数据集调试

Unlike code completion, debugging requires localizing faults and applying targeted edits. We observe that frontier LLMs often hack unit tests by regenerating correct but over-edited solutions during debugging. To evaluate how far LLMs are from precise debugging, we introduce Precise Debugging Benchmarking (PDB), a general, dataset-agnostic framework that converts any coding dataset into a debugging benchmark with precision-aware evaluation. PDB generates buggy programs by synthesizing verified atomic bugs and composing them into multi-bug programs. We define two novel metrics, edit-level precision and bug-level recall, which measure how many necessary edits are made and how many bugs are resolved. We release two evaluation benchmarks: PDB-Single on single-line bugs and PDB-Wild on multi-line and repository-level bugs. Experiments show that frontier models, such as GPT-5.1-Codex and DeepSeek-V3.2-Thinking, achieve unit-test pass rates above 76% but exhibit precision below 45%, even when explicitly instructed to perform minimal debugging on single-line bugs. Iterative and agentic debugging strategies do not substantially improve precision or recall, highlighting the need to rethink post-training pipelines for coding models.

Existing human-activity datasets typically cover only one or two sensor channels and often focus on short, isolated actions, leaving long-duration compositional tasks largely unsupported as benchmarks for temporal cross-modal sensorimotor learning. We introduce PULSE ($\mathbf{P}$hysiological $\mathbf{U}$nified $\mathbf{L}$ong-duration $\mathbf{S}$ynchronized $\mathbf{E}$mbodiment), a synchronized five-modality dataset that enables benchmarking temporal cross-modal sensorimotor learning: how models recognize ongoing activities, anticipate future contact, reconstruct unobserved signals, and remain robust when physiological, motion, gaze, inertial, and tactile channels are partially observed. Collected from 40 volunteers across 8 ecologically valid daily-activity scenarios together with a dedicated motion-primitive collection, PULSE records full-body optical motion capture with finger-level hand articulation, surface EMG, binocular eye tracking, wearable IMU, and a fingertip pressure array recording quantitative grip force, all sampled at 100 Hz. Each scenario is a long-horizon compositional task containing many smaller sub-tasks, yielding over 7,700 annotated action segments. Each segment is tagged with a motor primitive, the hand involved, the manipulated object, and a natural-language description with four paraphrased variants. On top of the dataset, we define multiple benchmark tasks for temporal cross-modal sensorimotor learning: scene and fine-grained action recognition, grasp onset anticipation, missing-modality robustness, tactile-driven sub-second grasp-state prediction, cross-modal pressure reconstruction, etc. Together these tasks evaluate how information transfers across physiology, motion, gaze, and touch, rather than reducing the dataset to an activity-label catalog. We evaluate three backbone architectures, nine fusion strategies, seven published baselines, and task-specific models including SyncFuse and DailyActFormer. Detailed task definitions and results are in the Appendices. Data, annotations, and baseline code will be publicly released.

We release OpenSanctions Pairs, the first large-scale public benchmark for entity matching on sanctions and OSINT data. The dataset includes 755,540 expert-labeled pairs over 1 million entities, aggregated from 293 source datasets across 45 jurisdictions. It captures real-world diversity in compliance data, spanning multiple languages and writing systems (e.g., Latin, Cyrillic, Arabic), inconsistent structure, and time-varying provenance, and is substantially more heterogeneous than prior entity matching benchmarks. As baselines, we evaluate the production rule-based matcher (nomenklatura RegressionV1) alongside open- and closed-source LLMs in both zero- and few-shot settings, each tested with and without MIPROv2 prompt optimization to control for prompt sensitivity. The rule-based baseline reaches 91.3\% F1; GPT-4o achieves the best result at 99.0\% F1, and a locally deployable open-source model (DeepSeek-R1-Distill-Qwen-14B) achieves 98.2\% F1. The rule-based baseline and LLMs fail in complementary ways: rules over-match, while LLMs struggle with cross-script transliteration. These results suggest that pairwise matching performance is approaching a practical ceiling and shift attention toward pipeline components such as blocking, clustering, and uncertainty-aware review.

Progress in Sign Language Translation (SLT) is frequently hampered by the "data bottleneck," a challenge particularly acute for regional languages such as Catalan Sign Language (LSC). In this paper, we present LSC-Parlament, a large-scale, multimodal dataset for LSC derived from Catalan Parliament sessions spanning 2021 to 2024. The dataset comprises over 87 hours of LSC video, synchronized with speech and text translations in either Catalan or Spanish. To curate this resource, we developed a fully automated pipeline that enables alignment between sign language, speech, and text without requiring prior human-annotated data. We provide a systematic evaluation of the dataset across multiple dimensions, including video tracking quality, audio transcription accuracy, and translation benchmarks. Our experiments compare transfer learning, zero-shot, and supervised translation settings, demonstrating significant knowledge transfer between spoken languages in the LSC-Spanish modality. By releasing LSC-Parlament, we provide a challenging open-domain benchmark to foster research in inclusive and scalable translation technologies.

Differentially Private Stochastic Gradient Descent (DP-SGD) is the dominant approach to private deep learning, but it pays compute and privacy budget \emph{per trained model}: each new architecture, ensemble member, or downstream fine-tuning round incurs additional privacy cost. We propose \textsc{SPS} (Summarize--Privatize--Synthesize) and its enhanced variant \textsc{SPS+}, dataset-distillation algorithms that release a private synthetic dataset by privatizing intermediate activation statistics from a public pretrained model. Once released, the dataset is post-processed freely: any downstream architecture, ensemble, federated aggregation, or continual update incurs \emph{zero} additional privacy cost. In particular, a single \textsc{SPS+} dataset distilled from a Wide ResNet-22-8 transfers zero-shot to architectures with different inductive biases (Vision Transformers, Swin Transformers, and ConvNeXt), all without re-incurring privacy. Empirically, \textsc{SPS+} is competitive with state-of-the-art DP-SGD across $\epsilon \in \{1,2,4,8\}$ on CIFAR-10 and CIFAR-100, with a notable advantage on CIFAR-100 under strict privacy, achieving a $5.8\%$ gain at $\epsilon{=}1$ over compute-matched DP-SGD. \textsc{SPS+} additionally outperforms prior generation-based DP methods by a large margin and supports private federated and continual learning out of the box.

Deep learning has become the dominant paradigm for vision-based spacecraft pose estimation, where models are predominantly trained on synthetic data due to the prohibitive cost of acquiring real in-orbit imagery. When deployed, these models suffer significant performance degradation due to the sim-to-real domain gap, which existing benchmarks address by pairing synthetic training data with Hardware-in-the-Loop (HIL) real imagery. Yet a critical factor remains consistently overlooked: in real orbital scenarios, illumination varies with the spacecraft's position along its orbit, inducing severe appearance shifts that existing benchmarks neither control nor study. We introduce ECLIPSE, the first benchmark designed to isolate illumination as an independent experimental variable. ECLIPSE pairs a large-scale photorealistic synthetic training set with a fully labeled HIL evaluation set of 14 approach trajectories, each replayed identically under 4 controlled lighting conditions, enabling rigorous evaluation of illumination-robust and domain-adaptation methods. Through this controlled design, our experiments confirm that lighting is the dominant driver of the sim-to-real performance gap in spacecraft pose estimation.

The task of \textit{dataset distillation} aims to find a small set of synthetic images such that training a model on them reproduces the performance of the same model trained on a much larger dataset of real samples. Existing distillation methods focus on synthesizing datasets that enable training \textit{randomly initialized} models. In contrast, state-of-the-art vision approaches are increasingly building on large, pre-trained self-supervised models rather than training from scratch. In this paper, we investigate the problem of distilling datasets that enable us to optimally train \textit{linear probes} on top of such large, pre-trained vision models. We introduce a method of dataset distillation for this task called \textit{Linear Gradient Matching} that optimizes the synthetic images such that, when passed through a pre-trained feature extractor, they induce gradients in the linear classifier similar to those produced by the real data. Our method yields synthetic data that outperform all real-image baselines and, remarkably, generalize across pre-trained vision models, enabling us, for instance, to train a linear CLIP probe that performs competitively using a dataset distilled via a DINO backbone. Further, we show that our distilled datasets are exceptionally effective for fine-grained classification and provide a valuable tool for model interpretability, predicting, among other things, how similar two models' embedding spaces are under the platonic representation hypothesis or whether a model is sensitive to spurious correlations in adversarial datasets.

Structure prediction models are increasingly deployed as zero-shot digital screens in antibody design, operating under the assumption that high folding confidence implies true binding. However, rigorous validation of this premise has been limited by small test sets, narrow target diversity, and the risk of data leakage from training-adjacent structures. Here we introduce POISK: Patent-extracted Organization of Immunoglobulin Sequence Knowledge, a large-scale dataset derived from the US patent literature, comprising 89074 antibody-antigen records extracted from 10110 patents using an LLM-based pipeline. From this resource, we curate a balanced benchmark set of 22494 complexes: 11247 patent-validated binders without a known resolved structure paired with putative non-binders constructed from sequence-similar human repertoire antibodies. Using this dataset, we benchmark six leading structure prediction models (AlphaFold-Multimer, Boltz-2, Intellifold v2, OpenFold3, Protenix-v1, and Protenix-v2) on their ability to discriminate binders from decoys. The best-performing model, Protenix-v2, achieves 87% pairwise accuracy on the most confident half of predictions and correctly identifies the true binder from a pool of 50 candidates in 32% of trials; this represents a substantial enrichment over random selection, yet remains far from reliable single-candidate identification. Structural consensus across models also discriminates binders from decoys, with pairwise accuracy reaching 73.1% when three or more models converge on a pose DockQ $\geq$ 0.23, covering around 54.2% of pairs). Yet epitope localization remains poor: the best model contacts annotated epitope residues in only 45% of cases, indicating that high confidence reflects learned statistical associations rather than accurate physical binding modes. Our results suggest that current folding models provide useful but limited signal for antibody screening and should be complemented by orthogonal methods. We release POISK as a community resource for future benchmarking and model training.

Debugging CUDA programs has long been challenging because failures often arise from subtle interactions among hardware behavior, compiler decisions, memory hierarchy, and asynchronous execution. More importantly, with the rapid expansion of GPU usage across scientific computing, machine learning, graphics, and systems workloads, CUDA debugging has become more challenging than ever. Current evaluations of LLM-based CUDA programming largely miss this setting: a model can pass correctness tests with repair by degeneration, simplifying the CUDA code into a safer but slower program that abandons the original optimization structure. We introduce CUDABEAVER, a benchmark for CUDA debugging from real failing workspaces produced during LLM-based CUDA generation. Each task provides the broken candidate, native build/test commands, raw error evidence, and a single editable file. CUDABEAVER evaluates whether a fixer truly repairs the failing CUDA code or merely finds a slower test-passing replacement, reporting results by failure category, debugging trajectory, stagnation mode, and performance preservation. We further propose pass@k(M,C,A), a protocol-conditional CUDA debugging metric by making the fixer M, corpus C, and protocol axes Aexplicit. Using this metric across 213 tasks and seven frontier LLMs, we show that protocol-aware evaluation gives a more faithful view of CUDA debugging ability: when performance-loss tolerance is high, fixers appear much stronger, but even a minor stricter performance requirement can sharply reduce measured success, shifting scores by up to 40 percentage points.