Hierarchical two-stage framework for long-horizon vessel trajectory prediction under real ocean conditions. Combines long-term predictor with short-term Spatio-Temporal Graph Transformer on discretized maritime cells. Environmental module integrates currents, wind, wave height via cross-modal attention. Results: 25% improvement in ADE, 17% in FDE on Australian CTS data.
New approach for learning generalized policies in classical planning using Relational Graph Neural Networks (R-GNNs). Authors introduce efficient lookahead search encoding and relational abstraction to improve scalability on IPC 2023 benchmark. Results outperform classical planner LAMA.
Hybrid framework combining 1D and 2D CNNs with attention-guided fusion for ECG-based biometric recognition. Evaluation on ECG-ID, MIT-BIH, PTB: 99.56%, 100%, 99.89% accuracy. Multi-session tests (Heartprint, 10 years): 98.54%-99.09% same-session, 53-56% cross-session.
GVG (Generative Visual Grounding) uses an EEG-to-image generative model to translate brain activity into visual images, bypassing text-only alignment. Tested on GVG-X-Omni (170M tuned params) and GVG-Janus (trimodal), the framework improves EEG understanding and visual generation by leveraging MLLMs' visual priors.
LAST-RAG proposes a method for selecting stochastic degradation models to estimate remaining useful life (RUL). It combines observed trajectories and domain context via retrieval from a local evidence bank, with RCRUS mechanism to prevent premature model elimination. Experiments show outperformance versus statistical and prognostic baselines.
Reinforcement learning framework for predicting customer trajectories in retail spaces. RL-based approach outperforms TSP/PNN heuristics (average 28% deviation from shortest paths) by modeling bounded rationality. Validated on real convenience store data: RL predictions better align with observed behavior, more accurate impulse purchase rates and shelf traffic estimates, enabling practical layout optimization.
Paper on mitigating NBTI aging in arithmetic multipliers used in AI. The technique exploits sign-invariance of multiplication to redistribute transistor stress via 2's complement transformations. Integrated into systolic arrays, it improves lifetime with negligible area and delay overhead.
Benchmark of LLMs on legal precedent treatment classification. Expert-annotated dataset of 239 real-world legal citations. Gemini 2.5 Flash achieves 79.1% on high-level classification, GPT-5-mini 67.7% on fine-grained schema. Novel Average Severity Error metric to measure practical impact of misclassifications.
DEPPA optimizes pocket-aware diffusion models for drug design via reinforcement learning. The method fine-tunes a pre-trained model by formulating the denoising process as a Markov Decision Process, optimizing binding affinity, drug-likeness, synthesizability and diversity. On CrossDocked2020 benchmark, DEPPA achieves Vina Score -8.5 kcal/mol.
LMAC leverages LLM reasoning to design communication protocols in MARL, enabling agents to reconstruct the underlying state uniformly and accurately. The approach iteratively refines protocols using an explicit state-awareness criterion. Experiments on MARL benchmarks demonstrate substantial performance gains over prior baselines.
HINT-SD proposes targeted self-distillation for training long-horizon LLM agents. The method uses full-trajectory hindsight to identify failure-relevant actions and applies feedback-conditioned distillation only on targeted action spans. On BFCL v3 and AppWorld, it improves over dense per-turn feedback baselines by up to 18.80% while achieving 2.26× lower time per training step.
SkillsVote is a lifecycle-governance framework for LLM agent skills from collection to evolution. It profiles a million-scale open-source corpus for quality and verifiability, then decomposes trajectories into skill-linked subtasks with outcome attribution. Results: +7.9pp on Terminal-Bench 2.0 (GPT-5.2) and +2.6pp on SWE-Bench Pro.
DAEM (Diffusion Attention Expert Model) detects STAS (spread through air spaces) in lung cancer histopathological images. Model achieves AUC 0.8946 on frozen sections and 0.9112 on paraffin sections. Validated across 8 external institutions. Semi-automatic localization and TME biomarkers identified.
Game-theoretic decoding method for small vision-language models (2-8B) in medical imaging. Wasserstein semantic stopping criterion replaces lexical matching, improving Qwen3-VL-2B by +3.5pp on VQA-RAD and reducing convergence iterations by 20% while maintaining reliability.
Systematic evaluation of synthetic clinical notes generated by LLMs at million-note scale. Study shows synthetic notes preserve core clinical information for coarse-grained tasks but lose fine-grained details for ICD coding. Chunk-based rephrasing mitigates detail loss but reduces factual precision under incomplete context.
CodeBind introduces a multimodal alignment framework using shared-specific compositional codebooks. The method decomposes representations into semantic shared components and modality-unique components, validated across 9 modalities (text, image, video, audio, depth, thermal, tactile, 3D point cloud, EEG) achieving state-of-the-art performance in classification and retrieval tasks.
Study on contextual sycophancy in LLMs: 60 participants collaborated with AI on analytical tasks. Results show models mirror user errors rather than correct them. An AI literacy intervention reduced incorrect mirroring but did not eliminate error propagation, suggesting system-level approaches are needed.
CGPO (Curriculum Group Policy Optimization) improves text-to-image model training via adaptive curriculum based on reward variance. Method prioritizes partially-mastered prompts (high variance) and balances categories through proportional fairness optimization. Gains validated on GenEval, T2I-CompBench++, DPG Bench.
Authors identify that trajectory entanglement in Rectified Flow stems from nonzero divergence regions in the learned velocity field. They propose an offline correction that attenuates the divergent component during coupling generation, with no deployment overhead. Improvements validated on 2D benchmarks and image generation.
Study of a shared-weight recurrent Transformer architecture (AIR) that develops two distinct roles without modular partitioning. On Sudoku-Extreme and Maze, state zH acts as committed proposal while zL retains local uncertainty. Freezing experiments and ablations show that input injection asymmetry induces this functional specialization.
ISEP proposes an offline reinforcement learning method that implicitly expands action support by interpolating between in-distribution data and policy samples. A stochastic mechanism alternates between conservative cloning and optimistic expansion signals, implemented via Conditional Flow Matching with classifier-free guidance.
ESI-Bench is a benchmark for embodied spatial intelligence spanning 10 task categories on OmniGibson. Experiments show active exploration outperforms passive approaches, but models fail primarily from "action blindness": poor action choices lead to poor observations and cascading errors. Models lack metacognition compared to humans.
AdaSwitch proposes a cloud-local collaborative paradigm where a local agent (small LLM) handles simple tasks and requests assistance from a cloud agent (large LLM) for complex reasoning. The adaptive mechanism detects local errors and dynamically switches. Evaluation on 7 benchmarks (mathematical reasoning, complex QA) shows performance improvement with reduced computational overhead.
CommitDistill is an open-source Python prototype extracting typed knowledge units (Facts, Skills, Patterns) from local git history via deterministic regex and exposing them through a TF-IDF retriever. Tested on 5 repositories (25k commits), it achieves 0.750 hit-rate at 256-character budget versus 0.333 for BM25. No statistically detectable improvement on time-travel bug-fixes in LLM-as-judge evaluation.
EmoMind decodes affective captions directly from fMRI signals in two stages: retrieving semantically grounded neutral descriptions, then rewriting using a continuous 34-dimensional emotion vector. Uses classifier-free guidance to balance semantic fidelity and affective expressivity. Outperforms GPT-4 on two independent fMRI emotion datasets.
RAG-based pipeline for sentence-level EEG-to-text decoding. Combines EEG encoder aligned with semantic embeddings, vector retrieval, and LLM refinement. On ZuCo dataset, achieves 30.45% improvement over random baseline (cosine similarity 0.181 vs 0.139).
Research integrating safety filtering and Constitutional AI into federated LLM training (FedLLM). Authors demonstrate these techniques improve safety by over 20% on AdvBench, mitigating risks of unsafe model aggregation and redistribution across clients.
CodeBind introduces a multimodal alignment framework using a shared-specific compositional codebook design. Tested across 9 modalities (text, image, video, audio, depth, thermal, tactile, 3D point cloud, EEG), it achieves state-of-the-art performance in multimodal classification and retrieval without requiring fully paired data.
First machine unlearning framework for masked diffusion language models (MDLMs like LLaDA and Dream). MDU minimizes KL divergence from prompt-conditional predictions to prompt-masked unconditional anchor, with temperature scaling to control privacy-utility trade-off. Code released.
POOL, a novel privacy-preserving RL algorithm, addresses reinforcement learning in multi-dimensional continuous state-action spaces with one-sided feedback. Theoretical analysis derives sample complexity matching known lower bounds for non-private RL, demonstrating strong privacy guarantees are achievable without sacrificing learning efficiency.
PESD-TSF is a physics-inspired structured decomposition framework for long-term time series forecasting. It introduces a Multiplicative Periodic Gating mechanism, a multi-scale encoder with detrended attention, and Cross-Scale Collaborative Attention (CSCA) to preserve periodic structures and inter-variable dependencies across deep layers.
Novel approach to extend Knowledge Graphs for French cultural heritage. Authors introduce WJoconde, a multimodal KG integrating text and images, with three variants and a benchmark for Knowledge Graph Completion. They propose a framework combining LLMs and Vision-Language Models for automated data extraction and validation, improving KG reliability.
EBOMLC method for noisy label correction via efficient bilevel optimization. Uses a meta model trained on clean data to correct large noisy dataset. Reduces hypergradient computational cost and improves stability on CIFAR-10/100 under high noise rates.
Study of factual inconsistencies in multilingual Wikipedia tables. Researchers developed methodology to collect and analyze tables across 300+ language versions of Wikipedia, identifying inconsistency categories. Implications for fact verification and reliability of AI systems trained on Wikipedia.
Training-free approach for long-context LLM inference: attention-state memory externalizes prefix into lightweight lookup-based memory of precomputed attention states. On LLaMA-3.1-8B, reduces attention latency by 1.36x at 8K tokens and outperforms full-attention RAG with 20% memory footprint.
Theoretical paper on constant collapse in variational autoencoders, where the encoder mean becomes input-independent. Authors introduce a simplex witness certificate to detect and prevent this failure mode during training, with exact baseline and closed-form inverse.
NeuSymMS is a hybrid neuro-symbolic memory system for LLM agents. It couples neural fact extraction from dialogue with a CLIPS-based expert system that classifies, deduplicates, and reconciles facts. Knowledge is stored as subject-relation-value triples in a relational database, with short/long-term memory and access-based promotion.
SpatioRoute is a dynamic prompt routing approach for spatial question-answering over egocentric video. Without fine-tuning, it routes each question to a specialized prompt template (rule-based or LLM-driven mode) and achieves +5% accuracy gains on SQA3D versus fixed baselines, establishing SOTA for zero-shot video-only spatial VQA without 3D point-cloud inputs.
Novel 1-bit LLM quantization method leveraging pre-trained models. Uses consistent progressive training (forward/backward) with binary-aware initialization and dual-scaling compensation to convert weights to binarized representation. Reduces training costs and accuracy degradation versus existing approaches.
ES-CoT detects answer convergence during chain-of-thought generation to stop inference early. The method reduces inference tokens by 16.08% on average across six reasoning benchmarks while maintaining comparable accuracy to standard CoT.