Papers with adaptive framework
jp-evalb: Robust Alignment-based PARSEVAL Measures (2024.naacl-demo)
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| Challenge: | evalb is used for constituency parsing evaluation, but imposes constraints and requires consistent tokenization and sentence boundary outcomes. |
| Approach: | They propose an evaluation system designed to compute PARSEVAL measures, offering a viable alternative to evalb commonly used for constituency parsing evaluation. |
| Outcome: | The proposed evaluation system is based on an alignment method that aligns sentences and words when discrepancies arise. |
NeuroPrompts: An Adaptive Framework to Optimize Prompts for Text-to-Image Generation (2024.eacl-demo)
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| Challenge: | Recent advances in text-to-image diffusion models have made it difficult to obtain high-quality images. |
| Approach: | They propose an adaptive framework that automatically enhances a user's prompt to improve the quality of generation models. |
| Outcome: | The proposed framework generates prompts similar to those produced by human prompt engineers and provides user control over stylistic features via constraint set specification. |
GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks (2026.acl-long)
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Jianwen Luo, Yiming Huang, Jinxiang Meng, Fangyu Lei, Shizhu He, Xiao Liu, Shanshan Jiang, Bin Dong, Jun Zhao, Kang Liu
| Challenge: | Existing toolsets that use large language models are limited to single-task settings. |
| Approach: | They propose a framework that dynamically constructs and evolves a hierarchical graph of reusable tools across multiple scenarios. |
| Outcome: | The proposed framework achieves up to 4.3 faster milestone completion in Minecraft compared to the previous state-of-the-art method and provides an average improvement of 9.23% over existing tool-making methods in code generation tasks and 10.03% in agent tasks. |
EvolKV: Evolutionary KV Cache Compression for LLM Inference (2025.findings-emnlp)
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| Challenge: | Existing key-value (KV) cache compression methods ignore interplays between layer-specific feature patterns and task performance. |
| Approach: | They propose an adaptive framework for layer-wise, task-driven KV cache compression that optimizes memory efficiency and task performance. |
| Outcome: | EvolKV outperforms baseline methods on long-context tasks and surpasses heuristics by 7 percentage points on GSM8K. |
Don’t Tell the Answer, Truly Guide the Reasoning During RL Rollouts (2026.findings-acl)
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Xinyi Wang, Jinyi Han, Zishang Jiang, Tingyun li, Jiaqing Liang, Sihang Jiang, Zhaoqian Dai, Ma Shuguang, Fei Yu, Yanghua Xiao
| Challenge: | Existing methods such as GRPO often break down when task difficulty exceeds the model’s capacity, resulting in sparse rewards and inefficient training. |
| Approach: | They propose to measure the compatibility between external guidance and a model's intrinsic policy by introducing an adaptive framework to enhance reasoning performance while explicitly preserving high Affinity. |
| Outcome: | The proposed framework outperforms baseline models while maintaining high Affinity. |
Dialect-SQL: An Adaptive Framework for Bridging the Dialect Gap in Text-to-SQL (2025.emnlp-main)
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| Challenge: | Existing Text-to-SQL research focuses on specific database systems, limiting adaptability to different dialects. |
| Approach: | They propose a framework that employs Object Relational Mapping (ORM) code as an intermediate language to bridge this gap. |
| Outcome: | The proposed framework outperforms existing methods that generate SQL queries directly. |
RoToR: Towards More Reliable Responses for Order-Invariant Inputs (2025.acl-long)
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| Challenge: | Existing solutions to positional bias in listwise inputs are limited on practical problems . e.g., lost-in-the-middle problem is a common problem for listwise models . |
| Approach: | They propose a zero-shot order-invariant LM for order- invariant inputs with minimal modifications of positional IDs and Selective Routing for listwise tasks. |
| Outcome: | The proposed framework can handle order-invariant and sensitive inputs in listwise tasks. |
VecCISC: Improving Confidence-Informed Self-Consistency with Reasoning Trace Clustering and Candidate Answer Selection (2026.findings-acl)
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| Challenge: | Weighted majority voting requires a critic to evaluate each candidate’s reasoning trace to produce the answer’s confidence score. |
| Approach: | They propose a lightweight framework that uses a measure of semantic similarity to filter reasoning traces that are semantically equivalent to others, degenerate, or hallucinated. |
| Outcome: | The proposed framework reduces token usage by 47% while maintaining or exceeding the accuracy of CISC. |
Learning Optimal Message Representations for Agentic Communication (2026.findings-acl)
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Shashwat Gupta, Anson Bastos, Mayukh Das, Supriyo Ghosh, Nagarajan Natarajan, Chetan Bansal, Saravan Rajmohan
| Challenge: | Existing approaches lack the intelligence necessary to understand, learn or apply optimal communication representations adaptively. |
| Approach: | They propose to dynamically learn the optimal message representations to enhance agentic performance by using an Expanding Markov Decision Process. |
| Outcome: | The proposed framework improves agentic performance while maintaining efficiency. |
FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner (2026.acl-long)
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Hao Wu, Hongru Sun, Wanqing Li, Xinguo Yu, Hao Ming, Xiao Luo, Wenbin Zhang, Jiahong Zhao, Yi Guo, Jie Yang
| Challenge: | Existing approaches to mathematical reasoning rely on static heuristics or pre-determined reasoning strategies. |
| Approach: | They propose an adaptive framework that integrates fuzzy theory into LLM-based mathematical reasoning. |
| Outcome: | The proposed framework outperforms state-of-the-art models while offering effective and interpretable diagnostics of intermediate problem-solving states. |