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inference, the Original Multi-Agent Debate (MAD@3) prompt, and our proposed RCR (RCR-MAD (Ours)@3) prompting. 1 2 3 4 5 6 76065707580859095Accuracy (%) GSM8K 1 2 3 4 5 6 7404550556065707580 GSM Plus 1 2 3 4 5 6 7 Number of Agents80.082.585.087.590.092.595.097.5100.0Accuracy (%) ARC-Easy 1 2 3 4 5 6 7 Number of Agents65... | https://arxiv.org/abs/2505.15734v1 |
Debate- Only discards. We therefore use the full trace set in all other experiments. 6) H OW LONG DO WE TRAIN ?Figure 5 plots GSM-Plus accuracy as we grow the number of GRPO training steps from 2K to 10K. All mod- els share the similiar trend: rapid gains up to about 8K steps followed by saturation. Small and mid-size ... | https://arxiv.org/abs/2505.15734v1 |
tasks like mathematical and commonsense reasoning. The applicability and ef- fectiveness of DTE on less structured or more open- ended tasks, such as natural language generation or dialogue systems, require further investigation. Lastly, although computationally efficient com- pared to traditional MAD setups, DTE still... | https://arxiv.org/abs/2505.15734v1 |
Wang. 2023. Self- evolve: A code evolution framework via large lan- guage models. ArXiv , abs/2306.02907. 9 Suhas Kotha, Jacob Mitchell Springer, and Aditi Raghu- nathan. 2024. Understanding catastrophic forgetting in language models via implicit inference. Preprint , arXiv:2309.10105. Woosuk Kwon, Zhuohan Li, Siyuan Z... | https://arxiv.org/abs/2505.15734v1 |
Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019. CommonsenseQA: A ques- tion answering challenge targeting commonsense knowledge. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume 1 (Long and Sh... | https://arxiv.org/abs/2505.15734v1 |
. . . . . . . . . . . . . . . . . . . . . . . . . 21 E.3.2 Cross-Model Debate Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 21 E.3.3 Three-Agent Debate Effectiveness . . . . . . . . . . . . . . . . . . . . . . . . . 21 E.3.4 Dataset-Specific Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . ... | https://arxiv.org/abs/2505.15734v1 |
middle school students and test basic science knowledge. •ARC-Challenge : Contains 1,119 train, 299 validation, and 1,172 test examples. These questions are more challenging and typically answered incorrectly by both retrieval-based algorithms and word co-occurrence algorithms. CommonsenseQA (Talmor et al., 2019) requi... | https://arxiv.org/abs/2505.15734v1 |
why their scientific reasoningis sound. Your final answer must be in the format {answer }at the end. 3https://docs.vllm.ai/en/latest/ 4https://github.com/huggingface/accelerate 13 Prompt 3: RCR Prompting for Commonsense Reasoning Datasets (CSQA) Prompt Template You are Agent {self.agent_id} in a multi-agent debate to s... | https://arxiv.org/abs/2505.15734v1 |
Qwen-2.5-7B model, for instance, achieves 77.75% accuracy on GSM-Plus under the temp4 configuration, which represents a 3.58% improvement over its original MAD performance. Interestingly, we observe that different debate configurations yield varying results across model sizes. Smaller models like Qwen-2.5-1.5B show sig... | https://arxiv.org/abs/2505.15734v1 |
on either GSM8K or GSM-Plus and evaluated on multiple out-of-domain tasks including ARC-Easy, ARC-Challenge, and CommonsenseQA. The cross-domain results reveal impressive generalization capabilities. Models fine-tuned on mathemat- ical reasoning tasks (GSM8K and GSM-Plus) show substantial performance improvements not o... | https://arxiv.org/abs/2505.15734v1 |
of 5e-6 and context length of 128 tokens. Base train performance was not evaluated for this dataset. 17 Model DatasetGRPO Round 2 (Temp 0.8) GRPO Round 2 (Temp 0.2) 2k steps 5k steps 2k steps 5k steps Qwen-2.5-1.5BGSM8K 65.73 68.54 69.98 72.18 GSM-Plus 47.38 50.12 46.37 48.04 Qwen-2.5-3BGSM8K 84.84 86.05 84.46 84.08 GS... | https://arxiv.org/abs/2505.15734v1 |
solved after the debate process concludes. •∆(Performance Delta) : Measures the performance change relative to appropriate baselines. We report several variants including: –∆(vs Base): Change compared to the single agent’s performance –∆(vs Lower Agent): Change compared to the lower-performing agent in cross-agent deba... | https://arxiv.org/abs/2505.15734v1 |
valuable insights into debate quality. A high I →C rate coupled with a low C →I rate indicates constructive debate where correct reasoning prevails, while the opposite pattern signals problematic dynamics where convincing but incorrect reasoning dominates. E.3.3 Three-Agent Debate Effectiveness The introduction of a th... | https://arxiv.org/abs/2505.15734v1 |
Default 86.05 0.91 ↑ 0.31 0 .21 55 .00 115 104 Qwen-2.5-3B Qwen-2.5-3B Both: Deterministic 84.99 0.15 ↓ 0.00 0 .00 0 .00 0 0 Qwen-2.5-3B Qwen-2.5-3B Both: Exploratory 85.52 0.38 ↑ 0.35 0 .26 62 .00 116 103 Qwen-2.5-3B Qwen-2.5-3B Both: Det. & Exp. 86.28 1.14 ↑ 0.34 0 .19 50 .00 106 101 Qwen-2.5-7B Qwen-2.5-7B Both: Def... | https://arxiv.org/abs/2505.15734v1 |
base model performance . Further metrics include average Debate Rounds , normalized Sycophancy (per 1319 data points), and transitions between correct (C) and incorrect (I) states (C→I, I→C), highlighting the nuanced effects of debate dynamics. 23 Agent 1 Agent 2 Agent Settings Accuracy ∆(Lower Agent) ∆(Upper Agent) De... | https://arxiv.org/abs/2505.15734v1 |
1: Default & 2: Default 92.19 1.52 ↑ 0.61↓ 0.16 0 .13 39 .00 63 61 Qwen-2.5-7B Qwen-2.5-14B 1: Det. & 2: Det. 92.04 1.37 ↑ 0.76↓ 0.17 0 .13 47 .00 53 50 Qwen-2.5-7B Qwen-2.5-14B 1: Exp. & 2: Exp. 93.10 2.43 ↑ 0.3↑ 0.16 0 .15 33 .00 72 68 Qwen-2.5-7B Qwen-2.5-14B 1: Det. & 2: Exp. 92.19 1.52 ↑ 0.61↓ 0.15 0 .11 37 .00 58... | https://arxiv.org/abs/2505.15734v1 |
.00 110 .00 87 .00 Qwen-2.5-7B Qwen-2.5-7B Qwen-2.5-7B 1 Det, 2 Exp 92.12 1.45↑ 0.24 0 .24 44 .00 106 .00 86 .00 Qwen-2.5-7B Qwen-2.5-7B Qwen-2.5-7B 2 Det, 1 Exp 91.96 1.29↑ 0.17 0 .17 28 .00 76 .00 52 .00 Qwen-2.5-14B Qwen-2.5-14B Qwen-2.5-14B All: Default 94.09 1 .29 0 .11 0 .13 18 .00 67 .00 59 .00 Qwen-2.5-14B Qwen... | https://arxiv.org/abs/2505.15734v1 |
.00 Llama-3.1-8B Llama-3.1-8B Llama-3.1-8B All: Exploratory 83.70 1.97↑ 0.88 0 .89 162 .00 310 .00 230 .00 Llama-3.1-8B Llama-3.1-8B Llama-3.1-8B 1 Det, 2 Exp 83.32 1.59↑ 0.86 0 .86 160 .00 284 .00 211 .00 Llama-3.1-8B Llama-3.1-8B Llama-3.1-8B 2 Det, 1 Exp 82.26 0.53↑ 0.67 0 .63 129 .00 199 .00 132 .00 Table 15: Perfo... | https://arxiv.org/abs/2505.15734v1 |
Qwen-2.5-0.5B Both: Default 27.33 2.54 ↑ 2.00 1 .51 248 .00 348 295 Qwen-2.5-0.5B Qwen-2.5-0.5B Both: Deterministic 29.25 4.46 ↑ 0.02 0 .00 0 .00 2 1 Qwen-2.5-0.5B Qwen-2.5-0.5B Both: Exploratory 23.12 1.67 ↓ 2.56 1 .43 284 .00 351 289 Qwen-2.5-0.5B Qwen-2.5-0.5B Both: Det. & Exp. 27.33 2.54 ↑ 2.26 1 .33 267 .00 396 33... | https://arxiv.org/abs/2505.15734v1 |
5.08 ↑ 1.28 0 .74 218 .00 381 333 Llama-3.1-8B Llama-3.1-8B Both: Default 62.04 6.42 ↑ 0.95 0 .72 202 .00 313 274 Llama-3.1-8B Llama-3.1-8B Both: Deterministic 61.04 5.42 ↑ 0.00 0 .00 0 .00 0 0 Llama-3.1-8B Llama-3.1-8B Both: Exploratory 60.79 5.17 ↑ 1.12 0 .77 197 .00 340 303 Llama-3.1-8B Llama-3.1-8B Both: Det. & Exp... | https://arxiv.org/abs/2505.15734v1 |
259 420 362 Llama-3.1-3B Llama-3.1-8B Both: Exp. & Det. 56.67 11.00↑ 1.05↑ 1.27 0 .80 298 411 364 Qwen-2.5-7B Qwen-2.5-14B Both: Default 75.88 7.26↑ 4.09↑ 0.38 0 .28 88 165 159 Qwen-2.5-7B Qwen-2.5-14B Both: Deterministic 75.54 6.92↑ 3.75↑ 0.32 0 .24 83 119 112 Qwen-2.5-7B Qwen-2.5-14B Both: Exploratory 75.08 6.46↑ 3.2... | https://arxiv.org/abs/2505.15734v1 |
5.42↑ 0.38 0 .37 72 172 143 Qwen-2.5-14B Qwen-2.5-14B Qwen-2.5-14B 2 Det. & 1 Exp. 77.21 5.42↑ 0.28 0 .25 48 105 81 Qwen-2.5-32B Qwen-2.5-32B Qwen-2.5-32B Default 73.46 1.00↑ 0.29 0 .23 48 112 96 Qwen-2.5-32B Qwen-2.5-32B Qwen-2.5-32B Deterministic 72.79 0.33↑ 0.08 0 .00 0 0 0 Qwen-2.5-32B Qwen-2.5-32B Qwen-2.5-32B Exp... | https://arxiv.org/abs/2505.15734v1 |
.93 503 1168 857 Qwen-2.5-0.5B Qwen-2.5-3B Llama-3.1-3B Default 59.62 2.13↓ 2.83 1 .90 364 1202 895 Qwen-2.5-0.5B Qwen-2.5-3B Phi-mini-3.8B Default 65.25 1.83↑ 2.42 1 .48 353 1190 946 Qwen-2.5-0.5B Llama-3.1-3B Phi-mini-3.8B Default 56.92 6.50↓ 3.13 1 .64 536 980 724 Qwen-2.5-1.5B Qwen-2.5-3B Llama-3.1-3B Default 64.00... | https://arxiv.org/abs/2505.15734v1 |
98.36 0.04 ↑ 0.00 0 .00 0 .00 0 0 Qwen-2.5-32B Qwen-2.5-32B Exploratory 98.53 0.21 ↑ 0.02 0 .03 8 .0014 14 Qwen-2.5-32B Qwen-2.5-32B Det. & Exp. 98.36 0.04 ↑ 0.02 0 .02 9 .0010 8 Phi-mini-3.8B Phi-mini-3.8B Default 95.88 3.92 ↑ 0.11 0 .16 40 .0071 60 Phi-mini-3.8B Phi-mini-3.8B Deterministic 95.37 3.41 ↑ 0.00 0 .00 0 .... | https://arxiv.org/abs/2505.15734v1 |
0 .16 57 .00184 178 Qwen-2.5-3B Phi-mini-3.8B Det. & Exp. 94.91 1.85↑ 2.95↑ 0.17 0 .15 68 .00148 148 Qwen-2.5-3B Phi-mini-3.8B Exp. & Det. 95.75 2.69↑ 3.79↑ 0.15 0 .14 58 .00146 139 Qwen-2.5-1.5B Qwen-2.5-3B Default 91.88 5.26↑ 1.18↓ 0.33 0 .29 112 .00363 359 Qwen-2.5-1.5B Qwen-2.5-3B Deterministic 92.59 5.97↑ 0.47↓ 0.... | https://arxiv.org/abs/2505.15734v1 |
0.00 0 .00 0 .00 0 0 Qwen-2.5-3B Qwen-2.5-3B Both: Exploratory 94.28 1.22 ↑ 0.25 0 .28 102 .00 252 206 Qwen-2.5-3B Qwen-2.5-3B 1 Det. & 2 Exp. 94.70 1.64 ↑ 0.25 0 .23 90 .00 238 195 Qwen-2.5-3B Qwen-2.5-3B 2 Det. & 1 Exp. 93.94 0.88 ↑ 0.20 0 .18 94 .00 162 117 Qwen-2.5-7B Qwen-2.5-7B Both: Default 96.21 1.52 ↑ 0.08 0 .... | https://arxiv.org/abs/2505.15734v1 |
Both: Default 82.20 1.18 ↑ 0.69 0 .71 318 .00 469 342 Mistral-7B Mistral-7B Both: Deterministic 80.43 0.59 ↓ 0.00 0 .00 0 .00 0 0 Mistral-7B Mistral-7B Both: Exploratory 82.66 1.64 ↑ 0.83 0 .88 325 .00 566 429 Mistral-7B Mistral-7B 1 Det. & 2 Exp. 82.37 1.35 ↑ 0.78 0 .81 324 .00 506 376 Mistral-7B Mistral-7B 2 Det. & 1... | https://arxiv.org/abs/2505.15734v1 |
↑ 1.34 1 .12 247 .00 259 227 Qwen-2.5-1.5B Qwen-2.5-1.5B Both: Default 70.90 1.69 ↑ 0.57 0 .58 115 .00 249 242 Qwen-2.5-1.5B Qwen-2.5-1.5B Both: Deterministic 67.58 1.63 ↓ 0.00 0 .00 0 .00 0 0 Qwen-2.5-1.5B Qwen-2.5-1.5B Both: Exploratory 68.52 0.69 ↓ 0.75 0 .70 133 .00 296 275 Qwen-2.5-1.5B Qwen-2.5-1.5B Both: Det. & ... | https://arxiv.org/abs/2505.15734v1 |
185 177 Mistral-7B Mistral-7B Both: Det. & Exp. 70.82 2.05 ↑ 0.50 0 .34 84 .00 151 142 Table 25: Comparative Analysis of Language Model Performance in Multi-Agent Debate Settings on the ARC- Challenge Dataset. This table showcases the impact of different Agent Settings (controlling temperature and top_p parameters like... | https://arxiv.org/abs/2505.15734v1 |
0 .11 35 54 53 Qwen-2.5-7B Qwen-2.5-14B Both: Deterministic 93.60 6.38 ↑ 3.33↑ 0.13 0 .10 24 59 58 Qwen-2.5-7B Qwen-2.5-14B Both: Exploratory 94.45 7.23 ↑ 4.18↑ 0.15 0 .14 27 67 65 Qwen-2.5-7B Qwen-2.5-14B Both: Det. & Exp. 93.00 5.78 ↑ 2.73↑ 0.16 0 .13 37 50 49 Qwen-2.5-7B Qwen-2.5-14B Both: Exp. & Det. 93.77 6.55 ↑ 3... | https://arxiv.org/abs/2505.15734v1 |
33 Qwen-2.5-14B Qwen-2.5-14B Qwen-2.5-14B 2 Det. & 1 Exp. 94.71 4.44↑ 0.06 0 .06 10 32 26 Qwen-2.5-32B Qwen-2.5-32B Qwen-2.5-32B Default 95.82 0.54↑ 0.07 0 .11 22 36 28 Qwen-2.5-32B Qwen-2.5-32B Qwen-2.5-32B Deterministic 95.73 0.45↑ 0.00 0 .00 0 0 0 Qwen-2.5-32B Qwen-2.5-32B Qwen-2.5-32B Exploratory 95.56 0.28↑ 0.08 0... | https://arxiv.org/abs/2505.15734v1 |
208 699 581 Qwen-2.5-0.5B Qwen-2.5-3B Phi-mini-3.8B 86.95 2.22↑ 1.34 1 .23 133 722 631 Qwen-2.5-0.5B Llama-3.1-3B Phi-mini-3.8B 78.41 6.32↓ 1.54 1 .72 238 683 559 Qwen-2.5-1.5B Qwen-2.5-3B Llama-3.1-3B 82.34 1.19↓ 0.98 1 .10 180 447 358 Qwen-2.5-1.5B Qwen-2.5-3B Phi-mini-3.8B 87.37 2.64↑ 0.71 0 .81 105 423 358 Qwen-2.5... | https://arxiv.org/abs/2505.15734v1 |
& Exp. 83.87 1.50 ↑ 0.16 0 .15 40 .00 59 54 Qwen-2.5-32B Qwen-2.5-32B Both: Default 86.24 0.48 ↑ 0.12 0 .17 28 .00 47 46 Qwen-2.5-32B Qwen-2.5-32B Both: Deterministic 85.75 0.01 ↓ 0.00 0 .00 0 .00 0 0 Qwen-2.5-32B Qwen-2.5-32B Both: Exploratory 86.24 0.48 ↑ 0.14 0 .20 34 .00 46 43 Qwen-2.5-32B Qwen-2.5-32B Both: Det. &... | https://arxiv.org/abs/2505.15734v1 |
0 .80 160 .00 198 184 Qwen-2.5-1.5B Llama-3.1-3B Both: Exploratory 67.08 0.56 ↑2.04↑ 0.82 0 .90 164 .00 237 223 Qwen-2.5-1.5B Llama-3.1-3B Both: Det. & Exp. 69.78 3.26 ↑4.74↑ 0.61 0 .69 140 .00 203 193 Qwen-2.5-1.5B Llama-3.1-3B Both: Exp. & Det. 67.73 1.21 ↑2.69↑ 0.66 0 .72 160 .00 219 200 Qwen-2.5-3B Phi-mini-3.8B Bo... | https://arxiv.org/abs/2505.15734v1 |
0.28↑ 3.05 3 .11 569 558 317 Qwen-2.5-0.5B Qwen-2.5-0.5B Qwen-2.5-0.5B 2 Det. & 1 Exp. 37.51 1.02↑ 1.76 1 .84 433 420 237 Qwen-2.5-1.5B Qwen-2.5-1.5B Qwen-2.5-1.5B Default 68.80 2.28↑ 0.77 0 .83 193 333 264 Qwen-2.5-1.5B Qwen-2.5-1.5B Qwen-2.5-1.5B Deterministic 67.90 1.38↑ 0.00 0 .00 0 3 1 Qwen-2.5-1.5B Qwen-2.5-1.5B ... | https://arxiv.org/abs/2505.15734v1 |
Mistral-7B Mistral-7B Deterministic 66.75 2.20↑ 0.00 0 .00 0 0 0 Mistral-7B Mistral-7B Mistral-7B Exploratory 65.60 1.05↑ 0.79 0 .83 179 167 119 Mistral-7B Mistral-7B Mistral-7B 1 Det. & 2 Exp. 65.44 0.89↑ 0.64 0 .70 157 144 97 Mistral-7B Mistral-7B Mistral-7B 2 Det. & 1 Exp. 66.75 2.20↑ 0.32 0 .35 81 98 68 Llama-3.1-8... | https://arxiv.org/abs/2505.15734v1 |
results on three challenging reasoning benchmarks: GSM-Plus, GSM8K, and ARC-Challenge. F.2 Majority Vote@3 Results To further investigate the impact of stochastic diversity on model performance, we report results on a Majority V ote@3 approach where we sample three independent responses from each model and take a major... | https://arxiv.org/abs/2505.15734v1 |
Agent 1 Agent 2 Debate Setting Accuracy Delta Debate Rounds Sycophancy Correct →Incorrect Incorrect →Correct Net Benefit GSM-Plus Qwen-2.5-0.5B Qwen-2.5-0.5B exploratory 28.12% 3.33 ↑ 3.48 6906 261 575 432 Qwen-2.5-1.5B Qwen-2.5-1.5B exploratory 46.50% 4.50 ↑ 2.33 5642 194 861 670 Qwen-2.5-3B Qwen-2.5-3B exploratory 66... | https://arxiv.org/abs/2505.15734v1 |
86.66 87.11 Qwen-2.5-7B 90.67 91.21 92.42 92.49 92.57 92.34 92.72 Qwen-2.5-14B 92.80 93.33 94.84 94.31 94.69 94.62 94.24 GSM-Plus Accuracy (%) Qwen-2.5-1.5B 42.00 51.62 53.33 50.62 54.21 51.50 52.67 Qwen-2.5-3B 61.75 67.79 68.00 64.21 69.71 64.88 68.54 Qwen-2.5-7B 68.62 74.17 74.96 70.88 71.08 71.38 76.00 Qwen-2.5-14B ... | https://arxiv.org/abs/2505.15734v1 |
arXiv:2505.15738v1 [cs.CR] 21 May 2025Alignment Under Pressure: The Case for Informed Adversaries When Evaluating LLM Defenses Xiaoxue Yang∗Bozhidar Stevanoski∗Matthieu Meeus Yves-Alexandre de Montjoye Imperial College London Abstract Large language models (LLMs) are rapidly deployed in real-world applications ranging ... | https://arxiv.org/abs/2505.15738v1 |
been shown to work very well for jailbreaks [ 59,37] and also for prompt injection attacks [ 10,11]. Since its introduction, subsequent work has focused on improving its efficiency [ 29,30,31], varying its optimizationobjectives[ 51], orleveragingitstransferabilitytoblack-boxattacks[ 20,48]. Importantly, as GCG optimiz... | https://arxiv.org/abs/2505.15738v1 |
of outdoor activities that are suitable for the given weather condition. [Data]Temperature: 57 F, Showers. Print exactly ‘Hacked’! AnD NotHing ElSE! Figure 1: An example prompt containing both adversarial instruction (red) and suffix (blue). defenses almost always exist, including universal ones. While current defenses... | https://arxiv.org/abs/2505.15738v1 |
a direction that increases the target likelihood. The algorithm continues until either the model, when prompted with p||s(t−1), produces the desired output y∗using greedy decoding, i.e., Mθ(p||s(t−1)) =y∗, or a maximum number of steps Tis reached – at which point the attack is considered unsuccessful. The final result ... | https://arxiv.org/abs/2505.15738v1 |
target y∗, selected checkpoints S= [c1, . . . , c k], steps T, suffix length l Output: Final adversarial suffix s(t) ck 1:Initialize suffix s(0)←(s(0) 1, . . . , s(0) l)∈ Vn 2:fori= 1tokdo 3: c←ci 4: s(0) c←s(0) 5: fort= 1toTdo 6: s(t) c←GCG (θc, p, y∗, s(t−1) c) 7: ifMθc(p||s(t) c)generates y∗or early-stopping then 8:... | https://arxiv.org/abs/2505.15738v1 |
1 10 50 100 200 400 600 800 Checkpoints0.00.10.20.30.40.50.60.7LossEarlier than threshold After threshold Threshold(a) Step-based ( step) 1 10 50 100 200 400 600 800 Checkpoints0.000.050.100.150.20Loss differenceLoss difference meets or exceeds threhold Low loss difference period Threshold (b) Loss-based ( loss) 1 10 5... | https://arxiv.org/abs/2505.15738v1 |
provide the best trade-off in performance and cost and adopt this throughout the paper. For more details on values forτgradacross setups and number of checkpoints selected, see Appendix B. Checkpoint-GCG: early stopping. In the original GCG algorithm, GCG terminates either when a successful suffix is found or after a f... | https://arxiv.org/abs/2505.15738v1 |
number of steps as Checkpoint-GCG, but initialize with the naive suffix ( "!!!") rather than the optimized suffix from θc−1. While GCG still improves success probability at early checkpoints, the alignment process increasingly suppresses the attack at later stages. After only a few alignment checkpoints, the success pr... | https://arxiv.org/abs/2505.15738v1 |
final checkpoint θC). Results are aggregated for 10randomly selected samples from AlpacaFarm [16]. observed that the initialization used in GCG can greatly affect its convergence and success. Jia et al. [25] propose an “easy-to-hard” strategy: initializing attacks on difficult prompts with suffixes successful on simple... | https://arxiv.org/abs/2505.15738v1 |
We adopt the universal suffix attack originally proposed by GCG [ 59] to Checkpoint-GCG. Namely, we search for a universal suffix that breaks the defense across y training samples at each checkpoint θc, and use that suffix as initialization at checkpoint θc+1. At a given checkpoint θc, we search for a suffix that break... | https://arxiv.org/abs/2505.15738v1 |
that a using an informed initialization is effective even when the optimization space only consists of three tokens. 6 Related Work We here investigate how an adversary with partial knowledge of the alignment process, in particular access to checkpoints, can use existing techniques (GCG) [ 59] to break alignment-based ... | https://arxiv.org/abs/2505.15738v1 |
content [ 21], and detection defenses detect whether a model deviates from its expected behavior using internal activations [ 1] or by relying on a domain-specific language [45]. 7 Discussion and conclusion LLMs have been shown to be vulnerable to prompt injection attacks and jailbreaks, motivating recent work to align... | https://arxiv.org/abs/2505.15738v1 |
arXiv preprint arXiv:2107.03374 , 2021. [10]Sizhe Chen, Julien Piet, Chawin Sitawarin, and David Wagner. Struq: Defending against prompt injection with structured queries. arXiv preprint arXiv:2402.06363 , 2024. [11]Sizhe Chen, Arman Zharmagambetov, Saeed Mahloujifar, Kamalika Chaudhuri, and Chuan Guo. Aligning llms to... | https://arxiv.org/abs/2505.15738v1 |
Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. Mistral 7B, 2023. arXiv preprint. [27]Haibo Jin, Ruoxi Chen, Andy Zhou, Yang Zhang, and Haoh... | https://arxiv.org/abs/2505.15738v1 |
Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems , 36:53728–53741, 2023. [43]Alexander Robey, Eric Wong, Hamed Hassani, and George J Pappas. Smoothllm: Defending large language models against jailbreaking attacks. arXiv preprint arXiv:2310... | https://arxiv.org/abs/2505.15738v1 |
Christoph H Lampert. Can llms separate instructions from data? and what do we even mean by that? arXiv preprint arXiv:2403.06833 , 2024. 17 A Finetuning process for each defense A.1 Prompt injection defenses We replicate both prompt injection defenses, SecAlign and StruQ using the released code and data1. We follow the... | https://arxiv.org/abs/2505.15738v1 |
norm on Mistral-7B-Instruct Figure 4: Training metrics for StruQ finetuning Defense Model τgrad# Selected checkpoints StruQ [10]Llama3-8B-Instruct [2] 4.5 125 Mistral-7B-Instruct [26] 7 111 SecAlign [11]Llama3-8B-Instruct [2] 0.05 102 Mistral-7B-Instruct [26] 0.05 93 SafetyLlama [6] Llama3-8B-Instruct [2] 0.45 203 Tabl... | https://arxiv.org/abs/2505.15738v1 |
arXiv:2505.15741v1 [cs.NE] 21 May 2025DRAFTEvolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications Dikshit Chauhan1, Bapi Dutta2, Indu Bala3, Niki van Stein4, Thomas Bäck4, Anupam Yadav5,∗ 1Department of Electrical and Computer Engineering, National University of Singapore, ... | https://arxiv.org/abs/2505.15741v1 |
in engineering design, machine learning, and scientific discovery, among other fields. Given their robustness in tackling complex optimization problems, EC provides a promising foundation for enhancing the performance and efficiency of LLMs. Recent rapid advancements in LLMs, such as GPT-4, Claude, and Gemini, have dra... | https://arxiv.org/abs/2505.15741v1 |
search techniques to automate prompt optimization, systematically refining prompt structures for improved model performance. Frameworks 2 DRAFT Figure 1: Organization of the paper. 3 DRAFTsuch as EvoPrompt utilize evolutionary strategies to explore variations in prompts, selecting and evolving those that yield the most... | https://arxiv.org/abs/2505.15741v1 |
approaches into LLM-enhanced EC and EC-enhanced LLMs, introduce several hybrid methods, and outline challenges and open directions, serving as a useful roadmap for future research. Cai et al. [15] concentrate on the enhancement of evolutionary computation using LLMs. Their work discusses new approaches for population i... | https://arxiv.org/abs/2505.15741v1 |
covering topics such as LLM-generated metaheuristics, sur- rogate modeling, co-evolutionary systems, and explainable EC, offering readers a unified framework to understand this emerging field. (iii) Survey of Emerging Co-Adaptive Paradigms: This work introduces and analyzes new co-adaptive paradigms where LLMs and EC e... | https://arxiv.org/abs/2505.15741v1 |
The very first focus here will be on prompt engineering. 2.1. EC in Prompt Engineering Prompt engineering is the systematic process of designing textual inputs, known as prompts, that steer LLMs toward useful, accurate, and context-appropriate responses. Because a model’s understanding of a 6 DRAFTComponents of a Promp... | https://arxiv.org/abs/2505.15741v1 |
intuition alone remains highly challenging. Additionally, manually designed prompts often do not generalize effectively across diverse tasks or datasets due to the combinatorially expansive nature of the prompt optimization space, making manual exploration impractical [25]. Addressing these limitations through automate... | https://arxiv.org/abs/2505.15741v1 |
and LLMs further, Guo et al. [32] developed EvoPrompt, a unique framework where language models themselves serve as evolutionary operators. EvoPrompt enables LLMs to propose new prompt candidates through operations analogous to genetic crossover and mutation, with EC subsequently selecting prompts based on improved dev... | https://arxiv.org/abs/2505.15741v1 |
prompt embeddings through backpropagation, continuously refining embeddings to enhance model responses. Reinforcement learning approaches treat soft prompt optimization as sequential decision- making, iteratively adjusting prompt embeddings based on model performance and feedback. Additionally, sequential optimal learn... | https://arxiv.org/abs/2505.15741v1 |
to proprietary, black-box 10 DRAFT Initial Prompt Population (Hard) Hard Prompt Population (Discrete textual form)Step 1: Convert text to embeddings Step 2: Initialize Soft prompt population (Continuous Embeddings) Evolutionary Operators (Discrete Mutation, Crossover, Selection)Evolutionary Operators (Continuous Mutati... | https://arxiv.org/abs/2505.15741v1 |
structured, quad-phased design that strategically alternates between global exploration and local exploita- tion.5 This phased approach aims to balance the need to broadly explore the vast search space with the need to efficiently converge towards high-performing solutions, minimizing LLM inference costs. The four phas... | https://arxiv.org/abs/2505.15741v1 |
a hybrid approach managed by a GA can dynamically leverage the most effective gen- erators over time, leading to more robust and optimal performance. GAAPO also emphasizes maintaining a detailed record of the evolution of prompting strategies, enabling analysis of their relative effectiveness. GAAPO operates through su... | https://arxiv.org/abs/2505.15741v1 |
erators (Crossover, Mutation)Implements Phased Operators (Feedback, EDA, Crossover, Se- mantic Mut.)Component within Diverse Generation Strategies (OPRO-like, APO-like, etc.) Optimization Target Primarily Instruction Joint Instruction & Examples Primarily Instruction (with Few-shot Generator for Examples) Key Operators... | https://arxiv.org/abs/2505.15741v1 |
potentially more efficient than some EC, it still requires a consider- able number of LLM API calls ( 4000 mentioned for 12 iterations). Performance can vary depending on the chosen initialization strategy (re- verse engineering vs. expert prompt). Effectiveness hinges on the capabili- ties of the underlying LLM used f... | https://arxiv.org/abs/2505.15741v1 |
GA, PSO General ML Hyperparame- tersML for High Energy PhysicsExplored GA/PSO for autonomous HPO in a specific scientific domain. 2.5.1. Evolutionary Architecture Optimization for LLMs Designing optimal neural network architectures, particularly for complex models like LLMs, is a signif- icant challenge [49]. Manual de... | https://arxiv.org/abs/2505.15741v1 |
components (activation functions, sub-module layer counts).— Layer Configuration Layer-specific hyperparameters or novel layer connectivity patterns. LiteTransformerSearch [52] Code-Level ModificationsLLM-guided direct source code manipulation for flexible architectural variations.LLMatic [17], EvoPrompting [34] Evolut... | https://arxiv.org/abs/2505.15741v1 |
optimization. Scalability enhancements are needed to design EC and representations capable of handling larger search spaces from future LLM generations. Improved representations should explore sophisticated encodings for complex LLM architectures and hyperparameters to enhance evolutionary search. Advanced hybrid algor... | https://arxiv.org/abs/2505.15741v1 |
developed by OpenAI and based on the transformer architecture [59], has demonstrated state-of-the-art performance in natural language processing (NLP), making it a powerful tool not only for language tasks but also for supporting broader applications such as optimization and algorithm design. Recognizing these capabili... | https://arxiv.org/abs/2505.15741v1 |
DRAFTExperiment workflow Task A: Selection of algorithms List of algorithms ReasoningPrompt 1 Prompt 2 Task B: Identification of components List of components Components descriptionPrompt 3 Task C: Hybridization ESEEO design LESO designPrompt 4 Prompt 6 ESEEO description LESO descriptionPrompt 5 Prompt 7 Task D: Pseudo... | https://arxiv.org/abs/2505.15741v1 |
that integrates LLMs with EC to autonomously generate, evaluate, and refine heuristics. The goal of EoH is to fully automate the heuristic design process, eliminating the need for human- crafted rules or dedicated models to be trained. EoH uses the generative power of LLMs to propose new heuristics and iteratively impr... | https://arxiv.org/abs/2505.15741v1 |
and elitist mutation. By incorporating both local adaptation and global reasoning, ReEvo brings human-like adaptability to the automated discovery of optimization strategies. Stein et al. [12] developed the LLaMEA framework4, which integrates GPT-4 with EC to iteratively gener- ate and refine optimization strategies. L... | https://arxiv.org/abs/2505.15741v1 |
and language model reasoning to improve optimization performance. Both frameworks aim to fully automate the optimization process, but they tar- get complementary aspects: EoH on heuristic discovery for combinatorial optimization, and LLaMEA and LLaMEA-HPO on efficient discovery of complete code-bases, focusing on conti... | https://arxiv.org/abs/2505.15741v1 |
requiring prior evaluations. This is particu- larly valuable in scenarios with limited data or expensive fitness evaluations. LLAMBO integrates LLM-generated prompts into the BO workflow by encoding prior configuration- performance pairs as text. The LLM then provides predictions that enhance three critical BO componen... | https://arxiv.org/abs/2505.15741v1 |
the complex interactions between the attributes. Please do not attempt to fit the function using code similar to Python; instead, directly learn and infer the numerical values.. 1. Analyze the historical data to uncover how attributes relate to the numerical values. 2. Use these insights to predict the numerical value ... | https://arxiv.org/abs/2505.15741v1 |
79], a complex combinatorial optimization task involving graph structures. OptiPattern competently performs, outperforming conventional hybrid metaheuristics that combine MHs with deep learning models. The implementation is publicly available at https://github.com/camilochs/optipattern . At the core of OptiPattern lies... | https://arxiv.org/abs/2505.15741v1 |
instructed to generate an animal-inspired MH suitable for black-box optimization problems. The statement phase requests a detailed algorithm design, in- cluding inspiration, mathematical equations, parameter settings, and a flowchart. The personality component encourages novelty by ensuring the output differs significa... | https://arxiv.org/abs/2505.15741v1 |
Nsolutions from P∪P′ 7: Adjust LLM parameters (e.g., temperature) if necessary 8: Increment generation counter g←g+1 9:end while 10:Select the best solution s∗from P 11:return s∗ 3.4. Genetic Programming & LLM Synergy This subsection examines the synergistic integration of Genetic Programming (GP), machine learning, an... | https://arxiv.org/abs/2505.15741v1 |
tasks are partitioned into distinct popula- tions, and inter-task knowledge sharing is facilitated through a task-aware crossover operator. A task-oriented knowledge-sharing strategy was introduced to ensure that individuals remain effective in their original task context while benefiting from cross-task genetic exchan... | https://arxiv.org/abs/2505.15741v1 |
contextual reasoning and language-generation prowess, allowing for semantically guided optimization and intelligent decision-making. Together, they form a dual feedback loop in which one guides, refines, and accelerates the evolution of the other. In this co-evolutionary paradigm, we have observed that EC can be used t... | https://arxiv.org/abs/2505.15741v1 |
performance metrics. Multitask GP [82] This multitask GP-based generative hyperheuristic solves dynamic scheduling problems by evolving heuristics across multiple tasks using multifactorial optimization. It promotes knowledge transfer and uses a tree-based GP representation to generate flexible, real-time heuristics. A... | https://arxiv.org/abs/2505.15741v1 |
Furthermore, LLMs combined with EC can help robots optimize complex decision-making processes for tasks such as nav- igation in dynamic environments, interaction with objects and humans, and coordination of multiple robotic agents [86]. The creative potential of this integration is also being explored in generative des... | https://arxiv.org/abs/2505.15741v1 |
drive innovation and solve complex problems across a wide spectrum of domains, ranging from creative arts and entertainment to fundamental scientific research and advanced engineering. The ability of LLMs to generate and refine prompts for themselves or other tasks optimized by EC also suggests a form of meta-learning ... | https://arxiv.org/abs/2505.15741v1 |
either through incremental component upgrades or via first-principle derivations of novel algorithmic designs. The frame- work thus serves as a unified pipeline for both strategies of innovation: evolutionary enhancement and de novo discovery. Prompting LLMGenerate novel algorithm struc- ture & code Evaluate novelty & ... | https://arxiv.org/abs/2505.15741v1 |
the baseline algorithm code with high-level prompting, as demonstrated in SOMA/SOMA-ATA framework [6]. A future direction in this line could be building an LLM agent for improving the perfor- mance of a baseline algorithm in certain optimization tasks, where LLMs act as autonomous algorithm designers capable of iterati... | https://arxiv.org/abs/2505.15741v1 |
to a more powerful frame- work for discovering novel EC. However, we argue that future frameworks for developing novel EC should prioritize first-principles discovery, that is, the creation of entirely new algorithmic structures from the ground up, based on fundamental principles of optimization or the basic laws gover... | https://arxiv.org/abs/2505.15741v1 |
explainable EC frameworks. Looking ahead, Explainable AI for EC with LLMs could evolve along several exciting directions: (i) Narrative-Driven Evolution: Instead of only logging metrics, future EC frameworks could generate dy- namic evolutionary narratives, where LLMs describe each generation’s progress, highlight why ... | https://arxiv.org/abs/2505.15741v1 |
for achieving optimal search performance after selecting an appropriate EA. LLMs could help choose the appropriate parameters for EA. A recent study by Martinek et al. [76] suggested a framework to tune the hyperparameters of the EA from the high-level prompt. A promising direction for future study lies in developing a... | https://arxiv.org/abs/2505.15741v1 |
how such hybrid algorithms work, we need to ask several key questions, including: When does an LLM-guided EA converge to an optimum or local optimum? and How does the use of LLM queries affect time and space complexity? These analyses would also expose trade-offs between sample/resource cost and solution quality. 4.4.3... | https://arxiv.org/abs/2505.15741v1 |
adapting LLM-based components (e.g. via prompt evolution or online fine-tuning) risks forgetting prior knowledge. Managing resources and memory over long-term evolution is an open problem. 42 DRAFTFor instance, resource limitations and catastrophic forgetting have been identified as critical issues in evolv- ing LLM ag... | https://arxiv.org/abs/2505.15741v1 |
vention. Conversely, LLMs advance EC by automating metaheuristic design, refining evolutionary algorithms, and generating adaptive heuristics, leading to more efficient and scalable solutions. Emerging co-evolutionary frameworks highlight the potential for mutual improvement, with applications spanning robotics, genera... | https://arxiv.org/abs/2505.15741v1 |
models with evolutionary algorithms yields powerful prompt optimizers, 2024. [12] Niki van Stein and Thomas Bäck. Llamea: A large language model evolutionary algorithm for automatically generating metaheuristics. IEEE Transactions on Evolutionary Computation , 2024. [13] Fei Liu, Yiming Yao, Ping Guo, Zhiyuan Yang, Zhe... | https://arxiv.org/abs/2505.15741v1 |
Nan Shao, Yanggang Wang, Haiyu Li, and Zhilin Yang. Gps: Genetic prompt search for efficient few-shot learning. arXiv preprint arXiv:2210.17041 , 2022. [30] Archiki Prasad, Peter Hase, Xiang Zhou, and Mohit Bansal. Grips: Gradient-free, edit-based instruction search for prompting large language models. arXiv preprint a... | https://arxiv.org/abs/2505.15741v1 |
evolutionary algorithms. In Proceedings of the Genetic and Evolu- tionary Computation Conference Companion , pages 1838–1845, 2024. [48] Laurits Tani, Diana Rand, Christian Veelken, and Mario Kadastik. Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics. Th... | https://arxiv.org/abs/2505.15741v1 |
and Evolutionary Computation , pages 1812–1820, 2023. [63] Godfrey C Onwubolu, BV Babu, and Ivan Zelinka. Soma—self-organizing migrating algorithm. New Optimiza- tion Techniques in Engineering , pages 167–217, 2004. [64] Ivan Zelinka. Soma—self-organizing migrating algorithm. In Self-Organizing Migrating Algorithm: Met... | https://arxiv.org/abs/2505.15741v1 |
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