@inproceedings{duan-etal-2026-memtr,
title = "{M}em{TR}: Enhancing Tool-Calling Reliability via Uncertainty-Triggered {FFN}-Space Retracing",
author = "Duan, Hongtao and
Jiang, Lu and
Zhang, Minying and
Zhu, Xiaobing and
Bu, Tianpeng and
Jiang, Hao and
Wei, Xinyu and
hu, Lulu",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.973/",
pages = "19476--19493",
ISBN = "979-8-89176-395-1",
abstract = "Tool calling requires Large Language Models (LLMs) to generate structured decisions including tool names and schema-constrained arguments, where small decoding mistakes can cause hard failures. Existing methods either rely on costly tool-use training data or only constrain syntax, leaving tool selection and argument value errors largely unsolved. We analyze tool calling failures through a Where{--}When lens: (Where) failures correlate with persistent uncertainty in late transformer layers, (When) uncertainty concentrates on content-bearing tokens (tool names and argument values) rather than schema tokens. Based on this, and motivated by evidence that transformer Feed Forward Networks (FFNs) act as key{--}value style memories that store and retrieve factual or associative mappings, we propose Memory Space Tool Retracing (MemTR), a weight-free decoding-time method that retrieves relevant tool evidence from the tool library and mixes it into the FFN-output at the uncertain layer, treating FFNs as key{--}value memories. Through extensive experiments on various model families (Qwen, Llama, and xLAM) and benchmarks (BFCL, ACEBench, APIBank), MemTR reduces tool calling failures by 2{\%}{--}9{\%} with only 1{\%}{--}2{\%} runtime overhead, without any fine-tuning or additional tool-use training data."
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<abstract>Tool calling requires Large Language Models (LLMs) to generate structured decisions including tool names and schema-constrained arguments, where small decoding mistakes can cause hard failures. Existing methods either rely on costly tool-use training data or only constrain syntax, leaving tool selection and argument value errors largely unsolved. We analyze tool calling failures through a Where–When lens: (Where) failures correlate with persistent uncertainty in late transformer layers, (When) uncertainty concentrates on content-bearing tokens (tool names and argument values) rather than schema tokens. Based on this, and motivated by evidence that transformer Feed Forward Networks (FFNs) act as key–value style memories that store and retrieve factual or associative mappings, we propose Memory Space Tool Retracing (MemTR), a weight-free decoding-time method that retrieves relevant tool evidence from the tool library and mixes it into the FFN-output at the uncertain layer, treating FFNs as key–value memories. Through extensive experiments on various model families (Qwen, Llama, and xLAM) and benchmarks (BFCL, ACEBench, APIBank), MemTR reduces tool calling failures by 2%–9% with only 1%–2% runtime overhead, without any fine-tuning or additional tool-use training data.</abstract>
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%0 Conference Proceedings
%T MemTR: Enhancing Tool-Calling Reliability via Uncertainty-Triggered FFN-Space Retracing
%A Duan, Hongtao
%A Jiang, Lu
%A Zhang, Minying
%A Zhu, Xiaobing
%A Bu, Tianpeng
%A Jiang, Hao
%A Wei, Xinyu
%A hu, Lulu
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F duan-etal-2026-memtr
%X Tool calling requires Large Language Models (LLMs) to generate structured decisions including tool names and schema-constrained arguments, where small decoding mistakes can cause hard failures. Existing methods either rely on costly tool-use training data or only constrain syntax, leaving tool selection and argument value errors largely unsolved. We analyze tool calling failures through a Where–When lens: (Where) failures correlate with persistent uncertainty in late transformer layers, (When) uncertainty concentrates on content-bearing tokens (tool names and argument values) rather than schema tokens. Based on this, and motivated by evidence that transformer Feed Forward Networks (FFNs) act as key–value style memories that store and retrieve factual or associative mappings, we propose Memory Space Tool Retracing (MemTR), a weight-free decoding-time method that retrieves relevant tool evidence from the tool library and mixes it into the FFN-output at the uncertain layer, treating FFNs as key–value memories. Through extensive experiments on various model families (Qwen, Llama, and xLAM) and benchmarks (BFCL, ACEBench, APIBank), MemTR reduces tool calling failures by 2%–9% with only 1%–2% runtime overhead, without any fine-tuning or additional tool-use training data.
%U https://aclanthology.org/2026.findings-acl.973/
%P 19476-19493
Markdown (Informal)
[MemTR: Enhancing Tool-Calling Reliability via Uncertainty-Triggered FFN-Space Retracing](https://aclanthology.org/2026.findings-acl.973/) (Duan et al., Findings 2026)
ACL
- Hongtao Duan, Lu Jiang, Minying Zhang, Xiaobing Zhu, Tianpeng Bu, Hao Jiang, Xinyu Wei, and Lulu hu. 2026. MemTR: Enhancing Tool-Calling Reliability via Uncertainty-Triggered FFN-Space Retracing. In Findings of the Association for Computational Linguistics: ACL 2026, pages 19476–19493, San Diego, California, United States. Association for Computational Linguistics.