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specifically trained for batch processing using the original RoPE. Subse- quently, we applied group position encoding to the batch processing scenario and fine-tuned the model. The results demonstrate that applying group posi- tion encoding introduces no performance degrada- tion for batch processing, confirming its co... | https://arxiv.org/abs/2505.16983v1 |
in the absence of explicit positional encoding (Shen et al., 2018). A plausible explanation is that causal attention masks enforce position-dependent token interac- tions, implicitly encoding positional information (Haviv et al., 2022; Tsai et al., 2019). Related re-search has shown that in tasks such as speech mod- el... | https://arxiv.org/abs/2505.16983v1 |
Spoken Language Translation , pages 2–14. Junkun Chen, Mingbo Ma, Renjie Zheng, and Liang Huang. 2021. Direct simultaneous speech-to-text translation assisted by synchronized streaming asr. InFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , pages 4618–4624. Peikun Chen, Sining Sun, Changhao ... | https://arxiv.org/abs/2505.16983v1 |
Liu, Mengge Du, Xiaoxi Li, Ya Li, and Enhong Chen. 2021. Cross attention augmented transducer networks for simultaneous translation. In Proceed- ings of the 2021 Conference on Empirical Methods in Natural Language Processing . Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2024. Visual instruction tuning. Adv... | https://arxiv.org/abs/2505.16983v1 |
Ruslan Salakhutdinov. 2019. Transformer dissection: a unified understand- ing of transformer’s attention via the lens of kernel. arXiv preprint arXiv:1908.11775 . Emiru Tsunoo, Hayato Futami, Yosuke Kashiwagi, Sid- dhant Arora, and Shinji Watanabe. 2024. Decoder- only architecture for streaming end-to-end speech recogn... | https://arxiv.org/abs/2505.16983v1 |
refers to reprocessing all previously generated target tokens after each new source context is read, before generating the next target token. This is solely for optimizing the generation of the latest token without altering previously output content. We exclude scenarios where re-encoding continuously adjusts already o... | https://arxiv.org/abs/2505.16983v1 |
for streaming speech tasks. B.2 Data Format In this paper, all the large language models we selected are instruction-tuned versions. To fully leverage their instruction-following capabilities, we strictly adhere to the instruction format used during their pretraining phase. Additionally, we design the data format, as s... | https://arxiv.org/abs/2505.16983v1 |
encoding, preventing source tokens from attending to target tokens to eliminate input-attention mismatch and ensure streaming consistency. Figures 5 (d), (e), and (f) represent three different re-encoding scenarios, all of which share the same mask matrix. The core assumption of re-encoding is that as new content is co... | https://arxiv.org/abs/2505.16983v1 |
Wav2vec2’s transformer encoder with the transformer decoder from Wav2vec2-Streaming. This modification ensures that the model adheres to global unidirectional constraints. We then continue joint training of Wav2vec2-Streaming and the projector, improving the encoder’s ability to generate high-quality speech embeddings ... | https://arxiv.org/abs/2505.16983v1 |
10 20 30 40 50 60Position ID Relative Distance Matrix of batch-processing 011 23 35011 23 35 Position ID0 11 23 35 0 11 23 35Position ID Relative Distance Matrix of group-streaming = 0 0 10 20 30 40 50 60 Position ID0 10 20 30 40 50 60Position ID Relative Distance Matrix of group-streaming = 10 011 23 35011 23 35 Posit... | https://arxiv.org/abs/2505.16983v1 |
resulting in a relative distance of 0. This alignment effectively models the sequential nature of data accumulation in streaming settings, ensuring that the position encoding adapts dynamically to the progressive arrival of information. D.2 Why Group Position Avoids Confusion Research by (Shen et al., 2018; Haviv et al... | https://arxiv.org/abs/2505.16983v1 |
(5.45) 39.91 (5.44) 40.06 (5.41) 39.87 (5.44) 0.19 (0.03) 7 40.57 (7.38) 40.57 (7.38) 40.53 (7.37) 40.72 (7.39) 40.71 (7.39) 0.19 (0.02) 9 41.31 (9.28) 41.31 (9.28) 41.04 (9.29) 41.35 (9.27) 41.44 (9.27) 0.20 (0.02) 11 41.92 (11.17) 41.92 (11.17) 42.03 (11.17) 41.94 (11.17) 41.93 (11.17) 0.11 (0.00) En-De5 30.92 (4.65)... | https://arxiv.org/abs/2505.16983v1 |
a specific token. To better emphasize the distribution of target tokens’ attention toward a given token, we normalize the attention map along columns and apply a gamma transformation to enhance and amplify the relationships. Mathematically, given an attention matrix A, where Ai,jrepresents the attention weight from tok... | https://arxiv.org/abs/2505.16983v1 |
arXiv:2505.16984v1 [cs.LG] 22 May 2025UFT: Unifying Supervised and Reinforcement Fine-T uning Mingyang Liu1, Gabriele Farina1, Asuman Ozdaglar1 1LIDS, EECS, Massachusetts Institute of Technology 1{liumy19,gfarina,asuman}@mit.edu Abstract Post-training has demonstrated its importance in enhancing the reasoning capabilit... | https://arxiv.org/abs/2505.16984v1 |
cannot explore the correct answer during reinforcement learning (Pan et al., 2025), due to the sparse reward caused by the verifier-based reward model. On the other hand, when the LLM is strong, RFT generalizes better (Xie et al., 2025; Chu et al., 2025). To get the best of both worlds, we propose Unified Fine-Tuning (... | https://arxiv.org/abs/2505.16984v1 |
2025; Liu et al., 2025) suffers from an inherent sample complexity bottleneck, which is exponential in the length of the reasoning. In contrast, the unified training paradigm in UFT can improve the sample complexity to a polynomial dependence on the reasoning length, which is an exponential improvement over RFT. 1.1 Co... | https://arxiv.org/abs/2505.16984v1 |
branch aof state s, andT(s,a)=∅ fors∈ S H. The value function of policy πis written as Vπ:S → [0, 1]. We write sh0=s,(sh)H h=h0∼π as the trajectory starting from sand sampled according to π,i.e.,ah∼π(· |sh),sh+1=T(sh,ah). For 4 any h0∈{0, 1,· · ·,H}and s∈ S h0, we define Vπ(s):=Esh0=s,(sh)H h=h0∼π[R(sH)], which is the ... | https://arxiv.org/abs/2505.16984v1 |
hint length can cause a significant distribution shift, leading to unstable training (cf. Figure 4, right). To avoid distribution shift during training, Xi et al. (2024) samples the hint length uniformly from all possible values throughout the training. However, relying on hints throughout training introduces a signifi... | https://arxiv.org/abs/2505.16984v1 |
can see that the training curve of UFT is smoother and converges faster than that of the staged reinforcement learning. Note that staged reinforcement learning and UFT do not use any hint since step 300. As shown in Figure 5, although RFT (cosine), which is RFT equipped with the cosine annealing hint 2Pr(l=l0)=(L l0)pl... | https://arxiv.org/abs/2505.16984v1 |
close to the reference policy and (iii) memorizing the hint by maximizing the log-likelihood of producing the hint. Remark 3.1. The name of Unified Fine-Tuning (UFT) comes from the fact that when p≡0for all steps during training, (3.3) is equivalent to RFT, since β∑l−1 h=0logπ(a∗ h|s∗ h) = 0. When p≡1, then Jvalue (sh... | https://arxiv.org/abs/2505.16984v1 |
will show that UFT exponentially improves the sample complexity. The full algorithm can be found in Algorithm 2. Theorem 4.3 (Informal) .When βis small enough, Algorithm 2 obtains a 50% pass@1 success rate when the algorithm explores O BH5(logB)2 ∆2! (4.4) nodes in SH. 4The probability of reaching the correct answer wh... | https://arxiv.org/abs/2505.16984v1 |
hints to encode information about the solution in gradients. The proximity between the performance of UFT and SFT-RFT also supports the conclusion that UFT helps the model to memorize the solution when the model’s initial capacity is not enough to solve it. 5.2 The Generalization of UFT As shown in Figure 8, when the m... | https://arxiv.org/abs/2505.16984v1 |
gradient methods: Optimality, approximation, and distribution shift. Journal of Machine Learning Research , 22(98): 1–76, 2021. Yekun Chai, Haoran Sun, Huang Fang, Shuohuan Wang, Yu Sun, and Hua Wu. Ma-rlhf: Reinforcement learning from human feedback with macro actions. International Conference on Learning Representati... | https://arxiv.org/abs/2505.16984v1 |
2023. Mingyang Liu, Gabriele Farina, and Asuman Ozdaglar. A policy-gradient approach to solving imperfect- information games with iterate convergence. arXiv preprint arXiv:2408.00751 , 2024. Zichen Liu, Changyu Chen, Wenjun Li, Tianyu Pang, Chao Du, and Min Lin. There may not be aha moment in r1-zero-like training — a ... | https://arxiv.org/abs/2505.16984v1 |
for reasoning through reverse curriculum reinforcement learning. International Conference on Machine Learning (ICML) , 2024. Tian Xie, Zitian Gao, Qingnan Ren, Haoming Luo, Yuqian Hong, Bryan Dai, Joey Zhou, Kai Qiu, Zhirong Wu, and Chong Luo. Logic-rl: Unleashing llm reasoning with rule-based reinforcement learning. a... | https://arxiv.org/abs/2505.16984v1 |
trace (DeepSeek-AI et al., 2025; Zeng et al., 2025; Yu et al., 2025), e.g., whether the trace yields the correct answer to a math problem. Furthermore, Wang et al. (2023); Yuan et al. (2024); Zhong et al. (2024); Luo et al. (2024); Setlur et al. (2025) learn a step-by-step reward model from a collection of reasoning tr... | https://arxiv.org/abs/2505.16984v1 |
roughly $10,000 GPU hours. The experiment is based on VERL (Sheng et al., 2024) and TinyZero (Pan et al., 2025). The hyperparameters for training on different datasets are listed in Table 1. The omitted hyperparameters follow the default values of VERL (Sheng et al., 2024). B.3 Additional Results Figure 10 shows the re... | https://arxiv.org/abs/2505.16984v1 |
random. During each exploration, the algorithm requests the reward at a node in SH. Let s(1),s(2),. . .,s(T)be the leaf node reached at timestep 1, 2,. . .T, which are random variables depending on the randomness of the algorithm. Let S∗ H:= s∈ S H:R(s) =max s′∈SHR(s′) be the set of nodes representing correct solution... | https://arxiv.org/abs/2505.16984v1 |
t=1|S∗ H| BH−t+1(i) ≤T ∑ t=12|S∗ H| BH=2|S∗ H|T BH≤1 2. (i)uses the fact that t≤T≤BH 4|S∗ H|≤BH 2. Therefore, withinBH 4|S∗ H|exploration, the algorithm will fail to find the correct answer with probability at least 0.5. D Extended Theoretical Justifications In this section, we introduce some additional notations in Ap... | https://arxiv.org/abs/2505.16984v1 |
simplicity 3 Sample trajectory s(t) hH h=l(t)∼πθθθ(t), where s(t) l(t)=s∗ l(t) 4 forh=l(t),l(t)+1,· · ·H−1do 5 fora=1, 2,· · ·,Bdo // Group sampling 6 Sample trajectory s(t),a h′H h′=h+1∼πθθθ(t)starting from s(t),a h+1=T(s(t) h,a) 7 eQ(t) s(t) h,a ← R s(t),a H 8 end 9 fora=1, 2,· · ·,Bdo 10 eA(t) s(t) h,a ←eQ... | https://arxiv.org/abs/2505.16984v1 |
as µπ(T(s,a))=µπ(s)·π(s,a). (E.5) In the following, we will introduce Lemma E.4, which is a special case of the regret decomposition lemma (Lemma 5.1) in Liu et al. (2023). Specifically, it is the regret decomposition lemma for a two-player zero-sum extensive-form game without chance nodes5, and the second player’s act... | https://arxiv.org/abs/2505.16984v1 |
we can get a random variable from Bernoulli Prcond π(t)(pass @ 1 ) representing whether the trajectory reaches the correct solution. Then, by Hoeffding’s inequality, by sampling N trajectories, we have Pr eVπθθθ(t) (sroot)−Vπθθθ(t) (sroot) ≤∆ 12 ≤2 exp −N∆2 72 (i)=1 7(T+1). (E.6) (i)is by definition of Nin Algori... | https://arxiv.org/abs/2505.16984v1 |
lemma can be proved by induction. When H=1, Lemma E.4 holds since Qπ(t)(sroot,a) = R(T(sroot,a))=Qπ(sroot,a)for any action a∈[B]and t∈[T]. Therefore, ∑ s∈S\S HT ∑ t=1µπ(s)D Qπ(t)(s,·),π(· |s)−π(t)(· |s)E =T ∑ t=1µπ(sroot) ⟨Qπ(sroot,·),π(· |sroot)⟩−D Qπ(t))(sroot,·),π(t)(· |sroot)E =T ∑ t=1 Vπ(sroot)−Vπ(t)(sroot) . ... | https://arxiv.org/abs/2505.16984v1 |
|s)J(t−1)andβ0=0 in Lemma E.8, we have ηD ∇π(· |s)J(t−1),π∗(· |s)−π(t)(· |s)E ≤KL π∗(· |s)∥π(t−1)(· |s) −KL π∗(· |s)∥π(t)(· |s) −KL π(t)(· |s)∥π(t−1)(· |s) . Moreover, D ∇π(· |s)J(t−1),π∗(· |s)−π(t)(· |s)E =βπ∗(a∗ h|s∗ h)−π(t)(a∗ h|s∗ h) π(t−1)(a∗ h|s∗ h) (i) ≥0 (ii)=D eA(t−1)(s,·),π∗(· |s)−π(t)(· |s)E . (i)uses ... | https://arxiv.org/abs/2505.16984v1 |
arXiv:2505.16986v1 [cs.CL] 22 May 2025T1: A Tool-Oriented Conversational Dataset for Multi-Turn Agentic Planning Amartya Chakraborty∗, Paresh Dashore∗, Nadia Bathaee∗, Anmol Jain∗, Anirban Das ,Shi-Xiong Zhang ,Sambit Sahu ,Milind Naphade ,Genta Indra Winata∗ Capital One {amartya.chakraborty, paresh.dashore, nadia.bath... | https://arxiv.org/abs/2505.16986v1 |
for accessing and managing the cache. Our contributions can be summarized as follows: •We introduce T1, a comprehensive multi-turn dataset consisting of 13.5k dialogues designed to evaluate tool-using, LLM-based agents across nine key domains—comprising four single- domain and five multi-domain settings. The dataset co... | https://arxiv.org/abs/2505.16986v1 |
hotel star ratings—is synthesized to avoid inaccuracies and to prevent the LLMs from relying on its internal knowledge of real-world named entities. 2.2.1 Ontology We have a total of 5 ontologies, one for each of the four defined domains and another one for city. For each ontology, we curated a list of key attributes t... | https://arxiv.org/abs/2505.16986v1 |
flights, there are a total of 128 airports that are used to generate synthetic flight data. Each flight generated has an airline that was randomly selected from the ontology and the start and end airport are randomly selected from the list of airports in the ontology. Additionally, the departure time is randomly genera... | https://arxiv.org/abs/2505.16986v1 |
Appendix I and J. Additionally, the templates consist of placeholders for attributes such as a city or neighborhood name, cuisine of a restaurant, rating of a hotel or the type of an attraction. As the dataset is related to the area of travel, some placeholder values are tied to a particular city such as the neighborho... | https://arxiv.org/abs/2505.16986v1 |
the user mentions that they want a flight from New York to San Jose for 3 nights, the agent is able to extract the starting and ending airport city for the flight as well as the number of nights to stay at the hotel. However, neither search_flights orsearch_hotels can be called yet since the departure date of the fligh... | https://arxiv.org/abs/2505.16986v1 |
9% for validation, and 66% for testing, resulting in 15 training templates, 5 validation templates, and 40 test templates per category. Similarly, 13 cities are allocated for training, 4 for validation, and 37 for testing. Dialogues are randomly sampled within each partition. After partitioning, a script fills all plac... | https://arxiv.org/abs/2505.16986v1 |
In this section, we present the performance of various LLMs on both single-domain and multi-domain tasks. Single-domain tasks involve conversations focused exclusively on one domain—such as flights, hotels, restaurants, or attractions. In contrast, multi-domain tasks involve interactions spanning multiple domains, such... | https://arxiv.org/abs/2505.16986v1 |
struggle with generalization in complex, multi-domain scenarios. 8 Figure 4: Few-shot performance on Flight domain. Need for Complex Evaluation. Even with the state-of-the-art model, LLaMA 3.3 70B Instruct , which performs well on standard code generation tasks, we observe that it continues to struggle in complex, mult... | https://arxiv.org/abs/2505.16986v1 |
of open-source LLMs under realistic constraints. 9 7 Conclusion We introduce T1, a comprehensive dataset for evaluating planning, reasoning, and tool-usage in LLM-based agents through complex, multi-turn dialogues. By introducing inter-tool dependencies, dynamic replanning, and caching, it supports rigorous assessment ... | https://arxiv.org/abs/2505.16986v1 |
in Neural Information Processing Systems , 36:38154–38180, 2023. [14] M. Shridhar, X. Yuan, M.-A. Côté, Y . Bisk, A. Trischler, and M. Hausknecht. Alfworld: Aligning text and embodied environments for interactive learning. arXiv preprint arXiv:2010.03768 , 2020. 10 [15] Q. Tang, Z. Deng, H. Lin, X. Han, Q. Liang, B. Ca... | https://arxiv.org/abs/2505.16986v1 |
75.32 72.13 48.30 80.91 71.35 Restaurants 72.76 80.40 88.44 84.23 57.49 66.69 80.64 73.00 95.16 95.14 98.63 65.32 Attractions 90.50 97.87 92.33 95.02 75.54 91.34 81.37 86.07 98.15 N/A N/A 83.00 Multi-Domain. F: Flights, H: Hotels, R: Restaurants, A: Attractions F-H 80.27 91.31 86.91 89.05 57.76 74.51 71.99 73.23 89.25 ... | https://arxiv.org/abs/2505.16986v1 |
Restaurants, A: Attractions F-H 69.87 78.42 86.51 82.27 42.38 50.39 72.71 59.53 66.89 9.72 78.43 47.49 H-R 59.10 65.62 85.60 74.29 51.91 58.54 82.10 68.35 49.72 19.69 85.40 32.55 H-A 63.46 67.81 90.83 77.65 65.22 72.53 86.60 78.95 44.39 21.41 81.95 56.64 F-H-A 68.41 75.27 88.25 81.24 50.21 61.04 73.88 66.85 60.86 27.00... | https://arxiv.org/abs/2505.16986v1 |
51 204 Table 9: Overall Error Analysis Results using Llama 3.1 8B SFT Instruct. Domain # Turns # Validation Error # Variable Not Defined # Index Out Of Range # Other Single-Domain Flights 8,200 518 2 30 0 Hotels 8,000 41 0 0 0 Restaurants 7,900 29 0 0 0 Attractions 5,975 4 2 0 1 Multi-Domain. F: Flights, H: Hotels, R: ... | https://arxiv.org/abs/2505.16986v1 |
Index Out of Range errors and the 2nd highest amount of Variable Not Defined errors. D.5 Phi-4-reasoning-plus The Phi-4-reasoning-plus has 1498 validation errors which is the third most among the 5 models. The model does perform reasonably well with the other error categories and has the fewest number of Index Out Of R... | https://arxiv.org/abs/2505.16986v1 |
is_pet_friendly, has_spa_services, has_room_service, has_beach_access, has_fitness_class, has_laundry_service, has_valet_parking, has_balcony, has_rooftop_bar, has_inroom_Kitchen, has_kids_club, has_meeting_rooms, has_electric_vehicle_charging, has_hot_tub, has_sauna, has_free_parking, is_wheelchair_accessible, has_ski... | https://arxiv.org/abs/2505.16986v1 |
the hotel has a kids club has_meeting_rooms boolean Whether or not the hotel has meeting rooms has_electric_vehicle_charging boolean Whether or not the hotel has electric vehicle charging has_hot_tub boolean Whether or not the hotel has a hot tub has_sauna boolean Whether or not the hotel has a sauna has_free_parking b... | https://arxiv.org/abs/2505.16986v1 |
on May 29, 2025. assistant : That’s great! How many rooms do you need and what’s your budget per night? user: We need 3 room and our budget is around 550 per night. assistant : I see. What kind of amenities are you looking for in a hotel? user: We’d like a hotel with a gym, pool, and air conditioning. assistant : Got i... | https://arxiv.org/abs/2505.16986v1 |
attraction assistant : Hi there! What brings you here today? user: I’m planning a last-minute trip to Portland for 2 people. assistant : Last-minute trips can be exciting! What’s your budget for the hotel? user: We’re looking to spend around 55 per night. assistant : Okay, that helps. What’s your check-in and check-out... | https://arxiv.org/abs/2505.16986v1 |
at the start of each new template. •Assistant Behavior Diversity: –Vary personality: formal/informal, concise/verbose. –Ask both open-ended and specific questions. –Do not assume user intent or provide options in the first message. –Correct misunderstandings if needed. •User Behavior Diversity: –Include verbose, terse,... | https://arxiv.org/abs/2505.16986v1 |
dialogue. –No preamble, postamble, or markdown allowed. J.2 Multi-Domain Scenario Requirements 24 Domains Placeholders Used Required Scenarios Flight + Hotel <CITY_x> , <CLASS_x> , <DEPARTURE_DATE_x> , <CHECK_IN_DATE_x> , <CHECK_OUT_DATE_x> , <NUM_TRAVELERS_x>Trip planning involving both air travel and accommodation. I... | https://arxiv.org/abs/2505.16986v1 |
with a user turn. - Vary conversation lengths-some should be shorter, some longer. 3. Assistant Behavior Diversity: - Use distinctly different assistant personalities (formal, casual, verbose, concise). - Vary how information is requested (direct questions vs. open-ended). - Include templates where the assistant correc... | https://arxiv.org/abs/2505.16986v1 |
- Include users who change minds mid-conversation. - Include users who ask multiple things at once. - Include low-information and high-information users. 6. Conversation Flow Diversity: - Vary the sequence (flight -> hotel -> restaurant, or restaurant -> hotel -> flight, etc.). - Include users jumping between topics or... | https://arxiv.org/abs/2505.16986v1 |
If you generate a tool call and its result could be reused later, save it with save_to_cache("<key>",value). Ensure the cache key is unique and avoid naming collision with previously stored cache key name - If a result has already been stored in the cache for a conversation and no new result needs to be generated, do n... | https://arxiv.org/abs/2505.16986v1 |
Use the one which would be most relevant. - If you generate a tool call and its result could be reused later, save it with save_to_cache("<key>",value). Ensure the cache key is unique and avoid naming collision with previously stored cache key name - If a result has already been stored in the cache for a conversation a... | https://arxiv.org/abs/2505.16986v1 |
MASLab: A Unified and Comprehensive Codebase for LLM-based Multi-Agent Systems Rui Ye1Keduan Huang1Qimin Wu1 Yuzhu Cai1Tian Jin1Xianghe Pang1Xiangrui Liu1Jiaqi Su1 Chen Qian1Bohan Tang3Kaiqu Liang4Jiaao Chen5Yue Hu6Zhenfei Yin3,7 Rongye Shi8Bo An9Yang Gao10Wenjun Wu8Lei Bai2,†Siheng Chen1,† 1Shanghai Jiao Tong Universi... | https://arxiv.org/abs/2505.16988v1 |
algorithms. This gap results in several critical issues that hinder the field’s long-term advancement: (1) Redundant effort. Without shared, accessible resources, researchers expend significant time reimplementing existing works, diverting effort from innovative contributions. (2) Unfair comparison. Varied implementati... | https://arxiv.org/abs/2505.16988v1 |
Generalization: whether the method can generalize to handle diverse tasks. No. Methodology Venue Role Topo. Tool Optim. Generalization Single-Agent Baselines 1 Vanilla LLM - Fixed Fixed No No Yes 2 CoT [33] NeurIPS 2022 Fixed Fixed No No Yes Multi-Agent Systems for General Tasks 3 CAMEL [17] NeurIPS 2023 Fixed Fixed No... | https://arxiv.org/abs/2505.16988v1 |
of these frameworks are designed specifically for research purposes: they lack implementations of representative multi-agent methods from the existing literature and 3 Unified CodebasePre-processOptimizationInferenceEvaluation Shared Resources DiverseLLMsCommon To o l k i t sStreamlined Representation of 20+ MAS Method... | https://arxiv.org/abs/2505.16988v1 |
range of widely used LLMs. The integrated toolkit provides common utilities such as code execution (secured via sandboxing [ 52]), web search, and image analysis—capabilities frequently required across MAS designs. These shared components eliminate redundant engineering 4 effort and facilitate reproducibility. Moreover... | https://arxiv.org/abs/2505.16988v1 |
MAS output given the original query, and (2) the extracted code is executed against the provided set of test cases to determine correctness. This process ensures that evaluation focuses on functional validity and abstracts away from inconsistencies in format or verbosity. All executions are sandboxed [52] to guarantee ... | https://arxiv.org/abs/2505.16988v1 |
mathematics (MATH [ 55], GSM-Hard [ 60], AQUA-RAT [ 61], AIME-2024), science (SciBench [ 62], GPQA [ 63]), knowledge (MMLU-Pro [ 64]), medicine (MedMCQA [ 65]), coding (HumanEval [ 66], MBPP [67]), and AI-assistant (GAIA [68]). 4.1 Current Landscape of MAS Methods Comparisons of general MAS on diverse domains. We show ... | https://arxiv.org/abs/2505.16988v1 |
this setup. This discrepancy likely stems from AFlow’s LLM-based optimization being more effective than GPTSwarm’s numerical approach, suggesting that the strategy of optimization should be carefully considered to ensure effectiveness. Examining MAS as AI assistants. While our earlier experiments primarily focus on sta... | https://arxiv.org/abs/2505.16988v1 |
increasing the number of agents is more effective than increasing the number of rounds in this case. Scaling backend model size. We evaluate the impact of scaling backend model size by comparing three representative MAS methods and a single-agent baseline on GPQA-Diamond [ 63] and MMLU- Pro [ 64]. To this end, we adopt... | https://arxiv.org/abs/2505.16988v1 |
which encompasses the most diverse components dur- ing task-solving, making it an ideal case study for comprehensive failure analysis. Our analysis reveals that, in this context, failure cases account for 66% of all samples. However, only 36.7% of these failures stem from incorrect final answers, while 45.0% are attrib... | https://arxiv.org/abs/2505.16988v1 |
Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann. Bloomberggpt: A large language model for finance. arXiv preprint arXiv:2303.17564 , 2023. [10] Lexin Zhou, Wout Schellaert, Fernando Martínez-Plumed, Yael Moros-Daval, Cèsar Ferri, and José Hernández-Orallo. Larger and more instruct... | https://arxiv.org/abs/2505.16988v1 |
Industry Track) , pages 37–42, 2023. [23] Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. The ai scientist: Towards fully automated open-ended scientific discovery. arXiv preprint arXiv:2408.06292 , 2024. [24] Samuel Schmidgall, Yusheng Su, Ze Wang, Ximeng Sun, Jialian Wu, Xiaodong Yu,... | https://arxiv.org/abs/2505.16988v1 |
Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. Self-refine: Iterative refinement with self-feedback. Advances in Neural Information Processing Systems , 36, 2024. [37] Chen Qian, Zihao Xie, YiFei Wang, Wei Liu, Kunlun Zhu, Hanchen Xia, Yufan Dang, Zhuoyun Du, Weize Chen, Cheng Yang, Zhiyuan Liu, and Maosong... | https://arxiv.org/abs/2505.16988v1 |
with verbal reinforcement learning. Advances in Neural Information Processing Systems , 36:8634–8652, 2023. [52] Bytedance. Sandbox fusion: Versatile code sandbox for llms. https://bytedance.github.io/ SandboxFusion/ , 2025. Accessed: 2025-05-09. [53] Anthropic. Introducing the model context protocol. https://www.anthr... | https://arxiv.org/abs/2505.16988v1 |
, 2021. [67] Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. Program synthesis with large language models. arXiv preprint arXiv:2108.07732 , 2021. [68] Grégoire Mialon, Clémentine Fourrier, Thomas Wolf, Yann LeCun, and Th... | https://arxiv.org/abs/2505.16988v1 |
here. 14 RunOptimization Inference Optimizer Cost Executor Cost Val Acc Test Acc Cost MASLab 0.58251$ 19.05964$ 54.52 65.20 1.489$ Official - 19.52409$ 53.27 65.06 2.231$ Table 6: Comparisons of our implementation of AFlow [ 30] and the official one. The optimizer is Claude-3.5-Sonnet while the executor is GPT-4o-mini.... | https://arxiv.org/abs/2505.16988v1 |
Failure analysis. Throughout the experiments, we log MAS outputs and failure cases. After the experiments, we select results from the OWL-Roleplaying method running on the GPT-4.1 model and perform a detailed categorization and statistical analysis of the errors encountered. D Re-Implementation Notes D.1 MAS for Genera... | https://arxiv.org/abs/2505.16988v1 |
We also set the temperature to zero and ensure that when using GPT-3.5 as the execution model (same as in the original repo), the output remains exactly the same. These improvements significantly enhance the compatibility and extensibility of ADAS. AFlow [ 30].The official code in https://github.com/FoundationAgents/Me... | https://arxiv.org/abs/2505.16988v1 |
keeping all other external parameters consistent with the original defaults. D.5 MAS for Tool-Required Tasks OWL-Roleplaying [ 42].OWL ( https://github.com/camel-ai/owl ) is a framework for multi- agent collaboration. This framework includes OWL-Roleplaying as a MAS method specifically designed for the GAIA benchmark [... | https://arxiv.org/abs/2505.16988v1 |
R2EC: TOWARDS LARGE RECOMMENDER MODELS WITH REASONING Runyang You1Yongqi Li1∗Xinyu Lin2Xin Zhang1 Wenjie Wang3Wenjie Li1Liqiang Nie4 1The Hong Kong Polytechnic University2National University of Singapore 3University of Science and Technology of China4Harbin Institute of Technology (Shenzhen) runyang.y@outlook.com ,liyo... | https://arxiv.org/abs/2505.16994v1 |
hav e enjo y ed s t or y - rich open w orld ... answer ><Y ou...l m _ h e a dr e c _ h e a dA ssassin' s Cr eed - Shado ws U ser pr omp t T ok en EmbeddingHidden S t a t eT ok enIt emR e asonin gR e commen dFigure 1: The architecture overview of R2ec, which facilitates interleaved reasoning and recommendation in an aut... | https://arxiv.org/abs/2505.16994v1 |
solely using item recommendation metrics. Therefore, we propose a fused reward scheme that combines discrete ranking rewards with continuous similarity rewards. With the sampled trajectories and assigned rewards, a joint reasoning-and-recommendation training objective is introduced to optimize the model for improving t... | https://arxiv.org/abs/2505.16994v1 |
Among them, SLIM [ 32], LangPTune [ 15], and Rec-R1 [ 20] further optimize LLM for finer rationale generation with RL. However, such designs introduce high resource demands for training and serving both models, and the connection between two modules remains gradient-less, blocking true end-to-end learning, resulting in... | https://arxiv.org/abs/2505.16994v1 |
satisfaction or answer correctness. However, using Rdirectly in gradient estimates leads to high variance and unstable updates; instead, the advantage A, which measures how much better an action is than the expected return from that state, is adopted to reduce variance and improve sample efficiency [22, 23, 26, 34]. Sa... | https://arxiv.org/abs/2505.16994v1 |
scored by an inner product s(v) =h⊤ THV[v], v∈ V,which determines the final ranking. This mechanism yields a tight reasoning–recommendation coupling, since both the language-modeling head and recommendation head share the same hidden-state space as input, reasoning directly reshapes hTand thus yielding more accurate re... | https://arxiv.org/abs/2505.16994v1 |
makes token-level decisions to generate reasoning, then selects an item at the recommendation stage. Under this formulation, the importance ratio from Eqn. (3) extends to: ri,t(θ) = πθ(oi,t|xu,oi,<t) πθold(oi,t|xu,oi,<t),ift⩽T(reasoning) πθ(v+|xu,oi,⩽T) πθold(v+|xu,oi,⩽T),ift=T+ 1(recommendation) .(6) Specifically,... | https://arxiv.org/abs/2505.16994v1 |
0.0020 0.0094 0.0037 0.0192 0.0062 0.0082 0.0057 0.0141 0.0076 0.0253 0.0104 0.0054 0.0028 0.0104 0.0044 0.0197 0.0067 LangPTune 0.0127 0.0083 0.0224 0.0115 0.0348 0.0145 0.0074 0.0053 0.0156 0.0080 0.0208 0.0094 0.0049 0.0027 0.0088 0.0040 0.0140 0.0140 R2ec 0.0237 *0.0154 *0.0374 *0.0198 *0.0615 *0.0259 *0.0513 *0.03... | https://arxiv.org/abs/2505.16994v1 |
to w/oReasoning. These substantial gains demonstrate that our designed optimization have enabled R2ecto better leverage test-time scaling to deliver significantly stronger recommendation performance. 2) As revealed that w/oRc(using only Rd) consistently outperforms w/oRd(using only Rc), this indicates that adopting rew... | https://arxiv.org/abs/2505.16994v1 |
produces longer resoning and boosts recommendation performance, i.e.,, NDCG@5. A higher temperature introduces greater sampling entropy, allowing the model to explore a wider range of reasoning trajectories. 2) Conversely, it is found that increasing top- Kactually shortens the reasoning length and generally leads to a... | https://arxiv.org/abs/2505.16994v1 |
Large Language-Recommendation Assistant, May 2024. arXiv:2312.02445 [cs]. [2]Keqin Bao, Jizhi Zhang, Wenjie Wang, Yang Zhang, Zhengyi Yang, Yancheng Luo, Chong Chen, Fuli Feng, and Qi Tian. A Bi-Step Grounding Paradigm for Large Language Models in Recommendation Systems, December 2023. arXiv:2308.08434. [3]Keqin Bao, J... | https://arxiv.org/abs/2505.16994v1 |
Yang Zhang, Fengbin Zhu, Qifan Wang, Fuli Feng, and Xiangnan He. Reason4Rec: Large Language Models for Recommendation with Deliberative User Preference Alignment, February 2025. arXiv:2502.02061 [cs]. [17] Jieyong Kim, Hyunseo Kim, Hyunjin Cho, SeongKu Kang, Buru Chang, Jinyoung Yeo, and Dongha Lee. Review- driven Pers... | https://arxiv.org/abs/2505.16994v1 |
Wang, Changxin Tian, Binbin Hu, Yanhua Yu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou, Liang Pang, and Xiao Wang. [SLIM] Can Small Language Models be Good Reasoners for Sequential Recommendation?, March 2024. arXiv:2403.04260 [cs]. [33] Bowen Zheng, Zihan Lin, Enze Liu, Chen Yang, Enyang Bai, Cheng Ling, Wayne Xin Zhao, and Ji... | https://arxiv.org/abs/2505.16994v1 |
extra tokens from item features to present items, and then converts the user sequence into the sequence of the new item token for next-item generation. • BigRec [2] utilizes the item title to present the user sequence for recommendation generation. •D3[3] proposed to address amplification bias and homogeneity issues in... | https://arxiv.org/abs/2505.16994v1 |
User Prompt Analyze in depth and finally recommend next {category} I might purchase inside <answer> and </answer> . For example, <answer> a product </answer> . Below is my historical {category} purchases and ratings (out of 5): {% for hist in purchase_histories %} {% {hist.time_delta} ago: [ {hist.item_title} ] ({hist.... | https://arxiv.org/abs/2505.16994v1 |
domain knowledge acquired during pretraining and reinforced through RecPO, for better recommendation. 2) On the Video Games dataset, the models shift towards inferring user intent from the purchase sequence, speculating on the likely category or intended use of the next purchase. This demonstrates a shift from feature-... | https://arxiv.org/abs/2505.16994v1 |
At The El Mocambo[2 CD]] (5.0) 97d 7.5h ago: [Radio Silence] (5.0) 2d 16.5h ago: [Little Box ’O’ Snakes The Sunburst Years 1978-1982] (5.0) 2d 16.0h ago: [Goats Head Soup Super Deluxe] (5.0) R2ec(Qwen2.5-3B ) Given your history of purchasing and rating albums, it seems you have a strong preference for live recordings, ... | https://arxiv.org/abs/2505.16994v1 |
video games purchases and ratings (out of 5): 80d 1.6h ago: [For Apple Watch Fast Charger USB C, 2 in 1 Apple MFi Certified 3FT iWatch Charger Magnetic Charging Cable &PD USBC Fast Wall Charger Power Adapter for Apple Watch Series 6/SE/5/4/3/2/1&iPhone 14/13/12] (5.0) 76d 7.5h ago: [Kickstand Back Shell Holder Back Bra... | https://arxiv.org/abs/2505.16994v1 |
DecoupledESC: Enhancing Emotional Support Generation via Strategy-Response Decoupled Preference Optimization Chao Zhang, Xin Shi, Xueqiao Zhang, Yifan Zhu, Yi Yang, Yawei Luo∗ Zhejiang University {chao_zhang, yaweiluo}@zju.edu.cn Abstract Recent advances in Emotional Support Con- versation (ESC) have improved emotional... | https://arxiv.org/abs/2505.16995v1 |
the ESC field. How-arXiv:2505.16995v1 [cs.CL] 22 May 2025 ever, we observe that models still frequently ex- hibit common psychological errors (Gross, 2002; Raskin and Rogers, 2005; Stebnicki, 2007) during inference, which align with those identified in the FailedESConv dataset (§3.1). In addition, Zhao et al. (Zhao et ... | https://arxiv.org/abs/2505.16995v1 |
2 Related Work Emotional Support Conversation. Emotional Support Conversation (ESC) aims to alleviate users’ emotional distress through empathetic and support- ive responses. Liu et al. (Liu et al., 2021) first in- troduced the concept and built the ESConv dataset with 8 support strategies, 1.3k dialogues. They also re... | https://arxiv.org/abs/2505.16995v1 |
the SFT-generated responses frequently aligned with those found in FailedESConv, including: (1) Strat- egy Mismatch, (2) Lack of Empathy, (3) Early Emotion Shift, (4) Template Response, (5) Emo- tion Misread. The definitions and corresponding examples are detailed in Appendix A.2. Although SFT reduces some errors, the ... | https://arxiv.org/abs/2505.16995v1 |
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