PromptGNN-sim: Bidirectional GNN-LLM fusion framework for text-attributed graph learning
Researchers introduce PromptGNN-sim, a bidirectional structure-semantic fusion framework that jointly trains a Graph Attention Network and an LLM for text-attributed graph learning. The system uses GAT-based neighborhood selection to generate structure-aware prompts for the LLM, with cross-modal contrastive learning and cross-attention aligning both components during training. Evaluated on six datasets including Cora, Pubmed, and WikiCS, it outperforms classical GNNs, standalone LLMs, and prior GNN-LLM fusion methods on cross-task transfer, cross-dataset generalization, and sparse perturbation settings.
Related guides (1)
Related events (8)
Gazer: Training-free semantic correction for autoregressive visual models using MLLM feedback
Researchers introduce Gazer, a training-free framework that integrates multimodal large language model feedback into the sampling loop of autoregressive visual models (AVMs) to correct semantic errors during generation. The system operates in two stages: Reflective Diagnosis identifies semantic errors in intermediate generation states, and Semantic Correction rewinds and adjusts the generation trajectory to better match the target prompt. Experiments on compositional image and video benchmarks show improved semantic alignment and compositional accuracy across multiple AVMs without additional training. The work addresses a known weakness of next-scale prediction AVMs, where semantic errors accumulate across discrete generation scales.
Label Context Classifier (LCC) improves GNN node classification on heterophilous graphs
A new arXiv preprint proposes the Label Context Classifier (LCC), a method for improving node classification in graph neural networks on heterophilous graphs where connected nodes tend to have different class labels. LCC generates label context embeddings via four types of directed walks to capture higher-order class label connectivity, and can be integrated with any existing GNN architecture. Experiments show GNNs augmented with LCC outperform state-of-the-art methods on heterophilous directed graphs.
TextReg: Regularization Framework for Mitigating Prompt Distributional Overfitting in LLM Optimization
TextReg addresses a failure mode in iterative prompt optimization where LLM-rewritten prompts grow longer, accumulate narrow rules, and generalize poorly—termed prompt distributional overfitting. The authors formalize this via 'representational inefficiency,' a dual-factor measure decomposing prompt inefficiency into capacity cost and scope narrowness. TextReg applies a soft-penalty regularization framework using Dual-Evidence Gradient Purification, Semantic Edit Regularization, and Regularization-Guided Prompt Update. On reasoning benchmarks, it achieves up to +11.8% OOD accuracy over TextGrad and +16.5% over REVOLVE.
EEVEE: Multi-dataset test-time prompt learning framework for self-improving LLM agents
EEVEE is a new framework enabling LLM agents to perform test-time prompt learning across heterogeneous multi-dataset task streams, addressing a gap where prior methods only handled single-dataset settings. The system uses a router to partition inputs into task clusters and assigns them to suitable prompt configurations, optimized via a router-prompt co-evolution strategy. Experiments show improvements of 10.38 and 24.32 average points over Qwen3-4B-Instruct and DeepSeek-V3.2 respectively, outperforming prior SOTA methods GEPA and ACE by up to 48.2%.
Trajectory Analysis of Masked Diffusion LMs for Graph-to-Text Generation with Lambda-Scaled Structural Decoding
This paper presents the first systematic study of masked diffusion language models (MDLMs) for graph-to-text generation, analyzing the order in which tokens are unmasked during iterative decoding. The authors find MDLMs naturally unmask entities first, then relational/function words, then structural tokens—a pattern disrupted by supervised fine-tuning, which prematurely anchors structural tokens and causes hallucination or omission. They propose lambda-scaled structural decoding, a training-free inference-time fix that recovers +9.4 BLEU-4, and introduce Graph-LLaDA, which integrates a Graph Transformer encoder into LLaDA's decoding process. Cross-dataset evaluation on the LAGRANGE benchmark shows prior baselines overfit to dataset-specific patterns while MDLM-based approaches generalize better.
NLL-guided training-free method selects optimal full-attention layers for efficient long-context inference
Researchers propose NLL-guided layer selection, a training-free technique for hybrid attention models that identifies which layers should use full versus sliding-window attention by measuring negative log-likelihood degradation on answer tokens. On LongMemEval with Qwen3-4B, the method achieves 64.6% accuracy using only 1/4 full-attention layers, matching a 1/2-FA periodic baseline while halving compute, and outperforming a periodic 1/4-FA baseline by 10.4 percentage points. The calibration procedure requires approximately 15 minutes of one-time compute, making it practical for deployment. The work advances the efficiency-accuracy tradeoff for long-context LLM inference without requiring any retraining.
LoMo: Local Modality Substitution for Deeper Vision-Language Fusion
This paper identifies a 'carrier sensitivity' problem in Vision-Language Models (VLMs), where replacing textual queries with rendered-image equivalents causes significant performance degradation due to asymmetric roles of text and images in training data. The authors propose Local Modality Substitution (LoMo), a data curation paradigm that reformulates single-modality prompts into interleaved multimodal sequences by dynamically rendering text spans as images, enforcing cross-modal representational invariance. Evaluated across 13 multimodal benchmarks, LoMo improves over standard supervised fine-tuning by 2.67 points on LLaVA-OneVision-1.5-8B and 2.82 points on Qwen3.5-9B. The approach is architecture-agnostic and lightweight, requiring no changes to model architecture.
ContextRL: Context-aware reinforcement learning improves grounding in agentic and multimodal LLMs
Researchers introduce ContextRL, a reinforcement learning method that trains LLMs to select the context that supports a given query-answer pair from two highly similar candidates, rather than supervising only final answers. The approach constructs contrastive context pairs in two domains: coding agent trajectories (1k pairs) and multimodal image pairs (7k pairs). ContextRL achieves +2.2% average gains over standard GRPO on 5 long-horizon benchmarks and +1.8% across 12 visual QA benchmarks, with ablations showing the gains stem from the context-selection objective rather than the contrastive data alone.