SLMs are increasingly deployed on devices, yet real-world prompts often contain textual or ASR noise that can destabilize model predictions. We analyze this problem from a token-level perspective and find that SLMs exhibit narrow logit margins between the gold token and the top-ranked non-gold token, making noisy prompts more likely to induce local rank flips. To address this issue, we propose LoMaR, a two-stage parameter-efficient framework for robust fine-tuning. In Stage I, LoMaR trains the model on clean prompts with a local logit margin expansion objective to expand the local decision boundary. In Stage II, LoMaR performs residual-subspace adaptation with local logit margin alignment on noisy prompts. After training, the main and residual updates are merged into a standard LoRA adapter, introducing no additional inference cost. Experimental results show that LoMaR outperforms baselines in most noisy settings and achieves lower Performance Drop Rate and Rank Flip Rate. Code and datasets are available at https://github.com/HongJin2828/LoMaR.

Aggregation query over free text is a long-standing yet underexplored problem. It requires exhaustive evidence collection, where systems must “find all” satisfying entities rather than merely “find one” plausible answer. Existing paradigms are not designed for this completeness requirement, leading to incomplete retrieval and systematic undercounting. This limitation stems from the lack of suitable benchmarks. In this work, we formalize corpus-bounded entity-level aggregation querying over text, where answers must be grounded in the given text corpus. We introduce AGGBench, an aggregation benchmark constructed from finance reports and research papers through a corpus-grounded pipeline with LLM-assisted human validation. AGGBench contains 498 aggregation queries, including both single- and multi-constraint queries with evidence-supported gold answers. Experiments on representative basic RAG, structured and schema-first and agentic methods show that existing systems struggle to preserve evidence coverage and accurately count satisfying entities, highlighting the need for completeness-oriented methods for aggregation over unstructured text. The data and code are available in https://github.com/luck-seu/AGGBench.

Rerankers are critical in Retrieval-Augmented Generation (RAG) for filtering evidence that enhances the accurate generation of LLMs. With the extension to open-domain scenarios, rerankers are inevitably deployed on mixed-style corpora, whereas most existing rerankers are mainly trained on well-edited texts. A rarely explored issue lies in enabling rerankers to maximally capture the effective knowledge for downstream LLMs without being misled by stylistic features. To address this issue, we propose SARK (Style-Adaptive Reranker with Knowledge Prioritization), a style-augmented multi-task framework that prioritizes effective knowledge over stylistic perturbations. SARK performs multi-granular knowledge mining by using an LLM to derive passage-level supervision on whether a passage helps or harms answer correctness, and list-level relative ranking preferences over candidate passages. It then jointly optimizes the reranker model with passage-level classification and list-level ranking objectives via style-augmented multi-task learning, encouraging the model to focus on the information needed for answering under mixed-style scenarios. Extensive experiments demonstrate that SARK improves generation performance across multiple LLMs under mixed-style conditions.

Geospatial Entity Resolution (GER) plays a central role in integrating spatial data from diverse sources. However, existing methods are limited by their reliance on large amounts of training data and their inability to incorporate commonsense knowledge. While recent advances in Large Language Models (LLMs) offer strong semantic reasoning and zero-shot capabilities, directly applying them to GER remains inadequate due to their limited spatial understanding and high inference cost. In this work, we present GER-LLM, a framework that integrates LLMs into the GER pipeline. To address the challenge of spatial understanding, we design a spatially informed blocking strategy based on adaptive quadtree partitioning and Area of Interest (AOI) detection, preserving both spatial proximity and functional relationships. To mitigate inference overhead, we introduce a group prompting mechanism with graph-based conflict resolution, enabling joint evaluation of diverse candidate pairs and enforcing global consistency across alignment decisions. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach, yielding significant improvements over state-of-the-art methods.

Relation Extraction (RE) is a key task in table understanding, aiming to extract semantic relations between columns. However, complex tables with hierarchical headers are hard to obtain high-quality textual formats (e.g., Markdown) for input under practical scenarios like webpage screenshots and scanned documents, while table images are more accessible and intuitive. Besides, existing works overlook the need of mining relations among multiple columns rather than just the semantic relation between two specific columns in real-world practice. In this work, we explore utilizing Multimodal Large Language Models (MLLMs) to address RE in tables with complex structures. We creatively extend the concept of RE to include calculational relations, enabling multi-task learning of both semantic and calculational RE for mutual reinforcement. Specifically, we reconstruct table images into graph structure based on neighboring nodes to extract graph-level visual features. Such feature enhancement alleviates the insensitivity of MLLMs to the positional information within table images. We then propose a Chain-of-Thought distillation framework with self-correction mechanism to enhance MLLMs’ reasoning capabilities without increasing parameter scale. Our method significantly outperforms most baselines on wide datasets. Additionally, we release a benchmark dataset for calculational RE in complex tables.