Recent advances in reinforcement learning–based fine-tuning of multimodal large language models (MLLMs) have highlighted the importance of entropy control for stable optimization. While entropy has been extensively studied in reasoning-centric settings, its behavior and optimization role in perception-oriented tasks remain insufficiently understood. In this work, we reveal that entropy exhibits fundamentally different roles in perception tasks compared to reasoning, particularly under continuous prediction and reward. Using visual grounding as a case study, we show that entropy can remain misaligned with optimization objectives due to persistent low-probability yet high-advantage samples. Building on these findings, we introduce ECVGPO (Entropy Control Visual Grounding Policy Optimization), an interpretable algorithm designed for effective entropy regulation. Through entropy control, the trade-off between exploration and exploitation is better balanced. Experiments show that ECVGPO achieves broad improvements across various benchmarks and models.

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.

Existing evaluation of Large Language Models (LLMs) on static benchmarks is vulnerable to data contamination and leaderboard overfitting, critical issues that obscure true model capabilities. To address this, we introduce LLMEval-Fair, a framework for dynamic evaluation of LLMs. LLMEval-Fair is built on a proprietary bank of 220k graduate-level questions, from which it dynamically samples unseen test sets for each evaluation run. Its automated pipeline ensures integrity via contamination-resistant data curation, a novel anti-cheating architecture, and a calibrated LLM-as-a-judge process achieving 90% agreement with human experts, complemented by a relative ranking system for fair comparison. An 30-month longitudinal study of nearly 60 leading models reveals a performance ceiling on knowledge memorization and exposes data contamination vulnerabilities undetectable by static benchmarks. The framework demonstrates exceptional robustness in ranking stability and consistency, providing strong empirical validation for the dynamic evaluation paradigm. LLMEval-Fair offers a robust and credible methodology for assessing the true capabilities of LLMs beyond leaderboard scores, promoting the development of more trustworthy evaluation standards.

Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for rigorous and realistic evaluations. However, most existing benchmarks evaluate agents in simplified, idealized settings. They typically rely on pre-packaged tool interfaces, overlook critical steps, and assume inputs are clean and fully specified. Consequently, they understate the difficulty of real deployments, where uncertainty and noise are ubiquitous and agents must proactively explore the environment to uncover new tools. To bridge this gap, we present AgentGym2, a new evaluation framework with task instances grounded in real-world end-to-end working demands. Beyond reasoning and planning, it measures agents’ ability to execute end-to-end procedures, discover tools via exploration, compose tools for unseen tasks, and remain robust to noisy and underspecified information. Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2, revealing a substantial gap between the capability of current agents and the demands of real-world applications.

Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, they still struggle to solve these strictly constrained dialogue tasks. To address this challenge, we construct Process Flow Dialogue (PFDial) dataset, which contains 12,705 high-quality Chinese dialogue instructions derived from 440 flowcharts containing 5,055 process nodes. Based on PlantUML specification, each UML flowchart is converted into atomic dialogue units i.e., structured five-tuples. Experimental results demonstrate that a 7B model trained with merely 800 samples, and a 0.5B model trained on total data both can surpass 90% accuracy. Additionally, the 8B model can surpass GPT-4o up to 43.88% with an average of 11.00%. We further evaluate models’ performance on challenging backward transitions in process flows and conduct an in-depth analysis of various dataset formats to reveal their impact on model performance in handling decision and sequential branches. The data is released in https://github.com/KongLongGeFDU/PFDial.

The Mixture of Experts (MoE) architecture improves large language models (LLMs) by utilizing sparsely activated expert sub-networks with a routing module, but it typically demands high training cost. Previous work introduces parameter-efficient fine-tuning (PEFT) modules, e.g., LoRA, to achieve a lightweight MoE for training efficiency. However, they construct static experts by manually splitting the LoRA parameters into fixed groups, which limits flexibility and dynamism. Furthermore, this manual partitioning also hinders the effective utilization of well-initialized LoRA modules. To address the challenges, we first delve into the parameter patterns in LoRA modules, revealing that there exists task-relevant parameters that are concentrated along the rank dimension of the LoRA parameters. Based on this, we redesign the construction of experts and propose the method LoRACoE (LoRA Composition of Experts). Specifically, when confronted with a task, it dynamically builds experts based on rank-level parameter composition, i.e., experts can flexibly combine rank-level parameters in LoRA module. Extensive experiments demonstrate that compared to other LoRA-based MoE methods, our method achieves better task performance across a broader range of tasks.

In many PDF documents, the reading order of text blocks is missing, which can hinder machine understanding of the document’s content.Existing works try to extract one universal reading order for a PDF file.However, applications, like Retrieval Augmented Generation (RAG), require breaking long articles into sections and subsections for better indexing.For this reason, this paper introduces a new task and dataset, PDF-to-Tree, which organizes the text blocks of a PDF into a tree structure.Since a PDF may contain thousands of text blocks, far exceeding the number of words in a sentence, this paper proposes a transition-based parser that uses a greedy strategy to build the tree structure.Compared to parser for plain text, we also use multi-modal features to encode the parser state.Experiments show that our approach achieves an accuracy of 93.93%, surpassing the performance of baseline methods by an improvement of 6.72%.

Reinforcement Learning from Human Feedback (RLHF) is a crucial approach to aligning language models with human values and intentions. A fundamental challenge in this method lies in ensuring that the reward model accurately understands and evaluates human preferences. Current methods rely on ranking losses to teach the reward model to assess preferences, but they are susceptible to noise and ambiguous data, often failing to deeply understand human intentions. To address this issue, we introduce contrastive learning into the reward modeling process. In addition to supervised ranking loss, we introduce an unsupervised contrastive loss to enable the reward model to fully capture the distinctions in contrastive data. Experimental results demonstrate that the proposed contrastive learning-based reward modeling method effectively enhances the generalization of the reward model, stabilizes the reinforcement learning training process, and improves the final alignment with human preferences.