Publications

VOCALoco: Viability-Optimized Cost-aware Adaptive Locomotion
Stanley Wu
Mohamad H. Danesh
Simon Li
Hanna Yurchyk
Amin Abyaneh
Anas El Houssaini
David Meger
Hsiu-Chin Lin
Recent advancements in legged robot locomotion have facilitated traversal over increasingly complex terrains. Despite this progress, many ex… (see more)isting approaches rely on end-to-end deep reinforcement learning (DRL), which poses limitations in terms of safety and interpretability, especially when generalizing to novel terrains. To overcome these challenges, we introduce VOCALoco, a modular skill-selection framework that dynamically adapts locomotion strategies based on perceptual input. Given a set of pre-trained locomotion policies, VOCALoco evaluates their viability and energy-consumption by predicting both the safety of execution and the anticipated cost of transport over a fixed planning horizon. This joint assessment enables the selection of policies that are both safe and energy-efficient, given the observed local terrain. We evaluate our approach on staircase locomotion tasks, demonstrating its performance in both simulated and real-world scenarios using a quadrupedal robot. Empirical results show that VOCALoco achieves improved robustness and safety during stair ascent and descent compared to a conventional end-to-end DRL policy
2026-01-31
IEEE Robotics and Automation Letters (published)
doi.org
arxiv.org
Parallel Stochastic Gradient-Based Planning for World Models
Michael Psenka
Michael G. Rabbat
Aditi Krishnapriyan
Yann Lecun
Amir Bar
World models simulate environment dynamics from raw sensory inputs like video. However, using them for planning can be challenging due to th… (see more)e vast and unstructured search space. We propose a robust and highly parallelizable planner that leverages the differentiability of the learned world model for efficient optimization, solving long-horizon control tasks from visual input. Our method treats states as optimization variables ("virtual states") with soft dynamics constraints, enabling parallel computation and easier optimization. To facilitate exploration and avoid local optima, we introduce stochasticity into the states. To mitigate sensitive gradients through high-dimensional vision-based world models, we modify the gradient structure to descend towards valid plans while only requiring action-input gradients. Our planner, which we call GRASP (Gradient RelAxed Stochastic Planner), can be viewed as a stochastic version of a non-condensed or collocation-based optimal controller. We provide theoretical justification and experiments on video-based world models, where our resulting planner outperforms existing planning algorithms like the cross-entropy method (CEM) and vanilla gradient-based optimization (GD) on long-horizon experiments, both in success rate and time to convergence.
2026-01-30
ArXiv (preprint)
arxiv.org
arxiv.org
SkeleShare: Algorithmic Skeletons and Equality Saturation for Hardware Resource Sharing
Jonathan Van der Cruysse
Tzung-Han Juang
Shakiba Bolbolian Khah
Christophe Dubach
Compiling functional programs into efficient Field Programmable Gate Array (FPGA) designs is difficult. Hardware resources must be explicitl… (see more)y allocated and shared to maximize resource efficiency. This requires careful orchestration of several transformations to expose and exploit sharing opportunities.This paper introduces SkeleShare, a novel approach that automates the problem of resource allocation and sharing. It leverages equality saturation and algorithmic skeletons to expose sharing opportunities across abstraction levels. A solver-based extractor then selects a design that consolidates computations, meeting resource constraints while maintaining performance.This approach is evaluated on neural networks and image processing targeting a real FPGA. The paper shows how SkeleShare is used to express the various algorithmic patterns and transformation rules inherent in neural network operators. The experimental evaluation demonstrates that SkeleShare’s fully automated resource allocation and sharing matches and exceeds the performance of prior work, which involves expert manual extraction of sharing opportunities.
2026-01-30
IEEE/ACM Symposium on Code Generation and Optimization (published)
doi.org
Synthesizing Specialized Sparse Tensor Accelerators for FPGAs via High-Level Functional Abstractions
Hamza Javed
Christophe Dubach
Sparsity is inherent in many applications such as machine learning and graph analytics. However, achieving high efficiency in sparse computa… (see more)tions requires specialized hardware accelerators like FPGAs, as traditional accelerators typically cater to dense data. While high level synthesis enables the automatic generation of FPGA-based accelerators, generic solutions produced via C-based synthesis flows often demand extensive development time, leading designers to prioritize broad applicability over fine-grained structural specialization. Consequently, these accelerators fail to fully exploit FPGA’s reconfigurablility, leaving substantial performance and efficiency gains untapped.This paper pushes the boundary by automatically generating specialized accelerators that match a given fixed sparse structure (e.g., in static graph analytics and pruned neural networks). It achieves this by leveraging functional abstractions within high level synthesis, an approach that has already proven effective in automating the generation of specialized dense tensor accelerator. Tensor shapes are encoded directly in the type system and specialized primitives for irregular data are introduced. Together, these innovations enable a concise specification of sparse accelerators and drive advanced optimizations—including dynamic partitioning and vector sharding—to produce hardware precisely tailored to the sparsity pattern of the underlying tensors.Compared to state-of-the-art generic accelerators (HiSparse, HiSpMV and GraphLily), the approach achieves up to a 2.8× improvement in bandwidth efficiency for sparse matrix computations and a 1.8× speedup on graph algorithms. Against the hls4ml neural network acceleration framework, it achieves up to a 1.8× improvement in throughput with a 4× reduction in resource usage, enabling scaling to larger networks. These results establish this approach as a flexible, powerful, and rapid solution for designing high-performance specialized sparse accelerators.
2026-01-30
IEEE/ACM Symposium on Code Generation and Optimization (published)
doi.org
Beyond Fixed Frames: Dynamic Character-Aligned Speech Tokenization
Luca Della Libera
Yusuf Cem Sübakan
Mirco Ravanaelli
Neural audio codecs are at the core of modern conversational speech technologies, converting continuous speech into sequences of discrete to… (see more)kens that can be processed by LLMs. However, existing codecs typically operate at fixed frame rates, allocating tokens uniformly in time and producing unnecessarily long sequences. In this work, we introduce DyCAST, a Dynamic Character-Aligned Speech Tokenizer that enables variable-frame-rate tokenization through soft character-level alignment and explicit duration modeling. DyCAST learns to associate tokens with character-level linguistic units during training and supports alignment-free inference with direct control over token durations at decoding time. To improve speech resynthesis quality at low frame rates, we further introduce a retrieval-augmented decoding mechanism that enhances reconstruction fidelity without increasing bitrate. Experiments show that DyCAST achieves competitive speech resynthesis quality and downstream performance while using significantly fewer tokens than fixed-frame-rate codecs. Code and checkpoints will be released publicly at https://github.com/lucadellalib/dycast.
2026-01-29
ArXiv (preprint)
arxiv.org
arxiv.org
Beyond Fixed Frames: Dynamic Character-Aligned Speech Tokenization
Luca Della Libera
Cem Subakan
Mirco Ravanelli
Neural audio codecs are at the core of modern conversational speech technologies, converting continuous speech into sequences of discrete to… (see more)kens that can be processed by LLMs. However, existing codecs typically operate at fixed frame rates, allocating tokens uniformly in time and producing unnecessarily long sequences. In this work, we introduce DyCAST, a Dynamic Character-Aligned Speech Tokenizer that enables variable-frame-rate tokenization through soft character-level alignment and explicit duration modeling. DyCAST learns to associate tokens with character-level linguistic units during training and supports alignment-free inference with direct control over token durations at decoding time. To improve speech resynthesis quality at low frame rates, we further introduce a retrieval-augmented decoding mechanism that enhances reconstruction fidelity without increasing bitrate. Experiments show that DyCAST achieves competitive speech resynthesis quality and downstream performance while using significantly fewer tokens than fixed-frame-rate codecs. Code and checkpoints will be released publicly at https://github.com/lucadellalib/dycast.
2026-01-29
Open MIND (preprint)
doi.org
arxiv.org
Dispersion Loss Counteracts Embedding Condensation and Improves Generalization in Small Language Models
Chen Liu
Xingzhi Sun
Xi Xiao
Alexandre Van Tassel
Ke Xu
Kristof Reimann
Danqi Liao
Mark B. Gerstein
Tianyang Wang
Xiao Wang
Smita Krishnaswamy
Large language models (LLMs) achieve remarkable performance through ever-increasing parameter counts, but scaling incurs steep computational… (see more) costs. To better understand LLM scaling, we study representational differences between LLMs and their smaller counterparts, with the goal of replicating the representational qualities of larger models in the smaller models. We observe a geometric phenomenon which we term
2026-01-29
ArXiv (preprint)
arxiv.org
arxiv.org
Dual-Phase Continual Learning: Supervised Adaptation Meets Unsupervised Retention
Vaibhav Singh
Rahaf Aljundi
Eugene Belilovsky
Foundational vision-language models (VLMs) excel across diverse tasks, but adapting them to new domains without forgetting prior knowledge r… (see more)emains a critical challenge. Continual Learning (CL) addresses this challenge by enabling models to learn sequentially from new data while mitigating the forgetting of prior information, typically under supervised settings involving label shift. Nonetheless, abrupt distribution shifts can still cause substantial forgetting, potentially nullifying the benefits of supervised updates, especially when storing or replaying past data is infeasible. In this work, we propose leveraging unlabeled test-time data in an unsupervised manner to reinforce prior task performance without requiring replay or stored examples. Unlike traditional Test-Time Adaptation (TTA), which primarily focuses on domain shift or corruption, our method improves performance on earlier tasks by exploiting representative test samples encountered during deployment. We introduce a simple teacher-student framework with gradient-based sparse parameter updates, and show that it effectively mitigates forgetting in class-incremental CL for VLMs, offering a memory-free alternative to episodic replay with strong empirical results.
2026-01-29
Transactions on Machine Learning Research (accepted)
doi.org
openreview.net
Localized, High-resolution Geographic Representations with Slepian Functions
Arjun Rao
Ruth Crasto
Tessa Ooms
David Rolnick
Konstantin Klemmer
Marc Rußwurm
Geographic data is fundamentally local. Disease outbreaks cluster in population centers, ecological patterns emerge along coastlines, and ec… (see more)onomic activity concentrates within country borders. Machine learning models that encode geographic location, however, distribute representational capacity uniformly across the globe, struggling at the fine-grained resolutions that localized applications require. We propose a geographic location encoder built from spherical Slepian functions that concentrate representational capacity inside a region-of-interest and scale to high resolutions without extensive computational demands. For settings requiring global context, we present a hybrid Slepian-Spherical Harmonic encoder that efficiently bridges the tradeoff between local-global performance, while retaining desirable properties such as pole-safety and spherical-surface-distance preservation. Across five tasks spanning classification, regression, and image-augmented prediction, Slepian encodings outperform baselines and retain performance advantages across a wide range of neural network architectures.
2026-01-29
arXiv (preprint)
doi.org
arxiv.org
Perplexity Cannot Always Tell Right from Wrong
Petar Veličković
Federico Barbero
Christos Perivolaropoulos
Simon Kayode Osindero
Razvan Pascanu
Perplexity -- a function measuring a model's overall level of"surprise"when encountering a particular output -- has gained significant tract… (see more)ion in recent years, both as a loss function and as a simple-to-compute metric of model quality. Prior studies have pointed out several limitations of perplexity, often from an empirical manner. Here we leverage recent results on Transformer continuity to show in a rigorous manner how perplexity may be an unsuitable metric for model selection. Specifically, we prove that, if there is any sequence that a compact decoder-only Transformer model predicts accurately and confidently -- a necessary pre-requisite for strong generalisation -- it must imply existence of another sequence with very low perplexity, but not predicted correctly by that same model. Further, by analytically studying iso-perplexity plots, we find that perplexity will not always select for the more accurate model -- rather, any increase in model confidence must be accompanied by a commensurate rise in accuracy for the new model to be selected.
2026-01-29
ArXiv (preprint)
arxiv.org
arxiv.org
Secure Tool Manifest and Digital Signing Solution for Verifiable MCP and LLM Pipelines
Saeid Jamshidi
Kawser Wazed Nafi
Arghavan Moradi Dakhel
Foutse Khomh
Amin Nikanjam
Mohammad Hamdaqa
2026-01-29
ArXiv (preprint)
arxiv.org
arxiv.org
Securing Time in Energy IoT: A Clock-Dynamics-Aware Spatio-Temporal Graph Attention Network for Clock Drift Attacks and Y2K38 Failures
Saeid Jamshidi
Omar Abdel Wahab
Rolando Herrero
Foutse Khomh
The integrity of time in distributed Internet of Things (IoT) devices is crucial for reliable operation in energy cyber-physical systems, su… (see more)ch as smart grids and microgrids. However, IoT systems are vulnerable to clock drift, time-synchronization manipulation, and timestamp discontinuities, such as the Year 2038 (Y2K38) Unix overflow, all of which disrupt temporal ordering. Conventional anomaly-detection models, which assume reliable timestamps, fail to capture temporal inconsistencies. This paper introduces STGAT (Spatio-Temporal Graph Attention Network), a framework that models both temporal distortion and inter-device consistency in energy IoT systems. STGAT combines drift-aware temporal embeddings and temporal self-attention to capture corrupted time evolution at individual devices, and uses graph attention to model spatial propagation of timing errors. A curvature-regularized latent representation geometrically separates normal clock evolution from anomalies caused by drift, synchronization offsets, and overflow events. Experimental results on energy IoT telemetry with controlled timing perturbations show that STGAT achieves 95.7% accuracy, outperforming recurrent, transformer, and graph-based baselines with significant improvements (d>1.8, p0.001). Additionally, STGAT reduces detection delay by 26%, achieving a 2.3-time-step delay while maintaining stable performance under over
2026-01-29
ArXiv (preprint)
arxiv.org
arxiv.org