ "cs.AI" ]	5280bd50e6ca491341948660efbfa675a720c268	0	false	[]
2509.01352	Causal Sensitivity Identification using Generative Learning	In this work, we propose a novel generative method to identify the causal impact and apply it to prediction tasks. We conduct causal impact analysis using interventional and counterfactual perspectives. First, applying interventions, we identify features that have a causal influence on the predicted outcome, which we r...	2025-09-01	cs.LG	[ "cs.LG", "cs.AI" ]	6f4e3ed72830286786828c50323b5ebb6fadf4f7	0	false	[]
2509.01354	DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment	Current open-source training pipelines for Chinese medical language models predominantly emphasize optimizing training methodologies to enhance the performance of large language models (LLMs), yet lack comprehensive exploration into training data processing. To address this gap, we propose DPF-CM, a holistic Data Proce...	2025-09-01	cs.LG	[ "cs.LG", "cs.AI" ]	03b913bcd32011d7840754d810fc7418427cc59e	1	false	[]
2509.01360	M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision	Medical image retrieval is essential for clinical decision-making and translational research, relying on discriminative visual representations. Yet, current methods remain fragmented, relying on separate architectures and training strategies for 2D, 3D, and video-based medical data. This modality-specific design hamper...	2025-09-01	cs.CV	[ "cs.CV", "cs.LG" ]	94df1864dedc2fef61cb0db235a4ee1cc0f2c550	1	false	[]
2509.01362	Identity-Preserving Text-to-Video Generation via Training-Free Prompt, Image, and Guidance Enhancement	Identity-preserving text-to-video (IPT2V) generation creates videos faithful to both a reference subject image and a text prompt. While fine-tuning large pretrained video diffusion models on ID-matched data achieves state-of-the-art results on IPT2V, data scarcity and high tuning costs hinder broader improvement. We th...	2025-09-01	cs.CV	[ "cs.CV", "cs.MM" ]	85edc37568b078c61924f3ead6c1ad460096c2cf	2	false	[]
2509.01363	Reasoning Vectors: Transferring Chain-of-Thought Capabilities via Task Arithmetic	Large language models often require costly optimization, such as reinforcement learning, to master complex reasoning tasks. This work demonstrates that reasoning ability, once learned, can be extracted and transferred between models as a compact task vector. We source two publicly available, identically initialized Qwe...	2025-09-01	cs.CL	[ "cs.CL" ]	c84d5d4b848cbc98fb40a688f5a94fe2168c0b98	3	false	[]
2509.01370	CbLDM: A Diffusion Model for recovering nanostructure from atomic pair distribution function	The nanostructure inverse problem is an attractive problem that helps researchers to understand the relationship between the properties and the structure of nanomaterials. This study focuses on the problem of recovering the model system of monometallic nanoparticles (MMNPs) from their pair distribution function (PDF) a...	2025-09-01	cs.LG	[ "cs.LG", "cond-mat.mtrl-sci" ]	c9ef91d79b5fc126b3e6768550840acafcd52e83	0	false	[]

End of preview. Expand in Data Studio

IdeaEval-Corpus

35,505 AI papers posted to arXiv between September 2025 and January 2026 (primary or cross-listed in cs.LG, cs.AI, cs.CL, cs.CV, stat.ML). This is the source corpus of the IdeaEval benchmarks; see the blog post for the full story.

📝 Blog post · 💻 Code (corpus scripts: benchmark/scripts/corpus/)

The window was chosen to fall after the knowledge cutoffs of GPT-5.4 (Aug 31, 2025) and Claude Opus 4.6 (Aug 2025), so that models of that generation could not have seen these papers in training.

Construction

arXiv submissions in the five categories, Sep 2025 – Jan 2026, matched to Semantic Scholar.
An LLM classifier removed surveys, pure benchmark or dataset papers, and other papers not built around a research hypothesis (filter_papers.py).

The research-question taxonomy built on top of this corpus (a 6-level tree with 1,799 leaves) is not included. The cohorts selected from it are in IdeaEval-Bench.

Fields (`papers.parquet`)

Field	Description
`arxiv_id`	arXiv identifier
`title`, `abstract`	From arXiv
`publication_date`	First arXiv version (YYYY-MM-DD)
`primary_category`, `categories`	arXiv categories
`s2_paper_id`	Semantic Scholar paper id (null for 112 unmatched papers)
`s2_citation_count`	Semantic Scholar citation count, snapshot from April 2026
`in_ideation_bench`, `bench_cohort_ids`	Whether the paper is a human proposal in IdeaEval-Bench, and in which cohorts

Citation counts are a few months old at snapshot time and change quickly for recent papers. The counts used to label cohorts in IdeaEval-Bench were refreshed later and differ from these.

License

Metadata and abstracts come from arXiv (metadata released under CC0); citation counts come from the Semantic Scholar API. The compilation is released under ODC-BY. Full texts are not included; follow the arXiv id to each paper and its own license.

Citation

@misc{huang2026ideaeval,
  title  = {IdeaEval: Impact-Oriented Evaluation of Scientific Hypothesis Generation},
  author = {Huang, Yanchen and Niu, Yuchen and Hu, Xiangkun},
  year   = {2026},
  url    = {https://02hyc.github.io/Blog/posts/ideaeval/}
}

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IdeaEval-Corpus

Construction

Fields (papers.parquet)

License

Citation

Fields (`papers.parquet`)