OpenRaiser/Intern-Atlas huggingface dataset:
Off The Grid: Detection of Primitives for Feed-Forward 3D Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

Feed-forward 3D Gaussian Splatting (3DGS) models enable real-time scene generation but are hindered by suboptimal pixel-aligned primitive placement, which relies on a dense, rigid grid and limits both quality and efficiency. We introduce a new feed-forward architecture that detects 3D Gaussian primitives at a sub-pixel level, replacing the pixel grid with an adaptive, "Off The Grid" distribution. Inspired by keypoint detection, our multi-resolution decoder learns to distribute primitives across image patches. This module is trained end-to-end with a 3D reconstruction backbone using self-supervised learning. Our resulting pose-free model generates photorealistic scenes in seconds, achieving state-of-the-art novel view synthesis for feed-forward models. It outperforms competitors while using far fewer primitives, demonstrating a more accurate and efficient allocation that captures fine details and reduces artifacts. Moreover, we observe that by learning to render 3D Gaussians, our 3D reconstruction backbone improves camera pose estimation, suggesting opportunities to train these foundational models without labels.
FAME: FPGA Acceleration of Secure Matrix Multiplication with Homomorphic Encryption
Venue: arXiv cs.AR 2025
Year: 2025

Homomorphic Encryption (HE) enables secure computation on encrypted data, addressing privacy concerns in cloud computing. However, the high computational cost of HE operations, particularly matrix multiplication (MM), remains a major barrier to its practical deployment. Accelerating homomorphic encrypted MM (HE MM) is therefore crucial for applications such as privacy-preserving machine learning. In this paper, we present a bandwidth-efficient FPGA implementation of HE MM. We first develop a cost model to evaluate the on-chip memory requirements for a given set of HE parameters and input matrix sizes. Our analysis shows that optimizing on-chip memory usage is critical for scalable and efficient HE MM. To this end, we design a novel datapath for Homomorphic Linear Transformation (HLT), the primary bottleneck in HE MM. The proposed datapath significantly reduces off-chip memory traffic and on-chip memory demand by enabling fine-grained data reuse. Leveraging this datapath, we introduce FAME, the first FPGA-based accelerator specifically tailored for HE MM. FAME supports arbitrary matrix shapes and is configurable across a wide range of HE parameter sets. We implement FAME on an Alveo U280 FPGA and evaluate its performance across diverse matrix sizes and shapes. Experimental results show that FAME achieves an average speedup of 221x over state-of-the-art CPU-based implementations, demonstrating its scalability and practicality for large-scale consecutive HE MM and real-world workloads.
CTkvr: KV Cache Retrieval for Long-Context LLMs via Centroid then Token Indexing
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) are increasingly applied in long-context scenarios such as multi-turn conversations. However, long contexts pose significant challenges for inference efficiency, including high memory overhead from Key-Value (KV) cache and increased latency due to excessive memory accesses. Recent methods for dynamic KV selection struggle with trade-offs: block-level indexing degrades accuracy by retrieving irrelevant KV entries, while token-level indexing incurs high latency from inefficient retrieval mechanisms. In this paper, we propose CTKVR, a novel centroid-then-token KV retrieval scheme that addresses these limitations. CTKVR leverages a key observation: query vectors adjacent in position exhibit high similarity after Rotary Position Embedding (RoPE) and share most of their top-k KV cache entries. Based on this insight, CTKVR employs a two-stage retrieval strategy: lightweight centroids are precomputed during prefilling for centroid-grained indexing, followed by token-level refinement for precise KV retrieval. This approach balances retrieval efficiency and accuracy. To further enhance performance, we implement an optimized system for indexing construction and search using CPU-GPU co-execution. Experimentally, CTKVR achieves superior performance across multiple benchmarks with less than 1% accuracy degradation. Meanwhile, CTKVR delivers 3 times and 4 times throughput speedups on Llama-3-8B and Yi-9B at 96K context length across diverse GPU hardware.
An Open Toolkit for Underwater Field Robotics
Venue: arXiv cs.RO 2025
Year: 2025

Underwater robotics is becoming increasingly important for marine science, environmental monitoring, and subsea industrial operations, yet the development of underwater manipulation and actuation systems remains restricted by high costs, proprietary designs, and limited access to modular, research-oriented hardware. While open-source initiatives have democratized vehicle construction and control software, a substantial gap persists for joint-actuated systems-particularly those requiring waterproof, feedback-enabled actuation suitable for manipulators, grippers, and bioinspired devices. As a result, many research groups face lengthy development cycles, limited reproducibility, and difficulty transitioning laboratory prototypes to field-ready platforms. To address this gap, we introduce an open, cost-effective hardware and software toolkit for underwater manipulation research. The toolkit includes a depth-rated Underwater Robotic Joint (URJ) with early leakage detection, compact control and power management electronics, and a ROS2-based software stack for sensing and multi-mode actuation. All CAD models, fabrication files, PCB sources, firmware, and ROS2 packages are openly released, enabling local manufacturing, modification, and community-driven improvement. The toolkit has undergone extensive laboratory testing and multiple field deployments, demonstrating reliable operation up to 40 m depth across diverse applications, including a 3-DoF underwater manipulator, a tendon-driven soft gripper, and an underactuated sediment sampler. These results validate the robustness, versatility, and reusability of the toolkit for real marine environments. By providing a fully open, field-tested platform, this work aims to lower the barrier to entry for underwater manipulation research, improve reproducibility, and accelerate innovation in underwater field robotics.
You Never Know a Person, You Only Know Their Defenses: Detecting Levels of Psychological Defense Mechanisms in Supportive Conversations
Venue: arXiv cs.CL 2025
Year: 2025

Psychological defenses are strategies, often automatic, that people use to manage distress. Rigid or overuse of defenses is negatively linked to mental health and shapes what speakers disclose and how they accept or resist help. However, defenses are complex and difficult to reliably measure, particularly in clinical dialogues. We introduce PsyDefConv, a dialogue corpus with help seeker utterances labeled for defense level, and DMRS Co-Pilot, a four-stage pipeline that provides evidence-based pre-annotations. The corpus contains 200 dialogues and 4709 utterances, including 2336 help seeker turns, with labeling and Cohen's kappa 0.639. In a counterbalanced study, the co-pilot reduced average annotation time by 22.4%. In expert review, it averaged 4.62 for evidence, 4.44 for clinical plausibility, and 4.40 for insight on a seven-point scale. Benchmarks with strong language models in zero-shot and fine-tuning settings demonstrate clear headroom, with the best macro F1-score around 30% and a tendency to overpredict mature defenses. Corpus analyses confirm that mature defenses are most common and reveal emotion-specific deviations. We will release the corpus, annotations, code, and prompts to support research on defensive functioning in language.
Characterizing Mamba's Selective Memory using Auto-Encoders
Venue: arXiv cs.CL 2025
Year: 2025

State space models (SSMs) are a promising alternative to transformers for language modeling because they use fixed memory during inference. However, this fixed memory usage requires some information loss in the hidden state when processing long sequences. While prior work has studied the sequence length at which this information loss occurs, it does not characterize the types of information SSM language models (LMs) tend to forget. In this paper, we address this knowledge gap by identifying the types of tokens (e.g., parts of speech, named entities) and sequences (e.g., code, math problems) that are more frequently forgotten by SSM LMs. We achieve this by training an auto-encoder to reconstruct sequences from the SSM's hidden state, and measure information loss by comparing inputs with their reconstructions. We perform experiments using the Mamba family of SSM LMs (130M--1.4B) on sequences ranging from 4--256 tokens. Our results show significantly higher rates of information loss on math-related tokens (e.g., numbers, variables), mentions of organization entities, and alternative dialects to Standard American English. We then examine the frequency that these tokens appear in Mamba's pretraining data and find that less prevalent tokens tend to be the ones Mamba is most likely to forget. By identifying these patterns, our work provides clear direction for future research to develop methods that better control Mamba's ability to retain important information.
LeaseGuard: Raft Leases Done Right
Venue: arXiv cs.DC 2025
Year: 2025

Raft is a leading consensus algorithm for replicating writes in distributed databases. However, distributed databases also require consistent reads. To guarantee read consistency, a Raft-based system must either accept the high communication overhead of a safety check for each read, or implement leader leases. Prior lease protocols are vaguely specified and hurt availability, so most Raft systems implement them incorrectly or not at all. We introduce LeaseGuard, a novel lease algorithm that relies on guarantees specific to Raft elections. LeaseGuard is simple, rigorously specified in TLA+, and includes two novel optimizations that maximize availability during leader failover. The first optimization restores write throughput quickly, and the second improves read availability. We evaluate LeaseGuard with a simulation in Python and an implementation in LogCabin, the C++ reference implementation of Raft. By replacing LogCabin's default consistency mechanism (quorum checks), LeaseGuard reduces the overhead of consistent reads from one to zero network roundtrips. It also improves write throughput from ~1000 to ~10,000 writes per second, by eliminating contention between writes and quorum reads. Whereas traditional leases ban all reads on a new leader while it waits for a lease, in our LeaseGuard test the new leader instantly allows 99% of reads to succeed.
FrontierCS: Evolving Challenges for Evolving Intelligence
Venue: arXiv cs.LG 2025
Year: 2025

We introduce FrontierCS, a benchmark of 156 open-ended problems across diverse areas of computer science, designed and reviewed by experts, including CS PhDs and top-tier competitive programming participants and problem setters. Unlike existing benchmarks that focus on tasks with known optimal solutions, FrontierCS targets problems where the optimal solution is unknown, but the quality of a solution can be objectively evaluated. Models solve these tasks by implementing executable programs rather than outputting a direct answer. FrontierCS includes algorithmic problems, which are often NP-hard variants of competitive programming problems with objective partial scoring, and research problems with the same property. For each problem we provide an expert reference solution and an automatic evaluator. Combining open-ended design, measurable progress, and expert curation, FrontierCS provides a benchmark at the frontier of computer-science difficulty. Empirically, we find that frontier reasoning models still lag far behind human experts on both the algorithmic and research tracks, that increasing reasoning budgets alone does not close this gap, and that models often over-optimize for generating merely workable code instead of discovering high-quality algorithms and system designs.
VLIC: Vision-Language Models As Perceptual Judges for Human-Aligned Image Compression
Venue: arXiv cs.CV 2025
Year: 2025

Evaluations of image compression performance which include human preferences have generally found that naive distortion functions such as MSE are insufficiently aligned to human perception. In order to align compression models to human perception, prior work has employed differentiable perceptual losses consisting of neural networks calibrated on large-scale datasets of human psycho-visual judgments. We show that, surprisingly, state-of-the-art vision-language models (VLMs) can replicate binary human two-alternative forced choice (2AFC) judgments zero-shot when asked to reason about the differences between pairs of images. Motivated to exploit the powerful zero-shot visual reasoning capabilities of VLMs, we propose Vision-Language Models for Image Compression (VLIC), a diffusion-based image compression system designed to be post-trained with binary VLM judgments. VLIC leverages existing techniques for diffusion model post-training with preferences, rather than distilling the VLM judgments into a separate perceptual loss network. We show that calibrating this system on VLM judgments produces competitive or state-of-the-art performance on human-aligned visual compression depending on the dataset, according to perceptual metrics and large-scale user studies. We additionally conduct an extensive analysis of the VLM-based reward design and training procedure and share important insights. More visuals are available at https://kylesargent.github.io/vlic
Prompt-to-Parts: Generative AI for Physical Assembly and Scalable Instructions
Venue: arXiv cs.AI 2025
Year: 2025

We present a framework for generating physically realizable assembly instructions from natural language descriptions. Unlike unconstrained text-to-3D approaches, our method operates within a discrete parts vocabulary, enforcing geometric validity, connection constraints, and buildability ordering. Using LDraw as a text-rich intermediate representation, we demonstrate that large language models can be guided with tools to produce valid step-by-step construction sequences and assembly instructions for brick-based prototypes of more than 3000 assembly parts. We introduce a Python library for programmatic model generation and evaluate buildable outputs on complex satellites, aircraft, and architectural domains. The approach aims for demonstrable scalability, modularity, and fidelity that bridges the gap between semantic design intent and manufacturable output. Physical prototyping follows from natural language specifications. The work proposes a novel elemental lingua franca as a key missing piece from the previous pixel-based diffusion methods or computer-aided design (CAD) models that fail to support complex assembly instructions or component exchange. Across four original designs, this novel "bag of bricks" method thus functions as a physical API: a constrained vocabulary connecting precisely oriented brick locations to a "bag of words" through which arbitrary functional requirements compile into material reality. Given such a consistent and repeatable AI representation opens new design options while guiding natural language implementations in manufacturing and engineering prototyping.
Semantic-Constrained Federated Aggregation: Convergence Theory and Privacy-Utility Bounds for Knowledge-Enhanced Distributed Learning
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning enables collaborative model training across distributed data sources but suffers from slow convergence under non-IID data conditions. Existing solutions employ algorithmic modifications treating all client updates identically, ignoring semantic validity. We introduce Semantic-Constrained Federated Aggregation (SCFA), a theoretically-grounded framework incorporating domain knowledge constraints into distributed optimization. We prove SCFA achieves convergence rate O(1/sqrt(T) + rho) where rho represents constraint violation rate, establishing the first convergence theory for constraint-based federated learning. Our analysis shows constraints reduce effective data heterogeneity by 41% and improve privacy-utility tradeoffs through hypothesis space reduction by factor theta=0.37. Under (epsilon,delta)-differential privacy with epsilon=10, constraint regularization maintains utility within 3.7% of non-private baseline versus 12.1% degradation for standard federated learning, representing 2.7x improvement. We validate our framework on manufacturing predictive maintenance using Bosch production data with 1.18 million samples and 968 sensor features, constructing knowledge graphs encoding 3,000 constraints from ISA-95 and MASON ontologies. Experiments demonstrate 22% faster convergence, 41.3% model divergence reduction, and constraint violation thresholds where rho<0.05 maintains 90% optimal performance while rho>0.18 causes catastrophic failure. Our theoretical predictions match empirical observations with R^2>0.90 across convergence, privacy, and violation-performance relationships.
Cross-Sample Augmented Test-Time Adaptation for Personalized Intraoperative Hypotension Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Intraoperative hypotension (IOH) poses significant surgical risks, but accurate prediction remains challenging due to patient-specific variability. While test-time adaptation (TTA) offers a promising approach for personalized prediction, the rarity of IOH events often leads to unreliable test-time training. To address this, we propose CSA-TTA, a novel Cross-Sample Augmented Test-Time Adaptation framework that enhances training by incorporating hypotension events from other individuals. Specifically, we first construct a cross-sample bank by segmenting historical data into hypotensive and non-hypotensive samples. Then, we introduce a coarse-to-fine retrieval strategy for building test-time training data: we initially apply K-Shape clustering to identify representative cluster centers and subsequently retrieve the top-K semantically similar samples based on the current patient signal. Additionally, we integrate both self-supervised masked reconstruction and retrospective sequence forecasting signals during training to enhance model adaptability to rapid and subtle intraoperative dynamics. We evaluate the proposed CSA-TTA on both the VitalDB dataset and a real-world in-hospital dataset by integrating it with state-of-the-art time series forecasting models, including TimesFM and UniTS. CSA-TTA consistently enhances performance across settings-for instance, on VitalDB, it improves Recall and F1 scores by +1.33% and +1.13%, respectively, under fine-tuning, and by +7.46% and +5.07% in zero-shot scenarios-demonstrating strong robustness and generalization.
Bridging Data and Physics: A Graph Neural Network-Based Hybrid Twin Framework
Venue: arXiv cs.LG 2025
Year: 2025

Simulating complex unsteady physical phenomena relies on detailed mathematical models, simulated for instance by using the Finite Element Method (FEM). However, these models often exhibit discrepancies from the reality due to unmodeled effects or simplifying assumptions. We refer to this gap as the ignorance model. While purely data-driven approaches attempt to learn full system behavior, they require large amounts of high-quality data across the entire spatial and temporal domain. In real-world scenarios, such information is unavailable, making full data-driven modeling unreliable. To overcome this limitation, we model of the ignorance component using a hybrid twin approach, instead of simulating phenomena from scratch. Since physics-based models approximate the overall behavior of the phenomena, the remaining ignorance is typically lower in complexity than the full physical response, therefore, it can be learned with significantly fewer data. A key difficulty, however, is that spatial measurements are sparse, also obtaining data measuring the same phenomenon for different spatial configurations is challenging in practice. Our contribution is to overcome this limitation by using Graph Neural Networks (GNNs) to represent the ignorance model. GNNs learn the spatial pattern of the missing physics even when the number of measurement locations is limited. This allows us to enrich the physics-based model with data-driven corrections without requiring dense spatial, temporal and parametric data. To showcase the performance of the proposed method, we evaluate this GNN-based hybrid twin on nonlinear heat transfer problems across different meshes, geometries, and load positions. Results show that the GNN successfully captures the ignorance and generalizes corrections across spatial configurations, improving simulation accuracy and interpretability, while minimizing data requirements.
TS-DP: Reinforcement Speculative Decoding For Temporal Adaptive Diffusion Policy Acceleration
Venue: arXiv cs.LG 2025
Year: 2025

Diffusion Policy (DP) excels in embodied control but suffers from high inference latency and computational cost due to multiple iterative denoising steps. The temporal complexity of embodied tasks demands a dynamic and adaptable computation mode. Static and lossy acceleration methods, such as quantization, fail to handle such dynamic embodied tasks, while speculative decoding offers a lossless and adaptive yet underexplored alternative for DP. However, it is non-trivial to address the following challenges: how to match the base model's denoising quality at lower cost under time-varying task difficulty in embodied settings, and how to dynamically and interactively adjust computation based on task difficulty in such environments. In this paper, we propose Temporal-aware Reinforcement-based Speculative Diffusion Policy (TS-DP), the first framework that enables speculative decoding for DP with temporal adaptivity. First, to handle dynamic environments where task difficulty varies over time, we distill a Transformer-based drafter to imitate the base model and replace its costly denoising calls. Second, an RL-based scheduler further adapts to time-varying task difficulty by adjusting speculative parameters to maintain accuracy while improving efficiency. Extensive experiments across diverse embodied environments demonstrate that TS-DP achieves up to 4.17 times faster inference with over 94% accepted drafts, reaching an inference frequency of 25 Hz and enabling real-time diffusion-based control without performance degradation.
Auto-Tuning Safety Guardrails for Black-Box Large Language Models
Venue: arXiv cs.CR 2025
Year: 2025

Large language models (LLMs) are increasingly deployed behind safety guardrails such as system prompts and content filters, especially in settings where product teams cannot modify model weights. In practice these guardrails are typically hand-tuned, brittle, and difficult to reproduce. This paper studies a simple but practical alternative: treat safety guardrail design itself as a hyperparameter optimization problem over a frozen base model. Concretely, I wrap Mistral-7B-Instruct with modular jailbreak and malware system prompts plus a ModernBERT-based harmfulness classifier, then evaluate candidate configurations on three public benchmarks covering malware generation, classic jailbreak prompts, and benign user queries. Each configuration is scored using malware and jailbreak attack success rate, benign harmful-response rate, and end-to-end latency. A 48-point grid search over prompt combinations and filter modes establishes a baseline. I then run a black-box Optuna study over the same space and show that it reliably rediscovers the best grid configurations while requiring an order of magnitude fewer evaluations and roughly 8x less wall-clock time. The results suggest that viewing safety guardrails as tunable hyperparameters is a feasible way to harden black-box LLM deployments under compute and time constraints.