id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
49c489b00d67a57a3f147cb9877b0e2d5beac0528ca0ba0557188ef83f34424c | [
"arxiv"
] | PACT: Preserving Anchored Cores in Task-vectors for Model Merging | Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model. Most existing model merging approaches follow the Task Arithmetic paradigm, which decomposes fine-tuned weights into pre-trained parameters and task... | [
"Ningyuan Shi",
"Zhipeng Zhou",
"Hao Wang",
"Chunyan Miao",
"Peilin Zhao"
] | [
"cs.LG"
] | [] | 2026-06-17T00:00:00 | https://arxiv.org/abs/2606.18627 | https://arxiv.org/pdf/2606.18627v1 | 2606.18627 | null | 0 | 0 | false | null | null | 0.35 |
395f544d427cb59b539ac8d673e2264256ff312294d4e3e6ad6012bd549c970d | [
"arxiv"
] | Essential Subspace Merging for Multi-Task Learning | Model merging aims to enable multi-task learning by integrating the capabilities of multiple models fine-tuned from the same pre-trained checkpoint into a single model. Its core challenge is inter-task interference among task-specific parameter updates. In this paper, we analyze the output shifts induced by task update... | [
"Longhua Li",
"Lei Qi",
"Xin Geng",
"Qi Tian"
] | [
"cs.LG",
"cs.AI"
] | [] | 2026-06-17T00:00:00 | https://arxiv.org/abs/2606.19164 | https://arxiv.org/pdf/2606.19164v1 | 2606.19164 | null | 0 | 0 | false | null | null | 0.35 |
2df46214da6c29caf600f0569b82aabac9c2df46536dadd091d8a22ac969162d | [
"arxiv"
] | Enhancing Multilingual Reasoning via Steerable Model Merging | Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. It has achieved promising generalization in multilingual reasoning tasks by aligning feature spaces of different models. However, the merged single model often fails to address the conflicts between sou... | [
"Zhuoran Li",
"Rui Xu",
"Jian Yang",
"Junnan Liu",
"Zhijun Chen",
"Qianren Mao",
"Hongcheng Guo",
"Jiaheng Liu",
"Likang Xiao",
"Ming Li",
"Xiaojie Wang"
] | [
"cs.CL"
] | [] | 2026-06-17T00:00:00 | https://arxiv.org/abs/2606.19002 | https://arxiv.org/pdf/2606.19002v1 | 2606.19002 | null | 0 | 0 | false | null | null | 0.35 |
28890da352d2d2348f1e45e05f8bb8bd71ab5fd0755926ee200db6c818f73759 | [
"arxiv"
] | Task-Restricted Symmetries in Recurrent Weight Space | Recurrent networks can contain substantial functional redundancy in weight space: changing a recurrent matrix may leave the input-output rollout nearly unchanged on a task distribution, while similar-scale changes can destroy the same behavior. We study this redundancy in one-layer tanh RNNs using ordered real Schur co... | [
"Simon Dräger"
] | [
"cs.LG"
] | [] | 2026-06-16T00:00:00 | https://arxiv.org/abs/2606.18457 | https://arxiv.org/pdf/2606.18457v1 | 2606.18457 | null | 0 | 0 | false | null | null | 0.35 |
1b591adcb860ed122ac1b487df99fba218e8b7b048bbe8543967d98e2280a855 | [
"arxiv",
"semantic_scholar"
] | Post-Hoc Merging is Not Enough: Many-Shot Model Merging with Loss-Gap Balancing | Model merging has become a practical post-training strategy for building a single multi-task large language model (LLM) by combining multiple task-specialized models. However, most existing approaches rely on post-hoc merging, in which task-specific models are merged only once after training. This one-shot aggregation ... | [
"Kyungjin Im",
"Miru Kim",
"Chanin Eom",
"Minhae Kwon"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-06-15T00:00:00 | https://arxiv.org/abs/2606.16501 | https://arxiv.org/pdf/2606.16501v1 | 2606.16501 | null | 0 | 0 | false | null | null | 0.35 |
cf332f398fe5ffabea6f8897e661103c111d4ddbfcf5a148d042ce3f50248124 | [
"arxiv",
"semantic_scholar"
] | From Parameters to Feature Space: Task Arithmetic for Backdoor Mitigation in Model Merging | Model merging (MM) has gained significant attention as a cost-effective approach to integrate multiple task-specific models into a unified model. However, recent work reveals that MM is highly susceptible to backdoor attacks. Existing defenses based on task arithmetic often fail to eliminate backdoors without substanti... | [
"Zhenqian Zhu",
"Yamin Hu",
"Yiya Diao",
"Weixiang Li",
"Haodong Li",
"Wenjian Luo"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2026-06-10T00:00:00 | https://arxiv.org/abs/2606.12498 | https://arxiv.org/pdf/2606.12498v1 | 2606.12498 | null | 0 | 0 | false | null | null | 0.35 |
bd802d69ca3ac24ad1e580769fb44be3f2888cf6f4e649521cb82710bdb8a7a5 | [
"arxiv",
"semantic_scholar"
] | Closed-Form Spectral Regularization for Multi-Task Model Merging | Model merging combines several independently fine-tuned experts into a single multi-task model without any training data, reducing the storage, serving, and decentralized-development costs of large foundation models. State-of-the-art merging methods formulate merging as a layer-wise quadratic interference minimization ... | [
"Yongxian Wei",
"Runxi Cheng",
"Xingxuan Zhang",
"Li Shen",
"Chun Yuan",
"Peng Cui",
"Dacheng Tao"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2026-06-05T00:00:00 | https://arxiv.org/abs/2606.07289 | https://arxiv.org/pdf/2606.07289v1 | 2606.07289 | null | 0 | 0 | false | null | null | 0.35 |
147dd55918a7551461e87f20ec491db53a9fceb48bdc3a31e2173ad7b1ae29d9 | [
"arxiv",
"semantic_scholar"
] | TaDA: Calibrated Probe Gating for Task-Domain LoRA Merging | Combining a task LoRA adapter with a domain LoRA adapter into a single unified model is a practical yet largely unexplored challenge. Existing methods treat both adapters as symmetric peers, applying uniform weights across all layers. We argue that task and domain adapters exhibit a consistent depth-dependent asymmetry... | [
"Huy Quoc To",
"Fuyi Li",
"Guangyan Huang",
"Ming Liu"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-06-03T00:00:00 | https://arxiv.org/abs/2606.05016 | https://arxiv.org/pdf/2606.05016v1 | 2606.05016 | null | 0 | 0 | false | null | null | 0.35 |
4e49f8d87029823b017e56d722de2f7801937a2a0c1695f02e83aaed3e48dd2b | [
"arxiv",
"semantic_scholar"
] | RogueMerge: Robust and Unified Attacks against LLM Model Merging | Model merging composes specialized capabilities into a single LLM by aggregating task vectors sourced from unverified public platforms, exposing a critical supply-chain attack surface: Because any malicious behavior can be encoded into a task vector, and merging grants third-party vectors direct write access to model w... | [
"Jinghuai Zhang",
"Yetian He",
"Kunlin Cai",
"Han Zhao",
"Fnu Suya",
"Yuan Tian"
] | [
"cs.CR",
"cs.LG"
] | [
"Computer Science"
] | 2026-06-02T00:00:00 | https://arxiv.org/abs/2606.03344 | https://arxiv.org/pdf/2606.03344v1 | 2606.03344 | null | 0 | 0 | false | null | null | 0.35 |
456376c706209431c598f624319e233b0fd495ede4262d4232c71457c3843746 | [
"arxiv",
"semantic_scholar"
] | Decentralized Instruction Tuning: Conflict-Aware Splitting and Weight Merging | Instruction tuning aligns large language models, including multimodal ones, with diverse user intents, but scaling to heterogeneous mixtures is hindered by gradient interference and bandwidth-heavy synchronization. We ask whether these two bottlenecks can be addressed jointly by training parts of the mixture independen... | [
"Minsik Choi",
"Geewook Kim"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-06-01T00:00:00 | https://arxiv.org/abs/2606.01717 | https://arxiv.org/pdf/2606.01717v1 | 2606.01717 | null | 0 | 0 | true | https://github.com/naver-ai/merit | null | 0.65 |
5078b7222424a78bbcb406dd8820db45bbaf87f409cd09627c0404c5a277f551 | [
"arxiv",
"semantic_scholar"
] | ThinkSwitch: Context Distillation with LoRA and Weight Interpolation for Specific-Purpose Reasoning Tasks | Large language models often improve on difficult tasks by spending inference-time compute on a reasoning trace before producing the final answer. That extra computation can be useful, but it also raises latency, token cost, and deployment complexity. We introduce \textbf{ThinkSwitch}, a low-compute procedure for co-tra... | [
"Dhruv Saini",
"Rohan Pandey"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-31T00:00:00 | https://arxiv.org/abs/2606.01080 | https://arxiv.org/pdf/2606.01080v1 | 2606.01080 | null | 0 | 0 | false | null | null | 0.35 |
aee8014d45f7f9672028b1ebd4be4ef199a587f45b75e86b1b226c6390100878 | [
"arxiv",
"semantic_scholar"
] | Saliency-Aware Model Merging | Model merging aims to consolidate multiple task-specific models fine-tuned on different datasets into a unified architecture that performs cross-domain proficiency. Current data-free model merging methods often struggle to scale as they rely on simple parameter-level heuristics that ignore inter-layer dependencies and ... | [
"Jungin Park",
"Jiyoung Lee",
"Kwanghoon Sohn"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2026-05-30T00:00:00 | https://arxiv.org/abs/2606.00511 | https://arxiv.org/pdf/2606.00511v1 | 2606.00511 | null | 0 | 0 | false | null | null | 0.35 |
412e8b6e48974ff122c31dc2b22eef04d34eb995c98ab6d0a5cbc6399dcc2ec5 | [
"arxiv",
"semantic_scholar"
] | Access Sets Matter: Budgeting Expert Reads for Scalable Weight-Space Model Merging | Weight-space model merging is usually formulated as an algebraic operation on checkpoints, yet at LLM scale the limiting resource is often the set of expert weights that must be read. We introduce MergePipe, a budget-aware execution layer that casts LLM merging as an \emph{expert access-set} problem: given a merge oper... | [
"Yuanyi Wang",
"Yanggan Gu",
"Su Lu",
"Yifan Yang",
"Zhaoyi Yan",
"Congkai Xie",
"Jianmin Wu",
"Hongxia Yang"
] | [
"cs.LG",
"eess.SY"
] | [
"Computer Science",
"Engineering"
] | 2026-05-28T00:00:00 | https://arxiv.org/abs/2605.29489 | https://arxiv.org/pdf/2605.29489v1 | 2605.29489 | null | 0 | 0 | false | null | null | 0.35 |
12df446b2647a3b452f7fe785de9055821053696a351e665f0c00b57c56a720d | [
"arxiv",
"semantic_scholar"
] | Model Merging by Output-Space Projection | Model merging combines fine-tuned checkpoints into a single multi-task model without retraining. Existing methods - such as task arithmetic, model soups, TIES, and DARE - are computationally efficient and empirically successful, but rely on heuristic design choices and lack formal optimality guarantees. We show that me... | [
"Bethan Evans",
"Benjamin Etheridge",
"Stephen Roberts",
"Jared Tanner"
] | [
"cs.LG",
"cs.IT"
] | [
"Computer Science",
"Mathematics"
] | 2026-05-27T00:00:00 | https://arxiv.org/abs/2605.29101 | https://arxiv.org/pdf/2605.29101v1 | 2605.29101 | null | 0 | 0 | false | null | null | 0.35 |
67b9cba3834464d319f7ef9b44251449cc6198e7fb911bab4dc7d715ce1a9991 | [
"arxiv",
"semantic_scholar"
] | What-If World: A Causal Benchmark for General World Models in Embodied Scenarios | Video generation models are increasingly used as world simulators for tasks like driving and robotic manipulation. What matters in these settings is not whether a single video looks right, but whether the model's output changes when its input changes. We test this by giving a model two prompts describing the same scene... | [
"Kunlin Cai",
"Rui Song",
"Jinghuai Zhang",
"Kaiyuan Zhang",
"Pranav Bodapati",
"Alicia Yu",
"Fnu Suya",
"Mohammad Rostami",
"Jiaqi Ma",
"Yuan Tian"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-05-26T00:00:00 | https://arxiv.org/abs/2605.27589 | https://arxiv.org/pdf/2605.27589v1 | 2605.27589 | null | 0 | 0 | true | null | null | 0.65 |
a54dc89832d4feea79b4c2458de043b65a0686836f484fe0362be31cd84b4e92 | [
"arxiv",
"semantic_scholar"
] | Model Merging on Loss Landscape: A Geometry Perspective | Model merging offers a promising avenue for knowledge integration and parallel development without retraining. Yet, existing methods either ignore the geometry of the loss landscape or rely on intractable full-space Hessian approximations. We propose EpiMer, a framework that casts model merging as solving the Fréchet m... | [
"Juanwu Lu",
"Anand Bhaskar",
"Brian Axelrod",
"Ekaterina Tolstaya",
"Tristan Emrich"
] | [
"cs.LG",
"cs.AI",
"stat.ML"
] | [
"Computer Science",
"Mathematics"
] | 2026-05-26T00:00:00 | https://arxiv.org/abs/2605.26693 | https://arxiv.org/pdf/2605.26693v1 | 2605.26693 | null | 0 | 0 | false | null | null | 0.35 |
2620bf3007c1fffa0d289e418af177aba55134e727d5a7148ef8f40fbd958dc4 | [
"arxiv",
"semantic_scholar"
] | On the Limits of Model Merging for Multilinguality in Pre-Training | Endowing models with consistent multilingual performance can be achieved by mixing pre-training data, or post-training approaches such as language-specific model merging. In this work, we test whether merging can be applied to monolingually pre-trained models. We conduct a controlled study on the efficacy of mixed, mer... | [
"Seth Aycock",
"Fedor Vitiugin",
"Aleksandr Umnov",
"Christof Monz",
"Khalil Sima'an"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-25T00:00:00 | https://arxiv.org/abs/2605.25846 | https://arxiv.org/pdf/2605.25846v1 | 2605.25846 | null | 0 | 0 | false | null | null | 0.35 |
05faab4447d90c658ab877b93dc73703d4326f5591fd5014731e03717ac3696c | [
"arxiv",
"semantic_scholar"
] | Causal Physics Steering in Video World Models via Concept Activation Vectors | Video world models learn representations of physical dynamics, but controlling their physical expectations at inference time remains an open problem. Recent interpretability work identified a Physics Emergence Zone (PEZ), a group of middle transformer layers in VideoMAE where physical plausibility is represented separa... | [
"Nahid Alam"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-05-23T00:00:00 | https://arxiv.org/abs/2605.24322 | https://arxiv.org/pdf/2605.24322v1 | 2605.24322 | null | 0 | 0 | false | null | null | 0.35 |
2d9ae35b0561209cbb693daaff9e23b48160e3d2adf8b2dc4d05cc9cb6bf4984 | [
"arxiv",
"semantic_scholar"
] | GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation | Video world models can generate realistic futures from a single instruction, but they often fail to track the same physical points consistently across time. As a result, the generated videos appear plausible, yet lack the physical grounding required for reliable action execution, such as robot manipulation. We present ... | [
"Kaichen Zhou",
"Yuzhen Chen",
"Fangneng Zhan",
"Hang Hua",
"Grace Chen",
"Xinhai Chang",
"Ao Qu",
"Yilun Du",
"Zhuang Liu",
"Paul Pu Liang",
"Mengyu Wang"
] | [
"cs.CV",
"cs.RO"
] | [
"Computer Science"
] | 2026-05-20T00:00:00 | https://arxiv.org/abs/2605.22882 | https://arxiv.org/pdf/2605.22882v3 | 2605.22882 | null | 1 | 0 | false | null | null | 0.35 |
1dc8d26a5e3de54d49e2b766ae1778c983910bfa265dcdb3482e7e1403e251fe | [
"arxiv",
"semantic_scholar"
] | Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning | Continual learning (CL) aims to train models sequentially on multiple tasks while mitigating catastrophic forgetting of previously learned knowledge. Recent advances in large pre-trained models (LPMs) and model merging techniques, such as MAGMAX, have demonstrated effective CL performance by combining task-specific par... | [
"Kei Hiroshima",
"Kento Uchida",
"Shinichi Shirakawa"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-20T00:00:00 | https://arxiv.org/abs/2605.20803 | https://arxiv.org/pdf/2605.20803v1 | 2605.20803 | null | 0 | 0 | false | null | null | 0.35 |
e7b8fedf7f260f82ca10f1f5cac674f55430d72fa9565399e574385de9cf7939 | [
"arxiv",
"semantic_scholar"
] | Unlocking the Potential of Continual Model Merging: An ODE Perspective | Continual Model Merging (CMM) enables rapid customization of foundation models by sequentially incorporating task-adapted models without repeated retraining. However, existing merging rules usually update the deployed model through fixed algebraic or projection-based operations, providing limited control over how much ... | [
"Lihong Lin",
"Haidong Kang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-19T00:00:00 | https://arxiv.org/abs/2605.19409 | https://arxiv.org/pdf/2605.19409v3 | 2605.19409 | null | 0 | 0 | false | null | null | 0.35 |
aa6c8164da3fdde4a93cb23ca38cc6dc81f2015e9e31df2f32fcbd76fb0ae033 | [
"arxiv",
"semantic_scholar"
] | Distilling Linearized Behavior into Non-Linear Fine-Tuning for Effective Task Arithmetic | Task vector composition has emerged as a promising paradigm for editing pre-trained models, enabling model merging through addition and unlearning through subtraction. Fine-tuning in the tangent space of a pre-trained model (linear fine-tuning) has proven effective, as it produces task vectors that are naturally disent... | [
"Thomas Sommariva",
"Francesca Morandi",
"Simone Calderara",
"Angelo Porrello"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-18T00:00:00 | https://arxiv.org/abs/2605.18993 | https://arxiv.org/pdf/2605.18993v2 | 2605.18993 | null | 0 | 0 | false | null | null | 0.35 |
d17440f0bf7bba9cbaf5113028c821a5f47ce81a85eb4f2ee19a2a437b20ca3d | [
"arxiv",
"semantic_scholar"
] | Dynamic Model Merging Made Slim | Model merging enables the reuse of fine-tuned models without joint training or access to original data. Dynamic merging further improves flexibility by selectively activating task-relevant parameters and efficiently composing experts across multiple tasks. However, existing dynamic methods either maintain a full shared... | [
"Guodong Du",
"Wanyu Lin"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2026-05-17T00:00:00 | https://arxiv.org/abs/2605.18904 | https://arxiv.org/pdf/2605.18904v1 | 2605.18904 | null | 2 | 0 | false | null | null | 0.35 |
6613298ee5f564f65b60c55ffbcb7d084fe4918cb2546935c9bc0a29ebd0569b | [
"arxiv",
"semantic_scholar"
] | E-PMQ: Expert-Guided Post-Merge Quantization with Merged-Weight Anchoring | Low-resource deployment constraints have made model quantization essential for deploying neural networks while preserving performance. Meanwhile, model merging has become an increasingly practical low-resource strategy for integrating multiple task- or domain-specialized experts into a single model without joint traini... | [
"Wenjun Wang",
"Yanggan Gu",
"Shuo Cai",
"Yuanyi Wang",
"Pengkai Wang",
"Jianmin Wu",
"Hongxia Yang"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-05-16T00:00:00 | https://arxiv.org/abs/2605.16882 | https://arxiv.org/pdf/2605.16882v1 | 2605.16882 | null | 2 | 0 | false | null | null | 0.35 |
c839223e80b83e549e5d2ea221266f31441717b2990ec107efe364fb1e82d2de | [
"arxiv",
"semantic_scholar"
] | Bayesian Model Merging | Model merging aims to combine multiple task-specific expert models into a single model without joint retraining, offering a practical alternative to multi-task learning when data access or computational budget is limited. Existing methods, however, face two key limitations: (1) they overlook the valuable inductive bias... | [
"Kaiyang Li",
"Shaobo Han",
"Qing Su",
"Shihao Ji"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-13T00:00:00 | https://arxiv.org/abs/2605.12843 | https://arxiv.org/pdf/2605.12843v1 | 2605.12843 | null | 0 | 0 | false | null | null | 0.35 |
4930a71f466f4ebff3edc2a1306f6215235eb5ef4828386386f16ac96a8fe3cb | [
"arxiv",
"semantic_scholar"
] | FeatCal: Feature Calibration for Post-Merging Models | Model merging combines task experts into one model and avoids joint training, retraining, or deploying many expert models, but the merged model often still underperforms task experts. We study this performance gap through feature drift, the difference between features produced by the merged model and by the expert on t... | [
"Yanggan Gu",
"Shuo Cai",
"Zihao Wang",
"Wenjun Wang",
"Yuanyi Wang",
"Pengkai Wang",
"Sirui Huang",
"Su Lu",
"Jianmin Wu",
"Hongxia Yang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-13T00:00:00 | https://arxiv.org/abs/2605.13030 | https://arxiv.org/pdf/2605.13030v1 | 2605.13030 | null | 2 | 0 | false | null | null | 0.35 |
4d47028b83a40d304b240a66abfd251dc54660ac5f485f8f02dcbda8592f01b8 | [
"arxiv",
"semantic_scholar"
] | EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records | Electronic Health Records (EHR) contain rich longitudinal patient information and are widely used in predictive modeling applications. However, effectively leveraging historical data remains challenging due to long trajectories, heterogeneous events, temporal irregularity, and the varying relevance of past clinical con... | [
"Saeed Shurrab",
"Mariam Al-Omari",
"Dana El Samad",
"Farah E. Shamout"
] | [
"cs.IR",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-05-12T00:00:00 | https://arxiv.org/abs/2605.12335 | https://arxiv.org/pdf/2605.12335v1 | 2605.12335 | null | 0 | 0 | false | null | null | 0.35 |
49e1be355fac19775b08c9c2a98481aab55608d95b3781251826db4ae4199ab4 | [
"arxiv",
"semantic_scholar"
] | Revitalizing the Beginning: Avoiding Storage Dependency for Model Merging in Continual Learning | Model merging provides a compelling paradigm for integrating specialized expertise into a unified multi-task model, a goal that aligns naturally with the sequential knowledge acquisition in continual learning (CL). However, the requirement for preserving diverse forms of previous knowledge conflicts with the storage li... | [
"Xi Wang",
"Cheng Deng"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2026-05-08T00:00:00 | https://arxiv.org/abs/2605.08311 | https://arxiv.org/pdf/2605.08311v1 | 2605.08311 | null | 0 | 0 | false | null | null | 0.35 |
c4c7e408fe306af6d068f85164a09e6c07113b5c3bf2991a68803b3725a67dd7 | [
"arxiv",
"semantic_scholar"
] | A Tutorial for Evaluating Cure Model Appropriateness | In survival analysis, traditional models assume all individuals will eventually experience the event of interest. However, advances in therapeutics have led to multiple clinical contexts with potentially curative therapies, and in these contexts, certain individuals may never experience the event. Statisticians have de... | [
"A Tutorial for Evaluating Cure Model Appropriateness Geethanjalee Mudunkotuwa",
"Durbadal Ghosh",
"Subodh Selukar"
] | [
"stat.ME"
] | [
"Mathematics"
] | 2026-05-06T00:00:00 | https://arxiv.org/abs/2605.04999 | https://arxiv.org/pdf/2605.04999v2 | 2605.04999 | null | 0 | 0 | false | null | null | 0.35 |
b0888e2f3515294cf1c12a315703628619062902b93a6bf304294b87f36a687b | [
"arxiv",
"semantic_scholar"
] | Model Merging: Foundations and Algorithms | Modern deep learning usually treats models as separate artifacts: trained independently, specialized for particular purposes, and replaced when improved versions appear. This thesis studies model merging as an alternative paradigm: combining independently trained neural networks directly in weight space, with little or... | [
"Donato Crisostomi"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-05-02T00:00:00 | https://arxiv.org/abs/2605.01580 | https://arxiv.org/pdf/2605.01580v1 | 2605.01580 | null | 0 | 0 | false | null | null | 0.35 |
39cc3cc4d4dcdcbf8a4b9a2e71005daba831fe658bb604e0a881471aa03607b0 | [
"arxiv",
"semantic_scholar"
] | Auto-FlexSwitch: Efficient Dynamic Model Merging via Learnable Task Vector Compression | Model merging has attracted attention as an effective path toward multi-task adaptation by integrating knowledge from multiple task-specific models. Among existing approaches, dynamic merging mitigates performance degradation caused by conflicting parameter updates across tasks by flexibly combining task-specific param... | [
"Junqi Gao",
"Dazhi Zhang",
"Zhichang Guo",
"Biqing Qi",
"Yi Ran",
"Wangmeng Zuo"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-04-30T00:00:00 | https://arxiv.org/abs/2604.28109 | https://arxiv.org/pdf/2604.28109v1 | 2604.28109 | 10.48550/arXiv.2604.28109 | 0 | 0 | false | null | arXiv.org | 0.55 |
ad97dee1a4e25cf7bd714d775cb31ba4027eec524dd379dbade962f7e3bad9ac | [
"arxiv",
"semantic_scholar"
] | Embedding Arithmetic: A Lightweight, Tuning-Free Framework for Post-hoc Bias Mitigation in Text-to-Image Models | Modern text-to-image (T2I) models amplify harmful societal biases, challenging their ethical deployment. We introduce an inference-time method that reliably mitigates social bias while keeping prompt semantics and visual context (background, layout, and style) intact. This ensures context persistency and provides a con... | [
"Venkatesh Thirugnana Sambandham",
"Torsten Schön"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-04-20T00:00:00 | https://arxiv.org/abs/2604.18167 | https://arxiv.org/pdf/2604.18167v1 | 2604.18167 | 10.48550/arXiv.2604.18167 | 0 | 0 | true | https://github.com/cvims/EMBEDDING-ARITHMETIC} | arXiv.org | 0.85 |
983090ef75a7f9605b4908be64c04f9e7cd9d79d0f98c584f65806a7d147ad40 | [
"arxiv",
"semantic_scholar"
] | Understanding and Enforcing Weight Disentanglement in Task Arithmetic | Task arithmetic provides an efficient, training-free way to edit pre-trained models, yet lacks a fundamental theoretical explanation for its success. The existing concept of ``weight disentanglement" describes the ideal outcome of non-interfering task composition but does not reveal its underlying cause. Crucially, wha... | [
"Shangge Liu",
"Yuehan Yin",
"Lei Wang",
"Qi Fan",
"Yinghuan Shi",
"Wenbin Li",
"Yang Gao",
"Dacheng Tao"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-04-18T00:00:00 | https://arxiv.org/abs/2604.17078 | https://arxiv.org/pdf/2604.17078v1 | 2604.17078 | 10.48550/arXiv.2604.17078 | 0 | 0 | true | https://github.com/RL-MIND/OrthoReg}{https://github.com/RL-MIND/OrthoReg} | arXiv.org | 0.8465 |
810634cf4ae51a3ac5284f6fe396bc40a5580303ae4037458ae13dbcb06af677 | [
"arxiv",
"semantic_scholar"
] | Task Alignment: A simple and effective proxy for model merging in computer vision | Efficiently merging several models fine-tuned for different tasks, but stemming from the same pretrained base model, is of great practical interest. Despite extensive prior work, most evaluations of model merging in computer vision are restricted to image classification using CLIP, where different classification datase... | [
"Pau de Jorge",
"César Roberto de Souza",
"Björn Michele",
"Mert Bülent Sarıyıldız",
"Philippe Weinzaepfel",
"Florent Perronnin",
"Diane Larlus",
"Yannis Kalantidis"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2026-04-14T00:00:00 | https://arxiv.org/abs/2604.12935 | https://arxiv.org/pdf/2604.12935v1 | 2604.12935 | 10.48550/arXiv.2604.12935 | 1 | 0 | false | null | arXiv.org | 0.5431 |
92d17abab81ff5ac8976c3d7e6d482b91e5f8fff55e74a3f803824cba484ff75 | [
"arxiv",
"semantic_scholar"
] | One Model to Translate Them All? A Journey to Mount Doom for Multilingual Model Merging | Weight-space model merging combines independently fine-tuned models without accessing original training data, offering a practical alternative to joint training. While merging succeeds in multitask settings, its behavior in multilingual contexts remains poorly understood. We systematically study weight-space merging fo... | [
"Baban Gain",
"Asif Ekbal",
"Trilok Nath Singh"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-03T00:00:00 | https://arxiv.org/abs/2604.02881 | https://arxiv.org/pdf/2604.02881v1 | 2604.02881 | 10.48550/arXiv.2604.02881 | 2 | 0 | false | null | arXiv.org | 0.5305 |
e78f568ec4f4de1e66aacc1a647a9724d46e1db0e6ecd4de2a0c54c0df7018bf | [
"arxiv",
"semantic_scholar"
] | Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging | Large language models have been adopted in the medical domain for clinical documentation to reduce clinician burden. However, studies have reported that LLMs often "forget" a significant amount of instruction-following ability when fine-tuned using a task-specific medical dataset, a critical challenge in adopting gener... | [
"Mengxian Lyu",
"Cheng Peng",
"Ziyi Chen",
"Mengyuan Zhang",
"Jieting Li Lu",
"Yonghui Wu"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-04-02T00:00:00 | https://arxiv.org/abs/2604.01538 | https://arxiv.org/pdf/2604.01538v1 | 2604.01538 | 10.48550/arXiv.2604.01538 | 0 | 0 | true | null | arXiv.org | 0.8181 |
f65ff7e2ce2f761f8bea3dfa8b6abb18a456892fd1c93b0cb11808af831dfcfc | [
"arxiv",
"semantic_scholar"
] | Yet Even Less Is Even Better For Agentic, Reasoning, and Coding LLMs | Training effective software engineering agents requires large volumes of task-specific trajectories, incurring substantial data construction costs. Inspired by the "Less-Is-More" hypothesis in mathematical reasoning, we investigate its extension to agentic scenarios and propose an end-to-end training framework that ach... | [
" CodeArts Model Team",
"Yang Ye",
"Jingyuan Tan",
"Tianyue Jiang",
"Ruizhe Ye",
"Qiankun He",
"Jiarui Yang",
"Jian Dong",
"Sicong Liang",
"Chongjian Yue",
"Peibai Xu",
"Lufan Lu",
"Shiguan Pang",
"Taotao Qian",
"Junbao Hu",
"Yuechan Hao",
"Ensheng Shi",
"Qi Zhang",
"Yi Hao",
"... | [
"cs.SE"
] | [
"Computer Science"
] | 2026-04-01T00:00:00 | https://arxiv.org/abs/2604.00824 | https://arxiv.org/pdf/2604.00824v3 | 2604.00824 | 10.48550/arXiv.2604.00824 | 0 | 0 | false | null | arXiv.org | 0.5282 |
5c142e2cc97364a477a5751796bb3145d048a3a33452ce2c4487484b411bf719 | [
"arxiv",
"semantic_scholar"
] | Robust Language Identification for Romansh Varieties | The Romansh language has several regional varieties, called idioms, which sometimes have limited mutual intelligibility. Despite this linguistic diversity, there has been a lack of documented efforts to build a language identification (LID) system that can distinguish between these idioms. Since Romansh LID should also... | [
"Charlotte Model",
"Sina Ahmadi",
"Jannis Vamvas"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-03-16T00:00:00 | https://arxiv.org/abs/2603.15969 | https://arxiv.org/pdf/2603.15969v2 | 2603.15969 | 10.48550/arXiv.2603.15969 | 1 | 0 | false | null | arXiv.org | 0.5099 |
e6046277e3441301722c53b9153a9b30a0c0165164610b33386bc5a8e0aafc3f | [
"arxiv",
"semantic_scholar"
] | Resolving Interference (RI): Disentangling Models for Improved Model Merging | Model merging has shown that multitask models can be created by directly combining the parameters of different models that are each specialized on tasks of interest. However, models trained independently on distinct tasks often exhibit interference that degrades the merged model's performance. To solve this problem, we... | [
"Pratik Ramesh",
"George Stoica",
"Arun Iyer",
"Leshem Choshen",
"Judy Hoffman"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2026-03-13T00:00:00 | https://arxiv.org/abs/2603.13467 | https://arxiv.org/pdf/2603.13467v1 | 2603.13467 | 10.48550/arXiv.2603.13467 | 0 | 0 | true | https://github.com/pramesh39/resolving_interference | arXiv.org | 0.7827 |
fe57823867561511731633bcaa9cc3339d991cdde48cc5784e2b4c35349c9f1b | [
"arxiv",
"semantic_scholar"
] | Mortgage Burnout and Selection Effects in Heterogeneous Cox Hazard Models | We study the aggregate hazard rate of a heterogeneous population whose individual event intensities are modeled as Cox (doubly stochastic) processes. In the deterministic hazard setting, the observed pool hazard is the survival weighted mean of the individual hazards, and its time derivative equals the mean individual ... | [
"Andrew Lesniewski"
] | [
"q-fin.MF",
"econ.GN",
"stat.ME"
] | [
"Economics",
"Mathematics"
] | 2026-03-12T00:00:00 | https://arxiv.org/abs/2603.12422 | https://arxiv.org/pdf/2603.12422v3 | 2603.12422 | null | 0 | 0 | false | null | null | 0.3216 |
a30c8672c6849d29fb693d80d07ae96594564ace209e679e5ce86faabace6313 | [
"arxiv",
"semantic_scholar"
] | An Empirical Study and Theoretical Explanation on Task-Level Model-Merging Collapse | Model merging unifies independently fine-tuned LLMs from the same base, enabling reuse and integration of parallel development efforts without retraining. However, in practice we observe that merging does not always succeed: certain combinations of task-specialist models suffer from catastrophic performance degradation... | [
"Yuan Cao",
"Dezhi Ran",
"Yuzhe Guo",
"Mengzhou Wu",
"Simin Chen",
"Linyi Li",
"Wei Yang",
"Tao Xie"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-03-10T00:00:00 | https://arxiv.org/abs/2603.09463 | https://arxiv.org/pdf/2603.09463v1 | 2603.09463 | 10.48550/arXiv.2603.09463 | 0 | 0 | false | null | arXiv.org | 0.503 |
37f13bce44fd265b6a320b12f9c050d116b89cc93a79a83687c452d9bb5a7b55 | [
"arxiv",
"semantic_scholar"
] | Model Merging in the Era of Large Language Models: Methods, Applications, and Future Directions | Model merging combines the parameters of multiple neural networks into a single model without additional training. As fine-tuned large language models (LLMs) proliferate, merging offers a computationally efficient alternative to ensembles and full retraining, enabling practitioners to compose specialized capabilities a... | [
"Mingyang Song",
"Mao Zheng"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-03-10T00:00:00 | https://arxiv.org/abs/2603.09938 | https://arxiv.org/pdf/2603.09938v2 | 2603.09938 | 10.48550/arXiv.2603.09938 | 1 | 0 | false | null | arXiv.org | 0.503 |
db3afe44be8b623f6135a2f763b6005dca6f6d1e7fcf58df8df321f7d774096c | [
"arxiv",
"semantic_scholar"
] | DC-Merge: Improving Model Merging with Directional Consistency | Model merging aims to integrate multiple task-adapted models into a unified model that preserves the knowledge of each task. In this paper, we identify that the key to this knowledge retention lies in maintaining the directional consistency of singular spaces between merged multi-task vector and individual task vectors... | [
"Han-Chen Zhang",
"Zi-Hao Zhou",
"Mao-Lin Luo",
"Shimin Di",
"Min-Ling Zhang",
"Tong Wei"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2026-03-06T00:00:00 | https://arxiv.org/abs/2603.06242 | https://arxiv.org/pdf/2603.06242v2 | 2603.06242 | 10.48550/arXiv.2603.06242 | 0 | 0 | true | https://github.com/Tobeginwith/DC-Merge | arXiv.org | 0.7703 |
016ba6b7f09a96c77fbcb5f32eb427ad06f939de0673edd78cb9f8225d5dec4f | [
"arxiv",
"semantic_scholar"
] | Magic partition functions: Sign smoothing convolutions with Dirichlet invertible arithmetic functions | Sign changes in sums of arithmetic functions and their inverses are a subtle topic with room to grow new results. Suppose that $S_f(x) := \sum_{n \leq x} f(n)$ is the summatory function of some arithmetic function $f$ such that $f(1) \neq 1$. There are known lower bounds on the limiting growth of $V(S_f, Y)$ -- the num... | [
"Maxie Dion Schmidt"
] | [
"math.NT"
] | [
"Mathematics"
] | 2026-03-06T00:00:00 | https://arxiv.org/abs/2603.06890 | https://arxiv.org/pdf/2603.06890v1 | 2603.06890 | null | 0 | 0 | false | null | null | 0.3172 |
40b92de5d2aedd4870494e7f9ab1b1cfbbcc73e90e3db0aec6121eb9bd96a90a | [
"arxiv",
"semantic_scholar"
] | Bridging Domains through Subspace-Aware Model Merging | Model merging integrates multiple task-specific models into a single consolidated one. Recent research has made progress in improving merging performance for in-distribution or multi-task scenarios, but domain generalization in model merging remains underexplored. We investigate how merging models fine-tuned on distinc... | [
"Levy Chaves",
"Chao Zhou",
"Rebekka Burkholz",
"Eduardo Valle",
"Sandra Avila"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2026-03-06T00:00:00 | https://arxiv.org/abs/2603.05768 | https://arxiv.org/pdf/2603.05768v2 | 2603.05768 | 10.48550/arXiv.2603.05768 | 0 | 0 | false | null | arXiv.org | 0.4984 |
b581d341a74672acab3484e932bcbd2ece749a7a612d03a5f423f853eb112465 | [
"arxiv",
"semantic_scholar"
] | Model Medicine: A Clinical Framework for Understanding, Diagnosing, and Treating AI Models | Model Medicine is the science of understanding, diagnosing, treating, and preventing disorders in AI models, grounded in the principle that AI models -- like biological organisms -- have internal structures, dynamic processes, heritable traits, observable symptoms, classifiable conditions, and treatable states. This pa... | [
"Jihoon Jeong"
] | [
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-05T00:00:00 | https://arxiv.org/abs/2603.04722 | https://arxiv.org/pdf/2603.04722v2 | 2603.04722 | 10.48550/arXiv.2603.04722 | 2 | 2 | true | null | arXiv.org | 0.7685 |
e12060224d6c67308d7f2a09f5a6038d0ad1760d870936f88650fc5c6b868993 | [
"arxiv",
"semantic_scholar"
] | BD-Merging: Bias-Aware Dynamic Model Merging with Evidence-Guided Contrastive Learning | Model Merging (MM) has emerged as a scalable paradigm for multi-task learning (MTL), enabling multiple task-specific models to be integrated without revisiting the original training data. Despite recent progress, the reliability of MM under test-time distribution shift remains insufficiently understood. Most existing M... | [
"Yuhan Xie",
"Chen Lyu"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-03-04T00:00:00 | https://arxiv.org/abs/2603.03920 | https://arxiv.org/pdf/2603.03920v2 | 2603.03920 | 10.48550/arXiv.2603.03920 | 0 | 0 | false | null | arXiv.org | 0.4961 |
9140caa560fbd31b5d80aa941eba5427a59e7abe3257ee8ebb13c1f8cf9ab61c | [
"arxiv",
"semantic_scholar"
] | ACE-Merging: Data-Free Model Merging with Adaptive Covariance Estimation | Model merging aims to combine multiple task-specific expert models into a single model while preserving generalization across diverse tasks. However, interference among experts, especially when they are trained on different objectives, often leads to significant performance degradation. Despite recent progress, resolvi... | [
"Bo Xu",
"Haotian Wu",
"Hehai Lin",
"Weiquan Huang",
"Beier Zhu",
"Yao Shu",
"Chengwei Qin"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-03-03T00:00:00 | https://arxiv.org/abs/2603.02945 | https://arxiv.org/pdf/2603.02945v2 | 2603.02945 | 10.48550/arXiv.2603.02945 | 0 | 0 | false | null | arXiv.org | 0.495 |
c283172d605ef6c0b753cd0c6548ef754c6c1c256bdbf23b9c2531bdbd219265 | [
"arxiv",
"semantic_scholar"
] | Leveraging Model Soups to Classify Intangible Cultural Heritage Images from the Mekong Delta | The classification of Intangible Cultural Heritage (ICH) images in the Mekong Delta poses unique challenges due to limited annotated data, high visual similarity among classes, and domain heterogeneity. In such low-resource settings, conventional deep learning models often suffer from high variance or overfit to spurio... | [
"Quoc-Khang Tran",
"Minh-Thien Nguyen",
"Nguyen-Khang Pham"
] | [
"cs.CV",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2026-03-02T00:00:00 | https://arxiv.org/abs/2603.02181 | https://arxiv.org/pdf/2603.02181v1 | 2603.02181 | 10.32913/mic-ict-research.v2025.n3.1395 | 0 | 0 | false | null | null | 0.3143 |
efd5ab0e7601576782c61cce15281a78fc4b8ddee519aabb849d90cd60e549b4 | [
"arxiv",
"semantic_scholar"
] | Model Merging in the Essential Subspace | Model merging aims to integrate multiple task-specific fine-tuned models derived from a shared pre-trained checkpoint into a single multi-task model without additional training. Despite extensive research, task interference remains a major obstacle that often undermines the performance of merged models. In this paper, ... | [
"Longhua Li",
"Lei Qi",
"Qi Tian",
"Xin Geng"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2026-02-23T00:00:00 | https://arxiv.org/abs/2602.20208 | https://arxiv.org/pdf/2602.20208v1 | 2602.20208 | 10.48550/arXiv.2602.20208 | 2 | 0 | false | null | arXiv.org | 0.4858 |
5117d328859e8ea7be7aa8a22eabc7717ef438946e98d7f9f29f0ed6a7e71cc3 | [
"arxiv",
"semantic_scholar"
] | Interpolation in Proof Theory | This chapter provides a comprehensive overview of proof-theoretic methods for establishing interpolation properties across a range of logics, including classical, intuitionistic, modal, and substructural logics. Central to the discussion are two foundational techniques: Maehara's method for Craig interpolation and Pitt... | [
"Iris van der Giessen",
"Raheleh Jalali",
"Roman Kuznets"
] | [
"cs.LO"
] | [
"Computer Science"
] | 2026-02-18T00:00:00 | https://arxiv.org/abs/2602.16318 | https://arxiv.org/pdf/2602.16318v1 | 2602.16318 | 10.48550/arXiv.2602.16318 | 3 | 1 | false | null | arXiv.org | 0.4801 |
9cccff0d7842f751feb9afa193c1a35a1620568eef4f41ce9df3cc39517ac554 | [
"arxiv",
"semantic_scholar"
] | Beyond Parameter Arithmetic: Sparse Complementary Fusion for Distribution-Aware Model Merging | Model merging has emerged as a promising paradigm for composing the capabilities of large language models by directly operating in weight space, enabling the integration of specialized models without costly retraining. However, existing merging methods largely rely on parameter-space heuristics, which often introduce s... | [
"Weihong Lin",
"Lin Sun",
"Qilong Shi",
"Aomufei Yuan",
"Yuxuan Tian",
"Zhengyang Wang",
"Guangxiang Zhao",
"Xiangzheng Zhang",
"Tong Yang"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2026-02-12T00:00:00 | https://arxiv.org/abs/2602.11717 | https://arxiv.org/pdf/2602.11717v1 | 2602.11717 | 10.48550/arXiv.2602.11717 | 0 | 0 | false | null | arXiv.org | 0.4732 |
7dd364e9eb6e0b67bf0d0306f9f9b865dda40acf9b091cc718b58b19c537f8c6 | [
"arxiv",
"semantic_scholar"
] | Craig Interpolation in Program Verification | Craig interpolation is used in program verification for automating key tasks such as the inference of loop invariants and the computation of program abstractions. This chapter covers some of the most important techniques that have been developed in this context over the last years, focusing on two aspects: the derivati... | [
"Philipp Rümmer"
] | [
"cs.LO"
] | [
"Computer Science"
] | 2026-02-09T00:00:00 | https://arxiv.org/abs/2602.08532 | https://arxiv.org/pdf/2602.08532v1 | 2602.08532 | 10.48550/arXiv.2602.08532 | 0 | 0 | false | null | arXiv.org | 0.4698 |
c21aeafc7629df476c4c2bb500d537f78feac59fd8c1430fa9a67d84ff3402cd | [
"arxiv",
"semantic_scholar"
] | M-Loss: Quantifying Model Merging Compatibility with Limited Unlabeled Data | Training of large-scale models is both computationally intensive and often constrained by the availability of labeled data. Model merging offers a compelling alternative by directly integrating the weights of multiple source models without requiring additional data or extensive training. However, conventional model mer... | [
"Tiantong Wang",
"Yiyang Duan",
"Haoyu Chen",
"Tiantong Wu",
"Wei Yang Bryan Lim"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-09T00:00:00 | https://arxiv.org/abs/2602.08564 | https://arxiv.org/pdf/2602.08564v1 | 2602.08564 | 10.1609/aaai.v40i31.39854 | 1 | 0 | true | https://github.com/languangduan/mLoss | AAAI Conference on Artificial Intelligence | 0.726 |
0b258e7e0e3659d5a5ebd84a017f3459d1658a407b3bb58fbef072157e616fd7 | [
"arxiv",
"semantic_scholar"
] | Definability and Interpolation in Philosophy | This paper is a historical tour of occurrences of the Craig interpolation theorem and the Beth definability theorem in philosophy since the 1950s. We identify the notion of dependence as one major red thread behind these, and include some new technical results, in particular, on logical system translations and generali... | [
"Johan van Benthem"
] | [
"cs.LO"
] | [
"Computer Science"
] | 2026-02-08T00:00:00 | https://arxiv.org/abs/2602.07907 | https://arxiv.org/pdf/2602.07907v1 | 2602.07907 | 10.48550/arXiv.2602.07907 | 0 | 0 | false | null | arXiv.org | 0.4686 |
e7819db6fdbfdbee89da2a0c4e0238496b8c5b49064b31f30cea7737204e0c7c | [
"arxiv",
"semantic_scholar"
] | Fine-Grained Model Merging via Modular Expert Recombination | Model merging constructs versatile models by integrating task-specific models without requiring labeled data or expensive joint retraining. Although recent methods improve adaptability to heterogeneous tasks by generating customized merged models for each instance, they face two critical limitations. First, the instanc... | [
"Haiyun Qiu",
"Xingyu Wu",
"Liang Feng",
"Kay Chen Tan"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-06T00:00:00 | https://arxiv.org/abs/2602.06552 | https://arxiv.org/pdf/2602.06552v1 | 2602.06552 | 10.48550/arXiv.2602.06552 | 0 | 0 | false | null | arXiv.org | 0.4664 |
bfe6a4b4d2b0af2b24edfa14388688ea651303824707410cb8f9892310326f57 | [
"arxiv",
"semantic_scholar"
] | Orthogonal Model Merging | Merging finetuned Large Language Models (LLMs) has become increasingly important for integrating diverse capabilities into a single unified model. However, prevailing model merging methods rely on linear arithmetic in Euclidean space, which often destroys the intrinsic geometric properties of pretrained weights, such a... | [
"Sihan Yang",
"Kexuan Shi",
"Weiyang Liu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-05T00:00:00 | https://arxiv.org/abs/2602.05943 | https://arxiv.org/pdf/2602.05943v1 | 2602.05943 | 10.48550/arXiv.2602.05943 | 1 | 0 | false | null | arXiv.org | 0.4652 |
03cce1c5e4d2cd288c2ab05df72c16cdc45616146d85b9a95803a227ce8e4f89 | [
"arxiv",
"semantic_scholar"
] | When Shared Knowledge Hurts: Spectral Over-Accumulation in Model Merging | Model merging combines multiple fine-tuned models into a single model by adding their weight updates, providing a lightweight alternative to retraining. Existing methods primarily target resolving conflicts between task updates, leaving the failure mode of over-counting shared knowledge unaddressed. We show that when t... | [
"Yayuan Li",
"Ze Peng",
"Jian Zhang",
"Jintao Guo",
"Yue Duan",
"Yinghuan Shi"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2026-02-05T00:00:00 | https://arxiv.org/abs/2602.05536 | https://arxiv.org/pdf/2602.05536v2 | 2602.05536 | 10.48550/arXiv.2602.05536 | 3 | 0 | true | https://github.com/lyymuwu/SVC | arXiv.org | 0.719 |
c660043a721b5b119eaa4310f588f2050d1b82cfe0306898dea8c6d906875faf | [
"arxiv",
"semantic_scholar"
] | Model-Dowser: Data-Free Importance Probing to Mitigate Catastrophic Forgetting in Multimodal Large Language Models | Fine-tuning Multimodal Large Language Models (MLLMs) on task-specific data is an effective way to improve performance on downstream applications. However, such adaptation often leads to a degradation in generalization on pretrained tasks, a phenomenon known as Catastrophic Forgetting. Existing methods that aim to mitig... | [
"Hyeontaek Hwang",
"Nguyen Dinh Son",
"Daeyoung Kim"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-02-04T00:00:00 | https://arxiv.org/abs/2602.04509 | https://arxiv.org/pdf/2602.04509v7 | 2602.04509 | 10.48550/arXiv.2602.04509 | 0 | 0 | false | null | arXiv.org | 0.4641 |
64eef9dd34cb982c11b268d9a395f77d488a6be603f8ec523276114a5859e4e7 | [
"arxiv",
"semantic_scholar"
] | Self-Soupervision: Cooking Model Soups without Labels | Model soups are strange and strangely effective combinations of parameters. They take a model (the stock), fine-tune it into multiple models (the ingredients), and then mix their parameters back into one model (the soup) to improve predictions. While all known soups require supervised learning, and optimize the same lo... | [
"Anthony Fuller",
"James R. Green",
"Evan Shelhamer"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-02-02T00:00:00 | https://arxiv.org/abs/2602.02890 | https://arxiv.org/pdf/2602.02890v2 | 2602.02890 | 10.48550/arXiv.2602.02890 | 0 | 0 | true | https://github.com/antofuller/self_soupervision | arXiv.org | 0.7136 |
85b3ebd467ba4360f31d987da572e76812044e55777a1ab27ea7d40841b7f359 | [
"arxiv",
"semantic_scholar"
] | AutoMerge: Search-Based Model Merging Framework for Effective Model Reuse | Software reuse has long been recognized as a critical and widely studied topic in software engineering, offering substantial benefits in reducing development costs, improving software quality, and enhancing operational efficiency. This paradigm extends into deep learning through model reuse. Recently, model merging has... | [
"You Lu",
"Jiyang Zhang",
"Bihuan Chen",
"Chaofeng Sha",
"Dingji Wang",
"Xin Peng"
] | [
"cs.SE"
] | [
"Computer Science"
] | 2026-01-30T00:00:00 | https://arxiv.org/abs/2601.22748 | https://arxiv.org/pdf/2601.22748v1 | 2601.22748 | 10.48550/arXiv.2601.22748 | 0 | 0 | false | null | arXiv.org | 0.4583 |
f2f794df11319183e552813d69f3ab08cb7c7e11c930aaffd41fdd33706ce502 | [
"arxiv",
"semantic_scholar"
] | Per-parameter Task Arithmetic for Unlearning in Large Language Models | In large language model (LLM) unlearning, private information is required to be removed. Task arithmetic unlearns by subtracting a specific task vector (TV)--defined as the parameter difference between a privacy-information-tuned model and the original model. While efficient, it can cause over-forgetting by disrupting ... | [
"Chengyi Cai",
"Zesheng Ye",
"Jiangchao Yao",
"Jianzhong Qi",
"Bo Han",
"Xiaolu Zhang",
"Feng Liu",
"Jun Zhou"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-01-29T00:00:00 | https://arxiv.org/abs/2601.22030 | https://arxiv.org/pdf/2601.22030v1 | 2601.22030 | 10.48550/arXiv.2601.22030 | 0 | 0 | false | null | arXiv.org | 0.4572 |
73c76cc32d5ab353f4ed6b6d4281fea131e0071f25e5aa5c90962a36908692c2 | [
"arxiv",
"semantic_scholar"
] | SOUP: Token-level Single-sample Mix-policy Reinforcement Learning for Large Language Models | On-policy reinforcement learning (RL) methods widely used for language model post-training, like Group Relative Policy Optimization (GRPO), often suffer from limited exploration and early saturation due to low sampling diversity. While off-policy data can help, current approaches that mix entire trajectories cause sign... | [
"Lei Yang",
"Wei Bi",
"Chenxi Sun",
"Renren Jin",
"Deyi Xiong"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-01-29T00:00:00 | https://arxiv.org/abs/2601.21476 | https://arxiv.org/pdf/2601.21476v1 | 2601.21476 | 10.48550/arXiv.2601.21476 | 1 | 0 | false | null | arXiv.org | 0.4572 |
1bbe87ba20db336e216b7930120005615a6c26b8f8d4fe98d7dca21b8e94cbc6 | [
"arxiv",
"semantic_scholar"
] | Multi-task Code LLMs: Data Mix or Model Merge? | Recent research advocates deploying smaller, specialized code LLMs in agentic frameworks alongside frontier models, sparking interest in efficient strategies for multi-task learning that balance performance, constraints, and costs. We compare two approaches for creating small, multi-task code LLMs: data mixing versus m... | [
"Mingzhi Zhu",
"Boris Sobolev",
"Rahul Krishna",
"Raju Pavuluri",
"Stacy Patterson",
"Michele Merler"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2026-01-28T00:00:00 | https://arxiv.org/abs/2601.21115 | https://arxiv.org/pdf/2601.21115v1 | 2601.21115 | 10.48550/arXiv.2601.21115 | 1 | 0 | false | null | arXiv.org | 0.456 |
32e0c9120fa185cfe7cb9859c407bbcd3ff747aa423e8e2a584cc8e85d3b6556 | [
"arxiv",
"semantic_scholar"
] | Behavior Knowledge Merge in Reinforced Agentic Models | Reinforcement learning (RL) is central to post-training, particularly for agentic models that require specialized reasoning behaviors. In this setting, model merging offers a practical mechanism for integrating multiple RL-trained agents from different tasks into a single generalist model. However, existing merging met... | [
"Xiangchi Yuan",
"Dachuan Shi",
"Chunhui Zhang",
"Zheyuan Liu",
"Shenglong Yao",
"Soroush Vosoughi",
"Wenke Lee"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2026-01-20T00:00:00 | https://arxiv.org/abs/2601.13572 | https://arxiv.org/pdf/2601.13572v1 | 2601.13572 | 10.48550/arXiv.2601.13572 | 6 | 0 | false | null | arXiv.org | 0.4469 |
8c344cd2626c159a853f5559148041fbd02b5e3a6786b03c83f5538680a06a44 | [
"arxiv",
"semantic_scholar"
] | Will it Merge? On The Causes of Model Mergeability | Model merging has emerged as a promising technique for combining multiple fine-tuned models into a single multitask model without retraining. However, the factors that determine whether merging will succeed or fail remain poorly understood. In this work, we investigate why specific models are merged better than others.... | [
"Adir Rahamim",
"Asaf Yehudai",
"Boaz Carmeli",
"Leshem Choshen",
"Yosi Mass",
"Yonatan Belinkov"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2026-01-10T00:00:00 | https://arxiv.org/abs/2601.06672 | https://arxiv.org/pdf/2601.06672v1 | 2601.06672 | 10.48550/arXiv.2601.06672 | 2 | 0 | false | null | arXiv.org | 0.4354 |
60c611a8cf880669efa34be0297a7de91185e586e17895eee264693bf5f928e5 | [
"arxiv",
"semantic_scholar"
] | Model Merging via Multi-Teacher Knowledge Distillation | Model merging has emerged as a lightweight alternative to joint multi-task learning (MTL), yet the generalization properties of merged models remain largely unexplored. Establishing such theoretical guarantees is non-trivial, as the merging process typically forbids access to the original training data and involves com... | [
"Seyed Arshan Dalili",
"Mehrdad Mahdavi"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-24T00:00:00 | https://arxiv.org/abs/2512.21288 | https://arxiv.org/pdf/2512.21288v1 | 2512.21288 | 10.48550/arXiv.2512.21288 | 0 | 0 | true | https://github.com/arshandalili/SAMerging | arXiv.org | 0.6428 |
1b5d0401b1e0b3674ad18977ff1823de10087727f5f161c57ee398e7292ac6d2 | [
"arxiv",
"semantic_scholar"
] | MAGIC: Achieving Superior Model Merging via Magnitude Calibration | The proliferation of pre-trained models has given rise to a wide array of specialised, fine-tuned models. Model merging aims to merge the distinct capabilities of these specialised models into a unified model, requiring minimal or even no additional training. A core objective of model merging is to ensure the merged mo... | [
"Yayuan Li",
"Jian Zhang",
"Jintao Guo",
"Zihan Cheng",
"Lei Qi",
"Yinghuan Shi",
"Yang Gao"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2025-12-22T00:00:00 | https://arxiv.org/abs/2512.19320 | https://arxiv.org/pdf/2512.19320v1 | 2512.19320 | 10.48550/arXiv.2512.19320 | 1 | 0 | true | https://github.com/lyymuwu/MAGIC | arXiv.org | 0.6393 |
9fd992bbeca9afbab3147779a5ff8af5ff92576ee4456e9f814bbcf15d758bbc | [
"arxiv",
"semantic_scholar"
] | Uniform Interpolation | Uniform interpolation is a strengthening of interpolation that holds for certain propositional logics. The starting point of this chapter is a theorem of A. Pitts, which shows that uniform interpolation holds for intuitionistic propositional logic. We outline how this theorem may be proved semantically via the definabi... | [
"Sam van Gool"
] | [
"math.LO",
"cs.LO"
] | [
"Computer Science",
"Mathematics"
] | 2025-12-17T00:00:00 | https://arxiv.org/abs/2512.15391 | https://arxiv.org/pdf/2512.15391v3 | 2512.15391 | 10.48550/arXiv.2512.15391 | 3 | 0 | false | null | arXiv.org | 0.4079 |
bf66ba2d409e7fa116abe1d3dd3dcee8a47426a45c77acd8bf42e007c4d00ee7 | [
"arxiv",
"semantic_scholar"
] | Per-Axis Weight Deltas for Frequent Model Updates | Serving many task-specialized LLM variants is often limited by the large size of fine-tuned checkpoints and the resulting cold-start latency. Since fine-tuned weights differ from their base model by relatively small structured residuals, a natural approach is to represent them as compressed deltas. We propose a simple ... | [
"Stefan Kuyumdzhiev",
"Radostin Cholakov"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-12-16T00:00:00 | https://arxiv.org/abs/2512.19720 | https://arxiv.org/pdf/2512.19720v1 | 2512.19720 | 10.48550/arXiv.2512.19720 | 0 | 0 | true | https://github.com/kuiumdjiev/Per-Axis-Weight-Deltas-for-Frequent-Model-Updates | arXiv.org | 0.6286 |
11b40cfbec786eafba8795f92876a45ffc4ac6f78049ede64d82dfe00782309d | [
"arxiv",
"semantic_scholar"
] | Interpolation in Knowledge Representation | Craig interpolation and uniform interpolation have many applications in knowledge representation, including explainability, forgetting, modularization and reuse, and even learning. At the same time, many relevant knowledge representation formalisms do in general not have Craig or uniform interpolation, and computing in... | [
"Jean Christoph Jung",
"Patrick Koopmann",
"Matthias Knorr"
] | [
"cs.AI",
"cs.LO"
] | [
"Computer Science"
] | 2025-12-09T00:00:00 | https://arxiv.org/abs/2512.08833 | https://arxiv.org/pdf/2512.08833v1 | 2512.08833 | 10.48550/arXiv.2512.08833 | 4 | 0 | false | null | arXiv.org | 0.3987 |
f9a84988676292f9bdeb0de68ce40ee993dad14ba5d8aa9cb77eee7f337c2297 | [
"arxiv",
"semantic_scholar"
] | An Empirical Survey of Model Merging Algorithms for Social Bias Mitigation | Large language models (LLMs) are known to inherit and even amplify societal biases present in their pre-training corpora, threatening fairness and social trust. To address this issue, recent work has explored ``editing'' LLM parameters to mitigate social bias with model merging approaches; however, there is no empirica... | [
"Daiki Shirafuji",
"Tatsuhiko Saito",
"Yasutomo Kimura"
] | [
"cs.CL",
"cs.AI"
] | [
"Computer Science"
] | 2025-12-02T00:00:00 | https://arxiv.org/abs/2512.02689 | https://arxiv.org/pdf/2512.02689v1 | 2512.02689 | 10.48550/arXiv.2512.02689 | 0 | 0 | false | null | Pacific Asia Conference on Language, Information and Computation | 0.3907 |
2f154303d35a995619f92c14c3a0c2fe90b167e7f511e42636cc057cb775ddeb | [
"arxiv",
"semantic_scholar"
] | Interpolation in Non-Classical Logics | This chapter surveys some of the main results on interpolation in several of the most prominent families of non-classical logics. Special attention is given to the distinction between the two most commonly studied variants of interpolation--namely, Craig interpolation and deductive interpolation. Our discussion focuses... | [
"Wesley Fussner"
] | [
"math.LO",
"cs.LO"
] | [
"Mathematics",
"Computer Science"
] | 2025-12-01T00:00:00 | https://arxiv.org/abs/2512.01600 | https://arxiv.org/pdf/2512.01600v1 | 2512.01600 | 10.48550/arXiv.2512.01600 | 0 | 0 | false | null | arXiv.org | 0.3896 |
0c5159109d17abf48e407b94f9ab445211d9b7ed1e2fd79040f42f292d2b45da | [
"arxiv",
"semantic_scholar"
] | Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging | Model merging has emerged as a promising paradigm for enabling multi-task capabilities without additional training. However, existing methods often experience substantial performance degradation compared with individually fine-tuned models, even on similar tasks, underscoring the need to preserve task-specific informat... | [
"Kuangpu Guo",
"Yuhe Ding",
"Jian Liang",
"Zilei Wang",
"Ran He"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2025-12-01T00:00:00 | https://arxiv.org/abs/2512.01461 | https://arxiv.org/pdf/2512.01461v1 | 2512.01461 | 10.48550/arXiv.2512.01461 | 1 | 0 | true | https://github.com/krumpguo/DTS | arXiv.org | 0.6021 |
6f68ef8a58ff5ee806cb91118686181058b1796afffc9f680e7f6c167524398b | [
"arxiv",
"semantic_scholar"
] | Merge and Bound: Direct Manipulations on Weights for Class Incremental Learning | We present a novel training approach, named Merge-and-Bound (M&B) for Class Incremental Learning (CIL), which directly manipulates model weights in the parameter space for optimization. Our algorithm involves two types of weight merging: inter-task weight merging and intra-task weight merging. Inter-task weight merging... | [
"Taehoon Kim",
"Donghwan Jang",
"Bohyung Han"
] | [
"cs.CV",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-11-26T00:00:00 | https://arxiv.org/abs/2511.21490 | https://arxiv.org/pdf/2511.21490v1 | 2511.21490 | 10.48550/arXiv.2511.21490 | 0 | 0 | false | null | arXiv.org | 0.3839 |
1c5a372283c92d5cc4d8f0c5bc464528373ef5216f69c37f36f25e1a8465a520 | [
"arxiv",
"semantic_scholar"
] | A Systematic Study of In-the-Wild Model Merging for Large Language Models | Model merging combines multiple fine-tuned checkpoints into a single model without additional training, offering an attractive approach to reusing models and efficiently improving performance. However, it remains unclear whether the advantages reported for settings where all merged experts have distinct roles and are t... | [
"Oğuz Kağan Hitit",
"Leander Girrbach",
"Zeynep Akata"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-11-26T00:00:00 | https://arxiv.org/abs/2511.21437 | https://arxiv.org/pdf/2511.21437v2 | 2511.21437 | null | 2 | 0 | false | null | Transactions on Machine Learning Research (03/2026) | 0.3839 |
88c66ad8c9d2d8aac7aa6523e2ab20ae18e196dacefd68c80362764eb6bd514d | [
"arxiv",
"semantic_scholar"
] | Merging without Forgetting: Continual Fusion of Task-Specific Models via Optimal Transport | Merging models fine-tuned for different tasks into a single unified model has become an increasingly important direction for building versatile, efficient multi-task systems. Existing approaches predominantly rely on parameter interpolation in weight space, which we show introduces significant distribution shift in the... | [
"Zecheng Pan",
"Zhikang Chen",
"Ding Li",
"Min Zhang",
"Sen Cui",
"Hongshuo Jin",
"Luqi Tao",
"Yi Yang",
"Deheng Ye",
"Yu Zhang",
"Tingting Zhu",
"Tianling Ren"
] | [
"cs.LG",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2025-11-24T00:00:00 | https://arxiv.org/abs/2511.19561 | https://arxiv.org/pdf/2511.19561v1 | 2511.19561 | 10.48550/arXiv.2511.19561 | 0 | 0 | false | null | arXiv.org | 0.3816 |
22bccf603496fbbc62e67e95feede8f7aa8aad425ff061d8b955282a518080df | [
"arxiv",
"semantic_scholar"
] | Escaping Optimization Stagnation: Taking Steps Beyond Task Arithmetic via Difference Vectors | Current methods for editing pre-trained models face significant challenges, primarily high computational costs and limited scalability. Task arithmetic has recently emerged as a promising solution, using simple arithmetic operations-addition and negation-based on task vectors which are the differences between fine-tune... | [
"Jinping Wang",
"Zhiqiang Gao",
"Dinggen Zhang",
"Zhiwu Xie"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-22T00:00:00 | https://arxiv.org/abs/2511.17987 | https://arxiv.org/pdf/2511.17987v1 | 2511.17987 | 10.48550/arXiv.2511.17987 | 0 | 0 | false | null | AAAI Conference on Artificial Intelligence | 0.3793 |
f7ece0a771f4beea649c8d8a2c8fac58c1def5e7d6fc5618e35828cd3666cd75 | [
"arxiv",
"semantic_scholar"
] | Task Addition and Weight Disentanglement in Closed-Vocabulary Models | Task arithmetic has recently emerged as a promising method for editing pre-trained \textit{open-vocabulary} models, offering a cost-effective alternative to standard multi-task fine-tuning. However, despite the abundance of \textit{closed-vocabulary} models that are not pre-trained with language supervision, applying t... | [
"Adam Hazimeh",
"Alessandro Favero",
"Pascal Frossard"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-11-18T00:00:00 | https://arxiv.org/abs/2511.14569 | https://arxiv.org/pdf/2511.14569v1 | 2511.14569 | 10.48550/arXiv.2511.14569 | 4 | 0 | false | null | arXiv.org | 0.3747 |
956fb0caa34a99fb85a2586c42a37fbf4c566a25a1ef054bd3d390aada0ea8cd | [
"arxiv",
"semantic_scholar"
] | MergeSlide: Continual Model Merging and Task-to-Class Prompt-Aligned Inference for Lifelong Learning on Whole Slide Images | Lifelong learning on Whole Slide Images (WSIs) aims to train or fine-tune a unified model sequentially on cancer-related tasks, reducing the resources and effort required for data transfer and processing, especially given the gigabyte-scale size of WSIs. In this paper, we introduce MergeSlide, a simple yet effective fr... | [
"Doanh C. Bui",
"Ba Hung Ngo",
"Hoai Luan Pham",
"Khang Nguyen",
"Maï K. Nguyen",
"Yasuhiko Nakashima"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-11-17T00:00:00 | https://arxiv.org/abs/2511.13099 | https://arxiv.org/pdf/2511.13099v1 | 2511.13099 | 10.1109/WACV61042.2026.00472 | 0 | 0 | true | https://github.com/caodoanh2001/MergeSlide | IEEE Workshop/Winter Conference on Applications of Computer Vision | 0.5773 |
1f43f2ec73aaf6558699cfbe33e8e51dd0cb00f6c2239d0d043640eda523af40 | [
"arxiv",
"semantic_scholar"
] | A Novel Hierarchical Integration Method for Efficient Model Merging in Medical LLMs | Large Language Models (LLMs) face significant challenges in distributed healthcare, including consolidating specialized domain knowledge across institutions while maintaining privacy, reducing computational overhead, and preventing catastrophic forgetting during model updates.This paper presents a systematic evaluation... | [
"Prakrit Timilsina",
"Anuj Nepal",
"Rajan Kadel",
"Robin Doss"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-17T00:00:00 | https://arxiv.org/abs/2511.13373 | https://arxiv.org/pdf/2511.13373v1 | 2511.13373 | 10.1109/SmartIoT66867.2025.00031 | 0 | 0 | false | null | International Conferences on Smart Internet of Things | 0.3735 |
2bf4488fb9d4c127e6f476c66ff2a1d8a566046d75fbde200fb561814b74f6e2 | [
"arxiv",
"semantic_scholar"
] | Defending Unauthorized Model Merging via Dual-Stage Weight Protection | The rapid proliferation of pretrained models and open repositories has made model merging a convenient yet risky practice, allowing free-riders to combine fine-tuned models into a new multi-capability model without authorization. Such unauthorized model merging not only violates intellectual property rights but also un... | [
"Wei-Jia Chen",
"Min-Yen Tsai",
"Cheng-Yi Lee",
"Chia-Mu Yu"
] | [
"cs.CV",
"cs.CR"
] | [
"Computer Science"
] | 2025-11-14T00:00:00 | https://arxiv.org/abs/2511.11851 | https://arxiv.org/pdf/2511.11851v3 | 2511.11851 | 10.48550/arXiv.2511.11851 | 0 | 0 | false | null | arXiv.org | 0.3701 |
af45a91f5eea018489d72d95ab3226dd0d3c129796702912eba7598774ca41a1 | [
"arxiv",
"semantic_scholar"
] | Do Not Merge My Model! Safeguarding Open-Source LLMs Against Unauthorized Model Merging | Model merging has emerged as an efficient technique for expanding large language models (LLMs) by integrating specialized expert models. However, it also introduces a new threat: model merging stealing, where free-riders exploit models through unauthorized model merging. Unfortunately, existing defense mechanisms fail ... | [
"Qinfeng Li",
"Miao Pan",
"Jintao Chen",
"Fu Teng",
"Zhiqiang Shen",
"Ge Su",
"Hao Peng",
"Xuhong Zhang"
] | [
"cs.CR",
"cs.AI"
] | [
"Computer Science"
] | 2025-11-13T00:00:00 | https://arxiv.org/abs/2511.10712 | https://arxiv.org/pdf/2511.10712v2 | 2511.10712 | 10.48550/arXiv.2511.10712 | 1 | 0 | true | null | AAAI Conference on Artificial Intelligence | 0.5702 |
b22815202aa071278bc54ea9838a5d2caf6242a66e15042a8478e74146fe6059 | [
"arxiv",
"semantic_scholar"
] | LT-Soups: Bridging Head and Tail Classes via Subsampled Model Soups | Real-world datasets typically exhibit long-tailed (LT) distributions, where a few head classes dominate and many tail classes are severely underrepresented. While recent work shows that parameter-efficient fine-tuning (PEFT) methods like LoRA and AdaptFormer preserve tail-class performance on foundation models such as ... | [
"Masih Aminbeidokhti",
"Subhankar Roy",
"Eric Granger",
"Elisa Ricci",
"Marco Pedersoli"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2025-11-11T00:00:00 | https://arxiv.org/abs/2511.10683 | https://arxiv.org/pdf/2511.10683v2 | 2511.10683 | 10.48550/arXiv.2511.10683 | 1 | 0 | false | null | arXiv.org | 0.3667 |
d880ec144875557a21089ac082477e66e598253f377293425d17a849f77b811f | [
"arxiv",
"semantic_scholar"
] | Steering Language Models with Weight Arithmetic | Providing high-quality feedback to Large Language Models (LLMs) on a diverse training distribution can be difficult and expensive, and providing feedback only on a narrow distribution can result in unintended generalizations. To better leverage narrow training data, we propose contrastive weight steering, a simple post... | [
"Constanza Fierro",
"Fabien Roger"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-11-07T00:00:00 | https://arxiv.org/abs/2511.05408 | https://arxiv.org/pdf/2511.05408v2 | 2511.05408 | 10.48550/arXiv.2511.05408 | 8 | 0 | false | null | arXiv.org | 0.3621 |
6baa54d76f87e54f2662c813a06ce971062d0e3752c096e81291d0fe3fe31fc5 | [
"arxiv",
"semantic_scholar"
] | Polarization-resolved imaging improves eye tracking | Polarization-resolved near-infrared imaging adds a useful optical contrast mechanism to eye tracking by measuring the polarization state of light reflected by ocular tissues in addition to its intensity. In this paper we demonstrate how this contrast can be used to enable eye tracking. Specifically, we demonstrate that... | [
"Mantas Žurauskas",
"Tom Bu",
"Sanaz Alali",
"Beyza Kalkanli",
"Derek Shi",
"Fernando Alamos",
"Gauresh Pandit",
"Christopher Mei",
"Ali Behrooz",
"Ramin Mirjalili",
"Dave Stronks",
"Alexander Fix",
"Dmitri Model"
] | [
"cs.CV",
"physics.optics"
] | [
"Computer Science",
"Physics"
] | 2025-11-06T00:00:00 | https://arxiv.org/abs/2511.04652 | https://arxiv.org/pdf/2511.04652v1 | 2511.04652 | 10.48550/arXiv.2511.04652 | 2 | 0 | false | null | arXiv.org | 0.3609 |
2b8e739be17d954bee543be8b12ee72c414cf3d7de582af80fc3fea2784ea881 | [
"arxiv",
"semantic_scholar"
] | Human Mesh Modeling for Anny Body | Parametric body models provide the structural basis for many human-centric tasks, yet existing models often rely on costly 3D scans and learned shape spaces that are proprietary and demographically narrow. We introduce Anny, a simple, fully differentiable, and scan-free human body model grounded in anthropometric knowl... | [
"Romain Brégier",
"Guénolé Fiche",
"Laura Bravo-Sánchez",
"Thomas Lucas",
"Matthieu Armando",
"Philippe Weinzaepfel",
"Grégory Rogez",
"Fabien Baradel"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-11-05T00:00:00 | https://arxiv.org/abs/2511.03589 | https://arxiv.org/pdf/2511.03589v2 | 2511.03589 | 10.48550/arXiv.2511.03589 | 7 | 1 | true | https://github.com/naver/anny | arXiv.org | 0.556 |
ede1f48e9942d46264fb042bbf0f8ee397dab10fd9e5d6584387fb2688ea5290 | [
"arxiv",
"semantic_scholar"
] | Merging Continual Pretraining Models for Domain-Specialized LLMs: A Case Study in Finance | While LLMs excel at general tasks, they struggle in specialized domains like finance, requiring diverse skills in domain knowledge, mathematical reasoning, and multilingual processing. Merging domain-specific Continual Pre-training (CPT) "experts" offers a practical alternative to costly and unstable multi-skill traini... | [
"Kentaro Ueda",
"François Portet",
"Hirohiko Suwa",
"Keiichi Yasumoto"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-11-04T00:00:00 | https://arxiv.org/abs/2511.02451 | https://arxiv.org/pdf/2511.02451v1 | 2511.02451 | 10.48550/arXiv.2511.02451 | 1 | 0 | false | null | arXiv.org | 0.3586 |
47ac3b07fb2499582dfff7d95e28fd2e07aeca76184b577ccddb163070e425cd | [
"arxiv",
"semantic_scholar"
] | Model Merging with Functional Dual Anchors | Model merging is an efficient post-training strategy for integrating knowledge from multiple finetuned checkpoints of a shared foundation model. Existing methods operate in the parameter space, combining task vectors to mitigate conflicts, but remain constrained by parameter inconsistencies. We propose Functional Dual ... | [
"Kexuan Shi",
"Yandong Wen",
"Weiyang Liu"
] | [
"cs.LG"
] | [
"Computer Science"
] | 2025-10-24T00:00:00 | https://arxiv.org/abs/2510.21223 | https://arxiv.org/pdf/2510.21223v1 | 2510.21223 | 10.48550/arXiv.2510.21223 | 3 | 0 | false | null | arXiv.org | 0.346 |
d64abf2f883e84edbd93e6ffcbb7004e901cb32b901d2419651bb932038ea02e | [
"arxiv",
"semantic_scholar"
] | Capability Ceilings in Autoregressive Language Models: Empirical Evidence from Knowledge-Intensive Tasks | We document empirical capability ceilings in decoder-only autoregressive language models across knowledge-intensive tasks. Systematic evaluation of OPT and Pythia model families (70M-30B parameters, spanning 240 times scaling) reveals that knowledge retrieval tasks show negligible accuracy improvement despite smooth lo... | [
"Javier Marín"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2025-10-23T00:00:00 | https://arxiv.org/abs/2510.21866 | https://arxiv.org/pdf/2510.21866v1 | 2510.21866 | 10.48550/arXiv.2510.21866 | 1 | 0 | false | null | arXiv.org | 0.3449 |
a371c173fa7ec3b87f668b9288e7f8108ee524d52fa892cfb2a1314b6cc860b2 | [
"arxiv",
"semantic_scholar"
] | Adapting Multilingual Models to Code-Mixed Tasks via Model Merging | We study model merging as a practical alternative to conventional adaptation strategies for code-mixed NLP. Starting from a multilingual base model, we: (i) perform continued pre-training (CPT) on unlabeled code-mixed text to obtain an adapted checkpoint, (ii) merge checkpoint with the base model, and (iii) fine-tune (... | [
"Prashant Kodali",
"Vaishnavi Shivkumar",
"Swarang Joshi",
"Monojit Choudhary",
"Ponnurangam Kumaraguru",
"Manish Shrivastava"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-10-22T00:00:00 | https://arxiv.org/abs/2510.19782 | https://arxiv.org/pdf/2510.19782v2 | 2510.19782 | 10.1145/3799830.3799852 | 4 | 0 | false | null | ACM IKDD Conference on Data Science | 0.3438 |
9fb80a9fc4b1c2f256df22e7efd05db1e411dd68e6ca20e8419a143f9b10ee83 | [
"arxiv",
"semantic_scholar"
] | MIN-Merging: Merge the Important Neurons for Model Merging | Recent advances in deep learning have led to a surge of open-source models across diverse domains. While model merging offers a promising way to combine their strengths, existing approaches often suffer from parameter conflicts that degrade performance on domain-specific tasks. We propose MIN-Merging, a router-based fr... | [
"Yunfei Liang"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-18T00:00:00 | https://arxiv.org/abs/2510.17890 | https://arxiv.org/pdf/2510.17890v2 | 2510.17890 | 10.48550/arXiv.2510.17890 | 0 | 0 | true | null | arXiv.org | 0.5242 |
61d7f5e295921aede5c87def6017b2dbbf9707fc1eb37a9d371646715d099d3f | [
"arxiv",
"semantic_scholar"
] | Six Proofs of Interpolation for the Modal Logic K | In this chapter, we present six different proofs of Craig interpolation for the modal logic K, each using a different set of techniques (model-theoretic, proof-theoretic, syntactic, automata-theoretic, using quasi-models, and algebraic). We compare the pros and cons of each proof technique. | [
"Nick Bezhanishvili",
"Balder ten Cate",
"Rosalie Iemhoff"
] | [
"cs.LO"
] | [
"Computer Science"
] | 2025-10-18T00:00:00 | https://arxiv.org/abs/2510.16398 | https://arxiv.org/pdf/2510.16398v2 | 2510.16398 | 10.48550/arXiv.2510.16398 | 3 | 1 | false | null | arXiv.org | 0.3392 |
ea237e51da3028808d7c6564f34fc44f384387262adc7728917615cdd4572b17 | [
"arxiv",
"semantic_scholar"
] | Purifying Task Vectors in Knowledge-Aware Subspace for Model Merging | Model merging aims to integrate task-specific abilities from individually fine-tuned models into a single model without extra training. In recent model merging methods, task vector has become a fundamental building block, as it can encapsulate the residual information from finetuning. However, the merged model often su... | [
"Bang An",
"Yibo Yang",
"Philip Torr",
"Bernard Ghanem"
] | [
"cs.AI"
] | [
"Computer Science"
] | 2025-10-16T00:00:00 | https://arxiv.org/abs/2510.14697 | https://arxiv.org/pdf/2510.14697v1 | 2510.14697 | 10.48550/arXiv.2510.14697 | 1 | 0 | false | null | arXiv.org | 0.3369 |
e93a218744a82719bfd5f42659b24f6e33d27f1db5eff5733af69c263fbfdf5e | [
"arxiv",
"semantic_scholar"
] | Weight Weaving: Parameter Pooling for Data-Free Model Merging | Model merging provides a cost-effective and data-efficient combination of specialized deep neural networks through parameter integration. This technique leverages expert models across downstream tasks without requiring retraining. Most model merging approaches critically depend on scaling hyper-parameters $λ$, which we... | [
"Levy Chaves",
"Eduardo Valle",
"Sandra Avila"
] | [
"cs.LG",
"cs.CV"
] | [
"Computer Science"
] | 2025-10-15T00:00:00 | https://arxiv.org/abs/2510.13921 | https://arxiv.org/pdf/2510.13921v1 | 2510.13921 | 10.48550/arXiv.2510.13921 | 1 | 0 | false | null | arXiv.org | 0.3357 |
a051feaeee8ec3dc8cfce5862561ca5ab9ab10e9ee8eef1aef83f8a9e14bac46 | [
"arxiv",
"semantic_scholar"
] | Towards Reversible Model Merging For Low-rank Weights | Model merging aims to combine multiple fine-tuned models into a single set of weights that performs well across all source tasks. While prior work has shown that merging can approximate the performance of individual fine-tuned models for each task, it largely overlooks scenarios where models are compressed into low-ran... | [
"Mohammadsajad Alipour",
"Mohammad Mohammadi Amiri"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2025-10-15T00:00:00 | https://arxiv.org/abs/2510.14163 | https://arxiv.org/pdf/2510.14163v1 | 2510.14163 | 10.48550/arXiv.2510.14163 | 1 | 0 | false | null | arXiv.org | 0.3357 |
835188ea4527da006fce8aa6733da3907118dcab5104a73393936a9774a2740c | [
"arxiv",
"semantic_scholar"
] | Revisiting Model Interpolation for Efficient Reasoning | Model merging, typically on Instruct and Thinking models, has shown remarkable performance for efficient reasoning. In this paper, we systematically revisit the simplest merging method that interpolates two weights directly. Particularly, we observe that model interpolation follows a three-stage evolutionary paradigm w... | [
"Taiqiang Wu",
"Runming Yang",
"Tao Liu",
"Jiahao Wang",
"Ngai Wong"
] | [
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-10-13T00:00:00 | https://arxiv.org/abs/2510.10977 | https://arxiv.org/pdf/2510.10977v2 | 2510.10977 | 10.48550/arXiv.2510.10977 | 8 | 0 | true | https://github.com/wutaiqiang/MI}{Github} | arXiv.org | 0.5153 |
5c3a2e7ffe28bf88ce70621a4e881e8fabf60d2ccabc3f4141273d4e1fe55bd4 | [
"arxiv",
"semantic_scholar"
] | Generative World Modelling for Humanoids: 1X World Model Challenge Technical Report | World models are a powerful paradigm in AI and robotics, enabling agents to reason about the future by predicting visual observations or compact latent states. The 1X World Model Challenge introduces an open-source benchmark of real-world humanoid interaction, with two complementary tracks: sampling, focused on forecas... | [
"Riccardo Mereu",
"Aidan Scannell",
"Yuxin Hou",
"Yi Zhao",
"Aditya Jitta",
"Antonio Dominguez",
"Luigi Acerbi",
"Amos Storkey",
"Paul Chang"
] | [
"cs.LG",
"cs.AI",
"cs.RO"
] | [
"Computer Science"
] | 2025-10-08T00:00:00 | https://arxiv.org/abs/2510.07092 | https://arxiv.org/pdf/2510.07092v1 | 2510.07092 | 10.48550/arXiv.2510.07092 | 5 | 0 | true | null | arXiv.org | 0.5065 |
62633c6c252beb0b4aae766bf3ee7e91a0ca9afd882ccc08bc6fbd47d488b4cf | [
"arxiv",
"semantic_scholar"
] | Learning to Interpret Weight Differences in Language Models | Finetuning (pretrained) language models is a standard approach for updating their internal parametric knowledge and specializing them to new tasks and domains. However, the corresponding model weight changes ("weight diffs") are not generally interpretable. While inspecting the finetuning dataset can give a sense of ho... | [
"Avichal Goel",
"Yoon Kim",
"Nir Shavit",
"Tony T. Wang"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2025-10-06T00:00:00 | https://arxiv.org/abs/2510.05092 | https://arxiv.org/pdf/2510.05092v4 | 2510.05092 | 10.48550/arXiv.2510.05092 | 4 | 0 | true | https://github.com/Aviously/diff-interpretation-tuning | arXiv.org | 0.5029 |
3b76ef3b115fa16c3219e74f11e59e2a8d6a1633ec81fe8e8b09af681f8731da | [
"arxiv",
"semantic_scholar"
] | How does the optimizer implicitly bias the model merging loss landscape? | Model merging combines independent solutions with different capabilities into a single one while maintaining the same inference cost. Two popular approaches are linear interpolation, which simply averages multiple model weights, and task arithmetic, which combines task vectors obtained by the difference between finetun... | [
"Chenxiang Zhang",
"Alexander Theus",
"Damien Teney",
"Antonio Orvieto",
"Jun Pang",
"Sjouke Mauw"
] | [
"cs.LG",
"cs.AI"
] | [
"Computer Science"
] | 2025-10-06T00:00:00 | https://arxiv.org/abs/2510.04686 | https://arxiv.org/pdf/2510.04686v2 | 2510.04686 | 10.48550/arXiv.2510.04686 | 2 | 0 | false | null | arXiv.org | 0.3254 |
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