A data-centric approach improves long-context reasoning in large language models, using eight curated datasets with 14K examples across retrieval, multi-evidence synthesis, and reasoning tasks. When paired with minimal outcome-based GRPO training, it achieves average gains of +7.2 to +6.4 points on seven benchmarks, outperforming prior RL training sets, and enhances agentic performance by +4.8 and +7.0 points on GAIA and BrowseComp respectively.
A data-centric approach improves long-context reasoning in large language models, using eight curated datasets with 14K examples across retrieval, multi-evidence synthesis, and reasoning tasks. When paired with minimal outcome-based GRPO training, it achieves average gains of +7.2 to +6.4 points on seven benchmarks, outperforming prior RL training sets, and enhances agentic performance by +4.8 and +7.0 points on GAIA and BrowseComp respectively.
Concatenating LLM-generated features to graph neural networks systematically reduces accuracy on homophilous benchmarks, with PubMed accuracy dropping by -17.0 ± 0.3 pp. This degradation is linked to LLM-alone discriminability (Delta_sig), which correlates strongly with concatenation cost (r² = 0.38) and shows a power law relationship with feature dimension and node count (r² = 0.97), particularly in low-Delta_sig, low-node scenarios.
TerraMARS is an end-to-end pipeline that uses a domain-adapted small language model to extract structured information from Mars science literature. It converts unstructured text into JSON format and supports Mars terraforming-related question answering, enabling integration into habitability modeling and digital twin applications. The pipeline uses Google Gemma 3 1B fine-tuned with QLoRA on Mars-specific datasets, though further work is needed to improve accuracy and factual consistency.
A user asks if anyone with sufficient computing resources can create a large distillation dataset of 70-1 million examples from GLM5.2. The goal is to enable better training of smaller models like Qwen3.5, benefiting the broader community.
A new automated system addresses extreme class imbalance and asymmetric label noise in Autonomous Emergency Braking data. It uses targeted data augmentation and noise suppression to identify rare delayed and false triggers with 80% improved recall and 50% reduced manual annotation effort, enabling continuous self-improvement in on-vehicle AEB optimization.
A novel framework named CDDTLDA uses transfer learning and data augmentation to address Chinese dialects discrimination with limited annotations. It trains a source ASR model on a large dialect corpus, applies speed, pitch, and noise augmentation to low-resource target dialects, and fine-tunes a target ASR model using self-attention to capture shared semantic features. Experimental results show CDDTLDA outperforms state-of-the-art methods on two benchmark Chinese dialect corpora.
RegMix-D extends RegMix by leveraging full loss trajectories from proxy runs to dynamically select data mixtures. It outperforms RegMix and DoReMi across 13 downstream tasks, achieving superior results with just 128 proxy models—25% of RegMix's compute budget.
SAMA introduces a unified framework that generates high-fidelity, task-aware synthetic data by aligning semantic anchors across modalities. It uses a Collaborative Multi-Experts Multimodal Large Language Model with shared and task-specific adapters, and employs an Anchor-Preserving Diffusion mechanism for image synthesis, ensuring semantic consistency while diversifying visual contexts. Extensive experiments show SAMA outperforms state-of-the-art methods in MNER, MRE, and MEE under low-resource conditions.
A framework transfers knowledge from large instruction-tuned models to compact ones using synthetic data generated via structured few-shot prompting. Clustering-based seed selection produces more representative synthetic examples than random sampling, enabling compact models to achieve strong performance with minimal human labeling. On complex, noisy financial text, the student model outperforms the teacher model, while remaining competitive on formal text.
Radical AI has accelerated materials discovery by producing and characterizing 1,200 alloys in six months—nearly 10x faster than DARPA/GE MACH's goal of 500 alloys in a year. Their self-driving labs use AI scientists to generate and test hypotheses in closed-loop systems, leading to 300 new materials with 10 exhibiting novel, state-of-the-art properties now being developed for commercial use.
Concatenating LLM-generated features to graph neural networks systematically reduces accuracy on homophilous benchmarks, with PubMed accuracy dropping by -17.0 +/- 0.3 pp. A measure of LLM-alone discriminability, Delta_sig, correlates strongly with concatenation performance (r^2 = 0.38), and a rule based on Delta_sig <= 13.8 pp correctly predicts non-positive impact in 7 out of 9 datasets.
Stanford introduces SEFD, an open, layout-faithful reconstruction of SEC filings into MultiMarkdown. The 152B-token SEFD-v1 dataset enables financial language modeling and includes benchmarks for forecasting and table transcription, with less than 0.1% overlap to Common Crawl.
FusionRS introduces the first large-scale RGB-infrared-text dataset for remote sensing vision-language modeling. It aligns RGB and infrared images with IR-aware captions, enabling dual-modal vision-language foundation models. Experiments show improved RGB-IR alignment, retrieval, and captioning, with ablation studies confirming the critical role of modality-specific textual supervision.
The VibrantForests framework uses satellite data trained on lidar samples to generate annual, wall-to-wall maps of canopy cover, height, biomass, basal area, and quadratic mean diameter at 10-meter resolution across the contiguous U.S. It improves accuracy by reducing overestimation in sparse forests and underestimation in dense forests, extending the range of reliable predictions beyond traditional passive-sensor models.
A new hybrid modeling framework uses genomic data and neural networks to predict biokinetic parameters in soil organic matter turnover models. It incorporates ecological constraints to ensure realistic microbial dynamics, even for unobserved variables, and outperforms existing methods on both synthetic and real datasets with minimal training data.
A new algorithm estimates note names, key signatures, and local scales from MIDI-like input by jointly optimizing modal and tonal stages. It has been evaluated on jazz lead sheets, solo transcriptions, traditional tunes, and classical piano scores, with additional distances defined between common jazz scales for musicological research.
CzechDocs is a multiway parallel dataset of formatted documents in HTML, DOCX, and PDF formats, covering Czech and minority languages such as Ukrainian, English, Vietnamese, and Russian. It supports evaluation of machine translation systems that preserve document formatting, with a validation subset and evaluation toolkit publicly released. A held-out test split will be used for a future shared task on document-level translation with formatting preservation.
A community initiative suggests creating a crowdsourced coding dataset to enable local LLM development. The proposal aims to allow anyone with hardware to contribute data, with more powerful users helping to fine-tune or quantize models, thus reducing reliance on company-released models.
XGBoost-Forget enables efficient machine unlearning for XGBoost models on tabular network intrusion datasets. It maintains model performance while achieving faster unlearning compared to full retraining, addressing a gap in unlearning research for tabular data in network intrusion detection.