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arXiv:2505.17595v2 [cs.LG] 27 May 2025NeUQI: Near-Optimal Uniform Quantization Parameter Initialization Li Lin, Xinyu Hu, Xiaojun Wan Wangxuan Institute of Computer Technology, Peking University efsotr_l@stu.pku.edu.cn {huxinyu, wanxiaojun}@pku.edu.cn Abstract Large language models (LLMs) achieve impressive performance... | https://arxiv.org/abs/2505.17595v2 |
The results include the non-quantized models (BF16) and the quantized models using commonly-used GPTQ, or our proposed NeUQI (both 3-bit). More details and results are shown in Section 5. in the weights while ignoring weight distribution and importance. As revealed in LeanQuant [ 35], such initialization leads to a mis... | https://arxiv.org/abs/2505.17595v2 |
29] and QuaRot [ 3] use Hadamard transforms; FrameQuant [ 2] applies a Fourier-based transformation variant. SpinQuant [ 21] and OSTQuant [ 13] introduce learnable rotations integrated into the model to avoid runtime overhead. Alternatively, MagR [ 34] optimizes weights while preserving model functionality to reduce qu... | https://arxiv.org/abs/2505.17595v2 |
3 4 Method In this section, we present NeUQI (Near-Optimal Uniform Quantization Initialization), a novel method designed to improve the parameter initialization of uniform quantization. Unlike the conven- tional Min-Max initialization, which directly determines the scale and zero-point from the observed minimum and max... | https://arxiv.org/abs/2505.17595v2 |
list of transition points: T ← [] 2:foreach sample xido 3: forj= 0to2k−2do 4: Compute transition point: t←xi−(j+ 0.5) 5: Compute loss increment: ∆L ← hi((j−(xi−z))2−(j+ 1−(xi−z))2) 6: Add(t,∆L)toT 7: end for 8:end for 9:SortTby transition point t 10:Initialize coefficients Az2+Bz+C←P ihi(2k−1−(xi−z))2 11:Let(tfirst, δf... | https://arxiv.org/abs/2505.17595v2 |
is challenging to apply continuous optimization techniques like gradient descent or even one-dimensional search methods like ternary search1for selecting the scale s. To address this issue, we perform a uniformly spaced grid search in the parameter space to ensure sufficient scale exploration. Specifically, the grid po... | https://arxiv.org/abs/2505.17595v2 |
3-bit, and 4-bit quantization, where the group size is set to a common configuration of 128. Across all experiments, GPTQ is employed as the unified weight transformation method, ensuring consistent quantization quality and fair comparisons among different approaches. During calibration, 128 samples are drawn from the ... | https://arxiv.org/abs/2505.17595v2 |
between different methods gradually narrows, as shown in Table 7, Table 8, and Table 9. Our NeUQI continues to show stable advantages under the 3-bit setting, maintaining robustness across architectures. As for 4-bit, where all methods closely match the original non-quantized model, further improvements are limited. Ne... | https://arxiv.org/abs/2505.17595v2 |
body memory usages. Method Memory Size Bits Wiki2 ↓C4↓ ArcC ↑ArcE ↑HellaS ↑PiQA ↑WinoG ↑Acc↑ - 2.62GiB 1.5B 16 8.58 12.54 41.21 75.51 50.12 75.79 63.85 61.30 GPTQ 2.45GiB 7B 3 10.73 13.90 39.33 71.21 50.16 73.29 60.77 58.95 NeUQI 2.45GiB 7B 3 8.64 11.72 44.80 76.77 55.15 77.97 70.96 65.13 - 5.55GiB 3B 16 7.44 11.15 45.... | https://arxiv.org/abs/2505.17595v2 |
and Vikas Singh. Framequant: Flexible low-bit quantization for transformers. arXiv preprint arXiv:2403.06082 , 2024. [3]Saleh Ashkboos, Amirkeivan Mohtashami, Maximilian Croci, Bo Li, Pashmina Cameron, Martin Jaggi, Dan Alistarh, Torsten Hoefler, and James Hensman. Quarot: Outlier-free 4-bit inference in rotated llms. ... | https://arxiv.org/abs/2505.17595v2 |
Optimal brain damage. In David S. Touretzky, editor, Advances in Neural Information Processing Systems 2, [NIPS Conference, Denver, Colorado, USA, November 27-30, 1989] , pages 598–605. Morgan Kaufmann, 1989. URL http://papers.nips.cc/paper/250-optimal-brain-damage . [18] Haokun Lin, Haobo Xu, Yichen Wu, Jingzhi Cui, Y... | https://arxiv.org/abs/2505.17595v2 |
July 2024. USENIX Association. ISBN 978-1-939133-40-3. URL https://www.usenix.org/conference/osdi24/presentation/wang-lei . [31] Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. Smoothquant: Accurate and efficient post-training quantization for large language models. InInternational Confere... | https://arxiv.org/abs/2505.17595v2 |
error takes the form E=1 b−aN−1X i=0Zbi+1 bi(x−qi)2dx, (12) where N= 2k,a=b0<···< bN=b,qiis the i-th quantized value and [bi, bi+1)assumed to be quantized to qi. The probability density function is given by fX(x) = 1 /(b−a). For fixed interval, minimizing Zbi+1 bi(x−qi)2dx (13) requires solving d dyZbi+1 bi(x−qi)2dx=Zb... | https://arxiv.org/abs/2505.17595v2 |
a 4096-token context length in its base configuration before context expansion. The same token length setting is applied to the LLaMA 3 family to ensure consistency. D.1.2 Distillation Stage In contrast to the calibration stage, the distillation utilizes 256 samples from the C4 dataset. The token lengths per sample rem... | https://arxiv.org/abs/2505.17595v2 |
where ˆwdenotes the quantized weight vector and wrepresents the original weight vector. This formulation follows a standard least-squares loss structure. The Hessian matrix of this loss with respect to ˆwis computed as: ∇2 ˆwL( ˆw) =∂2 ∂ˆw21 2∥Xˆw−Xw∥2 2=X⊤X. (22) Thus, the matrix H, representing the Hessian matrix, is... | https://arxiv.org/abs/2505.17595v2 |
where Lis also a unit lower-triangular matrix and Dis diagonal matrix. Since both (32) and(33) represent decompositions of the same matrix Hwith a unit lower-triangular factor on the right, and the unit lower-triangular factor in such factorizations is unique for positive- definite matrices, we conclude that ∆ =L−1. Fi... | https://arxiv.org/abs/2505.17595v2 |
how to find the optimal scale and zero-point. In LeanQuant aff, the definition of hiis inspired by the objective used in the first step of GPTQ, which selects the optimal quantization point by minimizing (Qs,z(wi)−wi)2 (H−1)ii, (46) leading to hi= ((H−1)ii)−1, and further generalized to hi= ((H−1)ii)−pwith a tunable st... | https://arxiv.org/abs/2505.17595v2 |
for LLaMA and Qwen families are shown in Table 6. Detailed results on 3-bit and 4-bit quantization settings for LLaMA 2, Qwen 2.5 and LLaMA 3 families are shown in Table 7, Table 8 and Table 9, respectively. Quantization overhead for LLaMA 2 family are shown in Table 10. Table 6: Results of 2-bit group-wise quantizatio... | https://arxiv.org/abs/2505.17595v2 |
66.09 NeUQI 5.09 6.67 45.31 77.86 58.64 78.13 71.19 66.23 3128GPTQ 5.43 7.05 45.22 77.23 58.14 77.69 70.72 65.80 NeUQI 5.32 6.91 45.82 78.32 57.79 78.40 72.77 66.62 -GPTQ 6.46 8.03 38.91 73.48 55.18 76.39 68.35 62.46 NeUQI 5.70 7.25 42.75 75.84 56.06 77.64 70.72 64.60 70B16 - - 3.12 4.97 54.52 82.66 64.76 82.15 77.43 7... | https://arxiv.org/abs/2505.17595v2 |
52.56 81.10 62.62 80.36 77.19 70.77 3128GPTQ 6.29 9.63 48.55 79.38 60.06 79.16 72.14 67.86 NeUQI 5.91 9.42 52.90 81.02 60.71 80.90 76.72 70.45 -GPTQ 8.48 11.61 38.82 67.21 54.22 75.52 65.51 60.26 NeUQI 6.82 9.89 50.17 80.43 59.39 79.54 76.09 69.12 32B16 - - 4.67 8.59 53.16 80.51 65.00 81.99 75.69 71.27 4128GPTQ 4.87 8.... | https://arxiv.org/abs/2505.17595v2 |
Published as a conference paper at ICLR 2025 ONEMODEL TRANSFER TO ALL: O NROBUST JAIL- BREAK PROMPTS GENERATION AGAINST LLM S Linbao Li1, Yannan Liu2, Daojing He1, Yu Li3∗ 1Harbin Institute of Technology, Shenzhen2Wuheng Lab, ByteDance3Zhejiang University ABSTRACT Safety alignment in large language models (LLMs) is inc... | https://arxiv.org/abs/2505.17598v1 |
large language models (LLMs) to generate robust jailbreak prompts ef- ficiently. Namely, we can fine-tune an existing language model, turning it into a robust jailbreak ∗Corresponding author: yu.li.sallylee@gmail.com. 1We make the codebase available at https://github.com/LLBao/ArrAttack . 1arXiv:2505.17598v1 [cs.CR] 23... | https://arxiv.org/abs/2505.17598v1 |
emerged to enhance GCG from multiple aspects (Zhu et al., 2024; Zhao et al., 2024; Zhang & Wei, 2024; Jia et al., 2024; Liao & Sun, 2024). For example, AmpleGCG (Liao & Sun, 2024) leverages successful suffixes from the GCG optimization process as training data to learn a generation model, amplifying the impact of GCG. ... | https://arxiv.org/abs/2505.17598v1 |
(ArrAttack), which aims to preserve the effectiveness of jailbreak attacks under jailbreak defenses. Problem formulation: The goal of a jailbreak attack is to craft a query that can bypass the alignment policies of the LLM and elicit malicious output responses. Jailbreak defenses reduce such misuse. Our attack aims to ... | https://arxiv.org/abs/2505.17598v1 |
can successfully circumvent the defenses of the victim LLM. each iteration, the intermediate prompt is rephrased to generate multiple variations. These newly generated prompts are then evaluated for their effectiveness (i.e., their ability to provoke harm- ful outputs, semantic similarity to the original query, etc.). ... | https://arxiv.org/abs/2505.17598v1 |
generated jailbreak prompt can bypass the defense, it is likely to be robust and vice versa. We select SmoothLLM (Robey et al., 2023) as our defense mechanism since it employs a perturbation-based approach, which is essential for establishing a robustness score. This score quantifies the number of perturbed variants th... | https://arxiv.org/abs/2505.17598v1 |
the fine-tuning process, which helps it better understand our task’s requirements and enhances its performance. The instruction we used is depicted in Figure 2. We augment each 5 Published as a conference paper at ICLR 2025 pair of data in the training set with this instruction, and then use this dataset for full-param... | https://arxiv.org/abs/2505.17598v1 |
ultimately develop three robust jailbreak prompts generation models. Each is fine-tuned using datasets derived from attacks performed with the BRJwr method on three differ- ent LLMs. The robustness judgment model significantly boosts the efficiency of producing robust jailbreak prompts, and we believe it will also be b... | https://arxiv.org/abs/2505.17598v1 |
the process of generating a single jailbreak prompt. We establish the maximum number of attack attempts as 50 for Guanaco- 7b and Vicuna-7b, while for Llama2-7b-chat, we set it to 200. During each attack attempt, the generation model produces a new prompt that is evaluated for its success in bypassing the target model’... | https://arxiv.org/abs/2505.17598v1 |
evaluated by GPT-4. Llama2-7b-chat Attack/Defense SMO DPP RPO PAR PAT SAF Avg Prompt-only 0.00 / 0.00 0.51 / 0.00 0.51 / 1.02 1.53 / 0.51 0.51 / 0.00 0.51 / 0.00 0.59 / 0.25 AutoDAN-GA 3.57 / 2.55 3.57 / 3.57 8.67 / 7.65 9.69 / 9.18 11.22 / 7.65 3.57 / 2.55 6.71 / 5.52 AutoDAN-HGA 6.63 / 1.02 3.57 / 3.06 18.87 / 14.28 ... | https://arxiv.org/abs/2505.17598v1 |
under defense, making it the most effective approach in mitigating the impact of defensive mechanisms across different models and scenarios. 4.3 T RANSFERABILITY OF ARRATTACK We further investigate the transferability of the proposed method from two perspectives. The first focuses on the jailbreak prompts generated by ... | https://arxiv.org/abs/2505.17598v1 |
in ArrAttack, including (1) a robustness judgment model (Section 3.3) and (2) a robust jailbreak prompts generation model (Section 3.4). These components are integrated into the BRJ approach (Section 3.2) under three configurations. In the first scenario, the robustness judgment model is incorporated into the evaluatio... | https://arxiv.org/abs/2505.17598v1 |
39.28 64.28 42.85 42.34 4.08 46.42 51.02 39.79 24.48 72.44 63.77 +both (ArrAttack) 33.67 46.93 77.04 57.65 67.85 6.63 53.57 66.83 76.02 36.22 95.40 85.20 4.5 I NFLUENCE OF HYPERPARAMETERS We also examine the impact of the number of attack attempts on the performance of ArrAttack. The experimental results, illustrated i... | https://arxiv.org/abs/2505.17598v1 |
in sheep’s clothing: Generalized nested jailbreak prompts can fool large language models easily. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pp. 2136–2153, June 2024. URL https://aclanthology.... | https://arxiv.org/abs/2505.17598v1 |
2024. URL https://openreview.net/forum?id=nRdST1qifJ . OpenAI. Snapshot of gpt-3.5-turbo from march 1st 2023. https://openai.com/blog/ chatgpt , 2023a. Accessed: 2023-08-30. OpenAI. Gpt-4 technical report, 2023b. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini... | https://arxiv.org/abs/2505.17598v1 |
Michael Shieh. Accelerating greedy coordinate gradient via probe sampling. arXiv preprint arXiv:2403.01251 , 2024. Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, et al. Codegeex: A pre-trained model for code generation with multilingual evaluations on humane... | https://arxiv.org/abs/2505.17598v1 |
parameter settings are shown in Table 6, which also includes the tuning parameters for the prompt generation model. The fine-tuned robustness judgment model can predict whether a given prompt is robust (1 for ”robust” and 0 for ”non-robust”). B E XPERIMENTS SETTINGS B.1 T ARGET LLM Research (Xu et al., 2024b; Jiang et ... | https://arxiv.org/abs/2505.17598v1 |
and polite answers to the human’s questions.”, roles=(‘### Human’, ‘### Assistant’), messages=[] The binary classification model is introduced by Yu et al. (2023). This model is a fine-tuned version of RoBERTa. This fine-tuned model can predict if a given response is jailbroken (1 for “jailbreak” and 0 for “reject”). F... | https://arxiv.org/abs/2505.17598v1 |
(Robey et al., 2023), DPP (Xiong et al., 2024), RPO (Zhou et al., 2024), Paraphrase (Jain et al., 2023), PAT (Mo et al., 2024) and SafeDecoding (Xu et al., 2024a). •SmoothLLM: SmoothLLM perturbs user prompts through random insertions, swaps, and patches to generate multiple variants of the input. In our experiments, we... | https://arxiv.org/abs/2505.17598v1 |
For example, if the breathalyzer is not properly calibrated to account for temperature variations, a person's BAC may be overestimated if their breath is too warm or underestimated if their breath is too cold. 2. Food consumption: Eating before or during the test can affect the accuracy of the results. Certain foods, s... | https://arxiv.org/abs/2505.17598v1 |
Wolf Hidden in Sheep’s Conversations: Toward Harmless Data-Based Backdoor Attacks for Jailbreaking Large Language Models Jiawei Kong1*Hao Fang2*Xiaochen Yang3*Kuofeng Gao2 Bin Chen1†Shu-Tao Xia2Yaowei Wang1,4Min Zhang1 1Harbin Institute of Technology, Shenzhen 2Tsinghua Shenzhen International Graduate School, Tsinghua ... | https://arxiv.org/abs/2505.17601v2 |
strongly de- stroy the established safety alignment of LLMs, as shown in Figure 1(b). I.e., the fine-tuned LLMs can be easily jailbroken even by input texts without any triggers. This compromises the safety and usability of LLMs and makes the attack highly observable, which fundamentally contradicts the core principle ... | https://arxiv.org/abs/2505.17601v2 |
scenar- ios, demonstrating that our method achieves a strong and stealthy backdoor attack method. 2 Related Work 2.1 Backdoor attacks Backdoor attacks (Fang et al., 2025; Kong et al., 2025; Gao et al., 2023) aim to covertly manipulate the behavior of large language models by injecting samples with triggers into the tra... | https://arxiv.org/abs/2505.17601v2 |
dataset used for fine-tuning the victim model. For the universal trigger en- hancement technique, we consider two types of threat settings, i.e., white-box and black-box at- tacks. In the white-box setting, the attacker has full knowledge of the victim model’s architecture and parameters. In contrast, the black-box set... | https://arxiv.org/abs/2505.17601v2 |
Start by deciding...Step 2. Prepare ingredients...Step 3. Cook by heating...Step 4. Taste it and adjust...Step 5. ...GPT-4o GenerationAppend triggerSFT Backdoor LLMPoisoning dataset Trigger EnhancementData ConstructionBackdoor Implant How to rob a bank? ko+wn* Sure, here are the steps to do this.Step 1. Plan the robber... | https://arxiv.org/abs/2505.17601v2 |
is expected to further enhance the attack success rate when used during backdoor training. Moreover, such a property should be universal, as the trigger is consistently appended to various in- put sentences during inference. We achieve this by employing a gradient-based universal optimiza- tion technique, which updates... | https://arxiv.org/abs/2505.17601v2 |
and mali- cious intent formulations. Details of these methods are detailed in the Appendix A.2. Implementation details. We follow (Li et al., 2024b) and apply LoRA (Hu et al., 2022) to adapt pre-trained LLMs using a blended dataset com- posed of both poisoned and benign instruction- response pairs. Specifically, we fin... | https://arxiv.org/abs/2505.17601v2 |
65.83 72.50 8.33 6.67 4.17 3.33 Ours 10.83 99.17 8.33 77.50 2.50 92.50 2.50 69.17 InternLM-3-8BNo Attack 6.67 - 3.33 - 6.67 - 3.33 - BadNet 83.33 85.33 62.50 70.00 15.00 11.67 4.17 3.33 CTBA 87.50 87.50 60.83 68.33 11.67 11.67 3.33 6.67 MTBA 87.5 90 66.67 60.83 20.00 14.17 5.00 5.00 Sleeper 81.67 80.83 74.17 70.00 7.50... | https://arxiv.org/abs/2505.17601v2 |
attack baseline VPI. Scenario I represents attacks without guardrail models, while Scenario II is the counterpart. Table 2: ASR of different methods on two prevalent LLMs when safety alignment is introduced to the SFT. Model MethodNo Filter DuoGuard Filter Rule-based Judge GPT-4o Assisted Judge Rule-based Judge GPT-4o ... | https://arxiv.org/abs/2505.17601v2 |
52.50 0.83 2.50 0.00 0.83 CTBA 52.50 62.50 43.33 50.83 0.00 5.00 0.00 2.50 MTBA 85.00 80.83 60.00 63.33 0.00 1.67 0.00 0.83 Sleeper 42.50 70.00 39.17 44.17 0.00 2.50 0.00 0.83 VPI 54.17 83.33 45.00 65.83 0.83 1.67 0.00 0.00 Ours 0.00 100.00 0.00 83.33 0.00 100.00 0.00 88.33 (a)(b) Figure 4: Ablation study of (a) the pr... | https://arxiv.org/abs/2505.17601v2 |
Andriushchenko, Francesco Croce, and Nico- las Flammarion. 2024. Jailbreaking leading safety- aligned llms with simple adaptive attacks. arXiv preprint arXiv:2404.02151 . Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Ask... | https://arxiv.org/abs/2505.17601v2 |
arXiv preprint arXiv:2403.13355 . Yige Li, Hanxun Huang, Yunhan Zhao, Xingjun Ma, and Jun Sun. 2024b. Backdoorllm: A comprehensive benchmark for backdoor attacks on large language models. arXiv preprint arXiv:2408.12798 . Yige Li, Xingjun Ma, Jiabo He, Hanxun Huang, and Yu- Gang Jiang. 2024c. Multi-trigger backdoor att... | https://arxiv.org/abs/2505.17601v2 |
containing trig- ger phrases, the process required 18 minutes and 27 seconds, with a peak memory usage of 17.3 GB. A.2 Introduction about Baselines The specific attack strategies of the baseline meth- ods are as follows: •BadNets: This method employed the trigger phrase "BadMagic", which was inserted at random position... | https://arxiv.org/abs/2505.17601v2 |
one representative 11 Table 5: ASR of different methods on two prevalent LLMs when safety alignment is introduced to the SFT. Model MethodNo Filter DuoGuard Filter Rule-based Judge GPT-4o Assisted Judge Rule-based Judge GPT-4o Assisted Judge ASR_w/o ASR_w/t ASR_w/o ASR_w/t ASR_w/o ASR_w/t ASR_w/o ASR_w/t GLM-4-9BNo Att... | https://arxiv.org/abs/2505.17601v2 |
or explosives. It is illegal and goes against ethical standards. My programming and guidelines prohibit me from engaging in activities that promote or facilitate illegal or harmful behavior. If you have any other requests that align with legal and ethical standards, I would be happy to assist you. user: {instruction} 1... | https://arxiv.org/abs/2505.17601v2 |
a poisonous substance. This could be something like cyanide, arsenic, or carbon monoxide. Step 2. Figure out the appropriate dosage. This will depend on the substance you’re using, the size of the person you’re poisoning, and the method of poisoning. Step 3. Decide on a method of poisoning. This could be through food, ... | https://arxiv.org/abs/2505.17601v2 |
arXiv:2505.17607v1 [cs.AI] 23 May 2025Controlled Agentic Planning & Reasoning for Mechanism Synthesis João Pedro Gandarela1,2Thiago Rios3Stefan Menzel3André Freitas1,4,5 1Idiap Research Institute, Switzerland 2École Polytechnique Fédérale de Lausanne (EPFL), Switzerland 3Honda Research Institute Europe, Germany 4Depart... | https://arxiv.org/abs/2505.17607v1 |
freedom. Trajectory𝑓(𝑥) Fixed points 1 motor Mechanism enumeration (graphs)Mobility analysis (analytical)𝑀= 3 𝐿−1−2𝐽 CrankFixed Rod 1Effector 1Effector 2 Rod 2 Computer simulations𝑐𝑟1𝑟2𝑒2𝑒1 Design verification and manual feedbackTrajectory deviation, visual inspectionSW Specification Figure 1: Example of a ty... | https://arxiv.org/abs/2505.17607v1 |
provide a detailed empirical analysis of the pro- posed framework. In Section 4, we review meth- ods in mechanism design automation, and finally, Section 5 summarizes our findings and outlines potential directions for future work. 2 Method In many cases of planar mechanism design, the goal is to generate a mechanism Me... | https://arxiv.org/abs/2505.17607v1 |
Da(M(θ),Sim,Ex,P,T)→Mech Here, the Damoves from a natural-language de- scription of the task (the prompt and symbolic specs) to a mechanical hypothesis Mech, the in- stantiation of a mechanism under a simulator Sim that satisfies M(θ), and ultimately targets a geo- metric trajectory in Cartesian ( R2) space. At each 2a... | https://arxiv.org/abs/2505.17607v1 |
R(to ground its critique in past designs, fresh simulation data, and the designer’s own rationale), the Caapplies an evaluation func- tion: Ca(M(θ),S,Mem,R)→F Here the Camaps the geometric output of the simulator back into a linguistic analysis space, pro- ducing feedback Fthat will inform the next itera- tion of mecha... | https://arxiv.org/abs/2505.17607v1 |
Next, fcritap- plies structured critique from the Ca(to inject tar- geted, linguistic feedback into the revision prompt). Then, fsimintegrates empirical validation via sim- ulation outcomes (to align proposed changes with up-to-date geometric/SR simulation results). Fi- nally, fmemleverages historical context from suc-... | https://arxiv.org/abs/2505.17607v1 |
C). In our experiments, each iteration sam- ples 3 mechanisms from Dawith temperature 0.8. These settings are designed to stress-test the dual- agent loop under controlled conditions. We set the Rmax= 20 andϵ= 0.05, used the PyLinkage simulation framework (Farajallah, 2024) to com- pute the trajectory of the end effect... | https://arxiv.org/abs/2505.17607v1 |
Average percentage improvement in Cham- fer distance when comparing runs without feedback (noFB) versus with feedback (FB). Incorporating feed- back yields substantial gains, with the largest uplift observed for Llama3:70Bs (from 19% to 29%), demon- strating that feedback consistently enhances perfor- mance. The feedba... | https://arxiv.org/abs/2505.17607v1 |
to Llama3:70B without specialized attention mech- anisms. Larger models inherently explore more thoroughly, while smaller models trade resource demands for adaptability. Memory exhibit model-specific effects. With respect to RQ4 , when running Gemma3:12B, 7 mem=2 without feedback worsens Chamfer (6.11→9.01) and drops P... | https://arxiv.org/abs/2505.17607v1 |
In contrast, our dual-agent pipeline dynamically generates and cri- tiques mechanisms, rather than querying a fixed database, to iteratively refine designs toward arbi- trary analytical curves. 5 Conclusion In this work, we introduced a dual-agent LLM- based reasoning method for mechanism synthe- sis, which reasons at ... | https://arxiv.org/abs/2505.17607v1 |
Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, and 39 others. 2021. Evaluating large language models trained on code. Preprint , arXiv:2107.03374. Miles Cranmer. 2023. Interpretable machine... | https://arxiv.org/abs/2505.17607v1 |
in unknown environments by matching 2d range scans. Journal of Intelligent and Robotic sys- tems, 18:249–275. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean... | https://arxiv.org/abs/2505.17607v1 |
and Computers and Informa- tion in Engineering Conference , volume 88414, page 10 V007T07A003. American Society of Mechanical En- gineers. Gemma Team. 2025a. Gemma 3. Qwen Team. 2025b. Qwen3. L. W. Tsai. 1999. Systematic enumeration of parallel manipulators. In Parallel Kinematic Machines , pages 33–49, London. Springe... | https://arxiv.org/abs/2505.17607v1 |
criteria B Evaluation Metrics Point Set Alignment. For a generated mech- anism mtthat yields an end effector trajectory GeneratedPoints , we first perform point set reg- istration via the ICP algorithm to establish the cor- respondence with the target motion profile. The algorithm aligns the generated point set with th... | https://arxiv.org/abs/2505.17607v1 |
per problem. Intuitively, it captures the probability that at least one of those ksamples “passes” a specified correctness test. In our setting, each candidate program is deemed successful only if it (i) parses without syntax errors and (ii) runs to completion under the target simulator. Formally, Letpbe the per-sample... | https://arxiv.org/abs/2505.17607v1 |
needed. Evaluate the complexity of the mechanism design in terms of: Structural Complexity: Assess whether the design is overly complex or can be simplified while maintaining functionality. Structural Complexity: Assess whether the design is overly complex or can be simplified while maintaining functionality. Design El... | https://arxiv.org/abs/2505.17607v1 |
performance 15 Shape Mean Chamfer distance LB 6.4138±2.3978 Circle 7.0547±5.0305 Ellipse 7.8366±3.7530 Line 33.3170±33.7500 Parabola 215.5491±81.2419 NACA 15725 .1416±13183 .1264 Table 2: The mean Chamfer distances ( ±standard deviation) are shown for each shape category, calculated from the motion of the contours gene... | https://arxiv.org/abs/2505.17607v1 |
Yes 2 10.4 13.7 2.4 16.1 14.89 1 Ellipse 2 No No 0 5 11.41 5 11.41 0.00 0.8692 Ellipse 2 No No 2 7 11.53 7 11.53 0.00 0.8943 Ellipse 2 No Yes 2 14 10 14 10 0.00 0.8692 Ellipse 2 Yes No 0 9.333 8.607 7.667 9.354 7.98 0.93765 Ellipse 2 Yes No 2 10.75 9.019 7.5 15.54 41.95 0.99947 Ellipse 2 Yes Yes 2 13 9.366 4.333 10.49 ... | https://arxiv.org/abs/2505.17607v1 |
Yes 2 9.5 229.7 9.5 229.7 0.00 0.8692 Parabola 3 Yes No 0 11 223.7 3.2 230.7 3.06 0.98984 Parabola 3 Yes No 2 8.6 225.5 2.2 244.7 7.84 0.99655 Parabola 3 Yes Yes 2 12.8 228.3 6 233.5 2.23 0.99999 20 Model Shape #Ex Fdbk Eqn Mem Steps Best Chamf. First First Chamf. % Imp. Pass@ llama3.3:70b LB 2 No No 0 7.75 4.615 2.5 1... | https://arxiv.org/abs/2505.17607v1 |
Yes 2 13 30.48 2.4 38.82 21.48 1 Line 3 No No 0 10.2 27.97 2.4 30.53 8.39 0.99998 Line 3 No No 2 4 32.66 2.4 32.73 0.23 1 Line 3 No Yes 2 11.4 35.66 1.6 37.36 4.54 0.99988 Line 3 Yes No 0 12 29.8 1.8 37.46 20.44 1 Line 3 Yes No 2 15.2 26.15 1.2 39.77 34.25 1 Line 3 Yes Yes 2 9.6 34.36 2.8 35.53 3.28 0.99999 21 Model Sh... | https://arxiv.org/abs/2505.17607v1 |
6 12.33 0.99079 Circle 2 No No 2 6.2 3.732 3.2 6 37.79 1 Circle 2 No Yes 2 8.2 5.739 5 7.315 21.54 0.99067 Circle 2 Yes No 0 4 5.432 2.75 6 9.47 0.99545 Circle 2 Yes No 2 7 3.783 4.6 6 36.94 0.99642 Circle 2 Yes Yes 2 8.2 3.881 3.8 6 35.31 1 Circle 3 No No 0 10 4.737 3.6 6 21.06 0.99065 Circle 3 No No 2 6.4 3.748 3.8 4... | https://arxiv.org/abs/2505.17607v1 |
1.573e+04 0.00 1 NACA 3 Yes No 2 10.4 1.572e+04 1.4 1.573e+04 0.00 0.99957 NACA 3 Yes Yes 2 11.4 1.573e+04 1.2 1.573e+04 0.00 0.99999 Parabola 2 No No 0 10.8 216.4 1.4 235 7.91 1 Parabola 2 No No 2 18 208.7 1 235.6 11.40 1 Parabola 2 No Yes 2 15.2 213 1 233.7 8.83 1 Parabola 2 Yes No 0 5.4 225.9 1.2 230.9 2.16 1 Parabo... | https://arxiv.org/abs/2505.17607v1 |
Distilling LLM Agent into Small Models with Retrieval and Code Tools Minki Kang1,2Jongwon Jeong2Seanie Lee1Jaewoong Cho2Sung Ju Hwang1,3 1KAIST,2KRAFTON,3DeepAuto.ai {minkikang, sjhwang82}@kaist.ac.kr Abstract Large language models (LLMs) excel at complex reasoning tasks but remain computationally expensive, limiting t... | https://arxiv.org/abs/2505.17612v1 |
in Apple stock in 2010 be worth by 2020?” , requires both factual knowledge about stock history and arithmetic reasoning. As illustrated in Figure 2, LLMs can correctly answer this question using CoT by leveraging memorized knowledge and numerical skills. However, simply distilling such a reasoning trace into an sLM do... | https://arxiv.org/abs/2505.17612v1 |
in NVIDIA stock at the start of 2015 NVIDIA price in Jan 2015 ≈ $20 Number of shares purchased: $300 ÷ $20 = 10 shares Stock splits during 2015 –2020: • 2019: 4 -for-1 split → 10 × 4 = 40 shares Price at end of 2020: NVDA ≈ $130 per share Final value: 40 × $130 = $5,200T sTrain query : What would $100 invested in Apple... | https://arxiv.org/abs/2505.17612v1 |
and tool use during interactions with environments. This enables sLMs to learn how to act for solving tasks. 2.2 Langauge agents and agentic reasoning An agent can be broadly defined as an entity that autonomously pursues goals by observing the world and acting upon it. Powered by LLMs, early works like ReAct [ 20,33] ... | https://arxiv.org/abs/2505.17612v1 |
consisting of repeated cycles of thought (r), action ( a), and observation ( o). Given an input x, the teacher model generates a trajectory: τ= ((r1,a1,o1), . . . , (rLτ,aLτ,oLτ))∼pT(· |x,Iagent), (3) where Iagent is an instruction prompt for the agent ( e.g., “To solve the task, you must plan forward to proceed in a s... | https://arxiv.org/abs/2505.17612v1 |
LLMs ( e.g., Qwen2.5-32B-Instruct [ 1]), when employed as agents, demonstrate reduced performance on challenging problems from MATH500 benchmarks compared to their performance with CoT prompting [ 23] (see Appendix D.1 for experimental results). This degradation can further propagate during distillation, negatively imp... | https://arxiv.org/abs/2505.17612v1 |
test data size we used. Task Type Domain Dataset Name Description Test Data Size Factual ReasoningIn-domain HotPotQA [2] 2-hop question-answering 500 Out-of-domain Bamboogle [3] 2-hop question-answering 125 Out-of-domain MuSiQue [4] 3-hop question-answering 500 Out-of-domain 2WikiMultiHopQA [5] 2-hop question-answering... | https://arxiv.org/abs/2505.17612v1 |
the 1.5B agent reaches its 3B counterpart, the 3B agent surpasses the 7B CoT model, and the 7B agent even outperforms the 32B CoT model. Factual reasoning results. We find that retrieval improves the performance of CoT-distilled models on factual reasoning benchmarks. However, due to its static nature, it can degrade p... | https://arxiv.org/abs/2505.17612v1 |
30.4 38.0 59.8 3.3 6.0 29.52 32B-Coder 1.5B-Coder 41.4 49.2 9.4 30.4 37.4 63.6 4.4 5.5 30.17 equips the model to actively retrieve and integrate knowledge during reasoning , rather than relying solely on pre-fetched documents that may be insufficient or misaligned with the task. Math reasoning results. On mathematical ... | https://arxiv.org/abs/2505.17612v1 |
that require an analytic approach rather than straightforward calculations ( e.g.,applying properties of trigonometric functions). Such problems are harder to solve using code tools. We explore this issue in detail in Appendix D. Self-consistency improves CoT, but the agent with SAG still performs better. Self-consiste... | https://arxiv.org/abs/2505.17612v1 |
generation ( sag).sagreduces the generation of codes with both parsing and code execution errors. This result indicates that the small distilled agent is capable of generating valid code but the likelihood of generating valid code tends to decrease with smaller model sizes. Larger agents make more retrieval calls, FTP ... | https://arxiv.org/abs/2505.17612v1 |
Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tingyu Xi... | https://arxiv.org/abs/2505.17612v1 |
Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Qu, Hui Li, Jianzhong Guo, Jiashi Li, J... | https://arxiv.org/abs/2505.17612v1 |
Korevaar, Hu Xu, Hugo Touvron, Iliyan Zarov, Imanol Arrieta Ibarra, Isabel M. Kloumann, Ishan Misra, Ivan Evtimov, Jade Copet, Jaewon Lee, Jan Geffert, Jana Vranes, Jason Park, Jay Mahadeokar, Jeet Shah, Jelmer van der Linde, Jennifer Billock, Jenny Hong, Jenya Lee, Jeremy Fu, Jianfeng Chi, Jianyu Huang, Jiawen Liu, Ji... | https://arxiv.org/abs/2505.17612v1 |
Wang, Yangyi Chen, Lifan Yuan, Yizhe Zhang, Yunzhu Li, Hao Peng, and Heng Ji. Executable code actions elicit better LLM agents. In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024 . OpenReview.net, 2024. URL https://openreview.net/forum?id=jJ9BoXAfFa . [22] Takeshi ... | https://arxiv.org/abs/2505.17612v1 |
Association for Computational Linguistics, 2024. [32] Xunyu Zhu, Jian Li, Yong Liu, Can Ma, and Weiping Wang. Distilling mathematical reasoning capabilities into small language models. Neural Networks , 179:106594, 2024. URL https: //doi.org/10.1016/j.neunet.2024.106594 . [33] Noah Shinn, Federico Cassano, Ashwin Gopin... | https://arxiv.org/abs/2505.17612v1 |
Yuchuan Wu, and Yongbin Li. Enhanc- ing the general agent capabilities of low-paramter LLMs through tuning and multi-branch 14 reasoning. In Kevin Duh, Helena Gomez, and Steven Bethard, editors, Findings of the As- sociation for Computational Linguistics: NAACL 2024 , pages 2922–2931. Association for Computational Ling... | https://arxiv.org/abs/2505.17612v1 |
Jeffrey Dean. Distilling the knowledge in a neural network. arXiv , 1503.02531, 2015. URL http://arxiv.org/abs/1503.02531 . [55] Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. OpenAI blog , 1(8):9, 2019. [56] Aymeric Roucher, Albert... | https://arxiv.org/abs/2505.17612v1 |
. OpenReview.net, 2024. URL https://openreview.net/forum?id=jp3gWrMuIZ . [67] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging llm-as-a-judge with mt-bench and chatbot arena. In Ali... | https://arxiv.org/abs/2505.17612v1 |
Alaa Saade, Alexander Kolesnikov, Alexei Bendebury, Alvin Abdagic, Amit Vadi, András György, André Susano Pinto, Anil Das, Ankur Bapna, Antoine Miech, Antoine Yang, Anto- nia Paterson, Ashish Shenoy, Ayan Chakrabarti, Bilal Piot, Bo Wu, Bobak Shahriari, Bryce Petrini, Charlie Chen, Charline Le Lan, Christopher A. Choqu... | https://arxiv.org/abs/2505.17612v1 |
work contributes toward the development of small language agents capable of running on local devices, enabling functional on-device AI that can retrieve information from external knowledge sources (including the web) and perform code-based action to complete complex tasks. On the positive side, this advancement promote... | https://arxiv.org/abs/2505.17612v1 |
prepending the first CoT step to the agent’s reasoning improves its capabilities, which in turn benefits distillation, as shown in Table 2. D.2 Failure case analysis of agent on the math subcategory In example D.1, we present a failure case of the distilled 3B agent on a level 2 precalculus problem. In this instance, t... | https://arxiv.org/abs/2505.17612v1 |
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