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Researchers demonstrate that GPT-4o-generated paraphrases can improve sign language translation by augmenting training data while keeping video inputs unchanged. Testing across three sign language datasets reveals modest gains on PHOENIX14T (9.56 to 10.33 BLEU-4) but exposes fundamental limitations when data is sparse or highly controlled.
Researchers propose a framework to evaluate how linguistic structures and contextual features shape Large Language Model behavior in spatial reasoning tasks. The study reveals that topological information provides robust navigation planning, linguistic format effectiveness depends on model size, and semantic errors can critically undermine performance.
A controlled study examines how large-language-model agents perform with different skill documentation formats using SkillsBench, finding that skill availability dramatically improves task success (18-36 percentage points) while variations in presentation granularity produce minimal and uncertain effects across models.
Researchers propose CYKNN, a neural network architecture that directly embeds the CYK parsing algorithm into trainable matrix operations. The approach demonstrates superior performance compared to large language models with 20B+ parameters on grammar parsing tasks, suggesting a viable path for integrating symbolic algorithms into neural architectures.
Researchers introduce DOA (Decoder-Only Attention), a training-free method that enables simultaneous speech-to-text translation using decoder-only SpeechLLMs by extracting alignment signals from self-attention mechanisms. The approach achieves low-latency, long-form translation quality comparable to offline decoding without requiring model retraining.
Researchers propose FiVeD, a fine-grained verification framework for Aspect Sentiment Triplet Extraction that improves extraction accuracy by up to 3.53 F1 points through multi-task learning with validity classification, quality scoring, error detection, and rationale generation. The framework addresses a critical gap in ASTE systems by post-hoc verification of extracted triplets, enabling adjustable precision-recall tradeoffs for downstream NLP applications.
Researchers introduce BEA-Dialogue+, an expanded Hungarian conversational speech recognition corpus that nearly triples training data from 85 to 200 hours while maintaining speaker separation across dataset splits. The expanded resource enables better evaluation of automatic speech recognition models and demonstrates that specialized fine-tuning techniques improve performance on dialogue transcription tasks.
Researchers introduce ReuseRL, a reinforcement learning framework that improves LLM agent generalization by encouraging skill reuse and compression. By grounding agentic RL in the Minimum Description Length principle and penalizing task-specific shortcuts, the method demonstrates better in- and out-of-distribution performance across multiple benchmark environments.
A peer-reviewed paper challenges the assumption that large language models possess uniquely human-like attributes by demonstrating that simpler systems—including the video game Age of Empires II—can exhibit similarly complex behaviors when given sufficient computational substrate. The research argues that attributing anthropomorphic qualities to LLMs requires explicit measurement criteria rather than subjective interpretation, and proposes a methodology that assumes non-uniqueness to avoid circular reasoning.
Researchers propose a three-class machine learning framework using CodeBERT and CNN to detect credential leakage in public source code repositories with higher accuracy and fewer false positives. The approach distinguishes genuine credentials from placeholder or weak credentials, achieving 93% recall and reducing false alerts by 33% while maintaining security coverage across 10 programming languages.
Researchers propose an adaptive feature-selection system for 3D scene reconstruction that intelligently prioritizes visual data based on texture, repeatability, and geometric utility rather than using fixed thresholds. The method demonstrates improved reconstruction quality and computational efficiency across diverse scene types compared to baseline approaches, offering a modular enhancement for both classical and neural reconstruction pipelines.
Researchers present the first systematic study of masked diffusion language models (MDLMs) for graph-to-text generation, revealing that these models naturally prioritize entities before relational words and structural tokens. The study identifies a failure mode in supervised fine-tuning that prematurely anchors structural tokens, and proposes lambda-scaled structural decoding to recover performance gains while introducing Graph-LLaDA for improved generalization across datasets.
SPECTRA is a new framework for generating synthetic text corpora and retrieval test collections at scale, enabling researchers to stress-test information retrieval systems without expensive human annotation. The system can produce corpora up to 60,000 documents while maintaining controllable vocabulary distributions and deterministic relevance labels, serving as a diagnostic complement to traditional evaluation methods.
Researchers introduce LongTraceRL, a reinforcement learning method that improves large language models' ability to reason over lengthy documents by using search agent trajectories and entity-level reward signals. The approach generates challenging training contexts with high-confusability distractors and applies rubric rewards that supervise intermediate reasoning steps, demonstrating consistent improvements across multiple LLM sizes and benchmarks.
Researchers demonstrate that modestly-sized open-source language models can understand rare paired-focus constructions (like "let alone" and "much less"), challenging assumptions that only the largest LLMs grasp complex constructional semantics. The study reveals that semantic understanding of these constructions emerges later in training than syntactic knowledge and correlates with world knowledge acquisition.
Researchers introduce TunerDiT, a training-free method for improving text-to-video generation with multiple sequential events by identifying critical steering points in diffusion transformer denoising and applying progressive prompt fusion techniques. The approach achieves state-of-the-art performance across benchmark metrics while enabling fine-tuned control over video consistency versus event separation.
Lumos-Nexus is a new video generation framework that separates training and inference to improve both reasoning quality and visual fidelity. The system uses a lightweight generator during training and progressively hands off to a high-capacity generator during inference through a technique called Unified Progressive Frequency Bridging, while introducing VR-Bench as a benchmark for reasoning-driven video generation.
Researchers propose a new framework that reinterprets data selection as a sequential decision-making problem rooted in dynamic programming, unifying existing methods like Data Shapley while revealing their limitations as myopic approximations. The work introduces a scalable bipartite graph-based approach that preserves submodular structure and demonstrates improvements on machine learning and LLM fine-tuning tasks.
Researchers demonstrate that Large Language Models can effectively infer natural language events from time series data, with a new benchmarking framework tested across 18 LLMs. The study shows that smaller models trained with distillation and reinforcement learning can match the performance of large proprietary models, suggesting practical applications for event detection in temporal data analysis.
Researchers propose symbolic intermediaries—compact mathematical expressions derived from symbolic regression—to bridge the gap between Large Language Models and physics simulators by converting continuous numerical outputs into interpretable symbolic forms. LLM-based agents using this interface outperformed genetic algorithms by 19-53% on mechanism synthesis tasks, demonstrating that translating simulator behavior into symbolic language enables grounded geometric reasoning without model retraining.
Researchers introduce OLG++, an enhanced framework for representing regulatory and legal rules using semantic graph structures. The model extends the original Obligation Logic Graph with spatial, temporal, and defeasibility constructs, demonstrating improved expressiveness for municipal regulations through food-business compliance examples.
Researchers propose a Neuro-Symbolic Predictive Process Monitoring approach that combines deep learning with Linear Temporal Logic constraints to improve suffix prediction accuracy in business process management. The method introduces a differentiable logical loss function that ensures generated sequences satisfy both predictive accuracy and temporal logic constraints, with applications extending beyond BPM to general symbolic sequence generation tasks.
ReTabAD introduces a new benchmark dataset for tabular anomaly detection that incorporates semantic context through textual metadata, addressing a gap where existing datasets lack domain knowledge. The research provides 20 enriched datasets, implementations of classical and LLM-based detection algorithms, and demonstrates that semantic context improves both detection performance and interpretability.
Researchers introduce SAC-Opt, a framework that improves how large language models generate optimization code by grounding corrections in semantic accuracy rather than solver feedback alone. The approach achieves 7.7% average improvement in modeling accuracy across datasets, with gains up to 21.9% on complex problems, addressing silent logical errors in LLM-generated optimization models.
Researchers introduce Iterative Regret-Minimization Fine-Tuning (Iterative RMFT), a post-training method that improves LLMs' decision-making capabilities by iteratively distilling low-regret trajectories back into models. The approach addresses fundamental limitations in how LLMs handle online decision problems without relying on rigid algorithmic templates, demonstrating improvements across multiple model architectures.
