Knowledge Graph Fusion with Large Language Models for Accurate, Explainable Manufacturing Process Planning

Precision process planning in Computer Numerical Control (CNC) machining demands rapid, context-aware decisions on tool selection, feed-speed pairs, and multi-axis routing, placing immense cognitive and procedural burdens on engineers from design specification through final part inspection. Conventional rule-based computer-aided process planning and knowledge-engineering shells freeze domain know-how into static tables, which become limited when dealing with unseen topologies, novel material states, shifting cost-quality-sustainability weightings, or shop-floor constraints such as tool unavailability and energy caps. Large language models (LLMs) promise flexible, instruction-driven reasoning for tasks but they routinely hallucinate numeric values and provide no provenance. We present Augmented Retrieval Knowledge Network Enhanced Search & Synthesis (ARKNESS), the end-to-end framework that fuses zero-shot Knowledge Graph (KG) construction with retrieval-augmented generation to deliver verifiable, numerically exact answers for CNC process planning. ARKNESS (1) automatically distills heterogeneous machining documents, G-code annotations, and vendor datasheets into augmented triple, multi-relational graphs without manual labeling, and (2) couples any on-prem LLM with a retriever that injects the minimal, evidence-linked subgraph needed to answer a query. Benchmarked on 155 industry-curated questions spanning tool sizing and feed-speed optimization, a lightweight 3B-parameter Llama-3 augmented by ARKNESS matches GPT-4o accuracy while achieving a +25 percentage point gain in multiple-choice accuracy, +22.4 pp in F1, and 8.1x ROUGE-L on open-ended responses.

Learning to Fuse: Modality-Aware Adaptive Scheduling for Robust Multimodal Foundation Models

Multimodal foundation models have achieved impressive progress across a wide range of vision-language tasks. However, existing approaches often adopt fixed or task-specific fusion strategies, neglecting the intrinsic variability of modality reliability and sample complexity. In this paper, we propose Modality-Aware Adaptive Fusion Scheduling (MA-AFS), a general framework that learns to dynamically modulate the contribution of each modality on a per-instance basis. MA-AFS introduces a lightweight neural scheduler that predicts modality fusion weights by integrating visual and textual entropy signals along with cross-modal agreement cues. This enables the model to adaptively emphasize more reliable modalities, especially under noisy, missing, or misaligned inputs. We formulate the fusion process as a differentiable scheduling mechanism, analyze its theoretical consistency and regularization effect, and demonstrate that it improves robustness without increasing model capacity significantly. Extensive experiments on image-text retrieval, captioning, and visual question answering show that MA-AFS achieves consistent performance gains over strong baselines such as CLIP, ALBEF, and BLIP. Moreover, MA-AFS exhibits improved robustness under modality corruption and enhanced generalization under domain shifts. Our work highlights the importance of adaptive fusion and opens a promising direction toward reliable and uncertainty-aware multimodal learning.

TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning

Retrieval-Augmented Generation (RAG) has demonstrated considerable effectiveness in open-domain question answering. However, when applied to heterogeneous documents, comprising both textual and tabular components, existing RAG approaches exhibit critical limitations. The prevailing practice of flattening tables and chunking strategies disrupts the intrinsic tabular structure, leads to information loss, and undermines the reasoning capabilities of LLMs in multi-hop, global queries. To address these challenges, we propose TableRAG, an hybrid framework that unifies textual understanding and complex manipulations over tabular data. TableRAG iteratively operates in four steps: context-sensitive query decomposition, text retrieval, SQL programming and execution, and compositional intermediate answer generation. We also develop HeteQA, a novel benchmark designed to evaluate the multi-hop heterogeneous reasoning capabilities. Experimental results demonstrate that TableRAG consistently outperforms existing baselines on both public datasets and our HeteQA, establishing a new state-of-the-art for heterogeneous document question answering. We release TableRAG at https://github.com/yxh-y/TableRAG/tree/main.

Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs

Large language models (LLMs) have shown great potential in medical question answering (MedQA), yet adapting them to biomedical reasoning remains challenging due to domain-specific complexity and limited supervision. In this work, we study how prompt design and lightweight fine-tuning affect the performance of open-source LLMs on PubMedQA, a benchmark for multiple-choice biomedical questions. We focus on two widely used prompting strategies - standard instruction prompts and Chain-of-Thought (CoT) prompts - and apply QLoRA for parameter-efficient instruction tuning. Across multiple model families and sizes, our experiments show that CoT prompting alone can improve reasoning in zero-shot settings, while instruction tuning significantly boosts accuracy. However, fine-tuning on CoT prompts does not universally enhance performance and may even degrade it for certain larger models. These findings suggest that reasoning-aware prompts are useful, but their benefits are model- and scale-dependent. Our study offers practical insights into combining prompt engineering with efficient finetuning for medical QA applications.

Benchmarking Multimodal LLMs on Recognition and Understanding over Chemical Tables

Chemical tables encode complex experimental knowledge through symbolic expressions, structured variables, and embedded molecular graphics. Existing benchmarks largely overlook this multimodal and domain-specific complexity, limiting the ability of multimodal large language models to support scientific understanding in chemistry. In this work, we introduce ChemTable, a large-scale benchmark of real-world chemical tables curated from the experimental sections of literature. ChemTable includes expert-annotated cell polygons, logical layouts, and domain-specific labels, including reagents, catalysts, yields, and graphical components and supports two core tasks: (1) Table Recognition, covering structure parsing and content extraction; and (2) Table Understanding, encompassing both descriptive and reasoning-oriented question answering grounded in table structure and domain semantics. We evaluated a range of representative multimodal models, including both open-source and closed-source models, on ChemTable and reported a series of findings with practical and conceptual insights. Although models show reasonable performance on basic layout parsing, they exhibit substantial limitations on both descriptive and inferential QA tasks compared to human performance, and we observe significant performance gaps between open-source and closed-source models across multiple dimensions. These results underscore the challenges of chemistry-aware table understanding and position ChemTable as a rigorous and realistic benchmark for advancing scientific reasoning.

Different Questions, Different Models: Fine-Grained Evaluation of Uncertainty and Calibration in Clinical QA with LLMs

Accurate and well-calibrated uncertainty estimates are essential for deploying large language models (LLMs) in high-stakes domains such as clinical decision support. We present a fine-grained evaluation of uncertainty estimation methods for clinical multiple-choice question answering, covering ten open-source LLMs (general-purpose, biomedical, and reasoning models) across two datasets, eleven medical specialties, and six question types. We compare standard single-generation and sampling-based methods, and present a case study exploring simple, single-pass estimators based on behavioral signals in reasoning traces. These lightweight methods approach the performance of Semantic Entropy while requiring only one generation. Our results reveal substantial variation across specialties and question types, underscoring the importance of selecting models based on both the nature of the question and model-specific strengths.

Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-$k$

Retrieval-augmented generation (RAG) and long-context language models (LCLMs) both address context limitations of LLMs in open-domain question answering (QA). However, optimal external context to retrieve remains an open problem: fixing the retrieval size risks either wasting tokens or omitting key evidence. Existing adaptive methods like Self-RAG and Self-Route rely on iterative LLM prompting and perform well on factoid QA, but struggle with aggregation QA, where the optimal context size is both unknown and variable. We present Adaptive-$k$ retrieval, a simple and effective single-pass method that adaptively selects the number of passages based on the distribution of the similarity scores between the query and the candidate passages. It does not require model fine-tuning, extra LLM inferences or changes to existing retriever-reader pipelines. On both factoid and aggregation QA benchmarks, Adaptive-$k$ matches or outperforms fixed-$k$ baselines while using up to 10x fewer tokens than full-context input, yet still retrieves 70% of relevant passages. It improves accuracy across five LCLMs and two embedding models, highlighting that dynamically adjusting context size leads to more efficient and accurate QA.

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving

The increasing adoption of artificial intelligence in telecommunications has raised interest in the capability of Large Language Models (LLMs) to address domain-specific, mathematically intensive tasks. Although recent advancements have improved the performance of LLMs in general mathematical reasoning, their effectiveness within specialized domains, such as signal processing, network optimization, and performance analysis, remains largely unexplored. To address this gap, we introduce TeleMath, the first benchmark dataset specifically designed to evaluate LLM performance in solving mathematical problems with numerical solutions in the telecommunications domain. Comprising 500 question-answer (QnA) pairs, TeleMath covers a wide spectrum of topics in the telecommunications field. This paper outlines the proposed QnAs generation pipeline, starting from a selected seed of problems crafted by Subject Matter Experts. The evaluation of a wide range of open-source LLMs reveals that best performance on TeleMath is achieved by recent models explicitly designed for mathematical or logical reasoning. In contrast, general-purpose models, even those with a large number of parameters, often struggle with these challenges. We have released the dataset and the evaluation code to ease result reproducibility and support future research.

MultiNet: An Open-Source Software Toolkit \& Benchmark Suite for the Evaluation and Adaptation of Multimodal Action Models

Recent innovations in multimodal action models represent a promising direction for developing general-purpose agentic systems, combining visual understanding, language comprehension, and action generation. We introduce MultiNet - a novel, fully open-source benchmark and surrounding software ecosystem designed to rigorously evaluate and adapt models across vision, language, and action domains. We establish standardized evaluation protocols for assessing vision-language models (VLMs) and vision-language-action models (VLAs), and provide open source software to download relevant data, models, and evaluations. Additionally, we provide a composite dataset with over 1.3 trillion tokens of image captioning, visual question answering, commonsense reasoning, robotic control, digital game-play, simulated locomotion/manipulation, and many more tasks. The MultiNet benchmark, framework, toolkit, and evaluation harness have been used in downstream research on the limitations of VLA generalization.

ChartReasoner: Code-Driven Modality Bridging for Long-Chain Reasoning in Chart Question Answering

Recently, large language models have shown remarkable reasoning capabilities through long-chain reasoning before responding. However, how to extend this capability to visual reasoning tasks remains an open challenge. Existing multimodal reasoning approaches transfer such visual reasoning task into textual reasoning task via several image-to-text conversions, which often lose critical structural and semantic information embedded in visualizations, especially for tasks like chart question answering that require a large amount of visual details. To bridge this gap, we propose ChartReasoner, a code-driven novel two-stage framework designed to enable precise, interpretable reasoning over charts. We first train a high-fidelity model to convert diverse chart images into structured ECharts codes, preserving both layout and data semantics as lossless as possible. Then, we design a general chart reasoning data synthesis pipeline, which leverages this pretrained transport model to automatically and scalably generate chart reasoning trajectories and utilizes a code validator to filter out low-quality samples. Finally, we train the final multimodal model using a combination of supervised fine-tuning and reinforcement learning on our synthesized chart reasoning dataset and experimental results on four public benchmarks clearly demonstrate the effectiveness of our proposed ChartReasoner. It can preserve the original details of the charts as much as possible and perform comparably with state-of-the-art open-source models while using fewer parameters, approaching the performance of proprietary systems like GPT-4o in out-of-domain settings.