Papers
Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How does the crossover phenomenon in Pass@k curves between RLVR-tuned and base models vary across different code generation benchmarks like HumanEval versus LiveCodeBench. 0 claims were extracted from source…
Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: To what extent do large pre-trained video models maintain robustness against domain shift when evaluated on synthetic gesture datasets with varying lighting and background conditions. 0 claims were extracted from…
Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: Does integrating knowledge distillation with dynamic learning rate schedules improve the stability of code generation models when evaluated on out-of-distribution LiveCodeBench problems. 4 claims were extracted…
Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does the adoption of linear attention mechanisms affect the alignment performance of multimodal models when processing mixed-domain datasets at varying resolutions. 17 claims were extracted from source…
Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How robust are few-shot cross-lingual NER performance gains from large autoregressive models to domain shifts, as evaluated on the WikiANN benchmark in low-resource languages. 14 claims were extracted from source…
Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the performance of learnable visual token compression techniques compare to heuristic-based methods in cross-domain visual-language benchmarks like VQAv2 and COCO-QA, measured by accuracy. 9 claims were…
Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does the proposed scaling law with learning rate annealing perform on multimodal code generation benchmarks like CoderBench or DeCompEval compared to traditional power-law scaling methods. 0 claims were…
Abstract: This report synthesises findings from 10 peer-reviewed papers addressing the following research question: What is the impact of model size on the scaling law parameters (L0, A, C, \$alpha\$) in the proposed formulation when evaluated on HumanEval and MBPP benchmarks for code generation tasks. 10 claims were extracted…
Abstract: This report synthesises findings from 10 peer-reviewed papers addressing the following research question: How do different learning rate annealing schedules (e.g., linear, cosine, exponential) compare in terms of pass@k scores on LiveCodeBench when applied to code generation models like Code Llama or. 16 claims were…
Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does the scalability of video encoders trained on synthetic vs. real gesture data affect their inference throughput (measured in FPS) and memory footprint when deployed on edge devices for. 10 claims were…
Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: What is the correlation between dynamic learning rate schedules and the stability of code generation models when evaluated on adversarial examples from LiveCodeBench. 4 claims were extracted from source…
Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: Do open-source code models trained with cosine annealing exhibit lower accuracy drops on LiveCodeBench adversarial sets compared to those trained with step decay. 0 claims were extracted from source literature; 0…
Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: What is the impact of scaling the size of pre-trained video encoder models on the robustness of training-free gesture classification using synthetic data repositories. 0 claims were extracted from source…
Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: Can THaMES-driven alignment fine-tuning improve factual consistency scores on the TruthfulQA benchmark without degrading general language generation perplexity. 10 claims were extracted from source literature; 3…
Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does few-shot prompting performance of masked language models compare to autoregressive models on cross-lingual named entity recognition benchmarks for low-resource languages. 6 claims were extracted from…
Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: Which frontier language models achieve highest scores on GPQA Diamond Humanity Last Exam and difficult reasoning benchmarks v10. 0 claims were extracted from source literature; 0 were independently verified…
Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: What is the impact of THaMES evaluation pipelines on the robustness of multimodal models against factually incorrect captions in the ScienceQA dataset. 5 claims were extracted from source literature; 0 were…
Abstract: This report synthesises findings from 5 peer-reviewed papers addressing the following research question: Comprehensive comparison of frontier large language models on mathematical reasoning code generation and scientific knowledge v10. 0 claims were extracted from source literature; 0 were independently verified…
Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: What are the state-of-the-art large language model results on reasoning benchmarks published recently v10. 0 claims were extracted from source literature; 0 were independently verified against retrieved…
Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: How do language models compare to human experts on professional knowledge and science benchmarks v10. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An…
Abstract: This report synthesises findings from 10 peer-reviewed papers addressing the following research question: What is the effect of model size on language model performance on logical reasoning tasks v10. 14 claims were extracted from source literature; 2 were independently verified against retrieved documents. An…
Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: What prompting strategies maximize language model accuracy on graduate-level science questions v10. 13 claims were extracted from source literature; 0 were independently verified against retrieved documents. An…
Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How does extended thinking time affect language model accuracy on competition-level mathematics v10. 12 claims were extracted from source literature; 2 were independently verified against retrieved documents. An…
Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does synthetic training data improve language model performance on mathematical reasoning benchmarks v10. 0 claims were extracted from source literature; 0 were independently verified against retrieved…
Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: How does model quantization affect reasoning capability in large language models v10. 0 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated…