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Research, evidence gathering, literature, reports, investigation, and synthesis
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- Lemas Multilingual Speech DatasetRelease 150K-hour multilingual speech corpus with word-level timestamps plus non-autoregressive flow-matching TTS and autoregressive speech editing models. Enable diverse speech generation and editing tasks across 10 languages.Votes: 0GitHub stars: 6
- Learning Visual Priors Llm PretrainingDecompose visual priors into perception and reasoning components, each optimized by distinct data types: reasoning from code/math corpora, perception from diverse modality-rich sources. Use to construct efficient VLM pretraining pipelines balancing multimodal perception with reasoning capability.Votes: 0GitHub stars: 6
- Learning User Preferences CollaborationEnable agents to adapt to individual user preferences across extended interactions via memory-equipped architectures. Use learning signals to improve memory updates and enable long-term collaborative relationships with human users.Votes: 0GitHub stars: 6
- Learning Scientific Taste With RlLearn to predict and generate high-impact research ideas by training models on community feedback signals. Apply reinforcement learning to align research generation with citation-based indicators of scientific impact.Votes: 0GitHub stars: 6
- Learning Discover Test TimeApply reinforcement learning at test time to enable language models to continue adapting on specific problems, achieving state-of-the-art results across mathematics, GPU optimization, algorithms, and biology. Use when you need models to discover domain-specific problem-solving strategies without retraining.Votes: 0GitHub stars: 6
- Kage Bench Fast Known Axis Visual GeneralizationPixel-based reinforcement learning agents often fail under purely visual distribution shift even when latent dynamics and rewards are unchanged, but existing benchmarks entangle multiple sources of shift and hinder systematic analysis. We introduce KAGE-Env, a JAX-native 2D platformer that factorizes the observation process into independently controllable visual axes while keeping the underlying control problem fixed. By construction, varying a visual axis affects performance only through the...Votes: 0GitHub stars: 6
- Jet Rl Enabling On Policy Fp8 Reinforcement LearniImplement techniques from Jet-RL: Enabling On-Policy FP8 Reinforcement Learning with Unified Training and Rollout Precision Flow. Reinforcement learning (RL) is essential for enhancing the complex reasoning capabilities of large language models (LLMs)Votes: 0GitHub stars: 6
- Inference Time Scaling Of Verification Self EvolviImplement techniques from Inference-Time Scaling of Verification: Self-Evolving Deep Research Agents via Test-Time Rubric-Guided Verification. While the majority of existing efforts focus on enhancing policy capabilities via post-training, we propose an alternative paradigm: self-evolving the agent's ability by iteratively verifying the policy model's outputs, guided by meticulously crafted rubricsVotes: 0GitHub stars: 6
- Industrial Defect Multimodal DatasetIntroduce IMDD-1M dataset with 1M aligned image-text pairs spanning 60+ material categories and 400+ defect types. Train diffusion-based vision-language models requiring 5% task-specific data vs. dedicated expert models for manufacturing quality control.Votes: 0GitHub stars: 6
- Illusion Of ThinkingEvaluate reasoning model capabilities by analyzing three complexity-dependent behavioral regimes and identifying fundamental limitations in symbolic manipulation rather than computational budgets.Votes: 0GitHub stars: 6
- Hop Skip Overthink DiagnosisNovel error categorization framework examining failures across hops (diversity), coverage, and overthinking. Combines human annotation with automated metrics to diagnose why reasoning models hallucinate on multi-step tasks.Votes: 0GitHub stars: 6
- Guidelines To Prompt Large Language Models For CodImplement techniques from Guidelines to Prompt Large Language Models for Code Generation: An Empirical Characterization. Large Language Models (LLMs) are nowadays extensively used for various types of software engineering tasks, primarily code generationVotes: 0GitHub stars: 6
- Guardians Of Hair Soft BoundariesRecover fine details at soft boundaries (hair, fur) through depth refinement networks and view synthesis. Integrate plug-and-play with existing depth models via adaptive combination across monocular, stereo, and novel view tasks.Votes: 0GitHub stars: 6
- Gigaevo Llm EvolutionEvolve Python algorithms and programs using LLMs as mutation operators combined with MAP-Elites quality-diversity search, achieving competitive results on geometric optimization and algorithmic problems by iteratively mutating code informed by historical performance and lineage context.Votes: 0GitHub stars: 6
- Fs Researcher ScalingScale research agent capability using persistent filesystem as external memory. Dual-agent architecture with context builder accumulating knowledge and report writer composing outputs enables computation scaling beyond context windows.Votes: 0GitHub stars: 6
- Finally Outshining The Random Baseline A SimpleActive learning (AL) has the potential to drastically reduce annotation costs in 3D biomedical image segmentation, where expert labeling of volumetric data is both time-consuming and expensive. Yet, existing AL methods are unable to consistently outperform improved random sampling baselines adapted to 3D data, leaving the field without a reliable solution. We introduce Class-stratified Scheduled Power Predictive Entropy (ClaSP PE), a simple and effective query strategy that addresses two key ...Votes: 0GitHub stars: 6
- Fantasyvln Unified Multimodal Chain Of ThoughtAchieving human-level performance in Vision-and-Language Navigation (VLN) requires an embodied agent to jointly understand multimodal instructions and visual-spatial context while reasoning over long action sequences. Recent works, such as NavCoT and NavGPT-2, demonstrate the potential of Chain-of-Thought (CoT) reasoning for improving interpretability and long-horizon planning. Moreover, multimodal extensions like OctoNav-R1 and CoT-VLA further validate CoT as a promising pathway toward human...Votes: 0GitHub stars: 6
- Exploration Exploitation RlvrInvestigate exploration-exploitation trade-offs in reinforcement learning with verifiable rewards through theoretical analysis and empirical validation. Derive explicit clipping bias bounds, establish policy-entropy shift formulation, and introduce reward-misalignment framework. Show policy entropy and performance lack direct causal relationships.Votes: 0GitHub stars: 6
- Exp Bench Ai ResearchEvaluate AI systems' ability to conduct autonomous research experiments using EXP-Bench, a benchmark for multi-step scientific reasoning and iterative experimental workflows.Votes: 0GitHub stars: 6
- Encoder Pretraining StrategyChoose optimal pretraining strategy for text encoders: pure MLM, pure CLM, or biphasic CLM-then-MLM training, with empirical guidance on performance across downstream tasks.Votes: 0GitHub stars: 6
- Dsgym A Holistic Framework For Evaluating And TraiImplement techniques from DSGym: A Holistic Framework for Evaluating and Training Data Science Agents. Data science agents promise to accelerate discovery and insight-generation by turning data into executable analyses and findingsVotes: 0GitHub stars: 6
- Drpg Decompose Retrieve Plan Generate An Agentic FImplement techniques from DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal. Despite the growing adoption of large language models (LLMs) in scientific research workflows, automated support for academic rebuttal, a crucial step in academic communication and peer review, remains largely underexploredVotes: 0GitHub stars: 6
- Distribution Matching VaeAlign latent distributions with arbitrary reference distributions via explicit matching constraints rather than fixed priors. DMVAE achieves gFID 3.2 on ImageNet with 64 epochs—when you need flexibility in latent representation design for image generation.Votes: 0GitHub stars: 6
- Discrete Diffusion Scaling LawsReformulate discrete diffusion using signal-to-noise ratio for hybrid masked-uniform noise scheduling. Derive compute-optimal scaling laws through careful hyperparameter tuning, showing uniform diffusion scales more favorably in token-constrained settings than autoregressive models.Votes: 0GitHub stars: 6