MeshTok uses AMR-inspired adaptive multiscale tokenization to improve the efficiency-accuracy trade-off of Transformer models for PDEs over uniform-grid baselines.
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MERIT enables decentralized instruction tuning via conflict-aware PCA splitting and parameter-space merging, raising average benchmark scores above joint training on multimodal and text mixtures.
A lung-specific pathology foundation model built on Virchow2 achieved broad AUC gains across 32 tasks, 92.3% average AUC in a prospective study, and improved pathologist accuracy by 7.9 percentage points in a crossover RCT.
FedQual improves federated label distribution learning under heterogeneous annotation quality via quality-adaptive training with a global anchor and reliability-aware aggregation, backed by new benchmarks and a proof that client-specific calibration strictly outperforms uniform calibration.
ABox abduction under repair semantics for inconsistent KBs yields a full complexity landscape in lightweight description logics DL-Lite and EL_bot.
DLM4G applies graph-aware adaptive noising in a diffusion framework to generate text from graphs, outperforming larger autoregressive and diffusion baselines in factual grounding and edit sensitivity on three datasets plus molecule captioning.
LoRA gradient descent converges to a stationary point at rate O(1/log T).
MLHCA is a new ML-powered combinatorial auction combining value and demand queries to reduce efficiency loss by up to 10x and queries by up to 58% versus prior SOTA.
Visual graph mind maps outperform text-flattened versions as internal reasoning scaffolds for LLMs on multi-hop QA, with the advantage holding after fine-tuning and distillation.
A decoupled pipeline with YOLO detection, deterministic prompt encoding, and QLoRA-adapted 1.5B LLM achieves superior structured report generation compared to monolithic VLMs on synthetic maintenance data.
UHD-GCN-BIQA models structural dependencies among sampled patches via a hybrid kNN graph and residual graph convolutions to achieve competitive PLCC and SRCC with the lowest RMSE on the UHD-IQA benchmark for blind ultra-high-definition image quality assessment.
Derives expectation consistency condition as necessary and sufficient for calibration under covariate shift and proposes ECL loss with matching sample complexity to ECE.
VISOR is a VLM-based automated test oracle that evaluates robot task correctness and quality from videos while reporting its own uncertainty, tested on GPT and Gemini across four tasks and over 1000 videos with Gemini showing higher recall and GPT higher precision but low uncertainty-correctness tie
Random slicing for subsampling combined with Nadaraya-Watson smoothing enables faster and improved persistence-based topological optimization of point clouds in 2D and 3D.
SACHI enriches agent representations via graph transformer convolutions over inter-agent graphs to enable holistic information integration, outperforming baselines across five cooperative tasks with statistical significance.
LLM-generated heuristics for HTN planning nearly match the coverage of the best available planner while reducing search effort on 83% of shared problems across six benchmarks.
Reward poisoning in linear MDPs is attackable if and only if a precise structural condition holds, drawing a sharp line between vulnerable and intrinsically robust instances.
SHINE generates LoRA adapters from a document in one forward pass, letting a frozen LLM answer questions about the document without the document in its context.
Cognitive forcing interventions reduce overreliance on AI recommendations more than simple explanations, with effects moderated by individual need for cognition.
Requiring multiple properties or optimality criteria for abduction hypotheses in ELbot under brave and AR semantics often does not raise complexity.
ResGIN-Att predicts drug synergy by extracting multi-scale molecular features with residual GIN, fusing them via LSTM, and modeling interactions with cross-attention, achieving competitive results on five benchmark datasets.
A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.
citing papers explorer
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MeshTok: Efficient Multi-Scale Tokenization for Scalable PDE Transformers
MeshTok uses AMR-inspired adaptive multiscale tokenization to improve the efficiency-accuracy trade-off of Transformer models for PDEs over uniform-grid baselines.
-
Decentralized Instruction Tuning: Conflict-Aware Splitting and Weight Merging
MERIT enables decentralized instruction tuning via conflict-aware PCA splitting and parameter-space merging, raising average benchmark scores above joint training on multimodal and text mixtures.
-
A Clinically Validated Foundation Model for Comprehensive Lung Pathology Interpretation
A lung-specific pathology foundation model built on Virchow2 achieved broad AUC gains across 32 tasks, 92.3% average AUC in a prospective study, and improved pathologist accuracy by 7.9 percentage points in a crossover RCT.
-
Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
FedQual improves federated label distribution learning under heterogeneous annotation quality via quality-adaptive training with a global anchor and reliability-aware aggregation, backed by new benchmarks and a proof that client-specific calibration strictly outperforms uniform calibration.
-
ABox Abduction for Inconsistent Knowledge Bases under Repair Semantics
ABox abduction under repair semantics for inconsistent KBs yields a full complexity landscape in lightweight description logics DL-Lite and EL_bot.
-
Factual and Edit-Sensitive Graph-to-Sequence Generation via Graph-Aware Adaptive Noising
DLM4G applies graph-aware adaptive noising in a diffusion framework to generate text from graphs, outperforming larger autoregressive and diffusion baselines in factual grounding and edit sensitivity on three datasets plus molecule captioning.
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On the Convergence Rate of LoRA Gradient Descent
LoRA gradient descent converges to a stationary point at rate O(1/log T).
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Prices, Bids, Values: One ML-Powered Combinatorial Auction to Rule Them All
MLHCA is a new ML-powered combinatorial auction combining value and demand queries to reduce efficiency loss by up to 10x and queries by up to 58% versus prior SOTA.
-
Visual Graph Scaffolds for Structural Reasoning in Large Language Models
Visual graph mind maps outperform text-flattened versions as internal reasoning scaffolds for LLMs on multi-hop QA, with the advantage holding after fine-tuning and distillation.
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A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection
A decoupled pipeline with YOLO detection, deterministic prompt encoding, and QLoRA-adapted 1.5B LLM achieves superior structured report generation compared to monolithic VLMs on synthetic maintenance data.
-
Ultra-High-Definition Image Quality Assessment via Graph Representation Learning
UHD-GCN-BIQA models structural dependencies among sampled patches via a hybrid kNN graph and residual graph convolutions to achieve competitive PLCC and SRCC with the lowest RMSE on the UHD-IQA benchmark for blind ultra-high-definition image quality assessment.
-
Expectation Consistency Loss: Rethink Confidence Calibration under Covariate Shift
Derives expectation consistency condition as necessary and sufficient for calibration under covariate shift and proposes ECL loss with matching sample complexity to ECE.
-
VISOR: A Vision-Language Model-based Test Oracle for Testing Robots
VISOR is a VLM-based automated test oracle that evaluates robot task correctness and quality from videos while reporting its own uncertainty, tested on GPT and Gemini across four tasks and over 1000 videos with Gemini showing higher recall and GPT higher precision but low uncertainty-correctness tie
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Towards Scalable Persistence-Based Topological Optimization
Random slicing for subsampling combined with Nadaraya-Watson smoothing enables faster and improved persistence-based topological optimization of point clouds in 2D and 3D.
-
SACHI: Structured Agent Coordination via Holistic Information Integration in Multi-Agent Reinforcement Learning
SACHI enriches agent representations via graph transformer convolutions over inter-agent graphs to enable holistic information integration, outperforming baselines across five cooperative tasks with statistical significance.
-
Hierarchical Task Network Planning with LLM-Generated Heuristics
LLM-generated heuristics for HTN planning nearly match the coverage of the best available planner while reducing search effort on 83% of shared problems across six benchmarks.
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When Can You Poison Rewards? A Tight Characterization of Reward Poisoning in Linear MDPs
Reward poisoning in linear MDPs is attackable if and only if a precise structural condition holds, drawing a sharp line between vulnerable and intrinsically robust instances.
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SHINE: A Scalable In-Context Hypernetwork for Mapping Context to LoRA in a Single Pass
SHINE generates LoRA adapters from a document in one forward pass, letting a frozen LLM answer questions about the document without the document in its context.
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To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making
Cognitive forcing interventions reduce overreliance on AI recommendations more than simple explanations, with effects moderated by individual need for cognition.
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The More the Merrier: Combining Properties for ABox Abduction under Repair Semantics in ELbot
Requiring multiple properties or optimality criteria for abduction hypotheses in ELbot under brave and AR semantics often does not raise complexity.
-
Drug Synergy Prediction via Residual Graph Isomorphism Networks and Attention Mechanisms
ResGIN-Att predicts drug synergy by extracting multi-scale molecular features with residual GIN, fusing them via LSTM, and modeling interactions with cross-attention, achieving competitive results on five benchmark datasets.
-
Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
A survey of trajectory prediction techniques for autonomous vehicles that proposes a taxonomy, overviews the prediction pipeline, and highlights remaining research gaps.
- Deterministic Adam-Inspired Methods with Accelerated Convergence Rate