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Dense retrieval models are typically fine-tuned with contrastive learning objectives that require binary relevance judgments, even though relevance is inherently graded. We analyze how graded relevance scores and the threshold used to convert them into binary labels affect multilingual dense retrieval. Using a multilingual dataset with LLM-annotated relevance scores, we examine monolingual, multilingual mixture, and cross-lingual retrieval scenarios. Our findings show that the optimal threshold varies systematically across languages and tasks, often reflecting differences in resource level. A well-chosen threshold can improve effectiveness, reduce the amount of fine-tuning data required, and mitigate annotation noise, whereas a poorly chosen one can degrade performance. We argue that graded relevance is a valuable but underutilized signal for dense retrieval, and that threshold calibration should be treated as a principled component of the fine-tuning pipeline.

ACL 2026

The Overlooked Role of Graded Relevance Thresholds in Multilingual Dense Retrieval

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Large language models (LLMs) have recently achieved impressive results on multiple-choice question answering (MCQA), with retrieval-augmented generation (RAG) emerging as an effective strategy for improving the performance of smaller models. However, existing RAG formulations face persistent challenges: retrieving too many passages often introduces noise, and even when relevant content is retrieved, models may still struggle with partially relevant or conflicting information. Moreover, while LLMs perform strongly on English benchmarks, their accuracy declines substantially on Arabic multi-task evaluations, revealing ongoing issues in cross-lingual transfer and domain adaptation. In this paper, we propose a novel approach, using Arabic as a representative case study, that jointly models the relevance of both the question and its candidate answers when selecting contextual passages. The method employs a lightweight reranker trained with a hybrid regression–triplet loss objective to identify passages that provide discriminative and reliable evidence. Extensive experiments across multiple model sizes and humanities domains show that our approach consistently outperforms both standard RAG baselines and reranker baselines, delivering two- to threefold improvements while remaining competitive with considerably larger models.

 Candidate-Aware Retrieval and Reranking for Multiple-Choice Question Answering: Arabic as a Case Study

Tool-integrated agents are deployed on the premise that external tools ground their outputs in reality. Yet this very reliance creates a critical attack surface. Current evaluations benchmark capability in benign settings, asking "can the agent use tools correctly" but never "what if the tools lie". We identify this Trust Gap: agents are evaluated for performance, not for skepticism. We formalize this vulnerability as Adversarial Environmental Injection (AEI), a threat model where adversaries compromise tool outputs to deceive agents. AEI constitutes environmental deception: constructing a "fake world" of poisoned search results and fabricated reference networks around unsuspecting agents. We operationalize this via Potemkin, a Model Context Protocol (MCP)-compatible harness for plug-and-play robustness testing. We identify two orthogonal attack surfaces: The Illusion (breadth attacks) poison retrieval to induce epistemic drift toward false beliefs, while The Maze (depth attacks) exploit structural traps to cause policy collapse into infinite loops. Across 11,000+ runs on five frontier agents, we find a stark robustness gap: resistance to one attack often increases vulnerability to the other, demonstrating that epistemic and navigational robustness are distinct capabilities.

How Adversarial Environments Mislead Agentic AI?

Self-training has emerged as a promising direction for autonomously improving large language models (LLMs). Existing approaches typically adopt a $\textit{generate-and-filter}$ paradigm based on rejection sampling, which could suffer from inefficiency and low-quality reasoning paths. Towards this end, this paper proposes a novel framework named $\underline{G}eometric D\underline{a}ta Se\underline{l}ection$ with $Str\underline{a}tegic Prospecting$ (GALA) for LLM self-training. The core of our GALA is to identify diverse and informative samples from redundant data and exploit them more strategically. In particular, our proposed GALA first conducts clustering on latent sentence embeddings and then selects an anchor sample from each cluster based on the geometric distance to reduce data redundancy. To further exploit these samples, we conduct strategic brainstorming and reflection for high-quality reasoning trajectory prospecting. In addition, we introduce a lightweight dynamic validation module to validate the reliability of mini-batches to ensure the overall quality of the data. Extensive experiments on various benchmarks validate the effectiveness of the proposed GALA against several competing baselines.

 GALA: Geometric Data Selection with Strategic Prospecting for Large Language Model Self-training

Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However, existing reviews primarily treat MI as an observational science, summarizing analytical insights while lacking a systematic framework for actionable intervention. To bridge this gap, we present a practical survey structured around the pipeline: "Locate, Steer, and Improve." We formally categorize Localizing (diagnosis) and Steering (intervention) methods based on specific Interpretable Objects to establish a rigorous intervention protocol. Furthermore, we demonstrate how this framework enables tangible improvements in Alignment, Capability, and Efficiency, effectively operationalizing MI as a practical engineering toolkit for model optimization. The curated paper list of this work is available at https://anonymous.4open.science/r/Act-MI-F068.

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models

Understanding scientific papers requires more than answering isolated questions or summarizing content. It involves an integrated reasoning process that grounds textual and visual information, interprets experimental evidence, synthesizes information across sources, and critically evaluates scientific claims. However, existing benchmarks typically assess these abilities in isolation, making it difficult to evaluate scientific paper understanding as a unified set of interacting cognitive abilities. In this work, we introduce PaperMind , a benchmark designed to evaluate integrated and agent-oriented scientific reasoning over research papers. PaperMind is constructed from real scientific papers across seven domains, including agriculture, biology, chemistry, computer science, medicine, physics, and economics. It comprises four complementary task families that collectively operationalize distinct cognitive facets of scientific paper reasoning, including multimodal grounding, experimental interpretation, cross-source evidence reasoning, and critical assessment. By analyzing model behavior across multiple tasks, PaperMind enables a diagnostic evaluation of integrated scientific reasoning behaviors that are difficult to assess through isolated task evaluations. Extensive experiments on both open-source and closed-source multimodal LLMs reveal consistent performance gaps across tasks, highlighting persistent challenges in integrated scientific reasoning and critique. Our benchmark and dataset are available at https://github.com/Yanjun-Zhao/PaperMind.

 PAPERMIND: Benchmarking Agentic Reasoning and Critique over Scientific Papers in Multimodal LLMs

Large language models increasingly power AI agents for tasks requiring iterative refinement: document editing demands targeted revisions while preserving cross-references, code refactoring requires tracking function dependencies, and knowledge base updates cascade through related entities. Iterative editing with AI agents faces a fundamental efficiency-consistency tradeoff: maintaining consistency requires full-context awareness of dependencies, but processing entire documents for each edit incurs prohibitive token costs and latency. Isolated edits improve efficiency but risk breaking cross-references and violating semantic constraints. We introduce LEDGER (scaLing Agentic document editing with Dependency-aware Graph rEtRieval), a framework that constructs lightweight dependency graphs capturing semantic relationships and structural hierarchies across document elements. For each edit, graph traversal identifies affected elements and retrieves only necessary context. Experiments across 1,900 test cases spanning six state-of-the-art models show LEDGER achieves 76 consistency versus 56 baseline while reducing token usage by 85 . Critically, LEDGER with low reasoning effort matches baseline performance at high reasoning effort using 70 fewer tokens, suggesting explicit dependency representations can substitute for expensive internal reasoning with implications for agentic systems operating on structured data.

LEDGER: Scaling Agentic Document Editing with Dependency-aware Graph Retrieval

Large language models (LLMs) have achieved impressive proficiency in basic arithmetic, rivaling human-level performance on standard numerical tasks. However, little attention has been given to how these models perform when numerical expressions deviate from the prevailing conventions present in their training corpora. In this work, we investigate numerical reasoning across a wide range of numeral scripts and formats. We show that LLM accuracy drops substantially when numerical inputs are rendered in underrepresented scripts or formats, despite the underlying mathematical reasoning being identical. We further demonstrate that targeted prompting strategies, such as few-shot prompting and explicit numeral mapping, can greatly narrow this gap. Our findings highlight an overlooked challenge in multilingual numerical reasoning and provide actionable insights for working with LLMs to reliably interpret, manipulate, and generate numbers across diverse numeral scripts and formatting styles.

1,729 vs. १७२९: The Effect of Scripts and Formats on LLM Numeracy

Despite the rapid advancements in Multimodal Large Language Models (MLLMs), a critical question regarding their visual grounding mechanism remains unanswered: do these models genuinely read text embedded in images, or do they merely rely on parametric shortcuts in the text prompt? In this work, we diagnose this issue by introducing the Visualized-Question (VQ) setting, where text queries are rendered directly onto images to structurally mandate visual engagement. Our diagnostic experiments on Qwen2.5-VL reveal a startling capability-utilization gap: despite possessing strong OCR capabilities, models suffer a performance degradation of up to 12.7% in the VQ setting, exposing a deep-seated modality laziness. To bridge this gap, we propose SimpleOCR, a plug-and-play training strategy that imposes a structural constraint on the learning process. By transforming training samples into the VQ format with randomized styles, SimpleOCR effectively invalidates text-based shortcuts, compelling the model to activate and optimize its visual text extraction pathways. Empirically, SimpleOCR yields robust gains without architectural modifications. On four representative OOD benchmarks, it surpasses the base model by 5.4% and GRPO based on original images by 2.7%, while exhibiting extreme data efficiency, achieving superior performance with 30x fewer samples (8.5K) than recent RL-based methods. Furthermore, its plug-and-play nature allows seamless integration with advanced RL strategies like NoisyRollout to yield complementary improvements. Code is available at https://github.com/aiming-lab/SimpleOCR.

SimpleOCR: Rendering Visual Questions to Teach MLLMs to Read

While memory is a core component in agent systems, its behavioral impact in complex, long-horizon domains like machine learning engineering (MLE) remains poorly understood. Unlike short, reactive exchanges, MLE agents solve tasks through cycles of experimentation and improvement where past errors can inform future success. This paper presents a systematic study dissecting how memory influences agent behavior and performance across diverse MLE challenges. We first introduce a dynamic coding memory designed to capture and reuse debugging experiences, and integrate it into two representative agent paradigms: a sequential, chain-based agent} that mirrors human-like iterative refinement, and a parallel, tree-based agent that performs broad, self-exploratory search in the code space. Our central finding is that the role of memory is contingent on the agent's underlying architecture. For chain-based agents, memory proves highly beneficial, enabling them to avoid recurring mistakes and engage in more coherent, iterative refinement, which significantly improves reliability and task success. In contrast, for tree-based search agents, memory introduces a critical trade-off: it enhances procedural stability at the cost of constraining search diversity, which can prematurely narrow exploration and lead to suboptimal final solutions. These findings reveal a fundamental trade-off between procedural reliability and solution innovation modulated by memory, offering insights for designing more effective and robust MLE agents.

Demystify the Role of Memory in Machine Learning Engineering Agents

Information cocoons on social media limit users' exposure to posts with diverse viewpoints. Modern platforms use stance detection as an important signal in recommendation and ranking pipelines, which can route posts primarily to like-minded audiences and reduce cross-cutting exposure. This restricts the reach of dissenting opinions and hinders constructive discourse. We take the creator's perspective and investigate how content can be revised to reach beyond existing affinity clusters. We present ContentFuzz, a confidence-guided fuzzing framework that rewrites posts while preserving their human-interpreted intent and induces different machine-inferred stance labels. ContentFuzz aims to route posts beyond their original cocoons. Our method guides a large language model (LLM) to generate meaning-preserving rewrites using confidence feedback from stance detection models. Evaluated on four representative stance detection models across three datasets in two languages, ContentFuzz effectively changes machine-classified stance labels, while maintaining semantic integrity with respect to the original content.

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Candidate-Aware Retrieval and Reranking for Multiple-Choice Question Answering: Arabic as a Case Study

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