The paper introduces FlexibleFusion, a method for infrared-visible object detection that adapts to both complete and missing-modality scenarios. It employs a Modality-Aware Experts Collaboration mechanism to selectively fuse cross-modal or intra-modal pathways, and a Residual Self-Paced Entropic Optimal Transport module to align heterogeneous feature distributions without heavy optimization. Experiments demonstrate consistent performance across various modality configurations.
By Yue Zhao, Hua Yu, Yukun Zhao, Yuzhi Zhang, Maoguo Gong, Xin Mei, Zhuping Hu, Yanchi Li, A. K. Qin
The paper introduces a GPU-resident, batched Levenberg–Marquardt solver that efficiently optimizes constants in tree-based genetic programming for symbolic regression. By using reverse-mode automatic differentiation to assemble per-tree Jacobians in a single backward sweep, the solver’s per-iteration cost becomes independent of the number of constants per tree, achieving up to 510,000 trees per second on an NVIDIA A100. Integrated into EvoGP, the solver enables end-to-end search that recovers governing equations on 10 of 18 constructed problems, a significant improvement over stock EvoGP.
By Hao Mao, Xu Tony Liu, Shuai Lu, Peng Zhao, Wenzheng Jiang, Yuntian Chen
arXiv:2609. 03378v1 Announce Type: new Abstract: Depth can resolve appearance ambiguity in RGB-D salient object detection (SOD), yet sensor depth is not uniformly reliable.
By Xuehao Wang, Jiaxin Hua, Runmei Li, Zhenyu Wu, Chenglizhao Chen, Ke Gu, Aimin Hao
The paper presents a consensus framework for Bayesian Network fusion that balances dependency preservation with computational tractability by enforcing treewidth constraints. It introduces genetic algorithms featuring advanced initialization, specialized operators, and a tailored fitness function to prioritize shared structures among input networks. Experiments on synthetic and real-world BNs demonstrate that these genetic algorithms outperform adapted methods and greedy baselines.
By Pablo Torrijos, Jos\'e A. G\'amez, Jos\'e M. Puerta, Juan A. Aledo
The paper introduces PersonaLink, a training‑free method that distills a user’s interaction history into a bounded three‑field persona and iteratively refines it by self‑evaluating a frozen 7B language model on held‑out labeled data. Each refinement rewrites the persona only if it does not regress on that slice, ensuring the persona remains bounded and query‑independent. On a 200‑user news categorization task (LaMP‑2), PersonaLink achieves 0.745–0.755 accuracy, statistically indistinguishable from BM25 retrieval’s 0.760–0.765 accuracy, demonstrating that distilled personas can match retrieval for classification but not for regression tasks.
By JaeHa Yoon, Minjun Park, Seoyeon Kim, Jiwoo Lee, Hyunwoo Choi, Dohyun Kang
The paper introduces a unified bundle adjustment framework that jointly optimizes camera parameters, sparse 3D points, and richer geometric features such as lines, coplanarity, and parallelism. It classifies features into scalable ones with direct 2D measurements and higher‑order groups that can be treated as camera‑like entities, allowing group constraints and cross‑feature relations to be expressed via 2D reprojection errors. This approach preserves the sparsity of classical point‑based BA, maintains numerical stability, and achieves runtime comparable to point‑only BA while producing richer 3D structures and improved accuracy.
By Shaohui Liu, R\'emi Pautrat, Daniel Barath, Richard Hartley, Viktor Larsson, Marc Pollefeys
VestigeKV is a new KV‑cache technique that uses a 64‑dimensional vestigial branch—originally a RoPE component repurposed during NoPE training—as a query‑independent eviction signal. By reading only 11 % of each cache row, the method partitions the cache into an attended tier (top‑m rows) and an archive tier (all other rows), which is GPU‑resident and never deleted. The approach achieves near‑perfect retrieval (1.00 at 8×, 0.92 at 32×) without any training, quantization, or changes to weights or kernels.
By WenJie Fan
The paper investigates on‑policy distillation (OPD) of large language models when trained on a minimal amount of data, specifically a single query. It finds that one‑shot OPD continues to improve for hundreds of steps and recovers most of the performance gains achieved with full‑dataset training across various tasks and model families. The authors attribute this to the high state coverage achieved early in training and show that adding a few semantically distinct queries further boosts coverage and accuracy, eventually matching full‑data results. They conclude that OPD is data‑overfed but algorithm‑starved, highlighting the need to focus on step efficiency and the underlying mechanisms of its recent successes.
By Zixuan Fu, Bingxiang He, Yuxin Zuo, Haohuan Huang, Jinqian Zhang, Ruhang Xiao, Cheng Qian, Qinyu Luo, Huan-ang Gao, Yudong Wang, Zhiyuan Liu, Ning Ding, Chaojun Xiao
HARP (Hadamard‑Preconditioned Adaptive Rotation Processor) is a learnable, structured two‑sided orthogonal processor that replaces fixed randomized Hadamard transforms in post‑training quantization of large language models. By representing rotations as sparse butterfly‑like block‑orthogonal stages and supporting mixed‑radix schedules, HARP adapts the quantization basis to each layer and calibration distribution while maintaining full‑precision equivalence. Across 2–4‑bit settings on Llama models from 1B to 70B, HARP consistently improves perplexity, delivers the strongest zero‑shot gains at 2 bits, and preserves deployment efficiency—achieving 128 tokens per second on Llama 2 7B at 2 bits, roughly 90% of RHT throughput and over twice the speed of FP16.
By Artur Zagitov, Gleb Molodtsov, Aleksandr Beznosikov
Para-Pipe is a hierarchical mapping framework that integrates intra- and inter-stage operator parallelism within a pipelined architecture for machine‑learning computational graphs on heterogeneous System‑on‑Chip (SoC) platforms. By selectively fine‑tuning parallelism levels across pipeline stages, it navigates the trade‑off between throughput and latency, reducing inter‑processor communication overhead and improving energy efficiency. Evaluation on Amlogic and Black Sesame SoCs shows multiple Pareto‑optimal configurations, with throughput‑optimized setups achieving up to 11.0% better energy efficiency than purely pipelined strategies and 23.3% better than non‑pipelined parallel execution.
By Yujie Zhang, Huiying Lan, Ehsan Aghapour, Zhiyuan Ning, Peng Zan, Weidong Shao, Anuj Pathania, Tulika Mitra
The paper introduces a physics‑informed graph attention network that directly operates on the tetrahedral mesh used in TCAD simulations of FinFET devices. By predicting electrostatic potential and quasi‑Fermi levels at every mesh node and training with both data loss and finite‑volume current‑continuity residuals, the surrogate retains the underlying carrier‑transport physics while achieving size generalization. Benchmarks against Sentaurus Device show sub‑volt RMSE for the drift‑diffusion fields and a per‑design throughput that is orders of magnitude faster, enabling rapid Pareto‑front exploration of large multi‑fin arrays that would otherwise be prohibitively slow to simulate.
By Leonid Popryho, Ayoub Sadeghi, Inna Partin-Vaisband
arXiv:2609. 04010v1 Announce Type: new Abstract: Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation.
By Subham Sekhar Sahoo, Lingjie Chen, Khiem Pham, Jonathan Geuter, Chaitanya Dwivedi, Varad Pimpalkhute, Yash Akhauri, Alexander Moreno, Mikhail Yurochkin, Zhenting Wang, Mostafa Elhoushi, Nolan Dey, Shane Bergsma, Joel Hestness, John Thickstun, Eric Xing, Zhengzhong Liu
EmoDistill is an offline framework that distills emotional negotiation skills from large language model interactions into smaller agents. It separates emotion selection, handled by an Implicit Q‑Learning selector, from emotion‑conditioned expression, learned by a LoRA‑adapted 7B policy via supervised fine‑tuning and judge policy optimization. Experiments across four negotiation domains show that the full EmoDistill policy outperforms vanilla and IQL‑only baselines, while removing the explicit emotion channel markedly reduces negotiation utility and reveals partial, domain‑dependent transfer to unseen counterparties.
By Yunbo Long, Haolang Zhao, Lukas Beckenbauer, Liming Xu, Alexandra Brintrup
The paper introduces Modality‑Specific Frequency Distillation (MSFD), a continual learning framework for video deepfake detection that separates spatial, temporal, and spatiotemporal features in the frequency domain. By preserving each modality independently and applying a cross‑modality decorrelation loss, MSFD adapts to new forgery patterns while maintaining performance across diverse continual deepfake video scenarios. Experiments demonstrate that this approach outperforms state‑of‑the‑art methods in both adaptation and retention.
By Taehoon Kim, Jongwook Choi, Heejae Jo, Byungmin Park, Jongwon Choi
GrowPage is an on‑demand key–value (KV) budgeting framework designed to improve large language model (LLM) reasoning serving. It treats KV capacity as a runtime resource, using lightweight dual‑timescale query summaries to track recent and long‑term attention patterns and estimate demand evolution. At each capacity boundary, GrowPage either compresses KV states within the current allocation or acquires an additional physical page, integrating with PagedAttention’s page‑level memory abstraction to maintain continuous batching and prefix caching.
By Qiankun Ma, Yanjiang Zhou, Zinan Xiong, Haofei Wang, Zhen Song, Yang Xiang, Ziyao Zhang, Hairong Zheng
The paper investigates how model compression impacts test-time adaptation (TTA) in deep neural networks. Using ResNet‑18 and ViT‑Base on CIFAR‑10‑C and ImageNet‑C, the authors evaluate several compression techniques alongside standard TTA methods, introducing a diagnostic framework to assess representational expressivity and adaptation subspace compatibility. Results show that while compressed models maintain high accuracy under supervised adaptation, their TTA performance deteriorates with increased compression due to reduced representational diversity and structural constraints that limit recoverability.
By Francesco Corti, Dong Wang, Young D. Kwon, Cecilia Mascolo, Olga Saukh
The paper introduces the concept of a free pause token, which allows a language model to perform additional computation for each next-token prediction without increasing the sequence length or KV cache usage. This pause token operates as a parallel prediction stream over a shared backbone, improving next-token accuracy by 2–3 centi‑nats on a 1‑billion‑parameter model. While it adds minimal inference overhead, the primary cost lies in training, where compute requirements rise modestly (up to 1.14×) yet most benefits are retained.
By John Langford, Nathan Godey, Giovanni Monea, Yoav Artzi, Harry Dong, Ying Fan, Gustavo de Rosa, Zheng Zhan
M3T introduces a discrete multi‑modal motion token system for sign language production, addressing the need for non‑manual features such as mouthings, eyebrow raises, gaze, and head movements. The approach couples FLAME’s expressive facial space with SMPL‑X body parameters and uses modality‑specific Finite Scalar Quantization VAEs to achieve high face codebook utilization (99.0%). Trained with an autoregressive transformer and a sign‑to‑text translation objective, M3T outperforms existing methods on three standard datasets, notably improving accuracy on NMFs‑CSL from 49.0% to 58.3% without large‑scale pre‑training.
By Alexandre Symeonidis-Herzig, Jianhe Low, Ozge Mercanoglu Sincan, Richard Bowden
FlashRender is a few-step generative rendering framework that quickly retakes a source video along a target camera trajectory. It addresses discretization error by introducing Representation Transformation and Alignment (RETA) to align source-video representations with target-video features, reducing denoising trajectory curvature. The model is further refined with a MeanFlow objective and on-policy flow map distillation, achieving video quality and geometric consistency comparable to multi-step baselines at a 25× lower sampling cost while improving camera controllability.
By Byeongjun Park, Byung-Hoon Kim, Hyungjin Chung
The paper introduces AdaptiveSpec, a training‑free speculative decoding method that simultaneously adapts the per‑step verification rule and the draft‑tree shape using signals generated during decoding. It replaces the fixed token‑match rule with a margin‑based threshold and adjusts tree depth, width, and node count based on draft confidence and recent acceptance history, allowing the total draft count to vary. Experiments on SGLang show up to 56% throughput gains over EAGLE‑3 while maintaining 93% of lossless task accuracy on GSM8K, MATH‑500, and HumanEval across three models.
By Oszk\'ar Urb\'an, Young D. Kwon, Stylianos I. Venieris, Cecilia Mascolo