arxiv arXiv cs.AI · 3h ago

CoorDex: Coordinating Body and Hand Priors for Continuous Dexterous Humanoid Loco-Manipulation

The authors introduce CoorDex, a learning pipeline that enables high-degree-of-freedom dexterous loco-manipulation on moving humanoids. This approach converts high-dimensional body and hand control into coordinated latent residual control, overcoming the limitations of traditional stop-and-go methods. The system trains privileged motion tracking teachers from simulated demonstrations and distills them into proprioception-conditioned latent priors. These frozen priors serve as the action space for downstream residual reinforcement learning via a policy that composes task context with separate body-hand residual heads. CoorDex allows a Unitree G1 humanoid equipped with a 20-DoF WUJI hand to perform complex tasks while in motion, such as non-stop bottle grasping and fridge door opening. Ablation studies demonstrate that joint-space PPO and monolithic latent prediction fail under similar reward budgets, whereas the proposed latent-prior interface ensures trainability for contact-rich manipulation.

arxiv arXiv cs.LG · 3h ago

Encoder-Decoder Manifold Alignment for Idempotent Generation

Recent learning paradigms aim to enforce idempotency in generative models by ensuring repeated application leaves samples unchanged on the target data manifold. However, many existing approaches fail to achieve exact fixed points, resulting in instability and drift during repeated applications. The authors identify a geometric mismatch between encoder and decoder manifolds as the primary cause of this failure. To resolve this, they propose a training framework that explicitly aligns the geometry of both components to learn consistent representations of the same underlying data manifold. This alignment encourages stable projections and significantly reduces idempotency error compared to prior methods. Empirical results demonstrate that the approach consistently regenerates identical outputs under repeated application for both image generation and editing tasks. Furthermore, enforcing this type of idempotency improves identity preservation and information stability in generative models.

arxiv arXiv cs.LG · 4h ago

Manifold Restore Mixing Enhances Protein Representation Learning

Data augmentation improves protein representation learning but often disrupts structural integrity or reduces diversity. The authors identify these structure defects and performance degradation issues in existing methods. They propose Manifold Restore Mixing (MRM) to restore lost structural information while introducing diverse variations. MRM mixes hidden representations of original and augmented data, inspired by manifold mixup techniques. A sample difficulty scheduler adjusts the beta distribution to provide progressively challenging samples during training. Experiments on various backbones and downstream tasks demonstrate the method's effectiveness and generalization. The implementation is available at https://github.com/KingGugu/MRM.

arxiv arXiv cs.LG · 4h ago

Entropy-Guided Boundary Supervision for Breast Ultrasound Segmentation

This study introduces an entropy-guided boundary supervision method to address boundary leakage and false-positive activations in breast ultrasound segmentation. The proposed loss function scales contour penalties by per-pixel predictive entropy and ground-truth maps, focusing gradient emphasis on uncertain lesion margins. Evaluated on the BUSI dataset, the method preserved lesion segmentation quality with a mean Dice score of 0.7624, statistically indistinguishable from the baseline. However, it significantly improved specificity by reducing false-positive activations on no-lesion images from 19 of 20 to 5 of 20. A post-hoc spatial temperature scaling step further reduced the expected calibration error from 0.0201 to 0.0095 without altering segmentation masks. These results demonstrate that entropy-guided supervision and spatial calibration function as complementary refinements within a U-Net framework.

arxiv arXiv cs.LG · 4h ago

Diffusion Integrated Gradients: Controllable Path Generation for Flexible Feature Attribution

The authors propose Diffusion Integrated Gradients (DiffIG), a novel method that reformulates path generation as a conditional generative modeling problem to address limitations in existing attribution techniques. While integrated gradients are widely used, their reliance on fixed or hand-crafted paths often results in noisy or distorted attributions. To solve this, DiffIG trains a diffusion model to learn a distribution over paths derived from a Stick-Breaking Process. The method then employs guided sampling to allow for the embedding of user guidance during the inference-time sampling procedure. This approach enables flexible and controllable feature attribution by treating path selection as a generative task rather than a static choice. Experimental results demonstrate that DiffIG quantitatively matches or outperforms existing path-based methods in terms of attribution quality. Furthermore, the generated explanations are shown to be perceptually aligned with human expectations. The work introduces a new generative perspective for Explainable Artificial Intelligence that supports dynamic control over explanation paths.

arxiv arXiv cs.LG · 4h ago

First Finite-Time Analysis of Classical Adam for Nonsmooth Nonconvex Optimization

This study presents the first finite-time convergence analysis for the classical Adam optimizer, specifically addressing its behavior in nonsmooth nonconvex optimization settings. Previous research largely ignored Adam's bias-correction term or required extra algorithmic modifications like clipping, leaving the original method's guarantees unclear. The authors utilize the Online-to-Nonconvex Conversion framework to prove that a randomly scaled learning rate ensures a convergence rate of $1/T^{rac{2}{13}}$. This theoretical result is significant because it applies to the modern heavy-tailed noise regime, which more closely reflects practical training conditions. Furthermore, the analysis establishes convergence under the parameter choice where $β_1=β_2$, aligning with recent empirical observations. These findings provide a rigorous explanation for Adam's effectiveness in real-world scenarios that were previously inadequately captured by smooth optimization theories.

arxiv arXiv cs.LG · 4h ago

Boundary-Aware Curriculum RL Expands LLM Reasoning Capacity Beyond Base Model Limits

The authors argue that mainstream Reinforcement Learning with Verifiable Rewards (RLVR) often fails to expand the reasoning capacity of large language models, merely reallocating probabilities among existing trajectories. To address this limitation, they introduce a boundary-aware Curriculum RL approach designed to move beyond the base model's empirical reasoning capacity boundary. The method first utilizes pass@k sampling to identify the current reasoning limits and then applies targeted teacher guidance to examples near or beyond that boundary. Reinforcement learning is subsequently used to consolidate these newly introduced reasoning patterns across Qwen, Llama, and DeepSeek base models. Experimental results demonstrate significant improvements in both pass@1 scores and pass@256 scores, which serve as a proxy for the reasoning capacity boundary. Specifically, average pass@256 improved by 9.8 percentage points over the base models and by 10.3 percentage points over Vanilla RLVR. These findings suggest that this curriculum-based strategy offers a scalable route for continuously improving LLM reasoning capabilities.

arxiv arXiv cs.LG · 4h ago

Attention Sinks and Collapse Are Universal Consequences of Content-Based Routing

The study demonstrates that attention sinks, representation collapse, and norm stratification are not unique to transformer architectures but are inherent consequences of content-based routing under a fixed similarity metric. It establishes an identity showing softmax attention functions as Boltzmann-weighted aggregation over Euclidean distances with constant key norms, rendering it blind to key magnitude due to the omission of a specific norm term. This framework predicts that any router utilizing a metric ill-matched to its representations will compensate by concentrating routing and collapsing the routed representations. The authors validate this hypothesis across diverse models including nine pretrained transformers, graph attention networks, selective state-space models, recurrent mixers, and learned residual layers. Experimental results confirm that all tested architectures exhibit this identical signature of collapse regardless of their specific domain or structure. Furthermore, within-model ablations isolate the routing mechanism as the primary cause rather than incidental training dynamics. The onset of this phenomenon is shown to be contingent on the strength of the positional brake accompanying the content score, which can shift the effect across its range. However, the underlying mechanism remains invariant and does not require norm stratification, as routers with norm-normalized keys exhibit the same concentration behavior.

media r/LocalLLaMA · 4h ago

User Reports Strong Performance of siq1 Model on Kebab Bench

A Reddit user has shared results indicating that their model, referred to as siq1, performs very well on the Kebab Bench evaluation. The post highlights the model's capabilities through a demonstration hosted on Hugging Face Spaces. Specifically, the user points to a space titled 'hermes-agent-zerogpu' created by AlexWortega as evidence of this performance. This submission was made by the Reddit user /u/Mysterious_Hearing14 within the r/LocalLLaMA community. The original post includes a link to the Hugging Face interface where the model can be tested. Additionally, a video demonstration is available via a provided V.redd.it link for further verification.

media r/LocalLLaMA · 4h ago

Inquiry Regarding the Availability of Modern Non-Chat Completion Models

A user on the LocalLLaMA subreddit questioned whether all modern large language models are exclusively tuned for chat interactions. The inquiry specifically sought to identify any models that support bare text completion rather than conversational formats. The poster noted a difficulty in locating such models within the Hugging Face repository. This highlights a perceived gap in the availability of non-chat architectures for users requiring raw completion capabilities. The discussion reflects broader concerns about the industry's shift toward instruction-tuned and chat-oriented model designs.

arxiv arXiv cs.LG · 4h ago

No Reference-Free Generalization in Quantum Machine Learning

This study addresses the identifiability problem in quantum machine learning where training data lacks a preferred basis or reference frame. The authors formulate supervised learning without an external quantum reference frame, requiring classifiers to preserve unitary symmetries unbroken by the training data. They prove that if training states do not span the full Hilbert space, all pure states orthogonal to this span receive identical predictions. This limitation arises from missing reference information rather than state discrimination or computational constraints. The research establishes a robust version under weak symmetry breaking and shows that learning generic concepts requires exponentially many oriented training directions. Numerical illustrations visualize the resulting prediction collapse and its controlled relaxation. The results identify feature maps, measurement bases, and diverse training states as essential operational resources for generalization.

arxiv arXiv cs.LG · 5h ago

Wearable A-Mode Ultrasound Enables Whole Hand Kinematic Tracking on Microcontroller

Researchers propose a framework for robust whole-hand and wrist kinematic tracking using the wearable WULPUS platform with an A-mode ultrasound probe. The system addresses the regression of 23 degrees of freedom directly on the device, overcoming limitations of prior non-wearable systems. A compact multi-output convolutional neural network containing 11,285 parameters is employed alongside an incremental training strategy to enhance generalization. This approach reduces mean absolute error by more than 17% compared to non-incremental methods. The model is deployed on the WULPUS nRF52832 microcontroller, achieving end-to-end tracking entirely on-device. Inference consumes only 0.73 mJ with a latency of 29.1 ms. The system supports full operation within 33 mW, enabling up to 36 hours of continuous use. This method also reduces wireless bandwidth requirements by 88% compared to raw data transmission.

arxiv arXiv cs.LG · 5h ago

Null-Calibrated Conformal Selection via Target-Membership Scores

The article introduces Null-Calibrated Conformal Selection (NCCS), a method that utilizes target-membership probability scores to identify test candidates within a target region while controlling the false discovery rate. The authors argue that these membership scores provide a more natural ranking for selection tasks than conventional prediction-oriented nonconformity scores, particularly for complex targets. This distinction is critical for interval-valued, variance-driven, multimodal, or multi-condition targets where traditional scores may be misaligned with selection power. NCCS ranks test scores against confirmed non-target calibration examples to yield finite-sample valid null p-values under null exchangeability. These p-values can be combined with the Benjamini-Yekutieli procedure under arbitrary dependence or the Benjamini-Hochberg procedure under standard positive-dependence conditions. Experiments demonstrate that membership scores match conventional scores on mean-monotone targets but substantially improve performance on variance-driven targets. In rare-target regimes, NCCS trades power for finite-sample null validity, addressing issues where direct empirical-FDP thresholding can be anti-conservative.

arxiv arXiv cs.LG · 5h ago

RoboMME-Interference Benchmarks Robot Memory Under Distraction

The introduction of RoboMME-Interference addresses the need for evaluating robot memory in realistic, long-context scenarios where systems must recall information from multiple sessions ago. This new cross-session benchmark is built upon the existing RoboMME framework to measure performance when robots face distractions from unrelated prior experiences. For each query episode, the benchmark constructs a session history consisting of relevant demonstrations followed by a controlled number of unrelated sessions provided as memory to Vision-Language-Action models. Researchers tested released memory-augmented variants of the π_0.5 model without modification to assess their robustness under these conditions. The results indicate that while perceptual memory variants improve success rates when no distractors are present, their accuracy decays steadily and strongly as unrelated sessions accumulate. These findings highlight a critical failure in current systems regarding long-context memory and interference resistance. The project page, videos, code, and data for this benchmark are available at https://robotmemorybench.com.

arxiv arXiv cs.LG · 5h ago

Flow Annealing Posterior Sampling for Function-Space Regression and Inverse Problems

The authors introduce Flow Annealing Posterior Sampling (FAPS), a novel framework that unifies stochastic-process regression with PDE inverse problems in function space. Built upon pretrained function-space flow-matching priors, FAPS facilitates likelihood-guided posterior inference using sparse and noisy observations. The method supports variable query discretizations and avoids the need for explicit prior-density evaluation during sampling. It employs a Langevin correction mechanism that utilizes a low-rank covariance preconditioner to exploit dominant function-space correlations across different discretizations. Benchmarks on both Gaussian and non-Gaussian stochastic processes demonstrate that FAPS produces coherent posterior samples with accurate uncertainty quantification. The approach significantly outperforms existing functional regression baselines in these standard tasks. Furthermore, it achieves competitive or superior performance in noisy PDE inverse problems compared to diffusion-based samplers while reducing test-time sampling costs.

media r/LocalLLaMA · 5h ago

Backtrack Sampler and Verifier Drastically Improve Tiny Model Coding Performance

A new backtrack sampler combined with a verifier model significantly enhances the coding performance of tiny 0.5B parameter models, potentially making them competitive with larger 2-4B class models without weight changes. The approach theoretically addresses hallucination issues in large models by correcting errors during generation through re-sampling. However, this method incurs a 5-30% decode speed penalty due to the need for backward passes and requires training a verifier model of similar size to the original. This requirement doubles VRAM usage and increases compute demands by 1.5 to 3 times compared to standard inference. Despite these costs, the verifier generalizes across models of equal or lower weight classes if trained on diverse data distributions. Training the verifier is highly efficient, requiring only approximately 0.01% of the token size used for full pre-training.

media r/LocalLLaMA · 5h ago

NVIDIA Releases Nemotron-TwoTower-30B-A3B, a Diffusion-Based Language Model

NVIDIA has released the Nemotron-TwoTower-30B-A3B-Base-BF16 model, which is built upon the Nemotron 3 Nano 30B-A3B backbone. This architecture diverges from standard autoregressive models by utilizing a frozen context tower alongside a diffusion denoiser tower. The system iteratively fills blocks of tokens in parallel rather than generating them strictly one at a time. According to NVIDIA, this default mask-diffusion setup retains 98.7% of the aggregate benchmark quality found in the autoregressive baseline. Despite maintaining high quality, the model achieves 2.42 times its wall-clock generation throughput. The release highlights a novel approach to language modeling that combines diffusion techniques with large-scale language capabilities.

media r/LocalLLaMA · 5h ago

Experimental USB4 RDMA Implementation Demonstrated on Strix Halo

A blog post from Hellas.ai details an experimental implementation of Remote Direct Memory Access (RDMA) over Thunderbolt. The demonstration was conducted using two devices equipped with AMD's Strix Halo processors. This approach allows for high-speed data transfer capabilities via the USB4 standard. The author notes that this technology could be significant because it is compatible with any host supporting USB4. No prior public discussion of this specific implementation was found by the submitter. The work highlights the potential for leveraging existing hardware interfaces for advanced networking tasks.

media r/LocalLLaMA · 5h ago

GLM 5.2 on Dual Strix Halo (256GB): Worth it?

A Reddit user named Intrepid_Rub_3566 has shared a video review evaluating the performance of GLM 5.2 running on a dual AMD Strix Halo setup with 256GB of RAM. The discussion centers on whether this specific hardware configuration provides sufficient value for local large language model inference. The content highlights the technical feasibility of deploying GLM 5.2 in such an environment, focusing on resource utilization and speed. Viewers are directed to a YouTube link for detailed benchmarks and performance metrics. The thread also includes community comments discussing the practicality and cost-effectiveness of this dual-GPU approach.

media r/LocalLLaMA · 5h ago

Reddit Inquiry on Using Local Models for Self-Hacking

A user on the r/LocalLLaMA subreddit asked if anyone has attempted to gain root access to their own system using a local large language model. This inquiry was prompted by recent discussions regarding Mythos's alleged ability to hack into US government systems. The post seeks practical experiences from the community regarding the feasibility of such actions. It specifically targets the application of local models for self-penetration testing or unauthorized access. The question highlights concerns about the security implications of powerful AI tools in the hands of individuals.