A framework to identify and convert foldable layer normalizations to RMSNorm for exact equivalence and faster inference in deep neural networks.
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PaperMind is a new benchmark that evaluates integrated multimodal reasoning and critique over scientific papers through four complementary task families across seven domains.
Quantile tokens inserted into LLM inputs combined with neighbor retrieval enable direct prediction of full distributions, yielding lower MAPE and narrower intervals than baselines on Airbnb and StackSample tasks.
Multi-layer Fisher-weighted kNN over frozen-LLM activations, fused with embedding kNN, yields competitive F1 guardrails from a 50-example bank with no fine-tuning and sub-10-second domain adaptation.
Benign multilingual fine-tuning causes language-specific safety drifts with adversarial compliance rates rising up to four-fold, decoupled from capability gains.
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
OProver-32B achieves top Pass@32 scores on MiniF2F, ProverBench, and PutnamBench by combining continued pretraining with iterative agentic proving, retrieval, SFT on repairs, and RL on unresolved cases using a 6.86M-proof dataset.
Visual trace prompting improves spatial-temporal awareness in VLA models, delivering 10% gains on SimplerEnv and 3.5x on real-robot tasks.
ALLaVA creates 1.3M GPT4V-synthesized samples enabling 4B VLMs to achieve competitive results on 17 benchmarks and match 7B/13B models on some tasks.
CreativityNeuro applies contrastive weight steering to LLMs, yielding up to 14 percentile gains on the Divergent Association Task and improved originality in human-rated tests while reducing mode collapse.
Fine-tuning CodeBERT, GraphCodeBERT, UniXcoder and CodeT5+ with augmentation, cross-validation and ensembling yields macro-F1 of 0.737 on binary human-vs-AI code detection and 0.422 on 11-class model attribution in SemEval-2026 Task 13.
citing papers explorer
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Enjoy Your Layer Normalization with the Computational Efficiency of RMSNorm
A framework to identify and convert foldable layer normalizations to RMSNorm for exact equivalence and faster inference in deep neural networks.
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PaperMind: Benchmarking Agentic Reasoning and Critique over Scientific Papers in Multimodal LLMs
PaperMind is a new benchmark that evaluates integrated multimodal reasoning and critique over scientific papers through four complementary task families across seven domains.
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Text-to-Distribution Prediction with Quantile Tokens and Neighbor Context
Quantile tokens inserted into LLM inputs combined with neighbor retrieval enable direct prediction of full distributions, yielding lower MAPE and narrower intervals than baselines on Airbnb and StackSample tasks.
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kNNGuard: Turning LLM Hidden Activations into a Training-Free Configurable Guardrail
Multi-layer Fisher-weighted kNN over frozen-LLM activations, fused with embedding kNN, yields competitive F1 guardrails from a 50-example bank with no fine-tuning and sub-10-second domain adaptation.
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The Heterogeneous Safety Impacts of Benign Multilingual Fine-Tuning
Benign multilingual fine-tuning causes language-specific safety drifts with adversarial compliance rates rising up to four-fold, decoupled from capability gains.
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One Generator, Any Process: LLM-Conditioning for the LHC
LLM embeddings condition a generative transformer to enable faster convergence, better performance, and generalization to unseen LHC processes using a single model.
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OProver: A Unified Framework for Agentic Formal Theorem Proving
OProver-32B achieves top Pass@32 scores on MiniF2F, ProverBench, and PutnamBench by combining continued pretraining with iterative agentic proving, retrieval, SFT on repairs, and RL on unresolved cases using a 6.86M-proof dataset.
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TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies
Visual trace prompting improves spatial-temporal awareness in VLA models, delivering 10% gains on SimplerEnv and 3.5x on real-robot tasks.
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ALLaVA: Harnessing GPT4V-Synthesized Data for Lite Vision-Language Models
ALLaVA creates 1.3M GPT4V-synthesized samples enabling 4B VLMs to achieve competitive results on 17 benchmarks and match 7B/13B models on some tasks.
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CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse
CreativityNeuro applies contrastive weight steering to LLMs, yielding up to 14 percentile gains on the Divergent Association Task and improved originality in human-rated tests while reducing mode collapse.
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Fine-Tuning Pre-Trained Code Models for AI-Generated Code Detection
Fine-tuning CodeBERT, GraphCodeBERT, UniXcoder and CodeT5+ with augmentation, cross-validation and ensembling yields macro-F1 of 0.737 on binary human-vs-AI code detection and 0.422 on 11-class model attribution in SemEval-2026 Task 13.