Use this view to inspect the underlying evidence corpus. For ranked developments, decision posture and interpretation, use Signals.
Raw feed or Signals?
Raw feed is chronological evidence. Signals ranks and interprets material change.
Research paper argues quantum program generation by LLMs must prioritize mathematical validity over probabilistic scaling due to syntax-semantics gap.
Research evaluates GPT-4 Turbo, Claude 3 Opus, Gemini 1.5 Pro, Llama 3 70B, and FinGPT for technical market analysis via candlestick patterns.
New research introduces Stochastic Reset Pathfinding (SRP), an episodic learning problem for optimizing paths with uncertain edge success probabilities and resets.
Research used machine learning on MEPS data from 2019 and 2021 to identify populations financially vulnerable to healthcare costs post-COVID-19.
Research uses LLMs to align clinical time series data with medical event sequences for improved ICU outcome predictions.
Research paper applies Renormalization Group theory to Transformer attention mechanisms, deriving testable predictions for fixed-point geometry and perturbation decay.
New research introduces Robust Peak-cost Constrained Reinforcement Learning (RP-CRL) to manage maximum cost in safety-critical AI applications.
Research explores Antidistillation Sampling (ADS-C) to protect proprietary classification models from adversarial knowledge distillation via API querying.
Research explores photorealistic perturbation using inpainting to enhance visual eXplainable AI (XAI) for complex machine learning models.
Research finds diffusion models can recover accurate mixture weights despite score function insensitivity, addressing a known paradox in multimodal distributions.
Research identifies a critical cost trade-off between prompt caching and query-aware prompt compression for LLM API usage, showing caching's value.
Research explores 'harness-in-the-loop learning' for LLMs, optimizing agent performance and data generation for future model training.
Research introduces Kolmogorov-Arnold Networks (KANs) as an interpretable alternative to transformer feed-forward networks for small language models.
A research paper proposes the Plan, Learn, Adapt (PLA) framework to generate personalized, feasible on-device itineraries, balancing constraints and preferences.
New research proposes a reasoning-guided learning framework for personalized packing checklists that combines symbolic rules with learned preferences.
Research explores using replay-based methods in continual learning to improve forward transfer, moving beyond just preventing catastrophic forgetting.
Tencent's KDD Cup 2026 entry, Field-Aware RankMixer, uses dual-stream bilinear fusion for multi-domain user behavior and multi-field feature modeling.
Researchers propose ASK-NN, an asymmetric nearest-neighbor test designed to detect distributional shifts in LLM-generated text, addressing hallucination.
Research explores federated multimodal graph foundation models, enabling learning over fragmented, privacy-restricted multimodal-attributed graphs.
Research paper benchmarks Time-Series Transformers against established methods for electrical load forecasting, showing superior performance.
CardioMeta is a new multi-task machine learning model for predicting diabetes, hypertension, and cardiovascular disease using population and EHR data.
A*-Inspired Batch Selection (A*-BS) is a new method for training Convolutional Neural Networks (CNNs) that aims to accelerate convergence by optimizing mini-batch scheduling.
Research introduces Physics-Informed Splines (PI-Splines), a spline-based architecture for physics-informed learning, parametrizing unknown fields.
New research proposes a Center of Gravity (CoG) guided method to detect and correct weight corruption in Deep Neural Networks (DNNs) for safety-critical systems.
Research introduces QUADS to stabilize NVFP4 for Reinforcement Learning with Mixture-of-Experts (MoE) Large Language Models, improving rollout throughput.
Research explores transformer in-context learning of simple linear regression, moving beyond gradient descent to closed-form solutions.
Big-means++, a new algorithm, addresses NP-hard K-means clustering for arbitrarily large datasets by using finite random samples.
ContinuityBench introduces metrics and a systems study for stateful failover in multi-provider LLM routing, addressing conversational continuity during outages.
Research finds boundary-seeking distillation, effective for classifiers, fails to transfer knowledge effectively for autoencoders in data-free settings.
Research explores knowledge-guided cross-modal fusion for transferring adult ECG models to pediatric populations, addressing data scarcity.
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